Credit Risk Assessment in Commercial Banks: A Comparative Study of Neural Networks and Traditional Algorithms

Abstract

Credit risk assessment is a critical aspect of commercial banking operations, ensuring prudent lending practices and safeguarding financial stability. Traditional credit risk assessment methods, relying on statistical models and rule-based algorithms, have long been the cornerstone of banking practices. This study conducts a comprehensive comparative analysis between neural networks and traditional algorithms in credit risk assessment within commercial banks. The research employs a dataset comprising historical credit information, encompassing various financial indicators and borrower characteristics. Evaluation metrics encompassing accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC) are utilized to assess the performance of these techniques. Neural networks exhibit robustness to noise and outliers, enhancing their reliability in real-world banking scenarios. While neural networks offer substantial advantages in credit risk assessment, their integration into commercial banking practices necessitates a balanced consideration of factors such as interpretability, regulatory compliance, and computational resources. Future research should focus on developing hybrid methodologies that leverage the strengths of both neural networks and traditional algorithms, fostering innovation in credit risk management while ensuring adherence to regulatory standards.