Data Science and Engineering (DSE) Record

This research investigates the critical strategic trade-offs between revenue maximization and loss control in credit risk modeling by comparing three distinct optimization strategies. The study addresses the gap between technical model performance and actual business outcomes, which is often overlooked in traditional machine learning applications. Using a synthetic dataset of 135,000 personal loans with 29 features, the study evaluates five machine learning models across multiple probability thresholds. The findings reveal that behavioral features capturing post-origination payment patterns drive 71% of predictive improvement, compared to only 29% from hyperparameter tuning. Strategy A (Income Maximization) achieved the highest profit of $662.54M with a 98.7% approval rate using Gradient Boosting at a 20% threshold. In contrast, Strategy B (Pure Loss Minimization) produced an impractical 0.04% approval rate, proving that unconstrained loss reduction leads to operational failure. Strategy C (Constrained Loss Minimization) implemented a 60% minimum approval constraint based on Federal Reserve standards, achieving $416.54M in profit with 40% lower losses than Strategy A. Critically, the choice of probability threshold demonstrated a five times greater financial impact than the choice of algorithm. These results provide strong empirical evidence that integrating specific business constraints is essential for effective and sustainable credit risk.