In the world of finance, the use of AI is tricky owing to privacy issues, the dreaded risk of hallucinations and the guardrails. Given that modern systems are increasingly getting foolproof, there is a huge opportunity for changing the industry with agents—all with a little fine-tuning.

Debarag Banerjee, chief AI and data officer at L&T Finance, spoke with AIM about how the firm is steadily moving away from rules-based automation towards a future led by agents.

The most important and real transformation happening under the hood is with Project Cyclops, L&T Finance’s proprietary AI stack for real-time, high-accuracy credit decisioning. 

“We launched this with our two-wheeler portfolio last year. It now handles 100% of those loans,” Banerjee said. “This year, we’ve extended it to our tractor business and are preparing to roll it out for small business loans.”

Project Cyclops pulls together various “trust signals”—from customer profiles to repayment behaviour—into an ensemble model that can instantly separate delinquent-risk borrowers from credit-worthy ones. “You upload your information and, just like that, you get a decision,” Banerjee said.

How to Build This in India?

In terms of data residency and compliance, L&T Finance is already future-proofing things. “Even for closed-source LLMs, we insist on endpoints hosted in India to prepare for laws like the Digital Personal Data Protection Act (DPDPA),” Banerjee added.

Open-source models naturally provide better control. “Since we host and manage the stack ourselves, the data is more secure. We ensure contractually that our EII data isn’t used for model retraining.” Much of the data used to fine-tune open-source LLMs is proprietary. Even when it’s not proprietary, the formulation under which the team trains them makes the contextual usage proprietary.

L&T Finance has also explored Indian LLMs built for Indic languages. “They’re a good start. But the number of parameters still matters.”

Meanwhile, Project Cyclops, the firm’s proprietary ML stack, continues to scale. It combines models across various trust signals — from customer data to repayment behaviour — and re-ensembles them to deliver real-time credit decisions.

The company deliberately took a multi-LLM route from day one. The goal is flexibility, not being locked into any single provider or model.

“Instead of being tied to LLMs from any one company, our stack can call any model our developer thinks is right for that task,” said Banerjee. This includes Google’s Gemini (multiple versions), OpenAI models through Azure, and several open-source LLMs hosted on GPU-as-a-service platforms.

They’ve also tested Meta’s Llama family (3.1, 3.2) and successfully fine-tuned them for performance comparable to larger models like Gemini, but with lower inference costs.

“In one of our other applications, we found medium-sized fine-tuned Llamas performing nearly as well as some of the premium models,” he noted. “We’re agnostic to geography or company, as long as data privacy is maintained and we retain full control.”

Tackling Bias and Ethics in AI Lending

When asked about ethical concerns in using AI for credit decisions, especially cases where background visuals or personal environment might influence model behaviour, Banerjee stressed two things: statistical validation and consent.

“Any trust signal we use has to hold up against statistically significant past data. If it’s frivolous, it gets discarded,” he said. “We are also very careful about consent. All data usage is fully transparent to the customer.”

He acknowledged the risk of adversarial behaviour, like customers gaming the system with artificial backgrounds. “But these kinds of patterns are caught through quality checks and operational safeguards,” Banerjee noted. “It’s a team effort — credit, risk, field ops, everyone must align for the system to work at scale.”

Traditional software relied on endless rules. With agent frameworks powered by LLMs and task-specific tools, Banerjee said that the effort to build systems has reduced drastically. “You can create something with a minimalist approach, deploy it, and let it improve over time. These are not just tools — they’re self-improving systems,” Banerjee said.

The future of agentic AI is both exciting and inevitable for Banerjee. “Agentic AI seems to have finally caught that right gap,” he observed. “We can already see it proving its mettle—not only in decision-making but in emulating human functions.”

He envisions agents becoming self-improving and minimalist in design. “Instead of writing rule after rule, you get to a working solution quickly, test it, improve it, and even reinforce it.”

Though we’re not fully there yet, the progress is palpable. “I’ve seen demos where agents can do EDA, generate train-test sets, build models, and write documentation—all with very little human intervention,” he said. “That future, where something like Project Cyclops is 90% agent-driven, would be wonderful.”

Regulation, Black Swans & the Human Touch

Will agents eventually monitor finance and trading platforms on their own? “There are still regulatory needs—maker, checker, monitor—which will stay,” he said. “And while you can create agents for predictable failures, black swans are by nature unpredictable.”

He also believes AI will create jobs. “One area where AI may generate jobs is in humans playing both white hat and black hat—looking for ways AI can fail or be misused and figuring out how to recover.” For L&T Finance, agentic AI is not just about tech, it’s about solving for India’s underserved.

“I was there when India got connected. Then came digital payments. Now, the next big inflection is digital access to credit for the bottom and middle of the pyramid,” he said. “India has the opportunity to leapfrog old credit systems because its consumers are digitally connected.”

The post L&T Finance Hopes Project Cyclops Would Be 90% Agentic One Day appeared first on Analytics India Magazine.

Mindsprint, a technology firm offering purpose-built AI-led solutions to modernise enterprise operations, generates about 20% of its earnings from AI-driven solutions, according to co-founder and CEO Suresh Sundararajan.

In a conversation with AIM, Sundararajan officially introduced Mindsprint’s portfolio of agentic AI-powered solutions and announced the expansion of its R&D hub in India. He revealed that the company’s AI offerings encompass a range of data analytics services and specialised accelerators.

Mindsprint’s Future Forward Lab oversees a suite of IP-driven AI solutions and accelerators designed to reduce time to market and enable rapid pilots, the company stated. 

The lab offers solutions like Sprint AP, a fully automated, agentic AI-powered accounts payable platform; ProcureSPRINT, a plug-and-play procurement solution with real-time spend visibility; SalesSPRINT, intelligent sales optimisation; TradeSPRINT, a live data-driven trade decisioning system for commodity markets; and Guardian Eye, an agentic AI-powered platform for continuously monitoring external threats and managing security vulnerabilities.

“Within this segment (20%), emerging technologies like generative AI and agentic AI constitute less than 10%,” Sundararajan said, adding that while emerging technologies like generative AI and agentic AI are promising, their adoption is still in the early stages.

CTO Sagar Porayil Vadakkinakathu said that Future Forward Lab is the place where the company’s AI-first ambition comes to life.

“We are not adding AI to legacy systems. Instead, we are engineering AI into the DNA of how businesses run. From autonomous trading to procurement intelligence and finance automation, every solution we build starts with measurable impact in mind,” he said.

In a press release, the company claimed it has embedded AI into everyday workflows “from HR and finance to project delivery”.

Data Strategy and Partnerships

Addressing the foundational role of data in AI success, Vadakkinakathu noted that many enterprises struggle with fragmented and poor-quality data. Mindsprint’s strong data engineering and platform teams focus on building unified data pipelines, ensuring data quality, and maintaining governance frameworks. He stressed that a solid data strategy is a prerequisite for effective AI and agentic AI deployments, enabling enterprises to realise the full potential of their AI investments.

Moreover, the CTO highlighted Mindsprint’s robust ecosystem of technology partnerships, which play a critical role in co-innovation and market expansion. He noted that key collaborators include ServiceNow, where Mindsprint recently launched a capability centre in Chennai, as well as SAP, Planview, and major cloud providers like AWS, Google Cloud Platform, and Microsoft Azure. These partnerships enable joint development of AI-driven solutions, he added.

The Importance of Diverse Talent

Vadakkinakathu highlighted the importance of hiring fresh graduates and nurturing a culture of continuous learning. Mindsprint actively recruits from diverse regions across India, recognising that fresh perspectives and diverse thoughts are essential in the AI era. The company encourages engineers to become “10x developers” by mastering not only programming fundamentals but also AI tools, DevOps, cloud technologies, and agile delivery practices.

“This approach aims to amplify productivity and accelerate software delivery lifecycles,” he said.

The CEO said the company has more than 3,200 employees and has onboarded around 300 to 400 people since the beginning of the year.

The company informed that 95% of its workforce, including non-tech teams, has completed a foundational certificate program in GenAI.

Besides, its proprietary Mindverse platform with more than 10 GenAI tools is powering delivery and development.

Mindsprint, which is headquartered in Singapore, launched its generative AI platform called MindVerse last year.

It also informed that a cross-functional AI Council has been tasked with identifying, prioritising, and scaling high-impact internal use cases for AI adoption.

Business Strategy

Mindsprint recently opened a new 87,000 sq ft facility in Chennai, featuring a next-gen customer experience centre and technology hub designed for innovation at scale.

The next phase of workspace expansion is planned in Bengaluru, purpose-built to accelerate collaboration, experimentation, and co-creation, the company said, adding that it recently opened offices in Sydney, Australia, and New Jersey.

“Anchored in India as our innovation nucleus, we’re expanding our client-facing presence worldwide and nurturing an AI-first culture that empowers teams to co-create the future,” Sundararajan said.

The CEO revealed that 5-6% of its earnings go into research and development, adding that its prime markets are North America and APAC. In contrast, its opportunity markets include the Middle East, Africa, and India.

“The company’s revenue is presently in the three-digit millions of dollars. It aims to double and triple its revenue to $400 to $500 million by 2030, if growth capital arrives,” he shared.

The company, which spun out of Olam Group and was previously called Olam Information Services Private Limited (OISL), presently sees 90% of its revenue from Olam Group companies.

“With Olam, we are operating under a multi-year, long-term service agreement at arm’s length, where they hold us as a supplier or vendor, and we work with them as they are our customers. Our plan is to significantly reduce Olam’s portion, mitigate risks, and gradually grow non-Olam customer revenue to 30 to 35% in the next five years,” Sundararajan shared.

He also informed that the company plans to divest to interested investors, including strategic players and private equity firms, as Olam Group has classified Mindsprint,  along with other assets, as non-strategic.

“We are hungry for growth, lofty and stretchy in our aspirations, demanding in terms of having high talent, and have targets to double or triple our revenues,” he concluded.

The post This is How Mindsprint is Generating 20% of its Earnings from AI appeared first on Analytics India Magazine.

During the Q4 FY25 earnings call, Sonata Software CEO Samir Dhir showcased the firm’s high confidence in generative AI services. “We expect AI enabled services to contribute 20% of our revenue over the next three years,” he said.

Dhir also added that the company is pursuing a $34 million pipeline in AI programs with over 100 clients. He also added that 97% of the workforce is trained with generative AI skills.

Sonata reported a 19.4% year-on-year (YoY) increase in revenue from operations for Q4FY25 at ₹2,617.2 crore, even as net profit dipped 2.56% to ₹107.53 crore, reflecting the impact of client decision delays and paused deals in a volatile global environment.

For the whole of FY25, revenue stood at ₹7,340.6 crores, reflecting a YoY growth of 23.4%.

Quarterly revenue, however, fell 4% sequentially to ₹702.3 crore. In dollar terms, FY25 revenue stood at $335.5 million (up 3.7% YoY), while Q4 revenue declined 6.6% QoQ to $81.3 million.

Despite the Q4 challenges, Sonata continues to see strong traction in AI with it being part of all deals. This has been a constant statement for most of the Indian IT firms who reported their quarterly results in April.

Speaking on one of the largest deals that Sonata secured this quarter, Dhir said that AI was the differentiator. “In the TMT sector, we’re increasingly seen as an AI company,” he said, while adding that as clients double down on AI investments in areas like customer support, there is still increasing demand.

Dhir said that AI adoption in software development is still in early stages—limited to testing and tools like GitHub Copilot. But Sonata expects it to deepen in the next 6 to 12 months, which might possibly affect the offerings of the firm to its clients.

Security and compliance remain concerns, but the firm’s AI-led offerings, including agent-based frameworks, are already creating clear business value and winning deals.

The company also acknowledged revenue conversion delays in Q4 due to global headwinds, including US tariffs impacting client IT budgets in retail and logistics. Despite this, the company added 14 new customers during the quarter.

In the domestic products and services business, revenue surged 23.4% YoY to ₹7,340.6 crore in FY25. However, Q4 revenue dropped 9.1% QoQ to ₹1,918.2 crore. Annual gross contribution increased 14.8% to ₹299.1 crore, while Q4 contribution declined 4.3% to ₹78.4 crore.

The company remains cautious for FY26 and did not give any guidance. 

Sonata Software just got added to the list of mid-sized IT firms outperforming their larger counterparts in Q4 FY25 in terms of quarterly growth and AI confidence. The firm has been investing in agentic AI for the last year and has clearly paid off this quarter.

The post Sonata Software Expects AI to Drive 20% Revenue in 3 Years appeared first on Analytics India Magazine.

Indian IT’s last quarterly results was the real report card of how the firms are pushing through macroeconomic conditions to stay on top of their services game. The growth of mid-size firms, and the flatlining and decline that the bigger firms saw was also a testament to the companies’ leadership.

Thanks to the deal pipelines, the stars of the show in the quarter remained firms like Persistent Systems, Hexaware, Mphasis, and Coforge, with a huge order book going into FY26. The CEOs of these mid-sized firms have all been in the driver’s seat for years—each with more than five years at the helm, and all bringing deep experience from earlier stints at larger IT firms.

The Slow-Growth CEOs

IT giants like Infosys, TCS, Tech Mahindra, and Wipro maintained a slimmer order book when compared to last year. These companies recently handed over the reins to new leaders with less than two years in the role. 

TCS’ K Krithivasan took over in June 2023, Tech Mahindra’s Mohit Joshi joined in December 2023, and Wipro’s Srinivas Pallia entered the scene in just April last year. Infosys, with Salil Parekh at the helm since 2018, still remains on a downward revenue trajectory, despite claiming confidence in generative AI for deals.

These giants like TCS, Infosys, and Tech Mahindra scraped together single-digit growth, or even shrank in Wipro’s case. Meanwhile, mid-cap firms saw their revenues surge. 

The fourth quarter results indicate that smaller firms are able to pull through quicker compared to the bigger firms. 

Zoho’s Sridhar Vembu had already established earlier that macroeconomic conditions are temporary and have not led to the sudden downfall of Indian IT. 

Changing the 30-year-old way of working is what mid-sized firms are seemingly able to pull off quickly. Firms such as Coforge grew by over 31%, Persistent by nearly 19%, Hexaware by almost 14%, Mphasis, though small, grew at 2.9%, but with a great outlook for the future. 

The CEOs of these firms have been working at the firm for at least five years.

The CEOs of the Year, and Why

Sudhir Singh became the CEO of Coforge in May 2017 after spending nine years at Infosys. Under his leadership, the company has emerged as one of the best-performing ones in the quarter.

To explain how the deals got impacted because of Singh, after acquiring Hyderabad-based Cigniti last year, the firm quickly signed a mega $1.56 billion deal with Sabre, a Texas-based travel tech firm. Add to that a strong pipeline of deals—Coforge, for instance, ended the last quarter with $2.1 billion worth of orders—and the contrast becomes even starker.

That not only made Coforge one of the few Indian firms to land a billion-dollar deal in recent times, but also propelled it into the list of top 10 IT services companies in the country. 

Meanwhile, despite a slow revenue growth, LTIMindtree reported another quarter with $1.5 billion in contract value, even though Debashis Chatterjee has been the CEO only since 2022. This shows that the agility of the firms with smaller teams possibly makes a difference. 

Persistent Systems doubled down on regulated verticals and brought in experienced leadership across sectors to help close big-ticket deals. Sandeep Kalra became the CEO in October 2020, but he had over 14 years of experience at HCLTech, and this quarter marked Persistent’s 20th consecutive quarter of revenue growth.

The strategy seems to be working, even in an otherwise cautious market. For Q4 FY25, the company reported a total contract value (TCV) of $517.5 million, compared to $594.1 million in the December quarter. Net new TCV was $329 million, slightly lower than $333.6 million in Q3 but higher than the $302 million reported in the same quarter last year.

Persistent reiterated its long-term goal of achieving $2 billion in annual revenue by FY27, highlighting continued progress toward this milestone. It has now become the 9th largest IT company in India.

The Case for AI in Deals

Then comes Hexaware’s CEO, Srikrishna Ramakarthikeyan, who took the post in August 2014, and has helped the company rise ever since. For the quarter ended March 31, 2025, Hexaware’s revenue stood at $371.5 million, a 12.4% increase year-on-year and an almost flat 0.2% QoQ growth.

Hexaware’s AI-driven approach enabled significant growth across clients and capabilities in Q1CY25, securing several major deals. The company now has three customers contributing over $75 million in revenue each, with one crossing the $100 million mark.

“We continued to execute well on the basics that power our growth — win market share through delivery excellence and invest in creating differentiated capabilities, talent, and platforms. The strength of our deal wins positions us strongly for a year of solid growth,” Ramakarthikeyan said.

Mphasis is another firm that stole the spotlight this year. With CEO Nitin Rakesh at the top since January 2017, Q4 FY25 was the strongest quarter in the last three years. 

The company’s revenue rose 2.6% sequentially in USD terms and 2.9% in constant currency, supported by strong deal wins and a record pipeline. Mphasis secured deal wins worth $390 million during the quarter, the highest in seven quarters, with 59% of those wins driven by AI-led initiatives. 

The company’s total contract value (TCV) pipeline was at record levels, growing 26% sequentially and 86% year-on-year. “In this uncertain macro environment, our focus is on continued investments in growth, keeping tech and AI at the core, and leveraging solutions to transform and modernise our clients’ technology and operations stack,” Rakesh said. 

All in all, mid-size firms’ CEOs with experience from bigger firms and stable years, have contributed to the growth, at least in terms of deals and order bookings.

While big IT companies are yet to carve out revenues specific to areas like GenAI, there’s still a long road ahead. But for now, the mid-caps seem hungrier and focused on closing newer deals.

The post CEO Stability is Paying Off for Mid-Size Indian IT  appeared first on Analytics India Magazine.

Global capability centres (GCCs) in India’s banking and financial services are now largely powered by advanced technologies like AI-ML, data analytics, and automation, redefining how financial services are delivered.

A key catalyst in this transformation is the adoption of real-time analytics, especially within the commercial insurance space. This is enabling sharper portfolio analysis, more effective risk engineering, and improved catastrophe modelling. 

Alongside this, there’s a rising focus on user experience (UX), as today’s customers expect digital solutions that are not only efficient but also intuitive and user-friendly.

According to ANSR’s ‘State of BFSI GCCs in India’ report, more than 90 BFSI enterprises operate approximately 185 GCCs across India, with many organisations maintaining multiple centres. Bengaluru stands out as the leading hub, followed by Delhi/NCR, Hyderabad, and Chennai.

India’s BFSI GCC ecosystem continues to thrive, employing nearly 20–25% of the total talent working in GCCs across all industries, highlighting its growing significance in the global financial services landscape.

Deep Tech with Anaptyss

In an interview with AIM, Anuj Khurana, CEO and co-founder of Anaptyss Inc, highlighted the company’s strong focus on AI-led innovation. “A major focus area for Anaptyss has been its substantial investment in an integrated centre of excellence (CoE) for AI-led digital services,” he said. 

This centre brings together tech architects, industry leaders, and advanced AI/ML technologies to create digital solutions that address real business challenges.

Anaptyss, a digitally enabled managed services company, has already developed several impactful solutions. One of its key products, ALFA, is an AI-powered system for anti-money laundering (AML) transaction monitoring that offers “over 75–80% accuracy in detecting false alerts”. 

The company also created a machine learning tool that allows for 40% faster validation of third-party credit risk models. In short, “GCCs are enabling insurers to reimagine their operations,” Shrinivas Ramanujan, chief operating officer at Opteamix, told AIM. 

“With AI, blockchain, and predictive analytics, these centres are becoming essential for digital transformation and customer engagement.”

Need for Deep-Tech Talent

The push for deep-tech talent is clearly strategic. BFSI GCCs are actively hiring AI engineers, data scientists, technology architects, and natural language processing (NLP) experts. 

Khurana mentioned that there’s also strong demand for talent in areas such as model risk management, credit risk, fraud analytics, and cryptocurrency. The critical skills these centres are looking for include programming languages like Python and R, risk management frameworks, AI and ML proficiency, and descriptive analytics.

To support this, Anaptyss is conducting focused hiring campaigns and partnering with top business schools and institutions such as the ICAI. The goal is to attract, retain, and grow talent capable of driving digital innovation.

Echoing the same, Guru Thiagarajan, head of Deutsche Bank’s India tech centre, previously told AIM how deep tech is helping his teams boost productivity. 

“The focus is on productivity—helping research analysts and bankers sift through vast amounts of data, summarise reports, and prepare for client meetings faster,” he said. “With generative AI and large language models, we can help our teams handle data-heavy tasks in a fraction of the time.”

India at the Centre of Transformation

India has become central to this shift. “Today, our delivery sites in Gurugram and Noida have dedicated technology infrastructure and facilities to support year-round R&D projects focused on developing AI-based solutions,” Khurana added.

The company has earmarked an annual budget to boost its innovation capability, and over 10% of its leadership roles are deeply involved in innovation programs.

Additionally, Anaptyss is leveraging emerging technologies, including data analytics, robotic process automation, AI, GenAI, agentic AI, machine learning, and natural language processing to develop proprietary digital solutions.

Additionally, the company’s ecosystem of ‘Digital Entrepreneurs in Residence’ allows Anaptyss to prototype and implement industry-specific and tailored digital solutions based on its deep-tech capabilities and in-depth understanding of the banking and financial services domains. 

Over 60% of the company’s program managers, technology architects, and data analysts are involved in developing AI-enabled digital solutions.

Rise of Neo Banks

Alongside this, the rise of neo banks—digital-first banking platforms—has accelerated the demand for agile, AI-powered solutions. 

Suman Sastry U, vice president at Everest Group, told AIM that these fintech players, like Freo, Jupiter, and Fi, are meeting the expectations of millennials and Gen Z users who want fast, flexible financial services. AI is central to their operations, powering everything from KYC compliance and credit assessment to fraud detection and customer support.

This disruption is also reshaping GCCs. Traditionally geared towards international markets, many GCCs are now turning their attention inward, serving Indian fintechs and banks. 

In many cases, neo banks have adopted a collaborative model by partnering with traditional banks, serving as their digital banking front ends. He further mentioned that partnerships such as Niyo-SBM India and Jupiter-Federal Bank demonstrate how this approach is expanding reach, enhancing customer experiences, and driving faster digital adoption.

The post BFSI GCCs Are Hungry for Deep-Tech Talent appeared first on Analytics India Magazine.

Infosys has announced a strategic collaboration with Yorkshire Building Society on Wednesday to accelerate its digital transformation. The partnership aims to provide the financial institution’s mortgage, commercial, and savings customers with a mobile-first banking experience that simplifies financial interactions.

The collaboration will leverage cloud, data, AI, and cybersecurity solutions to help Yorkshire Building Society achieve its 2030 strategic plan, focusing on enhancing customer and employee experiences through digital operations.

“This transformation will empower our members and colleagues with the tools and services needed to deliver great customer outcomes, including major investments such as faster payments and enhanced security,” Patrick Connolly, director of change delivery at Yorkshire Building Society said.

To this Dennis Gada, executive vice president and global head of banking & financial services at Infosys, commented that “We are bringing our full suite of next-generation technologies to help them improve customer experience for their members, with a deeper focus on end-to-end digital channels.”

Meanwhile, Infosys has also launched an AI-driven suite, Infosys Topaz for SAP S/4HANA Cloud, to support enterprises in transitioning to SAP’s cloud-based ERP platform. The offering integrates generative AI into every stage of digital transformation.

Built on Infosys’ cloud framework, the suite includes industry-specific pre-built solutions and implementation guides, aiming to accelerate SAP S/4HANA Cloud adoption and operational modernisation.
In the recent January–March quarter of FY25 earrings call,CEO Salil Parekh, showed confidence in generative AI. During the press conference, he said, “We’ve seen growing demand from clients to partner with them on AI. They’re moving from a use case approach to an AI led transformation approach,” he said, saying that this is using AI agents, which are playing more and more of a critical role.

The post Infosys Partners with Yorkshire Building Society to Accelerate its 2030 Strategic Plan with AI appeared first on Analytics India Magazine.

Fintech platform CRED has introduced Svalbard, a suite of credit experiences designed to empower members by transforming credit into a tool for financial confidence. With a focus on responsible credit behaviour, Svalbard offers innovations in credit score management, credit card control, and liquidity solutions.

CRED’s latest credit score experience introduces –

• Foresight: A machine learning-powered predictive analytics tool that analyses credit patterns to forecast the impact of financial decisions—like loans or missed payments—on credit scores.
• Compass: A goal-setting system that crafts personalised roadmaps, identifying key actions and predicting timelines to reach target credit scores.
• Aurora: A dynamic credit visualization tool that turns scores into evolving financial stories, using a custom typeface and a glowing Halo to highlight progress and offer guidance.
• Key Factors: A clear, colour-coded credit health report covering payment history, credit usage, and account longevity, with actionable insights to simplify credit scoring.

Svalbard enhances credit card management with a unified dashboard that consolidates dues across multiple cards, preventing unexpected commitments. A real-time monitoring system tracks unbilled transactions with 92% categorisation accuracy, while CRED Protect detects anomalies in statements to prevent hidden charges.

Additionally, over 400 live card versions offer interactive financial tools, making credit management seamless.

With most creditworthy individuals holding mutual funds yet rarely leveraging them for secured credit, CRED Cash+ introduces fully digital credit lines against mutual fund investments at rates starting from 8.99%. An automated bundling system minimises liquidation risk by assessing fund volatility

Kunal Shah, founder of CRED, emphasised the company’s mission: “With Svalbard, we’re transforming credit into a force for financial progress. In a country where most find debt stressful, we’re recognising and rewarding responsible behaviour, turning credit from a source of anxiety into a growth accelerant.”

Despite access to credit, financial anxiety remains a barrier—54% of creditworthy Indians would invest more, and 38% would start businesses if they had better credit confidence. With 79% experiencing stress around credit, CRED aims to change the narrative, making credit management seamless and empowering for its members.

The post CRED Launches Svalbard with AI-based Credit Management and Predictive Scoring appeared first on Analytics India Magazine.

In a mission to reshape the home loan experience, L&T Finance Limited (LTF) has launched Knowledgeable AI (KAI), an advanced AI-driven virtual advisor designed to simplify the borrowing process. KAI debuted on LTF’s newly revamped corporate website and was first introduced at the company’s RAISE’ 24 event. It marks a significant step in AI adoption within the financial sector.

Navigating home loans can often be daunting, filled with complex jargon, tedious calculations, and lengthy applications. KAI aims to eliminate these hurdles, particularly for first-time homebuyers, by providing instant, expert guidance at their fingertips. More than just a chatbot, KAI is an interactive AI-powered home loan consultant that offers real-time EMI calculations, personalised loan estimates, and intuitive support to simplify decision-making.

Powered by a specialised large language model (LLM) and retrieval-augmented generation (RAG) technology, KAI draws insights directly from LTF documents, ensuring users receive precise, contextual responses. Its interactive features allow users to adjust loan parameters with sliders, download EMI schedules, and even bookmark preferred options, making the process highly user-centric.

Sudipta Roy, managing director & CEO of L&T Finance, said, “With KAI, we are not just launching a chatbot; we are introducing a 24/7 AI-powered home loan guide. Our mission is to make home-buying simpler, more accessible, and stress-free. KAI does more than answer queries; it provides expert guidance across a range of home loan topics, ensuring a seamless experience for our customers.”

Banking in the AI era

Back in 2017, HDFC Bank introduced India’s first AI-driven banking chatbot, EVA (Electronic Virtual Assistant). Powered by Bengaluru-based startup Senseforth.ai, EVA was designed as a breakthrough in customer service, capable of handling millions of queries instantly across multiple platforms. EVA set the standard for AI-assisted customer interaction with the ability to pull information from thousands of sources and respond in under 0.4 seconds. 

By 2020, ICICI Bank expanded on this concept with its chatbot, iPal, integrating it with Amazon Alexa and Google Assistant. This allowed retail banking customers to perform various transactions through simple voice commands. However, the service was discontinued in August 2021.

More recently, in 2023, the State Bank of India (SBI) announced a strategic AI-driven initiative to enhance decision-making and operational efficiency. With plans to deploy advanced data warehouses and data lakes, SBI is also exploring partnerships with fintech firms and non-banking financial companies (NBFCs) to revolutionise co-lending practices and drive a more connected financial ecosystem.

The post L&T Finance Launches AI-Powered Home Loan Advisor appeared first on Analytics India Magazine.

Intel is set to face tough questions from investors about its search for a new CEO as it announced its quarterly results on Thursday. 

Revenue came in at $14.26 billion, beating the projected $13.81 billion. However, revenue declined 7% year-over-year, marking the third consecutive quarter of decline. Net loss for the quarter stood at $126 million, or 3 cents per share, compared to a net income of $2.67 billion, or 63 cents per share, a year earlier.

These results come as Intel grapples with falling PC demand, shrinking data centre market share, and uncertainty surrounding its leadership, as last month, it announced the retirement of CEO Pat Gelsinger after a 40-year career. 

Just after, the company announced David Zinsner, executive vice president and chief financial officer, and Michelle Johnston Holthaus, CEO of Intel Products, as interim co-CEOs. 

“Our Q1 outlook reflects seasonal weakness magnified by macro uncertainties, further inventory digestion and competitive dynamics,” said Zinsner during the call. To this, Holthaus added, “Dave and I are taking actions to enhance our competitive position and create shareholder value.”

This raised concerns about the future of its plan to expand into contract chip manufacturing—an initiative strongly backed by Gelsinger.

The chipmaker giant reported a fourth-quarter loss per share of $(0.03) on a GAAP basis, while non-GAAP earnings per share (EPS) stood at $0.13. For the full year, GAAP EPS was deeply negative at $(4.38), with non-GAAP EPS at $(0.13).  

Looking ahead, Intel expects Q1 2025 revenue to be between $11.7 billion and $12.7 billion, signalling further declines. 

The company also said in the release that it continues to lead the AI PC category. It’s on track to ship more than 100 million AI PCs by the end of 2025 and is working with more than 200 ISVs across more than 400 features to optimise its software on Intel silicon. 

No Falcon Shores in the Market Anymore

Intel has officially scrapped plans to bring Falcon Shores to market instead of repurposing it as an internal test chip. The decision comes as the company shifts its focus towards streamlining its roadmap and concentrating resources. This is bound to challenge Intel’s competitive edge in the Indian market compared to other companies like NVIDIA and AMD. 

“We have learned a lot as we have ramped up Gaudi, and we’re applying those learnings going forward,” Holthaus stated during the earnings call. “Based on industry feedback, we plan to leverage Falcon Shores as an internal test chip only without bringing it to market.”  

The company has now acknowledged that expectations for Falcon Shores had already been toned down last month. The move aligns with Intel’s strategy to develop a system-level AI data centre solution at rack scale centred around Jaguar Shores.

Intel’s AI Data Centre Struggles 

Intel sees long-term potential in the AI data centre market but admits it is not where it wants to be today. “This is an attractive market for us over time, but I am not happy with where we are today,” Holthaus said. 

However, the company has yet to establish a meaningful presence in the cloud-based AI data centre market. Intel is focusing on simplifying its AI roadmap and reallocating resources. 

Holthaus also highlighted a broader shift in Intel’s AI strategy, emphasising that AI is not a traditional market but an enabling technology that must integrate seamlessly across computing environments.

The post Intel Curbs Falcon Shores From Market, Quarterly Revenue Falls by 7% appeared first on Analytics India Magazine.

Meta Platforms exceeded Wall Street expectations in its fourth-quarter earnings, with CEO Mark Zuckerberg forecasting a significant year for AI. Despite an initial surge, the company’s stock rose in extended trading on Wednesday.

The social media giant reported quarterly revenue of $48.39 billion, surpassing analyst estimates of $47.04 billion. Earnings per share (EPS) stood at $8.02, exceeding the projected $6.77. Sales grew 21% compared to the same period last year. For the first quarter of 2025, Meta anticipates revenue between $39.5 billion and $41.8 billion, with analysts’ midpoint estimates at $41.73 billion.

Overall, the company reported a net profit of $20,838 million.

Despite the upbeat earnings report, Meta shares traded 1% higher at $684 in after-hours trading, retreating from an initial 5% spike.

Meta’s AI chatbot has reached 700 million monthly active users, and Zuckerberg expects it to cross 1 billion this year. “Once a service reaches that kind of scale, it usually develops a durable, long term advantage,” he told analysts. 

“In AI, I expect that this is going to be the year when a highly intelligent and personalised AI assistant reaches more than 1 billion people,” Zuckerberg said during the earnings call. “And I expect Meta AI to be that leading AI assistant.”

The company is maintaining a $60 billion to $65 billion capital expenditure plan for 2025 to strengthen its AI initiatives.

Zuckerberg also expressed optimism about the political climate under the new US administration, suggesting it would favour American tech dominance. “We now have a US administration that is proud of our leading companies, prioritises American technology winning, and that will defend our values and interests abroad,” he said.

However, analysts have raised concerns over Meta’s substantial AI investments as the conversation around Chinese AI model DeepSeek is making waves in the industry.

Meta’s headcount was 74,067 as of December 31, 2024, which is an increase of 10% YoY.

The post Meta Surpasses Earnings Estimates as Zuckerberg Predicts ‘Really Big Year’ in AI appeared first on Analytics India Magazine.

The Indian IPO market is witnessing a significant upswing, with several companies preparing to go public in 2025. The lineup includes Swiggy, Ather Energy, Reliance Jio, OfBusiness, Hexaware Technologies, and AI startup Fractal. The market’s growth trajectory in 2024, which saw a 149% increase in IPO value to $18.4 billion, has set a promising stage for the upcoming year. 

The Bombay Stock Exchange (BSE) anticipates a record-breaking streak continuing into 2025. Over 90 companies have filed their draft prospectuses, aiming to raise an estimated INR 1 trillion or $11.65 billion. About 34 companies have either already raised or are announcing their fundraising efforts.

Along similar lines, the BSE also witnessed a surge in investor confidence, with its share price doubling over the past year. Moreover, the trend of smaller city-based companies entering the stock market is expected to gain momentum in 2025.

Domestic Funds Taking the Charge

One of the most significant reasons for this upswing is India’s burgeoning retail investor base. Higher disposable incomes and financial literacy have driven increased participation in equity markets. Moreover, the rise of fintech platforms such as Zerodha or Groww and digital infrastructures has made IPO participation accessible to a broader audience.

Mahavir Lunawat, chairman of the Association of Investment Bankers of India, recently said that Indian firms used to take pride in raising funds abroad, but now foreign firms line up to raise funds in India. In an interview with BusinessLine a fortnight ago, he explained that around 851 IPOs had entered the market in the last six years, and around 1,000 companies will go public in the next two fiscal years.

Data from 2024 suggests that holding IPO stocks for at least six months yields better returns, reinforcing the need for thorough research and strategic investment planning. 

Krishnan V R, a quantitative research specialist at Marcellus Investment Managers, told AIM that this could be the right time for an IPO if organisations have a good business model that can sustainably ensure cash flows or credibly demonstrate a path to profitability through scaling. 

Highlighting the importance and the increased interest of domestic investors, Krishnan said, “Domestic mutual funds, which have become one of the largest incremental investors in Indian equity markets, are still seeing strong inflows,” he said, reiterating that December of 2024 saw a net inflow of INR 41,156 crore into equity mutual funds, the second highest inflow ever. 

“Though inflows could weaken if market returns are low, I still expect inflow to be strong enough to support primary market issuances at least over four quarters of FY2025,” Krishnan added.

Factors Affecting the IPOs

Domestic mutual funds and retail investors have dominated the market over the last year, while foreign investors (FII) have been reducing their allocation to Indian equities. This is also visible with the drop in the valuation of the Indian rupee last week.

“Indian rupee depreciation negatively affects FII investments, so I do not see much of an impact on the investor IPO demand, as long as domestic mutual fund flows are strong,” Krishnan said. He noted that if the newly listed companies are AI startups serving international clients with dollar-denominated revenues (similar to IT services), they could potentially benefit from a weaker rupee.

This was also reflected by Akash Aggarwal, managing director (investment banking) at Motilal Oswal Financial Services and former executive director at Axis Capital. Having participated in several IPOs in the past, Aggarwal said that despite the down market, IPOs are seeing decent subscriptions among Indian investors. “A majority of the money comes from Indian investors and not foreign investors,” Aggarwal told AIM. As of January 21, 2024, the BSE index has fallen by almost 10 percent compared to September 2024, when the market surpassed the 84,000 mark, its highest ever. 

He added that the drop might not affect small IPOs or the investment participation from FII. “Almost 70-80% of the interest is from domestic investors. Of the several companies that I am in touch with, some are going IPO, and I think this is the right time for it because it might take at least 9-12 months for them to launch the deal,” Aggarwal said, adding that if a company is mature enough, it should think about it.

He explained that the jump in the amount of retail investment is very significant. Compared to the average of 15-20 lakh applications last year, the average is now expected to be around 30-40 lakh since the last major IPO of Waaree Energies saw 98 lakh applications. 

The Need for a Strong Business Model Remains Paramount

“In my view, not every startup is ready for an IPO because most of the investors are looking for companies that are not burning cash for revenue. This makes these companies not ready for the public market,” Aggarwal said. These companies should look to raising funds from private investors, he added. He said he suggests this to most of the AI startups that he is in touch with, as generating revenue and achieving profitability is currently not proven for AI startups.

“Unless a startup has reached a certain level of maturity, it should not think about going public as it may not reach the expected valuation,” Aggarwal said. 

Many young and yet-to-be profitable companies have successfully been listed in the past 2-3 years, and several others are waiting to be listed from sunrise sectors like quick commerce, payments, etc. 

High valuation expectations and stiff competition in certain sectors could challenge companies’ efforts to attract investors. Therefore, it is crucial for companies to focus on long-term growth strategies rather than just short-term market trends. 

The post Now Might Be the Best Time for Indian IPOs appeared first on Analytics India Magazine.

Arva AI has successfully raised $3 million in a seed funding round led by Gradient, Google’s early-stage AI investment fund, Gradient. Y Combinator, Amino Capital, Olive Tree Capital, and other prominent fintech angels participated in the round. This milestone marks a significant step in redefining how financial institutions handle business verification using generative AI.

Know Your Business (KYB) verification, a critical compliance process to mitigate fraud and money laundering, has long posed challenges for financial institutions. Traditional KYB methods, which are heavily reliant on manual reviews, are not only time-consuming but also costly. As regulations tighten, banks and fintech companies face increasing pressure to streamline these processes without compromising on compliance standards.

Arva AI’s platform uses generative AI to automate KYB tasks and converts fragmented data from registries, social media, websites, and documents into actionable insights. The system’s advanced fraud detection and data extraction capabilities enable financial institutions to onboard businesses in seconds, which enhances both efficiency and compliance.

Founded in 2024 by Rhim Shah and Oliver Wales in San Francisco, Arva AI was accepted into the Y Combinator program’s winter batch (S24). Shah is also the product advisor of Kastle AI, a YC startup of the 24 batch that brings voice agents to automate and improve customer interaction in the mortgage industry. 

The founding team, including Shah, formerly of Revolut Business, and Wales, a former lead product engineer at Seal, brings deep expertise in fintech and AI. Their knowledge has been instrumental in crafting Arva’s innovative solutions.

“At Arva, our mission is to make business verification fast, accurate, and seamless,” Shah added. “Early adopters of Arva, including leading fintechs like Keep in Canada and Tola in the US, are already reaping the benefits of streamlined compliance.”

The funding will accelerate product development and expand Arva AI’s market presence to meet the growing demand for automated compliance solutions. Arva aims to develop a comprehensive suite of AI tools to handle repetitive, low to mid-risk compliance tasks. This will allow teams to focus on complex decision-making.

Indian Players

A number of Indian companies are already using AI to automate KYB processes. Fintech company Razorpay uses AI to automate business verification for onboarding customers and simplifying KYC and KYB processes. Similarly, Bengaluru-based Signzy, which offers an online identity verification service, utilises AI for the verification process. 

The post Indian-Founded Arva AI Raises $3 Million in Google-Backed Funding appeared first on Analytics India Magazine.

The fear of layoffs is palpable among Indian developers. AI takes it to new heights, especially considering that the financial industry or BFSI (banking, financial services, and insurance) is always on the verge of making its developers, quite ironically, bankrupt. In the next few years, we are expected to see an even bigger impact of AI on the fintech industry. 

If automation is already underway in the fintech industry, this begs the question of how the job market will be affected. To understand the same, Abhishant Pant, a full-time investor and founder of The Fintech Meetup, reached out to over 100 fintech founders, over 50 BFSI, and several VCs over the last week to understand the upcoming change in the landscape over the next three years. 

As per the results of the survey that Pant shared on LinkedIn, 30-50 % job loss is the base case impact of AI on BFSI.

Furthermore, Pant explained that 65% of the fintech founders believe anywhere around 30-50% of job loss occurs in the L1 category, which is the first line of support, including the help desk or global services desk. This is similar to the IT industry, where the support and services jobs are also under the threat of AI.

“Next year, we are targeting a 30% reduction in the workforce. In India, we are underestimating what’s going to hit us. It will hit hard,” Pant said that a founder told him.

In the BFSI industry, 70% of senior professionals at the CXO level believe job losses will be in the range of 10-30%. Because of better monitoring and coding tools, the developers’ jobs would definitely be affected, even though they expect that the impact would be minimal and only a boost in productivity. 

A Surge of Layoffs?

AIM spoke to Pant to understand more about the survey. Without revealing the names of the respondents, he said that the surveyed startups were all fintech companies with at least 100 employees. “Most of the people who are, for example, [in] web designing or tech support would be the most affected,” Pant explained.

Referring to the employees who are not critical for the code and basic programmers as “peripheral pieces”, Pant said they might get pushed to make the existing developers faster and better.

According to the survey, while some believe that AI will make existing developers three to five times faster and more efficient, others think that new jobs would be created that would be all about managing a team of AI agents with a lesser number of humans in the loop. 

However, not everyone is aligned on the same thought. For example, Zerodha CTO Kailash Nadh earlier told AIM that the company came up with an AI policy and job security since its employees were afraid of losing their jobs to AI.

Nadh clarified that Zerodha is one of the few companies that ensures there will be no job losses due to AI or any other technological advancements. “Last year, during the peak of the LLM hype, we made a decision to implement a policy explicitly stating that no one at Zerodha would lose their job solely due to the adoption of a particular technology.”

“Instead, we would provide avenues for employees to migrate to other roles,” he said, emphasising a human-centric approach to technology.

Sharing the perspective of The Fintech Meetup, Pant said that the financial industry could get recalibrated over the next one to three years due to the impact of AI, leading to job losses that could rise to 20-25%. Semi-skilled jobs would sustain the most impact, which he said “happen in a cushion environment” or “AC rooms”.

Pant fears that avoiding layoffs is going to be difficult since there is very little time for people to upskill. “There will be a period, maybe sometime [in] late 2025 or early 2026, from where there will be a churn, and that churn will be bad for one or two years. Maybe it will look less bad.”

This directly means that a surge of layoffs is expected in the fintech and BFSI sectors over the next few years as they continue to adopt AI tools.

Pant predicted that companies that are not publicly listed and are not under pressure from investors might be able to hold off layoffs for a while but will surely start laying off as soon as the pressure hits them in another two to three years.

“AI provides you with an advantage to reduce your cost…For companies that are living under the pressure of the market and are questioned by investors, their biggest expense is people,” Pant said, adding that these companies will have to remove some people from the organisation. 

The Impact is Clearly Visible

Most fintech companies are experimenting with generative AI in some way or the other. According to market research and data platform Tracxn, startups like INDMoney, IDfy, Perfios, PagarBook, CASHe, GoKwik, and several others are using AI tools for fraud detection and several other workflow tasks. 

In another survey, Moody’s Investor Service reveals that the fintech sector in India is at the forefront of adopting AI for risk management and compliance. While 18% of fintech participants reported active AI usage, the overall adoption rate across all sectors surveyed stood at just 9%. 

For example, Sharan Hegde, the founder of the 1% Club, recently cut 15% of his workforce to reduce costs by leveraging AI. Another similar example was Suumit Shah, CEO of e-commerce company Dukaan, who laid off 95% of his customer support team to replace them with AI in early 2023.

In October, PhonePe also cut 60% of its support staff as AI was able to drive its transaction speed by 40 times. This affected around 1,100 agents. “This efficiency was achieved by increasing automated customer service issue resolutions, powered by AI-driven chatbots, to over 90%,” PhonePe stated in a report.

In December last year, Paytm laid off around 1,000 employees, citing, “We are transforming our operations with AI-powered automation to drive efficiency, eliminating repetitive tasks and roles to drive efficiency across growth and costs, resulting in a slight reduction in our workforce in operations and marketing.” 

The spokesperson further added that this way, the company would be able to save 10-15% in employee costs as AI delivers better results than initially anticipated. 

Paytm CEO Vijay Shekhar Sharma had then said that he has been urging his employees and engineers to use Microsoft and Google’s AI tools to reduce development time and costs.

It is clear that there will be layoffs in fintech next year, and most of them will be due to AI and automation in lower-level jobs.

The post Indian Fintech Founders Expect Up to 50% Layoffs Driven by AI appeared first on Analytics India Magazine.

The Reserve Bank of India (RBI) has launched a high-level committee to establish a Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) in the financial sector. The initiative was announced alongside the December Monetary Policy Statement, underscoring the central bank’s commitment to balancing technological innovation with ethical governance.

Chaired by Pushpak Bhattacharyya, professor at IIT Bombay, the committee comprises notable figures from academia, government, and industry. Members include Debjani Ghosh, distinguished fellow at NITI Aayog and ex-NASSCOM President; Balaraman Ravindran, Head of IIT Madras’ Wadhwani School of Data Science and AI; and Abhishek Singh, Additional Secretary at the Ministry of Electronics and IT. Industry representatives from Microsoft India, HDFC Bank, and Trilegal also contribute their expertise.

The committee’s scope covers banks, non-banking financial companies (NBFCs), payment system operators (PSOs), and fintech entities. It will assess AI adoption in financial services domestically and globally, analyze regulatory frameworks, and identify risks associated with AI. Recommendations will include risk evaluation, mitigation strategies, and compliance frameworks to uphold ethical standards.

Key guidelines will be proposed for the responsible integration of AI models in India’s financial infrastructure, particularly catering to the growing fintech ecosystem. The RBI’s FinTech Department will provide secretarial support, and the committee is expected to deliver its report within six months of its inaugural meeting.

Through consultations with industry stakeholders and experts, the FREE-AI initiative aims to address the opportunities and challenges of AI, setting a benchmark for its ethical and sustainable use in the financial sector.

The post RBI Forms Panel to Regulate AI in Financial Sector appeared first on Analytics India Magazine.

Venture capitalist Vinod Khosla recently predicted that most consumer interactions online will involve AI agents handling tasks and filtering out marketers and bots. However, this raises a fundamental question: How are enterprises using AI agents to solve problems? 

Listed below are ten AI agents used in enterprise automation by different companies.

UiPath Platform

Developed by UiPath, this agent specialises in robotic process automation (RPA), enabling businesses to automate repetitive tasks across various applications like data entry, customer support, and financial processes. It is incorporated by companies like GE Healthcare to streamline medical record management and Wells Fargo for financial operations.

Power Automate

Developed by Microsoft as a part of Microsoft’s Power Platform, this agent helps automate repetitive tasks with AI integration and extensive app compatibility like email handling, notifications, and data updates. It’s used by companies like Toyota to automate supply chain notifications and Shell to streamline HR workflows.

Automation Anywhere

Developed by Automation Anywhere Inc, this agent offers an RPA platform with AI bots for end-to-end automation across different workflows and applications. It’s used by companies like Coca-Cola for supply chain optimisation and Cisco for automating customer services.

WorkFusion for RAP and Intelligent Automation

Developed by WorkFusion, this agent combines RPA with AI to streamline operations in sectors like finance and healthcare. Deutsche Bank uses it to automate transaction processes and UBS employs it for document compliance automation.

Blue Prism

Developed by the Blue Prism Group, this agent focuses on digital workforce automation, providing an AI-based RPA solution for large scale automation, helping with compliance, data processing and customer service. DHL uses Blue Prism for logistics and order processing, and Coca-Cola uses it for supply chain automation.

NICE Robotics Automation

Developed by NICE Ltd, this agent offers AI automation tools tailored for customer service, workforce optimisation and back office operations. Companies like AT&T integrate NICE automation for call centre management and Allianz employs it to automate insurance claim processing.

SAP Intelligent RPA 

Developed by SAP, it is a part of their Business Technology Platform aimed to offer process automation tailored for SAP ecosystems by automating ERP processes like finance, sales orders, and inventory management. Siemens uses SAP RPA for managing procurement processes, while BMW employs it to streamline logistics.

Pega RPA

Developed by Pegasystems, this agent is integrated with CRM and BPM systems, emphasising case management and customer service automation. It’s used by Bank of America for automated customer onboarding, while AIG uses it to handle insurance claims processing.

Appian RPA

Developed by Appian Corporation, this agent merges RPA with low code capabilities to automate workflows and integrate seamlessly with Appian’s low code automation platform. Companies like T-Mobile use Appian RPA for customer service workflows and General Electric incorporates it for data processing tasks.

AutomationEdge

Developed by AutomationEdge Group, this agent is an intelligent automation platform that integrates RPA, AI and IT process automation for end-to-end digital transformation. It offers automation for both IT and business processes, including ticketing, email processing, and data extraction. This agent is used by American Express for customer support and Wipro integrates it for IT process automation.

AI agents are in high demand in the industry. In the recent past, Oracle, Salesforce, and Microsoft have embraced AI agents as part of a larger trend in autonomous business functions. Oracle introduced over 50 AI agents within its Fusion Cloud Suite, targeting functions like HR and finance. Salesforce unveiled its Agentforce Partner Network, collaborating with companies like NVIDIA to expand AI-driven capabilities. Microsoft enhanced its Copilot agents to streamline workflows via integration with tools like SharePoint. 

This push highlights the potential of AI in reducing manual tasks and enhancing productivity across enterprises. With respect to streamline enterprise automation, the above mentioned few can be best suited for finance and HR activities. 

On the other hand, OpenAI has partnered with T-Mobile to develop AI customer service agents through a new platform called IntentCX, which uses OpenAI’s latest model to analyse customer interactions and optimise service responses. The platform aims to improve customer experiences by processing feedback and refining responses, with a full launch anticipated in the coming year. 

Additionally, T-Mobile is working with NVIDIA to further integrate AI in wireless networks, enhancing overall communication systems.

The post From Finance to Customer Service, These Top 10 AI Agent Drive Efficiency  appeared first on Analytics India Magazine.

Star Union Dai-ichi Life Insurance Co. Ltd. (SUD Life), one of India’s leading life insurers, has announced a strategic partnership with QuantumStreet AI, a global leader in AI-driven investment solutions. The collaboration will incorporate IBM watsonx-powered generative AI into SUD Life’s investment offerings, marking a significant development in the Indian investment landscape. The partnership was unveiled at the IBM Think Mumbai 2024 event, where QuantumStreet AI also launched its India service offerings.

The alliance between SUD Life and QuantumStreet AI will focus on developing investment products aimed at driving superior performance in the large market capitalisation sector. Leveraging the advanced capabilities of IBM’s watsonx AI platform, the companies aim to generate key insights to form the core of this investment solution, ultimately benefiting retail clients with responsible and trustworthy AI-powered tools.

“We are excited to partner with a world-leading fintech to bring innovative products to our clients. In today’s data-driven world, it is nearly impossible for individuals to process the vast amounts of information available, and AI has become an indispensable tool for fund managers, helping derive meaningful insights from growing data volumes. It is no longer a ‘nice-to-have’ but a ‘must-have,’” said Arindam Ghosh, Chief Technology Officer, and Prashant Sharma, Chief Investment Officer at Star Union Dai-ichi Life Insurance.

AI in Finance

QuantumStreet AI principals, Subhra Tripathy and Chris Natividad, echoed the enthusiasm for this collaboration: “This partnership reflects SUD Life’s innovative culture. We look forward to delivering our capabilities as we have done for leading banks, wealth managers, and pension funds around the world.”

As part of the collaboration, both companies plan to explore joint product development in Japan. Beyond India’s life insurance industry, QuantumStreet AI is poised to extend its services to asset management and wealth management sectors, where generative AI can be used for research, alpha generation, and risk management.

Sandip Patel, managing director of IBM India & South Asia, highlighted the transformative role of AI in India’s capital markets: “As India’s capital markets undergo pivotal changes, AI-driven investment solutions are becoming increasingly essential. This collaboration, with IBM watsonx at its core, sets a new standard for responsible investment practices in the Indian insurance industry.”

With IBM’s watsonx platform providing the foundation, the collaboration aims to deliver cutting-edge generative AI-powered insights. A few months ago, software company ServiceNow revealed plans to integrate watsonx.ai and IBM’s Granite foundation LLMs into its Now Assist GenAI experience for ServiceNow users. 

SUD Life is a joint venture between Dai-ichi Life, one of Japan’s largest insurers, and two prominent Indian banks, Bank of India and Union Bank of India. The company has a pan-India presence and is headquartered in Mumbai. On the other hand, QuantumStreet AI is an IBM Business Partner specialising in fintech, offering AI-powered investment solutions to institutional investors. The company operates out of San Francisco and Bangalore.

The post Star Union Dai-ichi Life and QuantumStreet AI Launch Investment Offerings Powered by IBM watsonx appeared first on Analytics India Magazine.

Oracle announced over 50 specialised AI agents integrated into its Oracle Fusion Cloud Applications Suite during CloudWorld 2024, Las Vegas. The AI agents will automate routine tasks across key areas like finance, HR, supply chain, sales, marketing, and customer service, enabling employees to focus on more strategic initiatives.

The new AI agents, powered by generative AI, are designed to automate end-to-end business processes and provide personalised insights and recommendations. 

“The latest Oracle Fusion Applications updates showcase the power of a single integrated suite with AI embedded in end-to-end workflows enabling customers to gain more value from their data,” said Steve Miranda, executive vice president of applications development, Oracle.

The AI agents will support functions such as employee shift scheduling, candidate sourcing, benefits management, order handling, asset maintenance, document processing, ledger monitoring, predictive forecasting, sales research, contract management, and incentive planning. 

For example, in HR, the ‘shift scheduling assistant’ helps manage employee shifts while ensuring compliance with regulations, and in finance, the ‘advanced prediction agent uses AI models to generate accurate revenue forecasts.

Oracle’s Fusion Applications Suite enables companies to standardise operations and break down silos by managing multiple business processes on a single cloud platform. This continuous innovation is updated regularly to help organisations adapt to changing business needs.

“I think that early use cases will be less completely autonomous and more human-assisted,” Miranda said in an exclusive interaction with AIM at CloudWorld 2024 Las Vegas, when asked whether these agents are fully autonomous. Moreover, he added that Oracle is heading towards an agentic workflow direction.

Oracle recently announced robust fiscal 2025 Q1 results, with total revenues reaching $13.3 billion, a 7% increase year-over-year in USD and 8% in constant currency. The company’s cloud services revenues surged 21% in USD and 22% in constant currency to $5.6 billion, driven by substantial gains in both cloud infrastructure and applications.

Oracle’s CEO, Safra Catz, highlighted the company’s expanding cloud services as a key driver of growth. “As Cloud Services became Oracle’s largest business, both our operating income and earnings per share growth accelerated,” said Catz.

Additionally, Oracle recently partnered with AWS, following its partnerships with Microsoft Azure and Google Cloud.

The post Oracle Unveils 50+ AI Agents to Automate Tasks Across Finance, HR, and More appeared first on Analytics India Magazine.

Managing finances isn’t easy for most of us. Data shows that an average consumer makes around 200 transactions every month. No wonder, nearly 7 in 10 of India’s affluent live with fragmented finances spread across multiple bank accounts, wallets, and UPI IDs. 

Further, according to an Amex Trendex survey, 69% of Indians prioritised financial health as part of their 2024 New Year resolution. On this front, growing savings (81%) and investing or expanding investments (75%) were the primary goals.

Building on this, CRED has recently introduced a feature called CRED Money. It leverages advanced data science to analyse spending patterns, provide personalised insights, and promote proactive personal financial management.

By making members more aware of their finances, CRED Money empowers them to take control and make informed financial decisions.

Recently, Avinash Shukla, a CRED member, tweeted about how the cash flow feature of CRED Money encouraged him to increase his investments beyond his spending.

So how does it work?

CRED Money uses the Account Aggregator (AA) framework, a key component of India’s digital public infrastructure, allowing consumers to securely and efficiently share their bank account information with authorised organisations.

With user consent, CRED’s AA partner, Finvu, consolidates data from various banks and transfers it directly to CRED Money.

Google Pay’s Similar Design 

Earlier in 2020, Google Pay had launched a similar update for Android and iOS, focusing on relationships with people and businesses. It offers insights into your spending and is built with multiple layers of security to keep your information private and safe. 

Instead of showing a long list of transactions, the app highlights the friends and businesses you transact with most frequently. 

If you connect your bank account or cards to Google Pay, the app provides periodic spending summaries and shows your trends and insights over time, giving a clearer view of your finances. 

Google Pay also organises your spending automatically, allowing you to search across your transactions in new ways. For example, you can search for “food”, “last month”, or “Mexican restaurants”, and Google Pay will instantly find the relevant transactions.

Is CRED a Super App?

It is to be noted that CRED, which is the credit card bill payment platform, eventually integrated UPI payment, competing against the likes of Paytm and PhonePe.  

Then there’s CRED Money, which also assists users by sending reminders and updates and allowing them to make payments through CRED UPI. 

Last year, the company launched CRED Garage, a one-stop shop to streamline vehicle management and enter motor insurance distribution, recharging FASTags, and accessing roadside assistance, and more. 

Speaking on the emergence of supper apps, CRED CEO Kunal Shah recently said, “Most people launch a very complex product to start with. A Swiss knife before a decent knife that works.”

He said that this also applies to super apps that numerous large conglomerates launch, attempting to offer 20 features all at once from day one, hinting at how CRED has been focusing on building one feature at a time and building value-adds sensibly. 

Responding to Justin.tv former CEO Emmett Shear’s take on Gall’s Law, Shah stated that large conglomerates often launch super apps that aim to offer a wide range of services right from the beginning. 

Emphasising the importance of simplicity and focus when starting a new venture or developing a product, he said these apps try to do many things at once, which can lead to complications and inefficiencies.

The post CRED Money – Personal Finance Manager with Advanced Data Science appeared first on Analytics India Magazine.

The State of AI in Accounting Report 2024, which explores the impact of AI on the accounting profession based on insights from 595 professionals, forecasts significant changes in the accounting industry thanks to AI. 

A staggering 71% of respondents foresee substantial transformation driven by AI. Despite this enthusiasm, the report reveals a notable gap: while 82% of accountants express interest or excitement about AI, only 25% actively invest in AI training for their teams.

Moreover, the report identifies three primary areas where AI is being utilised by accounting professionals: communication, task automation, and research. 

Currently, 59% of accountants use AI to compose emails, 36% to automate workflows, and 31% leverage AI tools for research purposes, highlighting the diverse applications of AI in enhancing efficiency and productivity within the accounting sector.

Here are 10 AI tools that are widely used in accounting and finance.

ClickUp

ClickUp Accounting is a cloud-based software for managing accounts and creating shareable reports. 

ClickUp Brain, an AI-powered virtual assistant, connects tasks, documents, and people, helping with financial management, project detailing, and meeting updates. Also, one can set up client/project workspaces, organise tasks into folders/lists by service type (audits, tax filings, monthly accounting).

Trullion

Trullion’s AI-powered accounting software solution offers significant time savings, growth opportunities, and impeccable financial oversight for accounting and audit teams. It automatically verifies the numbers against reporting and compliance requirements, identifying discrepancies and potential issues before they impact the business.

The platform leverages a proprietary financial rules engine, connects to hundreds of third-party data sources, and stays current with global compliance standards, ensuring comprehensive and up-to-date financial management.

Vic.ai

Vic.ai integrates seamlessly with leading ERP systems and accounting solutions, offering flexible and scalable AI-first capabilities through an open API. 

It optimises Accounts Payable processes, supports informed decision-making, and handles payment processing via card, cheque, and ACH, ensuring compatibility with all major ERPs for enhanced efficiency in financial operations.

Zeni

Zeni integrates AI to automate accounting, spending, and budgeting, simplifying financial operations with real-time data analysis for informed business decisions, blending AI with human expertise for effective expense tracking, bookkeeping, bill payments, reimbursements, and more.

Zeni provides personalised budgeting advice and a comprehensive one-page financial overview. It enables easy comparison of monthly, quarterly, and yearly reports to track progress, and simplifies data consolidation from receipts through a dedicated email address.

Docyt

Docyt AI enhances QuickBooks® with enterprise-level accounting automation, streamlining workflows for scalable business growth. One can choose from diverse plans, including expense management and automated bookkeeping for large operations. 

It helps access secure financial tools via its mobile app and automates revenue tracking and gaining insights across all streams with Docyt AI. One can accelerate month-end closings with real-time accounting and smart reporting capabilities.

Booke

Booke can transform financial processes with its AI-driven Robotic Bookkeeper for QuickBooks and Xero. It helps instantly organise invoices and receipts in any language or currency. It also assists in customising fields effortlessly with drag-and-drop, while the AI learns from one’s history to code transactions accurately.

Additionally, it helps resolve coding errors, categorise transactions, and automate tasks using AI. It streamlines month-end close with powerful automation, detecting and fixing errors quickly with Booke’s advanced features.

Bluedot

Blue Dot is an AI-driven tax compliance platform leveraging patented technology to help businesses ensure tax compliance, reduce spending vulnerabilities, and gain a comprehensive view of employee transactions. 

It utilises VAT Box to identify and calculate eligible VAT spending, employs AI for detecting and analysing wage tax information under Taxable Employee Benefits, and enhances expense management workflows with its proprietary AI-driven suite, applying checks and tax rules to maintain compliance.

Gridlex

Gridlex Sky, part of the Gridlex suite, integrates accounting, expenses, and ERP functionalities to streamline financial processes. It automates revenue and expense calculations, enhancing accuracy and saving time previously spent on manual tasks. 

This automation reduces errors, improves efficiency, and integrates seamlessly with Gridlex Ray for HR management and Gridlex Zip for CRM and customer service support, offering businesses a comprehensive platform for essential operations.

Truewind 

Truewind is an AI-powered software designed specifically for startups, offering reliable bookkeeping and detailed financial models with minimal errors. It accelerates month-end close processes for accounting firms and internal teams, reducing administrative burdens and increasing profitability.

Accounting firms benefit from Truewind’s specialised solution, which simplifies the month-end close without traditional checklist hassles. It integrates seamlessly, eliminating the need for manual checklist transfers into software, often required by other solutions.

Stampli

Stampli streamlines invoice management across all stakeholders—accounts payable (AP) staff, approvers, management, controllers, CFOs, and vendors—via a unified communications hub that integrates with each invoice. This fosters a seamless collaboration and rapid query resolution, accelerating processing times by 5x through timely access to critical information, thereby enhancing decision-making capabilities. 

Customers choose Stampli for its efficient invoice capture, coding, and approval processes, bolstering internal controls with detailed audit trails and real-time insights to optimise overall finance operations while ensuring audit readiness.

The post Top 10 AI Tools for Finance and Accounting in 2024 appeared first on Analytics India Magazine.

Financial consulting companies must definitely know the cost of using generative AI. That is why they are building their own customised LLMs for chatbots and other purposes to make sense of documents and not solely rely on expensive offerings by others.

The latest joiner in the LLM development league is JPMorgan, a financial consulting firm which deals in investment banking, commercial banking and other financial services. The firm has introduced DocLLM, a generative language model designed for multimodal document understanding.

It stands out as a lightweight extension to LLMs for analysing enterprise documents, spanning forms, invoices, reports, and contracts that carry intricate semantics at the intersection of textual and spatial modalities. 

Generalised models are sub-par

OpenAI’s ChatGPT Plus also allows users to scan and analyse documents. The feature is also available in ChatGPT Enterprise. But these models have been criticised for their privacy concerns, which makes enterprises and consulting companies, with all the financial data, scared to use them.

Moreover, GPT and other AI models have been experiencing hiccups when it comes to analysing documents. Most recently, the models were not able to analyse SEC filings, resulting in major backlash for the models. 

The top-performing configuration AI model, specifically OpenAI’s GPT-4-Turbo, achieved a mere 79% accuracy when equipped with the capability to analyse almost an entire filing in conjunction with the posed question. Frequently, the models exhibited reluctance to respond or would generate inaccurate information—or hallucinate—which did not align with the details found in SEC filings.

Anand Kannappan, co-founder of Patronus AI, a company which evaluates the security of AI models, expressed dissatisfaction with this level of performance, deeming it “absolutely unacceptable”. He emphasised on the necessity for a significantly higher accuracy rate to make the technology truly effective in automated and production-ready applications.

These discoveries underscore the difficulties that AI models encounter as major corporations, particularly those in regulated sectors like finance and consulting, strive to integrate state-of-the-art technology into their operations, whether for customer service or research purposes.

Thus, developing own models

These inaccuracies and securities were one of the reasons why BloombergGPT was launched, specifically for finance. It has been helping people make sense of financial documents, reports, and invoices. This also highlights the need for open source models, when it comes to dealing with financial information, where DocLLM definitely shines. 

 JPMorgan is making DocLLM open for other users as well. The two versions of DocLLM, one with 1 billion parameters is built on top of Falcon-1B architecture, and 7 billion parameter models are built on Llama2-7B. Being open source, the model provides safety and security to its users.

Similarly, KPMG had internally developed a system based on OpenAI models and called it KaiChat to aid its staff with exclusive data. PwC is set to invest $1 billion over three years to advance generative AI in its US operations, collaborating with Microsoft and OpenAI to automate tasks in tax, audit, and consulting.

EY leverages generative AI, integrating tax laws into an AI system for instant responses through a ChatGPT-like interface, particularly for tasks like payroll queries.

In August, McKinsey embraced the potential of LLMs with the launch of “Lilli”, designed to streamline and enhance the utilisation of the firm’s vast knowledge base. Wells Fargo also introduced Fargo in 2022, a virtual assistant powered by Google Cloud’s AI, for providing a personalised, convenient, and simple banking experience.

In October last year, Deloitte launched DARTbot, an internal chatbot for enhancing efficiency of Deloitte’s 18,000 US Audit & Assurance professionals.

But can they compete against OpenAI?

When OpenAI launched GPT-4, the much hyped BloombergGPT for the financial field slowly stopped gaining traction. Adi Polak said that models such as GPT-5 coming up soon can possibly outperform JPMorgan’s DocLLM, as they would be better at specialised and generalised tasks combined. 

“This could become the go-to model for document intelligence tasks, saving companies time and money. For example, insurance firms can automate claim assessments, while banks can speed loan processing,” said a user on X. To this, Polak replied that it would require a lot of fine tuning. 

Whenever OpenAI releases new features to ChatGPT, it gets blamed for affecting startups and others doing the same. When it introduced ‘Upload many types of documents’ this new ‘multimodal’ update, according to many, was expected to kill hundreds of startups. Some of the popular names include ChatPDF, AskYourPDF, and PDF.ai, and many more, which were basically wrappers of OpenAI’s models.

But for the time being, it is clear that consulting companies building their own LLMs for financial planning and decisions is better than relying on other offerings.

The post Why Are Consulting Firms Building LLMs appeared first on Analytics India Magazine.

At the end of 2023, we are probably standing at the high crest of the generative AI wave. From being termed the word of the year to being the crux of every major big tech company announcements this year, including Google, Microsoft, and more, it is a no-brainer that AI will continue to be crucial in 2024 too.

Will AI transform the tech ecosystem or be the hype that fizzles out, is something that remains to be seen. However, if we were to make AI predictions for 2024, the optimism from 2023 AI prediction persists. It’s likely that new AI superpowers and use-cases will emerge. Let’s get predicting.

US will not be the only AI superpower

While this year saw the dominance of the US in the AI space with big tech companies such as Meta, Microsoft, OpenAI, and Google releasing their LLMs and chatbots that got the world talking, other countries are slowly (but surely) catching up. 

This year saw the emergence of UAE as a promising force in the AI race. UAE’s Technology Innovation Institute (TII), a research institute supported by the government, released their LLM, Falcon, a 180 billion parameter open-source model this year. The government support combined with abundance of capital comfortably places UAE ahead in the LLM race. 

UAE is also focusing on building its demographic specific-models. Core 42, a subsidiary of tech company G42, released its Arabic-language model, Jais 30B. Furthermore, last week, the Advanced Technology Research Council (ATRC) in Abu Dhabi unveiled a new AI company, named A171.

Launch of A171 in Abu Dhabi. Source: Multiplatform AI

Meanwhile, US’s arch nemesis China too is finding its feet in the LLM race. Last week, Deep Seek, a Chinese company working on AGI, released DeepSeek LLM, a 67 billion parameter model. The open-source model is available in both English and Chinese, and outperforms Llama 2 and Claude-2. 

Though not fully there yet, the European Union too is slowly progressing in the LLM race. Paris-based AI startup that raised $113M in seed round, taking its valuation to $260M in June this year, released Mistral-7B, an open-source model which will be integrated with Vertex AI Notebooks, thereby finding an actual use-case with the tech giant. 

Furthermore, Germany-based AI research and development company Aleph Alpha recently raised $500M in Series B, pushing its valuation to $643M. These investments will probably reap the benefits in 2024, likely resulting in a string of AI investments in EU countries. 

Rallying for open source will gain steam

Founders and AI enthusiasts foresee a future where AI will mostly be democratised. The co-founder and CEO of Hugging Face, Clem Delangue, has predicted a number of things, out of which open-source LLMs is something he has heavily bet on. He believes that open-source LLMs will match the levels of best closed-source LLMs. 

The support for open-source is not just for promoting the whole LLM ecosystem, but from evading the dangers of over-reliance on a single or limited number of closed source models such as GPT-4, Anthropic’s Claude-2 and others.

When Sam Altman was recently ousted from OpenAI, companies that relied on GPT went into a frenzy as the future of the company was questioned, further compelling experts to advocate for open-source models. Meta’s open-source model Llama-2, has been adopted by companies for building a number of LLM models. 

There was also a movement for openness in AI development where 70 experts, including Meta’s chief scientist Yann LeCun, signed the letter. Furthermore, Tesla and x.ai leader Elon Musk has always been a promoter of open source models.

Rise of small language models

With immense costs that runs into millions of dollars, and high GPU utilisation associated with training large language models, big tech companies are looking to work on small language models (SLM). Furthermore, prototyping and customisation for specific tasks will work better on smaller models. 

Microsoft’s love for small language models was unveiled at the recent Ignite event where the company launched Phi2 for enterprises. Microsoft had earlier launched Orca, a 13 billion parameter, considered to be a smaller alternative to GPT-4. Meta’s Llama 7B, Falcon’s 1B, 7B, and Alibaba’s recent model Qwen 1.8B fall under the bucket of SLMs. With the rise of specific use cases in enterprises, SLMs will prove to be beneficial.

Generative AI in arts and science will flourish

The year set the wheel in motion with AI finding applicability across domains with two clear categories being rampantly spoken about — image/video generation and science, especially protein-folding. 

While protein folding applications had been making its way in the last few years, this year witnessed huge developments in that area. Google DeepMind released upgrades to AlphaFold models just a couple of months ago and are continuing its momentum. The models are also finding a way to help nature preserve its habitat. 

It won’t be wrong to say that generative AI has found maximum use cases in video and creative fields. This year saw a number of startups emerge in the space of generative AI text-to-image/video conversion. The recent Pika Labs, a text-to-video platform saw an influx of notable investors before the actual release of the product.

Other platforms such as Midjourney and Runway are  continuously releasing upgraded versions of the models. Indian startups have also emerged, thereby ringing the oncoming of generative AI use-cases in animation and video production. 

Delangue also predicts that there will be ‘big breakthroughs in time-series, biology and chemistry’. 

AGI still remains hazy

A topic so vastly debated in 2023, AGI discussions will continue in 2024 as well. In a race to achieve AGI, big tech companies are still wading their way to understand how to get there. On the one hand, OpenAI is working on Q* and PPO that will supposedly help reach AGI, Yann Le Cun, on the other hand, has not only been dissing OpenAI’s approach but has also stated that AI superintelligence will not happen in the next five years.

He believes we can get to cat- or dog-level AI before reaching the human-levels. 

Though Google Gemini, a powerful AI model, is slated to release next year, the hopes of AGI from it is still a distant dream. 

Going by the whirlwind of a year it has been for generative AI this year, it is unfathomable to exactly predict the diverse nature of which AI will continue to revolutionise the world. However, the generative AI hype is said to fade, and only actual use-cases will thrive.

With limitations in LLMs, especially in the finance sector, most companies integrating ChatGPT and other similar models are for either conversation or to improve their operational efficiencies. Revolutionary use cases are still awaited.

The post Challenging the Norm: Bold Predictions for Generative AI in 2024 appeared first on Analytics India Magazine.

Amid a not-so-good quarter, owing to increasing expenses and reduced client spending in a challenging macroeconomic environment when the companies’ profits dropped by 62% from Q2 of previous year, Tech Mahindra seems rather cheerful.

The company, in its latest earnings call, emphasised on the need for innovation and diversification, and shifted its focus towards sports, chess and cricket. 

Cricket Connection 

In collaboration with BCCI, Tech Mahindra set up an innovation lab to enhance fan experience at the Ahmedabad stadium. The innovation centre was launched during the IPL finals, this year. 

The Joint Innovation Center is set up at the Narendra Modi Stadium, and Gurnani confirmed that a similar innovation lab will come up at two more stadiums — Dharamshala and Wankhede, Mumbai. 

Interestingly, the Narendra Modi Stadium in Ahmedabad, which is the largest cricket stadium in the world, where the IPL finals for 2022 and 2023 were held, witnessed over 1 lakh spectators. This is also where the World Cup Finals for this year will be held, thereby, proving to be the right place for Tech Mahindra’s fan centre. 

The company’s foray into cricket vertical is not a new development. In 2020, during the pandemic, Tech Mahindra, partnered with IPL team Kings XI Punjab to enhance the fan experience and reach a wide audience. It launched an engagement app on Android and iOS for fans to connect with the team, and were also working on introducing holographic virtual fans in the stadium.

Furthermore, the company also collaborated with another IPL team in 2021. Team Rajasthan Royals partnered with the company to enhance fan loyalty and monetisation. The collab looked to triple the value of the team’s fan base by using Tech Mahindra’s digital platform, and also find ways to create revenue streams for the team. Digital campaigns via social media, email and other avenues were part of the plan. 

Sporting Revolution 

Moving beyond cricket, Tech Mahindra’s sports vertical has invested and partnered with diverse sports companies and universities too. In the earnings call, Gurnani confirmed that they are working with NFL (National Football League) and ‘Mahindra Racing’ that competes in electric FIA Formula E Championship (a motor racing team competing with an Indian racing licence), and have branded the platform as Fan NXT.NOW. 

In 2021, Tech Mahindra partnered with Loughborough University in England, whose expertise lies in exceptional athletes, world-class facilities, top-tier coaching, research capabilities, and active collaborations with sports entities. The partnership focussed to combine both their prowess by jointly advancing sports innovation and exploring options around 5G, AR, VR and further research on shaping the future of sports consumption.

A few years ago, Tech Mahindra partnered with Fanisko, a fan-engagement platform that enhances mobile fan retention and digital engagement. The collaboration was aimed to leverage cutting-edge technologies to improve fan engagement and introduce innovative monetisation models for sports organisations globally. 

Tech Mahindra is also synonymous with chess. The company is preparing to grow the league by introducing four additional teams in response to interest from both international and Indian businesses looking to invest in the franchise-based competition – Global Chess League. In June 2022, Tech Mahindra was the first corporate organisation to back the FIDE Chess Olympiad. 

Towards Diversification and Innovation

“We, as a company, will continue to invest in innovation; we’ll continue to invest in new verticals; and we’ll continue to diversify,” said the CEO and MD of Tech Mahindra, CP Gurnani, during the company’s earnings call. 

Tech Mahindra’s focus on generative AI has been rampant this year, with the company seeing it as means for talent utilisation and innovative work. The company announced its plans to train 8,000 employees to cater to the demand of generative AI and quantum computing.

This year, the company announced a number of initiatives such as Generative AI Studio, to drive customer excellence, and have even announced the Indus Project, which is working towards building an Indic language model, an effort towards making an indigenous language model for Indian context. 

The post Tech Mahindra Takes a Quarterly Dip Sportively appeared first on Analytics India Magazine.

NVIDIA’S recent quarterly report presents a curious picture of the company — a unique combination of record profit and loss. In this earnings call for the last quarter, the company reported a loss of $1.83 billion in gaming, which has long been its primary focus market. However, even as their gaming section saw a downturn, the company’s revenue rose to a record $15.01 billion in the data centre sector, representing a growth of 41% over the past year. 

While NVIDIA has been dedicating resources towards developing new solutions for their enterprise customers, it seems like gaming is taking a back seat. Last year alone, it released 7 new products for the enterprise while releasing only the RTX 40 series for the gaming market. However, according to NVIDIA chief Jensen Huang, gaming is recovering from the post-pandemic downturn, with gamers enthusiastically embracing the new Ada architecture GPUs. Compared to the last quarter the company has seen an increase of 16 percent in the gaming sector.

Gaming reality

Gaming has been NVIDIA’s bread and butter since inception. As per the information from the January 2023 Steam Hardware Survey, NVIDIA reigns supreme in the field. However, there is a twist in the story – users are still on old hardware. 

The green giant boasts 75.03% market share in PC gaming, but the list of most-used GPUs has a telling lack of the latest 40-series GPUs. The gaming community ditched the 40-series GPUs, mainly due to pricing issues and miscommunications on the capabilities of the chips. Most gamers are still using generations-old technology, with the top 5 dominated by 10-series and 20-series cards. The closest to a ‘latest GPU’ on the line comes in at number 3, a position taken by the RTX 3060 laptop GPU.

NVIDIA’s last big win in gaming was the launch of ray-tracing enabled GPUs in 2018, termed the ‘RTX’ line of GPUs. When combined with accompanying technologies like DLSS 3.0 and Tensor cores, these chips are currently the de-facto standard for high-fidelity gaming. However, the company’s downturn in the field may represent a saturation in the gaming market, where gamers are unwilling to upgrade for minimal improvements. 

The upgrade cycle for consumer GPUs hovers around 3-4 years, with the mean lifecycle for enterprise components pegged around 5.5 years. NVIDIA’s gaming GPUs are usually released in a 2-year cycle, but the pace of research and developments in the enterprise sector has crept up in the past few years. NVIDIA still caters to the consumer upgrade cycle, but it has circumvented the enterprise upgrade cycle by catering to cloud service providers. 

“Our strategy and goal is to put the DGX infrastructure in the cloud. By having NVIDIA DGX and NVIDIA’s infrastructure full stack in their cloud, we’re effectively attracting customers to the CSPs. This is a very, very exciting model for them…we’re going to be the best AI salespeople for the world’s clouds,” said Jensen Huang, the CEO of NVIDIA. 

Gaming revenue used to be neck-and-neck with enterprise revenue at NVIDIA, but their enterprise pivot has now come into focus. Along with the lacklustre 40-series launch, it signed an agreement with Microsoft to bring Xbox games to GeForce NOW, their cloud gaming platform. The company seems to have shifted towards AI hardware where they stand to gain a lot from doubling down on their already-pervasive industry presence.

Why enterprise? 

NVIDIA’s release schedule has been dominated by enterprise products for the last year, such as the Hopper architecture and DGX H100 systems. From its CUDA software stack to its partnership with major cloud service providers, to providing training compute for GenAI like ChatGPT, Stable Diffusion, and more, NVIDIA has a finger in every slice of the pie. 

Moreover, NVIDIA holds a near-monopoly in the training hardware space. A 2020 study pegs NVIDIA’s market share for AI accelerators at a whopping 80.2%. What’s more, the company has bigger plans — one of a future where every AI model is trained on the NVIDIA hardware, no matter the cloud service provider. In Huang’s own words, NVIDIA AI is “essentially the operating system of AI systems today”. 

Besides the partner program with cloud service providers, NVIDIA is actively working with companies from other industries. Last year, it joined forces with Deutsche Bank, Mercedes, Dell, Lockheed Martin, and Foxconn. What’s more, we are currently in the midst of a growth boom for AI chips, as seen by this study which predicts that this field will be valued at $263.6 billion by 2031 at a CAGR of 37.1%. With its market leader position, NVIDIA is poised to become the face of AI accelerator chips — the crown jewels of their enterprise strategy. 

When looking at gaming’s reduced revenues, a slowdown in the pace of new gaming products, Huang’s renewed enthusiasm towards AI compute, and a lack of significant partnerships with other companies in the gaming field, it seems as though NVIDIA is content with its crown for now. The installed base for NVIDIA’s hardware is extremely high, but this base might start eroding if the company goes in search of greener pastures in the arms of the enterprise sector. 

The post Decoding NVIDIA’S Recent Quarterly Report appeared first on Analytics India Magazine.

India has seen the banking sector adopt data analytics and AI faster than most countries. As per a survey by PwC-FICCI, AI applications are estimated to help banks make potential cost savings of $447 billion by 2023.

In an exclusive conversation with Analytics India Magazine, Sonali Kulkarni, lead, financial services, Accenture in India, shared her insights into the world of AI transforming the banking industry.  

AIM: Banks have been using data and AI for some time now. What has the adoption in India been like so far, and where do you see this headed?

Kulkarni: Despite a slow start, most mature banks in India have started their data and AI adoption journey by making foundational investments in analytics use cases, data lakes and customer journey digitalisation. They are using data and AI to improve decision-making across the banking value chain. For example, machine learning is being used to improve cross-sell efficiency, targeted digital marketing, estimate core balances, improve capital efficiency, and for smarter underwriting decisions.  

During the pandemic, there was a surge of investments in  data and analytics-driven risk discovery and mitigation to get early warnings on market and credit risk, forecast liquidity needs, identify delinquency patterns, improve collections strategies, and also for fraud detection. AI is also being used to support the Know your Customer (KYC) processes, for generating credit appraisal memos and regulatory compliance tasks such as filing suspicious transaction reports.

We expect banks to continue to innovate on new cross-sell and profit optimisation models to tap into their rich data reserves as well as pivot to building enterprise-wide intelligent automation and AI platforms. The need to compete with digital native players,  drive superior customer experiences and extract better value from digital investments will  drive innovation in this space.  Lastly, responsible and explainable AI practices will be a key focus for banks.

AIM: How can banks best approach AI-enabled business transformation so as to derive higher returns on investments?

Kulkarni: First, AI should be applied with a business intent rather than to fulfil a technology goal. Second, banks need to think of longer-term business outcomes and hence, must go beyond the siloed proof of concepts and apply AI across the organisation. Leadership commitment is vital to achieving this.

Banks need to build an AI core that integrates foundational capabilities across the organisation – such as cloud-native data lakes, AI services, data platforms and tools, robust security and governance. It is also key that banks do not just limit themselves to technology interventions but also focus on building data, digital and cloud skills in the workforce supplemented by new operating models, and data-driven ways of working.

AIM: How can banks use AI and analytics to unlock value in relatively untapped segments such as treasury operations and corporate banking?

Kulkarni: Treasury operations can benefit from curated AI algorithms that can analyse large volumes of data with greater accuracy, and thereby help treasuries manage risk and  forecast liquidity better. They can also manage cash more effectively and efficiently, and implement controls in a timely manner.

AI and analytics can play a pivotal role in coverage management and improving client servicing in corporate banking. AI-driven insights, when applied to portfolio management on an ongoing basis, can enable proactive monitoring. They can also help the bank pre-empt customer queries and servicing issues and empower relationship managers to take proactive or preventive steps. AI driven nudges can also help relationship managers prioritise key tasks.

Lastly, the true value that data and analytics can drive is to offer the bank a 360-degree view of the customer and break the silos between the corporate banking and retail banking businesses. Data and analytics can address needs of a client across their entire ecosystem, including at an organisation level (corporate banking), for their suppliers (SME banking) and their employees (retail banking). This approach would unlock value for customers as well as for the entire bank.

AIM: New entrants to the financial services sector may not have significant customer data to derive insights from. How can they get started?

Kulkarni: Leveraging data is an ongoing journey, and every organisation is at a different stage of this journey. New entrants to financial services that do not have significant customer data can begin by focusing on low-hanging fruits, such as heuristic analytics, where insights from the available data are driven through business judgement or predictive insights from an expert opinion. And as they accumulate more data, sophisticated analytics can be leveraged to replace expert judgement with machine learning models. They can also leverage partnerships to tailor customer insights, bootstrapping techniques or use proven pre-built analytical models, which have worked in environments with similar customer profiles or data sets.

AIM: How can Indian banks cultivate a more data-driven culture? What kind of infrastructure and skills do they need to invest in?

Kulkarni: Business leaders at banks need to advocate that every decision be supported with data-backed insights so that this approach permeates into the bank’s processes and culture. This must be supported by a structured change management program that embeds desired outcomes into routine banking processes.

Banks need to develop a comprehensive data strategy and make investments in an enterprise-wide data and analytics foundation, data governance, and management processes. A key element is a modern data and analytics platform that identifies and collates connected and contextual data from within and outside the bank and can convert data into insights for easy consumption. This platform needs to be supported by enabling architecture such as cloud-based accelerators and self-service tools. Where needed, there must be willingness to transition from legacy data systems to a scalable and modular data architecture, reconfigure processes and systems to support easy sharing of data across the organisation.

Investing in data leadership and cultivating data literacy throughout the organisation is equally important.  Banks must build or hire for data, AI, analytical skills and related multi-disciplinary skills such as data visualisation, data storytelling and behavioural sciences.

AIM: Experts say that cloud adoption is vital to banks becoming more data-driven. Can you elaborate?

Kulkarni: Limited data storage and compute power are rendering on-premise data lakes and analytics environments sluggish and expensive. The cloud can help overcome this challenge since it is scalable and offers elastic storage and computation. It allows banks to integrate data from different sources and make it more accessible in real time. This can enable agile reports, more sophisticated analytical models and insights for faster decision-making.

Factors such as regulatory compliance related to data residency, customer data protection, security, and return on investments must be taken into consideration while envisaging a bank’s cloud adoption journey.

The post Optimising the Indian Banking industry using Artificial Intelligence appeared first on Analytics India Magazine.

Chennai-based pure-play analytics firm ‘LatentView’ announced its first quarterly results for FY23 today. The company reported a revenue of INR 120 crore, witnessing the highest ever revenue in this quarter, growing at 37 per cent YoY. Its financial services vertical reportedly grew by 17 per cent, and the technology vertical, one of the company’s largest verticals, grew by 15 per cent during FY23 Q1. 

However, LatentView’s EBITDA margin for the quarter witnessed a marginal drop at 29 per cent, compared to the previous quarter, which was at 30.5 per cent. The reason—latest investments made by the company in the front end and marketing activities, alongside hiring via campus hires i.e., a total of 103 employees were added in the last few quarters, out of which 85 were campus hires. 

“These employees go through 2-3 months of training programme/bootcamp before [being] deployed on the projects”, shares Rajan Venkatesh, CFO at LatentView. 

At the earnings call, Venkatesh revealed that the company recorded its highest PBT, which stood at INR 41.8 crores; a growth of 46.4 per cent YoY; and 3.7 per cent QoQ. Further, its cash and investments as of June 30 2022, stood at INR 587 crores, where the company considers spending this cash in the coming months across various avenues, M&As and more. 

Analytics revenue breakdown

Breaking down revenue from an analytics services perspective, LatentView chief Rajan Sethuraman told Analytics India Magazine that about 10~15 per cent of the revenue comes from upfront analytics consulting and road-mapping work. Meanwhile, data engineering, data platforms and architecture as well as deploying data solutions contributed about 25 per cent of its revenue. “In total, these two make up about 40 per cent”, Sethuraman adds. 

The remaining 60 per cent of the revenue is split into two: According to LatentView—it’s called ‘look back’ and ‘look ahead’ analytics. 

‘Look back’ is a diagnostic, descriptive type of work, contributing about 40 per cent of their revenue. The remaining ‘look ahead’, which is oriented more towards predictive and prescriptive analytics—using AI and ML models—i.e., about 20 per cent.  

From an industry perspective, 65 per cent of revenue stems from technology and digital native companies. Sector-wise, banking, financial services, retail and consumer segments witnessed huge demand for their services, besides automotive, oil and gas, logistics and manufacturing, and others. 

“We added three new accounts”, exclaims Sethuraman. In addition, he said that they had significant growth in many of their existing accounts. “Last year, we added about 18 accounts. This year, we started with three in the first quarter, but right now, there are conversations with eight new accounts, which we expect to close in quarter two”, Sethuraman adds. 

Citing the early days of deals, Rajan Sethuraman said that the quantum of the worth of the first SOW (statement of work) was around the $200~250 range. 

“In recent times, that has gone up significantly. Today, when we take on new accounts, most start at least half a million ($500K). And we are expecting that trend to continue”, concludes Sethuraman.

The post LatentView records its highest ever quarterly revenue appeared first on Analytics India Magazine.

Rocket Capital Investment (RCI), in association with MachineHack, is set to launch the longest blockchain tournament from June 13 to September 05 — a twelve-week hackathon to source and incentivise the best in machine learning applications for finance. The tournament is made up of a series of challenges, where the participants stand a chance to win over INR 4,00,000 cash & 200,000+ MUSA coins.

Being a Licensed Financial Institution headquartered in Singapore, RCI combines their financial expertise with external machine learning forecasts through a blockchain tournament on financial markets. 

The company believes that a key need for RCI is to feed its engine with thousands of machine learning predictions from data scientists to improve the accuracy of the Meta-Model Rocket used to make portfolio asset allocation. 

Through this competition, RCI aims to use a decentralised platform to source and incentivise the best in machine-learning applications for the finance industry. Alongside, data scientists and ML practitioners can now be rewarded weekly with MUSA coins for their data science skills. This can also help them create a decentralised track record of their performance.

Start Date: June 13, 2022

End Date: September 05, 2022

Register Now

All about the #CryptoPrediction challenge 

In this challenge, RCI asks the data scientists & ML Practitioners to submit financial predictions using the weekly dataset. RCI evaluates participants’ performance and pays them rewards at the end of the week. 

Let’s not forget that the MUSA token drives the challenge, where participants stake their MUSA coins to submit their predictions and earn rewards based on their stake and the performance of their submissions. The community competition gets up to 200,000 tokens and a 500 USDT prize (INR 40,000) at the end of every week, for the next 12 weeks. 

How does it work?

The #CryptoPrediction tournament is made up of weekly challenges, where each week:

• RCI distributes a dataset (every Monday at 11:00 AM, till September 05) and opens the submission window. 
• Participants can start submitting their crypto predictions of the financial markets.
• At the end of the week, RCI closes the submission window (every Tuesday at 5:30 AM, till September 05).
• Post that, the predictions are evaluated against the real-world data, and the rewards are paid out in MUSA tokens & cash amount.

Dataset

The dataset consists of

• #first column (symbol) is the ticker
• # ‘target_DC1’ is the log of return for the delta between the current close to the previous close
• # ‘target’ is the y which is ranked by Era using ‘target_DC1.’
• # the rest of the columns are features to be used for ML

Start Date: 13th June 2022

End Date: 05th September 2022

Register Now

Prerequisites for the #CryptoPrediction challenge

• Follow https://twitter.com/rocketcapinv on Twitter

• Follow https://www.linkedin.com/company/rocketcapitalinvestment/ 

• Join the RocketCapital Discord channel at https://discord.com/invite/r6NbnCSrzn

• Open an ETH wallet and configure tournament dApp ( https://www.youtube.com/watch?v=Lb_A8J2wJMw&t=1s)

• Request MUSA airdrop on this site ( https://rocketcapital.ai/airdrop/)

• Check for airdropped MUSA tokens on your wallet or competition account

• Use MUSA tokens for staking ( https://www.youtube.com/watch?v=5CLKh776mtI&t=50s)

• Each week download the dataset and send the predictions ( https://www.youtube.com/watch?v=A9iUuV1RVdo )

• The challenge will only allow for one submission each week per wallet address (One Account Per Wallet Address), and submissions from multiple accounts will lead to disqualification. All registered users are eligible to participate in the hackathon.

• Privately sharing code or data outside of teams is not permitted.

• Rewards will be paid to the wallet address used to submit, so please coordinate with your team regarding the distribution of rewards.

• Each team can have a maximum of 5 members aged above 18. The one who shared the request (once accepted by another participant) will become a team leader. 

• Only a team leader can create a team (including sharing requests & accepting members). A team or an individual participant will be treated as one entity. A single participant adding a submission will be discarded once they join a team.

• Changes to team members are not permitted. Once the team is formed, it is not possible to delete the team.

How will you be evaluated?

 The submission will be evaluated using the Spearman Correlation metric. One can use ‘scipy.stats.spearmanr(prediction, target)’ to calculate the same. [mean_squared_error(y_true, y_pred, squared=False)]

 The Final Score represents the score achieved based on the Best Score on the public leaderboard.

Rewards will be given to participants according to the above scores, considering the correlation among predictions (refer to RCI Competition for technical details).

 Sklearn models support the predict() method to generate the predicted values. The participant should submit a .csv file with the same number of rows of the test dataset. The submission will return an Invalid Score if you have extra rows or columns. The file should have exactly two columns (ticker and predicted value).

 PLEASE NOTE:

• Do not shuffle the sequence of the test series.
• If you are using pandas, use this submission code-submission_df.to_csv(‘my_submission_file.csv’, index=False)

 To stake MUSA coins and make submissions, participants will need a Metamask Wallet. This will help you manage your tokens and take action on the blockchain.

Check out this video to learn how to set up your Metamask Wallet.

You can also read the instructions here.

Start Date: 13th June 2022

End Date: 05th September 2022

Register Now

The post The wait is over: Rocket Capital to launch #CryptoPrediction challenge in India — the longest blockchain tournament on financial markets appeared first on Analytics India Magazine.

Time series analysis and modelling are complex procedures in the field of data science. We always find that time series modelling is a combination of different processes and time taking processes. So to reduce the time complexity in time series modelling there is always a requirement of automacy. The prophet is a toolkit that aims to provide automacy in time series modelling. In this article, we are going to discuss the prophet toolkit provided by Meta (Facebook) and how we can use it. The major points to be discussed in the article are listed below.

Table of contents

1. What is the Prophet?
2. Implementation 
   1. Loading data
   2.  Importing modules 
   3. Initiating model 
   4. Making predictions
   5. Plotting prediction   

Let’s start with understanding what the Prophet is.

What is the Prophet?

The prophet is a toolkit or library for time series analysis that is available to us as an open-source. Utilizing this toolkit we can perform time series analysis and forecasting very easily and fast. This toolkit has various features that can make our time series analysis procedure accurate and efficient. Some of the best features are automatic hyperparameter tuning, skilful and accurate forecasting. Model plotting and compatibility with R and python language. 

This toolkit is developed by the researcher of Facebook that is known as Meta now. The modules designed under this toolkit can be referred to as additive time series models for forecasting. One thing that the researcher of the toolkit claims is it is best with time series that have seasonal effects. This toolkit provides fully automated features to deal with messy time series data with less manual effort. Using it we can work on detecting outliers, missing data, and irrelevant changes in the time series.

We can install this toolkit in our environment using the following line of codes:

!pip install prophet

After installation, we are ready to use this toolkit for time series analysis. 

Implementation

Loading data 

In this article, we are going to use the share price data of the State Bank of India. To get the present data we are required to use the yfinance toolkit that can be downloaded in the environment using the following lines of codes:

!pip install yfinance

After installation, we are required to import some modules that are related to the DateTime values.

import datetime as dt
from datetime import datetime as dt
from dateutil.relativedelta import relativedelta

After this import, we can define the end and start date of the data like following

end = dt.today()
start = dt.today() - relativedelta(years=1)

After defining the date range we can use the yfinance to download the data for the given date range.

import yfinance as yf  
data = yf.download('SBIN.NS', start, end)

Output: 

Let’s check our data.

data.head()

Output:

Let’s plot the data so that we can understand how the time series is going.

from matplotlib import pyplot
data.plot(y = 'Close')
pyplot.show()

Output:

The reason behind plotting the data is I wanted to let the reader know we can use this toolkit with the time series having huge seasonality. In the above plot, we can see that there is a trend in the data that is going up and a monthly seasonality that is reducing the price of the shares of SBI.

Treating such data manually will need a lot of processing and the procedure will become so much time taking. Let’s check the prophet how much it is capable of dealing with such data.

Importing modules 

from prophet import Prophet as ph
from prophet.plot import plot_plotly, plot_components_plotly

Here in the above, we can see that we have imported only two modules one is for modelling and the second one is for plotting the results from the model.

Initiating model 

We can initiate a basic model using the object prophet. The toolkit provides the forecasting procedure into this object and then we can fit this using the .fit() command on any time series. One thing that is required by the prophet toolkit is it need a date-time column named as ds and the target variable named as ‘y’. Using the following line we can change the column name and fit the data into the model.

data = data.rename(columns={'Close': 'y'})
model = ph()
model.fit(data)

Output:

‘Here we can see some of the information about the modelling using prophet. In the above codes, we have called the object prophet using the model instance and on the model instance, we fit our data. 

Making predictions

After initiating the model instance we are ready to make predictions but before generating the prediction we are required to make some dates of the future. Let’s see the tails of our imported data.

data.tail(10)

Output:

Here we can see that we have data till data 2022-03-14, we can make dates using the make_future_dataframe module of the toolkit.

future_dates = model.make_future_dataframe(periods=30)
future_dates.tail(10)

Output:

Here we have created dates for a whole month. Now, these blank dates require some values. Using the older data we can estimate them. This is what a time series modelling procedure does and here we are using the prophet toolkit for time series modelling.

We can utilize this toolkit for forecasting in the following way:

future = model.predict(future_dates)

Lets see how the predicted data is :

future[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(10)

Output:

Here we can see that the toolkit provided object predicts values in range here yhat is an exact prediction and yhat_lower and yhat_upper are the upper and lower range of the predictions.

Plotting prediction 

We can also utilize modules from the prophet for plotting the predictions. 

fig2 = model.plot(future)

Output:

In the above plot, we can see what the predictions are and also we can see that to make the prediction object has utilized an average from the older values, outliers are separated and predictions have a maintained seasonality.

Let’s segregate the predicted time series in its components that can provide us with more detailed information about the predictions.

fig3 = model.plot_components(future)

Output:

Here we can see the trend of the prediction and the yearly components shows the seasonality of the predictions and how the series is changing weakly.

We can also use prophet with plotly to generate interactive plots. For example, using the below lines of codes we can make an interactive plot of prediction with filters that are telling us reports in weekly, monthly, half-yearly, yearly, and whole-time series filters.

plot_plotly(model, future)

Output:

Here we can not post the interaction with the plot we can check this plot in this notebook. Here we have completed our procedure and see how we can generate predictions using the prophet toolkit with real-life data.  

Final words 

In this article, we have gone through the prophet toolkit, using this toolkit we can perform effective, efficient, and accurate time series modelling. We can see in the above all the things we performed required only one or two lines of code.

References     

• Link for the codes
• Prophet documentation 

The post A guide to time series prediction using Meta’s prophet toolkit appeared first on Analytics India Magazine.

A survey conducted by PwC in collaboration with FICCI showed that about 83 per cent of the respondents believed that AI is key to offering a better customer experience in Indian financial service companies. Titled ‘Uncovering the ground truth: AI in Indian financial services,’ the respondents included people from the banking and financial services industry. 

The report also said that almost 82 per cent of these companies are using chatbots for servicing customers. While chatbots were found to be the most common function of AI that was in use, 65 per cent of them were using fraud detection AI engines, followed by 56 per cent who use virtual assistants. Of the respondents, 57 per cent said that they trusted that AI would give them a competitive edge. 

“Maturity of using and adopting AI-enabled solutions with a deeper understanding of not just the business case, technology and data, but also the risks around security, privacy and accountability, will differentiate the leaders from the rest,” said Sudipta Ghosh, partner at the data analytics division in PwC.  

“AI is used every day within payments, credit risk, investment recommendations and particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. Indian BFSI organisations looking to move ahead on the AI adoption curve can use AI to boost revenues through increased personalisation of services and embedding intelligence in automation and digital ecosystem partnerships,” Vivek Belgavi, partner at the FinTech division of PwC, stated. 

However, 60 per cent of the respondents believed that privacy concerns were worrying. 

The post 83 per cent of Indian BFSI players think AI is compulsory for better customer experience appeared first on Analytics India Magazine.

“A goal without a plan is just a wish”- that’s how the saying goes. The Global Operations Analytics team at AB InBev started with a dream of transforming business operations through analytics, but it made sure that the wish had a plan and hence converted it into the goal.

The team started with five members heavily focused on using analytics in operations to go ahead and deliver $18 million in EBITDA savings. The head-start with a highly focused team enabled leadership to pump in funding to scale the solutions to all the zones where AB InBev does business. In 2020, the team spanned five more zones and scaled the team 3x. 

Even during COVID-19 in 2020, the team was able to realise a value of $30 million in EBITDA. The same trend followed in 2021, where the team added $58 million in EBITDA and productised the flagship projects, thus enabling a zero-touch ML pipeline execution.

Data Science

The data science team consists of AI and ML experts ranging from 4-to 16 years of industry experience having a product mindset. The team supports all facets of the business in operations and dives into building an MVP in no time. This enables the buy-in from the business to develop a solution that is best suited for the business. All the processes in AB InBev have an analytics footprint from the Global Operations Analytics team, with the USP of the team being “Limitless boundaries in operations and deliver high value”.

Productization

Another arm of Global Operations Analytics  ensures the project is taken to the stage of fruition. There is a lot of drive and rigour from the team that the consumption of every solution reaches every user, and the adoption rate stays on the upswing. In order to do that, the team engages in the development of an interface for the solution, which is supported by a data pipeline and driven by the algorithms.

Tax analytics

In the VAT domain, the structure of VAT keeps changing from country to country in the way it’s levied and the way it’s imposed. Every year, the ABI’s Accounts-to-report (ATR) teams process more than $20 billion worth of taxes and more through the goods and services that ABI provides. The complexity, legacy systems, and high volume of transactions make this process prone to error.

Sometimes, because of manual intervention by all the people working on the tax computation, there can be over claims and under claims. If there is a situation where an underclaim or overclaim happens, we can have a dispute, and it will delay the whole process itself. Once the government raises a dispute, they will put their points forward. So the money gets stuck during this period which can act as a working capital.

The team has created a structure to prevent overpayments, underpayments and penalties charged to the company due to erroneous tax applications. In addition, it has created an algorithm that typically detects where anomalies pop up or if incorrect VAT postings occur.

Architecture

There is an AP (Account payable) and AR (Account receivable) invoice. It goes into SAP, where the financial data resides. The automated invoice posting gets extracted and finally goes to the tax analytics engine, where anomaly detection occurs. These are then sent to the concerned team in a certain file. They would make those corrections. In SAP, the corrected VAT posting would go with the entire exercise being automated and made intelligent by using analytics and data science.

Cash flow forecasting

The objective here is to develop a robust analytical model that provides finance team(s) with an accurate cash flow forecast that helps with financial planning and maintains the business’s solvency.

If the Accounts Receivable is stuck, there is a lack of cash with the company, and it is deprived of working capital. It could have used that money that was coming for some project. AB InBev wanted to know the point of time at which the cash is going to be with us and how much. 

Account payable plays a crucial role as well. Though it is controllable, businesses would like to see, based on the current financial ecosystem, when they are able to make the payment. This is where the forecasting engine has been built. It gives a flavour of what amount will be coming to the company and what amount will be payable in what amount in time.

Procure to Pay

The procure-to-pay process is one of the most business-critical technology stacks. It extends beyond your enterprise and involves suppliers. Therefore, it is crucial to adopt a seamless, efficient, and human-centric approach to designing and developing procurement solutions. When AB InBev is trying to procure goods, it encounters various challenges. Initially, it used to track one metric payment on time, but it was not giving the company the entire picture. So the team now looks at overpayment and early payment as well. 

The payment leakage detector solution helps PTP teams identify duplicate invoice postings. The solution is powered by AI and leverages the last three years of data to find similar patterns between existing and new invoice postings through NLP and pattern matching. 

Smart Collections System 

AB InBev’s Order-to-Cash (OTC) teams are responsible for processing over $50 billion worth of invoices every year. However, even after having fully optimised solutions in place, the company has encountered many issues. The hurdles arise out of system dependencies and the business silos of various departments. These problems can create invoicing disputes that result in financial and strategic burdens on ABI business. Invoicing disputes can arise due to a variety of reasons such as inept order processing due to inefficiencies in the order management system, inconsistent payment terms, inaccurate and incomplete invoicing etc.

Some repercussions due to invoicing disputes include 

• Nonadherence to scheduling agreements leads to undercharging issues.
•  Delayed collections and recovery leading to impact on DSO (Days of Sales Outstanding) 
• Negative impact on customer relationships, incorrect discounts

To solve such problems, the analytics team at ABI designed the Smart Collections System. This AI-powered platform assists the OTC teams in avoiding revenue leakages through solving the invoicing disputes in the OTC cycle and ensuring the timely collection of correct payments. 

Three modules:

• Early Warning System 

This uses heuristic rules and fuzzy logic to solve the invoicing issues during order generation.

• Short Paid Analytics 

With the help of Optical Character Recognition and Natural Language Processing (NLP), assists collections teams in short payment recovery from the customers (Proof of Deliveries).

• Delay Prediction Model

This model uses cutting edge machine learning algorithms to predict the customers who would delay their payments. This significantly helps the collections team to recover the dues and impact the company’s working capital.

Credit Risk

The objective here is to enhance current credit allocation by leveraging transaction history and analysing drivers for noncompliance.

Here the data used are the invoice data (total invoices, delayed invoices, invoice amount), customer data (credit utilisation, payment term), Credit Bureau data and AR ageing data (AR ageing, balance, overdue).

It goes into two machine learning models.

• Model 1 – to determine credit risk category.
• Model 2 – to determine payment terms and credit limit.

Outcome

• Customers grouped into Credit Risk categories
• Dynamic Payment term determined for each customer
• Credit Limit determined for each customer

With analytics and AI taking such a central role in key business decisions, 

AB In Bev as a leader is paving the way (and staying ahead) for integrating analytics and new-age technologies in business verticals. The use of cutting-edge tech in various aspects of the company’s finance systems has helped it optimise its performance and stay ahead in the game from its competitors.

The post How the world’s largest brewer AB InBev is changing the back-end finance operations with AI appeared first on Analytics India Magazine.

Artificial intelligence and machine learning have infiltrated across industries, including banking, insurance and financial services. It will inevitably affect customer experiences, loans and credit scores, fraud mitigation and investment advisories.

Scotland-based NatWest Group, formerly The Royal Bank of Scotland Group, is a banking and insurance holding company. To understand how NatWest is employing AI and ML to enhance customer experience and support business ambitions, Analytics India Magazine caught up with Neeraj Goyal, Head of Technology, Global Hub India at NatWest Group. Neeraj comes with over 20 years of expertise in the global financial industry as an accomplished IT executive. He has been with NatWest Group for more than ten years and served earlier as an Information Technology officer at JPMorgan Chase & Co.

AIM: What is your role as Head of Technology at NatWest Group India?

Neeraj: Technology in India plays a critical role in helping NatWest Group achieve its purpose of championing potential to help people, families, and businesses to thrive. Over the past several years, technology in India has evolved into a mature delivery business unit and is a key enabler in the execution of the bank’s strategy.

My role is to build out technology in India as a differentiated, high performing digital IT Hub, influencing the Group’s top line and bottom line. Additionally, building a learning organisation with a high-performance culture and innovative mindset and making NatWest Group India a great place to work for its employees. 

AIM: Do share with us your plans on integrating data analytics, machine learning and tech-based solutions in the next 2-3 years.

Neeraj: Over the next 2-3 years, we look to rapidly adopt public cloud capabilities for our analytical and machine learning workload. We already have a good foundation of tools on-premise for this, but we need the cloud’s elasticity and advanced analytics and deep learning capabilities. This is primarily required to be able to support the business ambitions, ranging from highly sophisticated fraud detection models to protect our customers to enhancing customer experience by anticipating the needs of our customers using Customer Lifetime Value ML modelling, which got an industry award in the UK (DataIQ). 

The journey to the cloud will involve re-engineering and re-platforming a number of our existing solutions. This task demands very close collaboration with the various business stakeholders over the next three years.

AIM: How well do you think the tech at NatWest is equipped to tackle fraud in banking and financial services?

Neeraj: NatWest Group has been doing particularly well in containing fraud losses. Our strategic investments in AI, ML, behavioural biometrics, and device profiling have resulted in the lowest fraud loss percentage in the top five banks of the UK. Our Mobile Banking app has been recognised as the best, safest banking app in the UK by the ‘Which?’ magazine. As a bank, we have a multi-pronged approach that is helping us achieve these results.

AIM: How does NatWest take care of the data privacy issues of its customers?

Neeraj: Customer data privacy is of utmost importance to NatWest Group. We have very strict controls on who gets access to data in the bank, including role-based access controls in our data systems, encryption in transit and at rest, pseudonymisation (tokenisation), very high level of background checks and screening for our employees, and advanced detection and alerting systems for flagging up any data loss instances. In addition, our Innovation & Solutions teams are constantly reviewing the market for innovative solutions to strengthen this position further.

As we continue to embrace the use of technologies like AI and ML to drive our decision making, we understand the associated privacy risks entailed by the usage of these newer technologies. Our customers trust us not just with their finances but also with their information. To prevent market abuse and detect inappropriate disclosures of information, we use different types of monitoring and surveillance. 

Our Privacy and Client Confidentiality (P&CC) Policy effectively governs the processing of customer and personal data by all parts of the NatWest Group in all jurisdictions. We also have a clear set of internal guiding principles (Info SAFE) for colleagues regarding how customers’ and colleagues’ information should be stored, used, and shared. Most importantly, regular training ensures there is awareness around privacy and client confidentiality policies and procedures to support our colleagues with the knowledge required to keep our customers safe.

AIM: What all opportunities are open for young graduates in India in the technological domain?

Neeraj: NatWest Group is a technology-led bank; there are exciting opportunities for young graduates in India in this domain, allowing them to build niche technologies skills. Some of these exciting opportunities are:

• Build a world-class scalable, performance, and cost-effective credit risk decisioning engine leveraging AWS Cloud services (like EC2, S3, Lambda, etc.) in order to serve the borrowing needs of our 19 million retail and commercial customers across diverse product offerings like mortgages, personal loans, credit cards, business loans, etc.
• Enable “speed of change” and lower cost for business by breaking large applications into smaller microservices-based architecture leveraging private and public cloud platforms.
• Build “Open Banking APIs” and open the same to our partner banks and FinTechs to help our customers get access to better services.

AIM: Also, what kind of specific skills do you look for?

Neeraj: We look for the right attitude, aptitude, potential, technical acumen, and passion for technology, which we gauge through an online assessment comprising algorithmic, aptitude and coding questions, followed by interviews.

Subsequently, we invest in building the skill through an extensive 8-week long training boot camp, which comprises training in technical (Java, Object-Oriented-Programming/OOPS concepts, SQL, etc.) and domain areas (data security, credit risk, retail banking, commercial banking, etc.), hands-on assignments, assessments, and hackathon events.

AIM: Can you list some of the difficulties you face as the head of technology at NatWest Group India?

Neeraj: Talking about the difficulties, I can say the following two are of utmost importance and should be taken care of:

• A rapid change in the technology landscape leads to skill gaps for future and legacy areas along with changing regulations.
• In the present scenario, employee wellbeing is a key concern and focus area.

The post How ML Ensures Data Privacy At NatWest Group appeared first on Analytics India Magazine.