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Time of India
05-07-2025
- Business
- Time of India
Lending with empathy: Automation to Augmented Intelligence
It's 2.14 a.m. A loan application has just pinged the system at the bank. A home décor business owner in Jaipur is seeking a working capital loan. Her credit score sits at 680 – dragged down by delayed payments on a personal credit card 36 months ago and a limited history of formal borrowing. The old lending playbook might have flagged this as a red flag. But this time, the system doesn't just look at the pre- defined rules. It looks at her financial behaviour. With her consent, data flows in digitally via the Account Aggregator framework. Within 2 minutes, Artificial Intelligence (AI) parses her GST data, sees consistent filings since she started the business and spots steady exports. It identifies seasonal spikes in her banking transactions and cross-maps the invoices with the payments. It notices she's stocking up ahead for a festival and recognizes her as a regular vendor for a well-reputed lifestyle brand in the U.S. Playing by the rules of the old world, her file would have landed on an underwriter's desk, waiting for a decision: Should we give her a loan? Instead, the new playbook empathizes. An AI sales agent nudges a personalized smart product fit offer. An underwriter agent kicks in, reviewing the risk markers. An operations agent assures regulatory compliance, data validation and workflow orchestration. A GenAI assistant turns all of this into a clean, simple decision rationale, not just for credit officers but also for compliance, operations and sales teams. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Spots on face... Totally gone with Japan's Whitening Gel YUKINOUE雪之上 Learn More Undo The shift from automation to embedding intelligence to now, driving agentic autonomy is evident. With data at its core, the era of decision-capable AI is here. Too much data: The age of analysis paralysis Live Events The last decade saw financial institutions digitise processes, interfaces, and data capture. For example, one large public sector bank in India adopted a digital lending platform and was able to launch 15 products across retail, corporate, MSME, and agriculture. In just four years, they disbursed over ₹35,000 crores, processed more than 10 million digital transactions, and achieved 3x growth in their digital loan book. That was automation – the first phase of digitization. Necessary, but in today's context not enough. According to a BCG Report, productivity in mature markets has plateaued at just 1% CAGR – largely because digitization only automates the 'happy paths'. It's great for straight through journeys but when complexity kicks in – which is often – the system fails to deliver. As digital channels gained popularity financial institutions got access to a wealth of digitally sourced data. However, the challenge was in harnessing its full potential to drive growth effectively. Mukesh Ambani famously described data as 'the new oil' – but like oil, it's only valuable when refined. Traditional digitization ensured speed and efficiency, but it lacked one crucial element: insights. That's where AI flipped the script shifting the focus from automation to intelligence. The rise of behavioural intelligence A loan application isn't the end – it's the start of a relationship. Every transaction, pattern or moment of friction is a signal. The pressing question is: how do we decode these signals? That's where behavioural scoring steps in, especially when the borrower is new-to-credit or when their financial footprint doesn't fit neatly into legacy models. This is the reality for millions of individuals and MSMEs across emerging markets. By analyzing various factors like cashflow patterns, liquidity, collections efficiency, customer concentration, loan behaviour, governance quality, anomalous behaviour, etc., for individuals as well as for MSMEs - financial institutions are building richer, more dynamic borrower profiles. It's a data to DNA approach – every insight distilled in one powerful score that helps lenders drive growth across the customer lifecycle - acquisition, credit-decisioning, monitoring, portfolio analytics and cross-sell/up- sell. Leading Indian banks and NBFCs are adopting behavioural scores as part of their detailed customer assessment. One of the top private banks was able to build a ₹15,000 crore MSME loan book, propelling a substantial 40% growth within a year with an enhanced 'Go-No-Go' in under 2 minutes, all while maintaining a Gross Non-Performing Asset (GNPA) of less than 1%. A large NBFC has assessed 50,000+ applications in five months using a behavioural scoring approach and disbursed loans of upto ₹50,00,000 based on banking transactions alone. Intelligent signals for the MSME ecosystem The MSME sector is complex. Cashflow fluctuates due to seasonality or long, uncertain credit periods, and it is particularly vulnerable to macroeconomic shocks. A commodity price surge in China or a tariff shift in the US can ripple through supply chains and destabilize small enterprises overnight. That's why better signals are critical for everyone in the ecosystem: lenders, policymakers, and the MSMEs themselves. AI layered on multiple consent-led digitally sourced data points is a game changer. It distils deep insights into a single, adaptive behavioural score - one that evolves with the business. For MSMEs, it serves as a compass to reflect, and course correct. For lenders, it provides unbiased insights into the customers' business performance, credit behaviour and overall persona. Additionally, it provides entity- level, portfolio-level and macro-level insights highlighting potential risks and opportunities. For sectors facing challenges due to market dynamics, these insights help policymakers recalibrate schemes and deliver support when it's needed the most. From scoring to storytelling One score tells many tales, as it holds different meanings for different stakeholders, each viewing it through their own lens. GenAI translates complex insights into contextual and actionable conversations for every stakeholder. Trained on hundreds of thousands of lending interactions, portfolio trends, and sectoral signals, AI including GenAI can answer questions for all the stakeholders. For a lender the question might be: 'Which segment in my portfolio poses the highest risk under the current macroeconomic conditions?'. An MSME might ask: 'How can I boost my business performance through supply-chain optimization or an effective pricing strategy? And which government schemes am I eligible for?' A policymaker might ask: 'Which scheme or sectoral intervention will deliver the most on ground impact?' These valuable AI-driven insights that are accessible via systems, platforms, chatbots or voice assistants help underwriters, borrowers, and policymakers make decisions with confidence. A new kind of teamwork: Enter AI Agents Agentic AI is setting a new standard by introducing autonomous agents into the mix. It is redefining lending by making independent decisions, swiftly adapting to changing environments and acting purposefully to meet specific goals. Think of them as your digital coworkers. Crucially, this isn't about removing humans from the lending equation, it's about empowering them with intelligence. At every stage of the lending lifecycle, from sales to underwriting to operations – AI agents are stepping in. Whether it is for identifying smart product recommendations, generating credit decisions backed by behavioural scores or eliminating process bottlenecks, these agents unlock speed, scale and efficiency with precision. Lenders continue to maintain a 'human in the loop' approach. By starting in co-pilot mode and graduating to autopilot in low-risk cases, they can scale without compromising governance. The road ahead: From Artificial to Augmented Intelligence Ironically, AI powered decisioning isn't here to replace humans, it's here to restore the human touch. We're returning to a time when our bank truly knew us. Earlier, they knew us by our faces and our stories – they still know us through our stories but told by data. As we transition from automation to augmented intelligence, lenders are empowered to say yes more often to the right customers, at the right time and for the right reasons. 2.20 a.m. Application approved. Decisioned not by automation but by augmented intelligence. The writer is Founder & Managing Director at Jocata.
Economic Times
05-07-2025
- Business
- Economic Times
Lending with empathy: Automation to Augmented Intelligence
iStock AI for MSMEs serves as a compass to reflect, and course correct. For lenders, it provides unbiased insights into the customers' business performance, credit behaviour and overall persona. It's 2.14 a.m. A loan application has just pinged the system at the bank. A home décor business owner in Jaipur is seeking a working capital loan. Her credit score sits at 680 – dragged down by delayed payments on a personal credit card 36 months ago and a limited history of formal borrowing. The old lending playbook might have flagged this as a red flag. But this time, the system doesn't just look at the pre- defined rules. It looks at her financial behaviour. With her consent, data flows in digitally via the Account Aggregator framework. Within 2 minutes, Artificial Intelligence (AI) parses her GST data, sees consistent filings since she started the business and spots steady exports. It identifies seasonal spikes in her banking transactions and cross-maps the invoices with the payments. It notices she's stocking up ahead for a festival and recognizes her as a regular vendor for a well-reputed lifestyle brand in the U.S. Playing by the rules of the old world, her file would have landed on an underwriter's desk, waiting for a decision: Should we give her a loan? Instead, the new playbook empathizes. An AI sales agent nudges a personalized smart product fit offer. An underwriter agent kicks in, reviewing the risk markers. An operations agent assures regulatory compliance, data validation and workflow orchestration. A GenAI assistant turns all of this into a clean, simple decision rationale, not just for credit officers but also for compliance, operations and sales teams. The shift from automation to embedding intelligence to now, driving agentic autonomy is evident. With data at its core, the era of decision-capable AI is here. Too much data: The age of analysis paralysis The last decade saw financial institutions digitise processes, interfaces, and data capture. For example, one large public sector bank in India adopted a digital lending platform and was able to launch 15 products across retail, corporate, MSME, and agriculture. In just four years, they disbursed over ₹35,000 crores, processed more than 10 million digital transactions, and achieved 3x growth in their digital loan book. That was automation – the first phase of digitization. Necessary, but in today's context not enough. According to a BCG Report, productivity in mature markets has plateaued at just 1% CAGR – largely because digitization only automates the 'happy paths'. It's great for straight through journeys but when complexity kicks in – which is often – the system fails to deliver. As digital channels gained popularity financial institutions got access to a wealth of digitally sourced data. However, the challenge was in harnessing its full potential to drive growth effectively. Mukesh Ambani famously described data as 'the new oil' – but like oil, it's only valuable when refined. Traditional digitization ensured speed and efficiency, but it lacked one crucial element: insights. That's where AI flipped the script shifting the focus from automation to intelligence. The rise of behavioural intelligence A loan application isn't the end – it's the start of a relationship. Every transaction, pattern or moment of friction is a signal. The pressing question is: how do we decode these signals? That's where behavioural scoring steps in, especially when the borrower is new-to-credit or when their financial footprint doesn't fit neatly into legacy models. This is the reality for millions of individuals and MSMEs across emerging analyzing various factors like cashflow patterns, liquidity, collections efficiency, customer concentration, loan behaviour, governance quality, anomalous behaviour, etc., for individuals as well as for MSMEs - financial institutions are building richer, more dynamic borrower profiles. It's a data to DNA approach – every insight distilled in one powerful score that helps lenders drive growth across the customer lifecycle - acquisition, credit-decisioning, monitoring, portfolio analytics and cross-sell/up- Indian banks and NBFCs are adopting behavioural scores as part of their detailed customer assessment. One of the top private banks was able to build a ₹15,000 crore MSME loan book, propelling a substantial 40% growth within a year with an enhanced 'Go-No-Go' in under 2 minutes, all while maintaining a Gross Non-Performing Asset (GNPA) of less than 1%. A large NBFC has assessed 50,000+ applications in five months using a behavioural scoring approach and disbursed loans of upto ₹50,00,000 based on banking transactions alone. Intelligent signals for the MSME ecosystem The MSME sector is complex. Cashflow fluctuates due to seasonality or long, uncertain credit periods, and it is particularly vulnerable to macroeconomic shocks. A commodity price surge in China or a tariff shift in the US can ripple through supply chains and destabilize small enterprises overnight. That's why better signals are critical for everyone in the ecosystem: lenders, policymakers, and the MSMEs layered on multiple consent-led digitally sourced data points is a game changer. It distils deep insights into a single, adaptive behavioural score - one that evolves with the business. For MSMEs, it serves as a compass to reflect, and course correct. For lenders, it provides unbiased insights into the customers' business performance, credit behaviour and overall persona. Additionally, it provides entity- level, portfolio-level and macro-level insights highlighting potential risks and opportunities. For sectors facing challenges due to market dynamics, these insights help policymakers recalibrate schemes and deliver support when it's needed the most. From scoring to storytelling One score tells many tales, as it holds different meanings for different stakeholders, each viewing it through their own lens. GenAI translates complex insights into contextual and actionable conversations for every on hundreds of thousands of lending interactions, portfolio trends, and sectoral signals, AI including GenAI can answer questions for all the stakeholders. For a lender the question might be: 'Which segment in my portfolio poses the highest risk under the current macroeconomic conditions?'. An MSME might ask: 'How can I boost my business performance through supply-chain optimization or an effective pricing strategy? And which government schemes am I eligible for?' A policymaker might ask: 'Which scheme or sectoral intervention will deliver the most on ground impact?'These valuable AI-driven insights that are accessible via systems, platforms, chatbots or voice assistants help underwriters, borrowers, and policymakers make decisions with confidence. A new kind of teamwork: Enter AI Agents Agentic AI is setting a new standard by introducing autonomous agents into the mix. It is redefining lending by making independent decisions, swiftly adapting to changing environments and acting purposefully to meet specific goals. Think of them as your digital coworkers. Crucially, this isn't about removing humans from the lending equation, it's about empowering them with intelligence. At every stage of the lending lifecycle, from sales to underwriting to operations – AI agents are stepping in. Whether it is for identifying smart product recommendations, generating credit decisions backed by behavioural scores or eliminating process bottlenecks, these agents unlock speed, scale and efficiency with continue to maintain a 'human in the loop' approach. By starting in co-pilot mode and graduating to autopilot in low-risk cases, they can scale without compromising governance. The road ahead: From Artificial to Augmented Intelligence Ironically, AI powered decisioning isn't here to replace humans, it's here to restore the human touch. We're returning to a time when our bank truly knew us. Earlier, they knew us by our faces and our stories – they still know us through our stories but told by data. As we transition from automation to augmented intelligence, lenders are empowered to say yes more often to the right customers, at the right time and for the right reasons. 2.20 a.m. Application approved. Decisioned not by automation but by augmented intelligence. The writer is Founder & Managing Director at Jocata.
Time of India
03-06-2025
- Business
- Time of India
Finding Gen XX among GenAI leaders
There are nearly 7,000 active women-led startups in India. The number may seem huge, but it is only 7.5% of the total pool, according to data from Tracxn. In the tech space, these startups have so far collectively raised $26.4 billion, with 2021 attracting the most funds at $6.3 billion. Since then, the number of women-led startups and capital raised by them has consistently declined. The gaps in the tech landscape being headlined by GenAI are already visible. A recent BCG Report said that in India, there are 65% more male executives in GenAI at the head or director level than female. At the junior level, women's representation is 33%, which drops to 19% at the senior level. But there are signs of a shift. Experts say that GenAI may have the potential to correct the imbalance to some extent, as it democratises software and web to Nasscom, 30% of startups in its GenAI foundry already have women founders. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Giao dịch vàng CFDs với sàn môi giới tin cậy IC Markets Tìm hiểu thêm Undo Breaking the glass ceiling Kunal Bahl, cofounder of Snapdeal and Titan Capital, said historically, tech innovation has been male-dominated—driven by risk-taking, access to capital, and networks that favoured men. Live Events 'But, more women are founding companies, especially in sectors like consumer tech, fintech, healthtech, and increasingly in core tech as well,' he told ET, calling it a meaningful shift. Discover the stories of your interest Blockchain 5 Stories Cyber-safety 7 Stories Fintech 9 Stories E-comm 9 Stories ML 8 Stories Edtech 6 Stories Several initiatives such as AI Kiran, led by Kirthiga Reddy, founder, Verix, CXXO led by Vani Kola, founder and MD, Kalaari Capital, and Peak XV's Spark Fellowship, are fueling growth platforms for women leaders. For instance, the AI Kiran initiative aims to build a community of a million women leaders in AI. The first cohort of 250 AI Kiran women had 27% founders or CEOs of AI-driven companies. 'History has shown the importance of early adoption–else like the gender equity gap, we'll again be looking at 134 years to close the gender AI gap,' said Reddy who was also the first managing director of Facebook India and the first female investment partner at the $130-billion SoftBank Vision Fund. She said gender representation is crucial in AI that must reflect diverse perspectives to ensure fairness, inclusivity and ethical innovation. ETtech GenAI – an open canvas Meghana Jagadeesh, founder and chief executive of GoCodeo, has taken a bold leap to compete with the world's leading companies in the AI-assistive coding space. She explained that GenAI has democratised human interaction with machines. 'Time is ripe for women to shape tech businesses. We don't need to be expert coders, web developers or data scientists to build AI use-cases,' she said, adding that she herself emerged to be an AI founder from doing sales and marketing roles in her early career. Ritu Mehrotra, founder, United We Care (UWC), a mental health and emotional wellness company said that her inclusive team has played an instrumental role in designing empathy in the tech stack. 'Empathy is something women are naturally wired for.' Backed by Cred's Kunal Shah, UWC has broken the world record for intent-detection, automated speech recognition, she said. 'Building reasoning on top of emotional intelligence was not easy because today's AI models are not sensitised for gender, cultural, linguistic and regional diversity,' she pointed out adding that building inclusive tech is more important now than ever. Every woman is a data point which is reshaping the AI models trained for tomorrow, she explained. AI is also enabling founders like Angana Saikia of early-stage Chittoo, an AI platform for English learners, to set up companies for societal upliftment. 'GenAI as a technology has unlocked language learning for the deepest pockets of India…We have signed up users from remote villages which are unheard of,' said Saikia. As a female entrepreneur, Saikia said she often struggled for attention in male-dominated boardrooms, but that didn't deter her from setting up three ventures after quitting a job. Silent biases A Harvard Business School study showed that female founders raise seven times less funding than their male counterparts. Societal biases around maternity, lack of confidence in visionary leadership and access to resources have weighed down the progress of women entrepreneurs. In a fast-evolving technology like GenAI, gender equity is not only seen as an economic prospect but also a strategic need. And being the only woman in a room doesn't help. The need for bridging the gap is now when the ecosystem is still shaping up. Nasscom highlighted how generative AI could be a driver to this change. As per its survey, 90% of women leaders view it as crucial for career advancement, with 95-100% willing to invest extra time in mastering these technologies.
