Commercial Bank Credit Grading Model Using Genetic Optimization Neural Network and Cluster Analysis.

Area of Science:

• Financial Risk Management
• Computational Finance
• Banking Technology

Background:

• Commercial banks face increasing credit risk due to financial market volatility.
• Existing credit risk assessment models may lack accuracy and efficiency.
• A robust and dependable credit risk assessment method is crucial for financial institutions.

Purpose of the Study:

• To propose and develop a credit risk assessment model for commercial banks.
• To enhance the accuracy and efficiency of credit risk evaluation.
• To provide commercial banks with an effective tool for managing credit risk.

Main Methods:

• Utilized cluster analysis for indicator classification.
• Employed a factor model to select representative indicators, reducing complexity.
• Developed a credit risk assessment model based on Genetic Algorithm-based Neural Network (GANN).
• Incorporated advancements in data preprocessing and genetic operations.

Main Results:

• The proposed GANN model achieved a highest accuracy rate of 94.17%.
• This accuracy surpasses that of the BPNN algorithm (89.46%) and the immune algorithm (90.14%).
• The optimization algorithm demonstrated improved convergence speed and search efficiency.

Conclusions:

• The developed GANN model is a feasible and effective method for commercial bank credit risk assessment.
• The model offers a significant improvement over traditional algorithms in accuracy and efficiency.
• This approach provides a dependable solution for mitigating credit risk in volatile financial markets.