Credit card fraud mitigation plays a significant role in modern society. While fraud detection systems are essential, they often struggle to keep pace with the constantly evolving fraud techniques. As a result, fraud investigation is an important complementary process required for continuously improving detection models, identifying emerging fraud patterns, providing case explanations of to stakeholders, and maintaining customers' trust. However, fraud analysts are overwhelmed with an enormous number of alerts generated by credit card transaction monitoring systems. Each alert investigation requires careful attention, domain expertise, and thorough documentation of the investigation outcomes, leading to alert fatigue. To address this challenge, we introduce the first Fraud Investigation Assistant (FIA) framework, which employs multimodal large language models (LLMs) to automate key steps of credit card fraud investigation and generate explanatory reports. FIA leverages the reasoning, code execution, and vision capabilities of LLMs to collect relevant and logically consistent evidence while maintaining relatively short investigation trajectories. Experiments with the Sparkov and CCTD datasets show that FIA gradually improves the F1 score while investigating borderline cases, reaching 8% improvement after only 1,500 additional investigations. These results suggest that LLM-based agents can assist with automating substantial parts of the fraud investigation process and may be particularly useful for resolving ambiguous alerts.

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