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Commercial banks play an important role in the financial system, and their role in the processes of capital circulation, capital integration, capital resource allocation, and total social demand and supply adjustment is irreplaceable. This paper proposes the idea of constructing a credit risk evaluation system for commercial banks based on BPNN by combining qualitative and quantitative analysis, based on computer technology and management theory. Its main goal is to use a neural network’s self-learning ability to perform complex loan risk assessment. BPNN is capable of self-learning, self-adaptation, and knowledge acquisition, as well as dealing well with uncertainty. It is a nonlinear method that eliminates obvious subjective and artificial factors, resulting in a more objective and effective evaluation result. Experiments show that the established model is effective in regulating personal credit management and reducing investment risks for commercial banks, as well as providing a new decision-making framework for banks’ personal credit business.

A Self-Learning BP Neural Network Assessment Algorithm for Credit Risk of Commercial Bank