FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
The continuous growth of the e-commerce industry attracts fraudsters who
exploit stolen credit card details. Companies often investigate suspicious
transactions in order to retain customer trust and address gaps in their fraud
detection systems. However, analysts are overwhelmed with an enormous number of
alerts from credit card transaction monitoring systems. Each alert
investigation requires from the fraud analysts careful attention, specialized
knowledge, and precise documentation of the outcomes, leading to alert fatigue.
To address this, we propose a fraud analyst assistant (FAA) framework, which
employs multi-modal large language models (LLMs) to automate credit card fraud
investigations and generate explanatory reports. The FAA framework leverages
the reasoning, code execution, and vision capabilities of LLMs to conduct
planning, evidence collection, and analysis in each investigation step. A
comprehensive empirical evaluation of 500 credit card fraud investigations
demonstrates that the FAA framework produces reliable and efficient
investigations comprising seven steps on average. Thus we found that the FAA
framework can automate large parts of the workload and help reduce the
challenges faced by fraud analysts.
