Lending Logic

How lenders assess borrowers, deal with uncertainty, and make credit decisions under imperfect information.

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    From Scorecards to AI: Why Automating Credit Decisions Is Harder Than It Looks

    Amit Balooni

    What happens when a credit model becomes more confident than the evidence beneath it? Credit automation works best when the borrower, data and decision are predictable. Outside that boundary, better models can create greater confidence without greater understanding. From scorecards and machine learning to AI, the real challenge is knowing what can be automated, what still requires judgement and how institutions should govern the space between them.

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    India’s DPI Stack: A Banker’s Guide

    FrankBanker Research

    India’s DPI story is often told through scale. This guide looks at it through structure. It maps the digital rails, protocols, access layers, and public data systems that increasingly shape lending, onboarding, verification, payments, and underwriting in India. From Aadhaar, UPI, DigiLocker, and Account Aggregator to OCEN, ONDC, ULI, and the expanding universe of public data APIs, the guide explains what each layer does, where it fits, and where the gaps still remain.

  • Credit Risk Blindspots: Hidden Biases in Lending

    Amit Balooni

    Bias is an inherent human tendency, and recognising and minimising it requires deliberate effort. No matter how advanced the financial models or how experienced the analysts, Credit Risk decisions are inevitably influenced by personal experiences and context. The challenge lies in identifying when this reliance on past-experience crosses the line into bias.