Detect Anomalous Patterns in Claims Submissions

Fraudulent claims often exhibit subtle deviations from normal patterns. LLMs can analyze large datasets of historical claims to learn baseline behaviors and then identify submissions that are statistical outliers or contain unusual combinations of factors (e.g., injury type vs. accident description, treatment duration vs. diagnosis). These anomalies are flagged for further investigation. For example, an LLM might flag a series of claims from a specific medical provider with unusually consistent billing codes across different patients.