Credit risk modeling is the backbone of responsible lending. In India, where informal credit markets coexist with formal banking, building accurate default prediction models is both a science and an art.
Understanding the Data Landscape
Indian banks have access to rich data sources: CIBIL scores, Equifax reports, bank statements, and GST filings. The challenge is not data scarcity — it's data integration.
Feature Engineering for Indian Context
Standard Western models miss India-specific signals. Seasonal income patterns for agricultural borrowers, EMI bounce history, and regional economic indicators all carry predictive power that generic models overlook.
Model Selection and RBI Compliance
RBI mandates that credit decisions must be explainable. Black-box models won't pass audit. Logistic regression with clear coefficients, or tree-based models with SHAP values, are the pragmatic choices.
From Notebook to Production
The real test is deploying your model where loan officers can use it. A Streamlit dashboard connected to your core banking system, running entirely on-premise, satisfies both usability and compliance requirements.