AI-Driven Delinquency Risk Analysis & Collections Strategy Intern — TATA (Virtual)
Performed AI-driven delinquency risk analysis using customer credit risk datasets to support collections strategy. Conducted EDA to uncover key risk drivers such as credit utilization and missed payments, then trained a Random Forest model with SMOTE to address class imbalance. Produced risk-based segmentation and proposed an AI-driven collections framework based on model insights. • Analyzed credit risk data to identify early delinquency indicators • Performed EDA to find key risk drivers (e.g., utilization, missed payments) • Trained Random Forest with SMOTE for imbalanced classification • Developed customer segmentation to guide targeted collections strategy