Credit score models are undergoing a transformation: Subhankar Mishra

More frequent credit bureau updates are reshaping underwriting, helping lenders curb loan stacking and assess borrowers with greater precision

The Reserve Bank of India last January nudged credit information companies, or credit bureaus, to update data every two weeks, shifting from a monthly cycle. This helps better credit hygiene and we may not be far away from lenders coming up with bespoke offerings on the back of nearly real-time data. Subhankar Mishra, head of business strategy and interim managing director at Equifax India, tells Raghu Mohan in an email interview what is in store. Edited excerpts: 

How is the periodic data update at credit bureaus changing lenders’ attitudes? 

The transition towards more frequent, near-real-time data reporting has fundamentally shifted the lending ecosystem from a reactive risk posture to a proactive risk management framework. Historically, reliance on legacy, monthly data cycles introduced an inherent information lag. Data recency is no longer a strategic luxury — it is the foundational bedrock of responsible lending in a high-velocity credit economy. Today, with high-frequency updates, lenders have an unprecedented view into credit velocity, specifically short-term inquiry surges and rapid liability accumulation. This allows institutions to identify early signs of credit stress or over-extension long before it manifests as a default. We view this shift as a critical enabler for lenders to build highly dynamic, resilient portfolios that can adapt to consumer behaviour in days rather than quarters.

 

In terms of onboarding customers, has this led to a material change in tackling delinquencies and credit over-leverage? 

The compressed data window has driven a material, positive structural change at the point of customer onboarding. The primary benefit has been the mitigation of loan stacking — a challenge where borrowers could exploit the data lag to secure multiple parallel unsecured loans, such as various buy-now-pay-later lines or instant digital personal loans, simultaneously across different institutions. Real-time incremental reporting effectively neutralises this arbitrage window. For instance, if a consumer’s credit utilisation spikes rapidly across platforms, lenders can now proactively adjust credit limits or pause onboarding before the borrower becomes overleveraged. 

By equipping lenders with an accurate, contemporaneous view of a borrower’s total leverage on the exact day of application, the industry is seeing a healthier containment of early-stage delinquencies. This ensures that credit growth across the industry remains sustainable and structurally sound. Ultimately, by leveraging alternative data rails and advanced analytics, we are not just expanding the credit grid; we are ensuring that the transition from new-to-credit (NTC) to being responsibly leveraged is seamless and sustainable.

 

A dichotomy is that lenders are reluctant to serve customers below a score threshold. Will newer technologies reduce the primacy accorded to credit scores when profiling and onboarding customers? As in, how do you see underwriting models evolving? 

It is important to clarify that traditional credit scores are not losing their relevance; rather, they are evolving. The score remains an invaluable, highly efficient anchor for risk segmentation. As we see it, the evolution of underwriting isn’t about replacing the credit score; it is about supercharging it with multi-dimensional, real-time data assets that reflect today’s economic realities. However, the underwriting models wrapped around it are undergoing a significant transformation. 

We are moving away from uni-dimensional credit profiling towards a multi-layered, holistic risk assessment. The integration of advanced analytics, Cloud-native technology, and robust alternative data frameworks allows lenders to look beyond just payment history. Underwriting models are shifting from purely asset-and-liability-centric views to real-time cash-flow analysis. This hybrid approach combining core bureau scores with deeper behavioural and transactional insights creates a more precise, predictive model that allows lenders to confidently serve a broader spectrum of consumers without compromising risk parameters.

 

How do you see lenders refining their approach to NTC, gig workers and those outside the big cities? 

Expanding access to these segments and geographies presents a massive growth opportunity that requires a specialised data strategy. Lenders are refining their models to ingest surrogate data parameters, such as utility behaviours, digital transaction flows and platform-specific performance metrics for gig workers. By leveraging advanced analytical capabilities and alternative data suites, lenders can evaluate income consistency and repayment capability rather than relying solely on a formal salary slip.