Credit Risk Assessment Model Based on AHP and BP Neural Network

Under the present financial situation, accurate credit risk assessment is required for the sustainable development of lending institutions. According to the data of Lending Club, a well-known P2P lending website, this paper establishes a composite credit risk assessment model based on the combination of the analytic hierarchy process (AHP) and back propagation (BP) neural network. AHP is employed to rank and evaluate hierarchically the various factors affecting credit risk in a well-structured approach of risk factor analysis. The robust predictive power of the BP neural network is then used to establish a predictive model with the ability to identify patterns and correlations in the data. The model is constructed and validated on a large Lending Club dataset covering a wide range of borrower characteristics and loan performance outcomes. The study contributes to credit risk modeling by the systematic, data-driven approach, a novel credit scoring framework improving the accuracy and effectiveness of credit risk assessment and with the potential to enhance the lending decision-making process for institutions.