Abstract
In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.
Highlights
At present, the traditional method of proportional analysis is still mainly used to evaluate the credit of enterprises in the Republic of China. e biggest disadvantage of this method is that the determination of indicators and weights in credit evaluation has great subjectivity, which is bound to increase the credit risks of commercial banks
To evaluate the enterprise credit rating widely, competently, quantitatively, precisely, and suitably, this paper uses the classical genetic algorithm, one of the widely used optimization algorithms, to enhance the thresholds and weights of the classical BP neural network (NN) to establish a credit rating model for an enterprise, which is grounded on the new genetic algorithm integrated into the BP neural network. e main characteristics of the proposed model are (i) fast convergence speed, (ii) global optimization, and (iii) accurate evaluation of the credit rating. e credit rating model well adapts dynamically to the work of enterprise credit rating and has certain
To evaluate the enterprise credit rating comprehensively, efficiently, objectively, accurately, and conveniently, this paper practices the integration of a genetic algorithm to improve and enhance the thresholds and weights of the BP neural network model
Summary
The traditional method of proportional analysis is still mainly used to evaluate the credit of enterprises in the Republic of China. e biggest disadvantage of this method is that the determination of indicators and weights in credit evaluation has great subjectivity, which is bound to increase the credit risks of commercial banks. A credit rating model based on the BP neural network, which is further enhanced and optimized, by a genetic algorithm is proposed. (1) is paper proposes a credit rating approach grounded on the BP neural network that uses the classical genetic algorithm to increase the model accuracy and generate reasonable rating recommendations (2) Characteristics such as self-adaptiveness and selflearning of the classical genetic algorithm are utilized to modify, enhance, and improve the thresholds and weights of the neural network connections (3) Experimental results and our evaluations on real datasets show that the suggested hybrid genetic BP neural network model has higher accuracy as compared to the classical BP neural network e structure of the remaining part of this paper is as follows.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
