Integrating Case-Based Reasoning with LLM for Expense Fraud Detection

Xiaoyu Ge, Jiao Xu

Abstract: Organizations face major challenges in detecting employee expense fraud because of scarce data, intricate fraud patterns, and the requirement for explainable results. This paper implements a novel application that integrates Case-Based Reasoning (CBR) with Large Language Models (LLMs) to address these challenges. Our system represents expense activities as spatiotemporal events, uses LLMs to generate fraud detection rules within a constrained function space, and applies CBR to retrieve similar cases, minimize hallucinations, and improve explainability. The system implements a complete CBR cycle—retrieve similar fraud patterns, reuse detection rules, revise rules through LLM interaction, and retain verified cases. We evaluated the system with more than 200,000 real-world expense events and the results show that the integration of CBR with LLMs effectively constrains hallucinations while generating high-quality, explainable fraud detection rules. This approach offers a practical solution for applying AI in high-stakes domains requiring reliability and explainability.

External IDs:dblp:conf/iccbr/GeX25