Synergizing Generative AI and Machine Learning for Financial Credit Risk Forecasting and Code Auditing

Abstract

Financial stability and efficiency of costs and time require the credit risk assessment process to evaluate models and comparisons while assessing future business impacts on the commercial banking sector. Accurate credit risk evaluation remains fundamental because financial institutions need it to prevent defaults while developing superior lending methods. A new AI framework based on Generative AI coupled with BERT technology presents itself for financial credit risk forecasting tasks. The model advances data representation by producing synthetic information and improves generalization through expert feature choice mechanisms while delivering fairness through automatic code evaluation systems. The Bank Credit Card dataset evaluation shows BERT surpasses conventional models to deliver 99.31% accuracy together with 99.61% precision 99.76% recall and 99.87% F1-score. BERT produces superior classification results than SVM and Decision Tree in addition to Logistic Regression as verified through comparative analysis. In order to better adapt to changing financial market conditions, future research will focus on creating hybrid models and real-time credit risk monitoring. The study's findings support the application of deep learning in financial risk management.