Analysis of Bank Credit Risk Evaluation Model Based on BP Neural Network

Abstract

Commercial banks are of great value to social and economic development. Therefore, how to accurately evaluate their credit risk and establish a credit risk prevention system has important theoretical and practical significance. This paper combines BP neural network with a mutation genetic algorithm, focuses on the credit risk assessment of commercial banks, applies neural network as the main modeling tool of the credit risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of neural network, so as to give better play to the efficiency of neural network. After verification of various evaluation models, the accuracy of the evaluation model designed in this paper is more than 65%, while the acceptability of the evaluation results optimized by the mutation genetic algorithm is more than 85%. Compared with the accuracy of about 50% of the traditional credit scoring method, the accuracy of the credit risk evaluation using neural network technology is improved by more than 10%. It is proved that the performance of the optimized algorithm is better than that of the traditional neural network algorithm. It has important theoretical and practical significance for the establishment of the credit risk prevention system of commercial banks.

The Risk Management of Commercial Banks

“…Neural network models are self-organizing and self-learning systems that do not require a defined function form before application, hence avoiding any potential errors brought on by a subjective function form [11]. Based on this, the rest of this section will discuss the studies by Shan [6] and Wang [12], which primarily describe the implementation of several neural network models in commercial banks' credit risk management.…”

“…According to the data, in contrast to the BP, which has a poor accuracy of fitting but a high accuracy of prediction, the GRNN has a high accuracy of fitting but a low accuracy of prediction. BP neural network and a mutation genetic algorithm were integrated by Wang [12], who concentrated on the credit risk evaluation in commercial banks. In his study, the mutation genetic algorithm was used to improve the major parameter combination of the neural network in order to maximize neural network efficiency.…”

Credit-Risk Prediction Model Using Hybrid Deep—Machine-Learning Based Algorithms

“…Therefore, it is crucial from both a theoretical and practical standpoint to appropriately assess their credit risk and set up a credit-risk prevention mechanism. Using a combination of a BP-neural network with a mutation genetic algorithm, [6] focuses on the credit-risk assessment of the commercial banks, uses the neural network as the primary modeling tool of the credit-risk assessment of commercial banks, and uses the mutation genetic algorithm to optimize the main parameter combination of the neural network in order to enhance the neural network's efficiency. Following the validation of several assessment models, the accuracy of the model created in their work is greater than 65%, and the evaluation outcomes improved by the mutation genetic algorithm are more than 85% acceptable.…”

Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network

“…At its essence, rail transportation involves the movement of trains along tracks laid out across diverse terrains, connecting cities, regions, and countries [7]. Whether transporting bulk commodities like coal and grain or facilitating the daily commute for millions of individuals, railroads offer numerous advantages, including energy efficiency, costeffectiveness, and reduced environmental impact compared to other modes of transportation [8]. From highspeed passenger trains whisking travelers between urban centers to freight trains hauling goods across continents, railroads play a pivotal role in shaping economies and societies worldwide [9].…”