Enhancing Financial Fraud Detection Using LLMs and Advanced Data Analytics

The complexity and volume of financial fraud poses a significant challenges to the state of the art and evolving detection systems. This makes it necessary for an in depth study and come up with provable techniques with effective approaches. The proposed work focus on algorithmic improvement of the fraud detection technique for financial frauds by integrating Large Language Models (LLMs) along with data analytics techniques for inference. Morevover, by leveraging the extensive linguistic understanding and pattern recognition capabilities of LLMs, combined with analytical methods, this research develops a robust framework that improves both the accuracy and speed of fraud detection mechanisms. Our proposed methodology utilizes a designed data collection pipelines, sourcing an array of transactional and communicational datasets from multiple financial institutions. The core of our research involved the development and training of a customized LLM to identify potential fraudulent activities by analyzing textual and numerical data patterns that are typically indicative of fraud. Along with LLMs, state of the art data analytics techniques, such as anomaly detection algorithms and predictive modeling, were utilized to refine the detection process further. The results shows an improvement in detecting fraudulent transactions, with the proposed model achieving a higher precision and recall compared to traditional fraud detection systems. The LLM-based framework efficiently identified complex fraud schemes, which were previously undetected by conventional methods, by effectively analyzing discrepancies and anomalies in large datasets. The proposed approach enhances the accuracy and reduces the response time, thereby enabling financial institutions to safeguard their operations and client assets more effectively.