Credit Risk Assessment Models in Commercial Banking: Approaches, Evolution, and a Proposed Hybrid Risk Evaluation Framework

Credit risk assessment remains a cornerstone of commercial banking, underpinning lending decisions, portfolio management, and overall financial stability. Over the years, the evolution of credit risk models has reflected the dynamic interplay between regulatory reforms, technological advancements, and the growing complexity of financial markets. Traditional approaches, such as expert judgment, credit scoring, and financial ratio analysis, provided the initial foundations for evaluating borrower creditworthiness. However, these methods often lacked predictive accuracy and consistency, particularly in volatile economic environments. The development of statistical models, including discriminant analysis, logistic regression, and survival analysis, introduced greater rigor and objectivity, offering banks structured tools to estimate default probabilities and loss exposures. The contemporary landscape has been shaped by advanced quantitative techniques and machine learning algorithms, enabling the processing of large datasets, behavioral patterns, and alternative credit information. These approaches improve predictive power but also raise challenges related to model interpretability, data quality, and ethical concerns such as bias and fairness. Additionally, regulatory frameworks like Basel II and Basel III have influenced model development by emphasizing risk-sensitive capital requirements, stress testing, and portfolio-level risk aggregation. Drawing on this trajectory, the paper proposes a hybrid risk evaluation framework that integrates traditional financial indicators, advanced statistical techniques, and machine learning tools into a multi-layered assessment structure. The framework emphasizes transparency, adaptability, and sustainability by combining the interpretability of traditional models with the predictive strength of modern analytics. It also embeds environmental, social, and governance (ESG) metrics to align credit risk evaluation with broader sustainability goals. By incorporating diverse data sources and balancing quantitative rigor with qualitative insights, the hybrid model aims to strengthen decision-making, improve early warning systems, and enhance resilience against systemic shocks. Ultimately, the proposed framework provides commercial banks with a comprehensive tool to navigate evolving credit risk environments, ensuring both regulatory compliance and long-term competitiveness.