RAG-Enhanced LLM Framework For Real-Time Fraud Detection In Financial Systems
DOI:
https://doi.org/10.29284/2zbf4r73Keywords:
Retrieval-Augmented Generation (RAG); Financial fraud detection; Graph neural networks; Large language models; Transaction anomaly detection .Abstract
Financial fraud remains a significant and evolving challenge and has become a multifaceted problem in modern financial systems, particularly due to the spread of digital payment systems and online financial services around the globe. Conventional rule-based fraud detection systems, which rely on fixed rules, are less effective in detecting complex and other rapidly changing patterns of fraud. The vast majority of the recent advancements in artificial intelligence have also seen the introduction of machine learning and deep learning algorithms that can analyse huge amounts of financial transactions and track abnormal behaviours. However, most existing fraud detection approaches rely primarily on structured transaction categories and cannot put into practice contextual data revolving around financial action. This review examines the rising prominence of large language model frameworks with retrieval as a part of real-time fraud detection in financial systems. The literature in the machine learning, graph analytics, and knowledge-enhanced language models’ area is critically analysed to identify methodological trends, computational architectures, and performance characteristics of the new systems of fraud detection. As it may be observed with comparative analysis, ensemble machine learning models, deep neural networks and graph neural networks have significantly improved detection accuracy compared with classical statistical methods. Nonetheless, these techniques typically suffer a weakness when it comes to the ability to integrate unstructured data and contextual information pertinent to financial fraud investigation. Retrieval-augmented architectures can provide this answer since during the inference process, fraud detection systems view external sources of knowledge as dynamic repositories that can be consulted in the process. The latter systems integrate contextual information on financial behaviour with transaction analytics, and, furthermore, can enhance detection accuracy, interpretability, and adaptability to new schemes of fraud. The major research challenges include scalability in high-frequency transaction settings, combining heterogeneous data sources, explainability of model results, and privacy of sensitive financial data. Further research in the hybrid systems of graph analytics and retrieval-augmented reasoning systems and streaming analytics systems may help to develop the next generation fraud detection systems, which can meet the ever-growing financial threat.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
