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

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Bhushan P. Chaudhari , Santhosh Chitraju Gopal Verma ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.32628/cseit25112761

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.

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Artificial Intelligence

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Audit Code (set theory) Business Generative grammar Computer science Artificial intelligence Machine learning Finance Accounting Programming language Set (abstract data type)

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Type: article
Language: en
Landing Page: https://doi.org/10.32628/cseit25112761
PDF: https://www.ijsrcseit.com/index.php/home/article/download/CSEIT25112761/CSEIT25112761
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Cited By: 2
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DOI: https://doi.org/10.32628/cseit25112761

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

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-04-01

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Authors: Bhushan P. Chaudhari, Santhosh Chitraju Gopal Verma

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Landing page: https://doi.org/10.32628/cseit25112761

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Concepts: Audit, Code (set theory), Business, Generative grammar, Computer science, Artificial intelligence, Machine learning, Finance, Accounting, Programming language, Set (abstract data type)

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Cited by: 2

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