Development of a Retail Loan Default Prediction and Early Warning System for Indian Banks

Objective: To design and implement a predictive analytics solution that identifies potential retail loan defaults early, enabling timely interventions for Indian banks.

Problem Context: Retail loan defaults are a growing concern for Indian banks due to economic fluctuations, borrower profile diversity, and limited early-warning mechanisms. High non-performing assets (NPAs) impact profitability and regulatory compliance, making proactive risk management essential.

Approach: The project will use historical loan data from sources such as Kaggle's 'Indian Lending Data' and RBI datasets, applying machine learning models (logistic regression, decision trees, ensemble methods) to predict default risk. Feature engineering will focus on demographics, credit history, transaction patterns, and macroeconomic indicators specific to the Indian retail banking context.

Deliverables: The team will deliver a data-driven predictive model with an interactive dashboard (using Power BI or Tableau) for risk monitoring, detailed model evaluation (precision, recall, ROC-AUC), and guidelines for operational integration. Business recommendations will outline triggers for early intervention with high-risk borrowers.

Business Impact: The solution will help banks reduce NPAs, improve credit risk management, and optimize recovery strategies. It informs decisions on loan approvals, restructuring, and targeted communication, enhancing overall asset quality and regulatory compliance.