Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection

ArXi:2512.13040v2 Announce Type: replace-cross Detecting fraud in financial transactions typically relies on tabular models that demand heavy feature engineering to handle high-dimensional data and offer limited interpretability, making it difficult for humans to understand predictions. Large Language Models (LLMs), in contrast, can produce human-readable explanations and facilitate feature analysis, potentially reducing the manual workload of fraud analysts and informing system refinements.