Credit default prediction based on genetic algorithm improved neural network

The article delves into the fundamental objective of credit risk management, which entails evaluating the likelihood of borrowers defaulting on their obligations. This statement underscores the significance of precise risk quantification to mitigate losses, as well as the necessity of a robust risk assessment framework that is bolstered by model algorithms and data. The utilization of extensive datasets and the progression of machine learning algorithms have facilitated the examination of borrowers' data, enabling the prediction of default probabilities and the subsequent evaluation of loan risk. This article provides an overview of the progression of credit assessment models, starting from statistical models and advancing to machine learning algorithms and composite algorithms. It emphasizes the use of ensemble learning algorithms and underscores the benefits of composite algorithms in enhancing the accuracy of these models. The paper introduces the ant colony-BP (back propagation) neural network model as a potential remedy for the limitations observed in conventional BP neural network models. Furthermore, the article delineates the sequential phases entailed in the ant colony approach. The paper examines various data processing strategies, including feature reduction, handling imbalance, and resampling methods. Among these techniques, the SMOTE-Tomek algorithm is found to be particularly effective in mitigating data imbalance. The present paper conducts a comparative analysis of the ant colony-BP neural network model and the XGBoost model, highlighting the superior performance metrics exhibited by the former. The essay finishes by highlighting the practical consequences of the research findings and their significance in the context of data preprocessing and model selection within the financial industry.