Optimization Of Loan Classification Criteria For Reducing Credit Risk And Increasing Bank Profitability

Loan classification criteria are fundamental to commercial-bank credit-risk management because they determine when an exposure is treated as performing, requires intensified monitoring, or presents materially elevated risk. Poorly optimized criteria can create two opposite failures: late recognition of deterioration and excessive classification of viable borrowers as risky. The first increases credit losses and provisioning pressure, while the second reduces lending income and can weaken customer relationships. This thesis examines the optimization of loan classification criteria as an economic and analytical decision problem. The proposed approach combines borrower financial indicators, payment behavior, facility characteristics, collateral, sectoral exposure, and macroeconomic conditions with probability-of-default estimates and risk-based thresholds. Particular attention is given to discrimination, calibration, false-negative and false-positive costs, portfolio segmentation, stress testing, and risk-adjusted profitability. The thesis argues that the optimal classification rule should not be selected solely by predictive accuracy. Instead, criteria should minimize the total economic cost of credit risk while preserving profitable lending opportunities and regulatory discipline. A conceptual implementation framework is developed in which model outputs are translated into differentiated monitoring, pricing, provisioning, and collection actions. The approach can help banks identify deterioration earlier, allocate capital more efficiently, improve expected-loss estimation, and increase risk-adjusted returns. The main conclusion is that optimized classification criteria create value when they transform better risk information into timely and consistent managerial decisions.