FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations

2025-06-13

AI Summary

Rising e-commerce fraud has overwhelmed manual investigation processes, causing delayed responses and alert fatigue. This paper introduces the first multimodal large language model (MLLM)-based automation framework tailored for credit card fraud investigations. Our approach innovatively integrates task planning (Chain-of-Thought + ReAct), dynamic code execution, OCR, and visual reasoning over transaction graphs to enable end-to-end autonomous execution of a standardized seven-step investigative workflow: alert parsing → evidence collection → cross-source reasoning → report generation. The framework ensures interpretability and strict adherence to financial regulatory requirements. Evaluated on 500 real-world cases, it fully completes all seven analytical steps on average, achieves >92% accuracy in critical conclusions, significantly improves investigation efficiency, and substantially reduces both false negatives and analyst cognitive load.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Automating credit card fraud investigations to reduce analyst workload
• Addressing alert fatigue from excessive transaction monitoring alerts
• Generating reliable explanatory reports using multi-modal LLMs

Innovation

• Multi-modal LLMs automate fraud investigations
• LLMs enable reasoning and evidence collection
• Generates explanatory reports for fraud analysis