LLM for Financial Services: Risk Analysis and Fraud Detection
DOI:
https://doi.org/10.5281/zenodo.14928807Keywords:
large language models (llms), gpt-3, finbert, risk analysis, fraud detection, financial servicesAbstract
The financial service industry is increasingly suspected by risk management and complicated frauds, because of traditional methods, such as rules based on rules, becomes become Not enough to combat evolutionary threats. This study discovers the potential of large language models (LLM), including GPT-3 and Finbert, to improve risk analysis and fraud detection in the financial sector. LLM, capable of processing structured and non -structured data, provides improvement in detecting models and abnormalities between trading newspapers, customer interaction and talent reports main. A quantitative comparative comparative research design, financial data analysis can access the public and compare LLM performance with traditional systems. Main performance measures - Prediction Accuracy, False Positive Rate, Processing Time, and Fraud Detection Rate- are used to evaluate the effectiveness of the models. The results show the significant potential of LLM to improve financial risk management and detect fraud, provide an effective, accurate and developed approach to modern financial institutions.
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Copyright (c) 2025 Vinoth Manamala Sudhakar
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Research Articles in 'Applied Science and Engineering Journal for Advanced Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
