By: Joydip Gupta, Head of APAC, Scienaptic AI
With traditional credit underwriting methods falling short in today’s dynamic market, AI is revolutionising how credit risk is assessed, particularly for individuals who have been historically excluded from formal credit systems.
Laxmi, a rural toy seller, receives an unexpected notification: her loan application, submitted just a day earlier, has been approved. Curious, she opens the app to discover a loan offer that exactly matches her financial requirements and has a highly competitive interest rate. This was not a stroke of luck—it was Artificial Intelligence (AI) at work behind the scenes, analysing her spending patterns, anticipating her financial needs, and presenting her with a perfect solution.
Why does traditional Credit Risk Assessment fail?
India is home to a vast and diverse population with a significant portion underserved by traditional credit systems. Over 400 million adults are credit unserved, and an additional 160 million are underserved. This segment of the population, often classified as “new-to-credit” (NTC), has limited access to formal financial services, largely due to the rigid frameworks of conventional credit assessment.
Traditional risk assessment models focus heavily on historical financial data and credit bureau scores. This approach is inadequate in addressing the needs of those without an established credit history, such as gig workers, small business owners, and those with inconsistent income streams. To bridge this gap, AI-powered credit risk assessment is emerging as a game-changer, leveraging alternative data, advanced algorithms, and new-age credit BREs to offer a more nuanced and inclusive approach.
Alternative data: Providing a complete 360o data view
Traditional credit scores focus on a borrower’s financial history, often failing to capture the full financial picture of those outside the formal economy. AI models can incorporate a wide array of alternative data sources, such as account aggregator data, financial transactions, GST or tax returns, Point-of-Sale (POS) and wallet activity, and even social media behaviour, providing a more holistic view of a borrower’s financial standing.
In India, where informal income sources and non-traditional financial behaviours are common, this shift towards alternative data is particularly relevant. For example, adding GST returns, digital payment history, and POS transaction data allows lenders to assess the creditworthiness of MSMEs and self-employed borrowers who might not have regular income streams or substantial financial records.
AI risk models: Predicting risk with precision
AI models do not just rely on historical data; they predict future risk more accurately than traditional scorecards. Advanced AI algorithms can analyse vast amounts of structured and unstructured data in real-time, identifying patterns and correlations that human analysts might miss. This capability is crucial in predicting borrower behaviour, especially in a country like India, where the market is dynamic, and income streams can vary widely.
FIs using AI have reported 25-60 percent accuracy improvements in predicting whether an applicant will default, enabling more reliable risk assessments and reducing non-performing loans. Predictive models also allow lenders to auto-approve or auto-reject a larger number of applications, reducing the average underwriting turnaround time (TAT) and providing a smoother experience for borrowers.
AI-driven Credit Decision Engines (BRE): Smarter credit decisions
Adopting flexible, analytics-driven credit decisioning engines is a game changer. Traditional underwriting often relies on static lending strategies that struggle to adapt to changing market conditions, creating bottlenecks in decision-making. AI decisioning engines allow risk teams to quickly identify fluctuations in customer behaviour and their impact on portfolio quality and provide recommendations for policy changes. Advanced decisioning engines deliver 30-45 percent higher approval rates and 20-25 percent reduction in default rates, while increasing STP rates by 50-65%, boosting efficiency and improving customer satisfaction.
Financial inclusion and personalisation: The AI advantage
One of the most profound impacts of AI in credit risk assessment is its ability to drive financial inclusion. Using alternate data and sharper algorithms, AI opens the door to underserved populations, offering them access to credit products that were previously out of reach. This has significant implications for India’s economic growth, as credit access can be a powerful enabler for individuals and small businesses, helping them invest, grow, and thrive.
Another area where AI is driving significant change is in the personalisation of lending products. AI enables financial institutions to design credit offerings that are tailored to the individual needs of borrowers, whether consumers or MSMEs. For instance, risk-based pricing models powered by AI can offer personalised interest rates, loan tenures, and repayment schedules based on an individual’s unique financial profile.
AI’s future: A strategic imperative for Indian FIs
As AI continues to evolve, its role in credit risk assessment will get stronger. For Indian FIs, the adoption of AI-driven credit models is not just a technological upgrade—it is a strategic imperative. Those who invest in AI today will be better positioned to serve an increasingly diverse and dynamic market, offering faster, fairer, and more inclusive credit products.
For the nation’s economy, AI is more than just a tool for efficiency; it is a pathway to a more equitable and resilient financial future. By leveraging data-driven insights and predictive analytics, FIs can unlock new opportunities for growth while minimising risk, driving profitability and financial inclusion.
