Fraud Detection and Quality Control

Description and Importance

Spots invalid claims (e.g., duplicates, forgeries) to protect funds. Increasingly AI-driven; fraud costs billions annually.

Step-by-Step Process

Screening: AI scans for patterns (e.g., identical docs across claims). 

Flagging: Alert on anomalies (e.g., geolocation mismatches). 

Investigation: Manual deep dives or hire PIs for suspicions. 

Audits: Random 5–15% checks with external sources. 

Rejections: Deny with evidence; report to authorities if criminal. 

Training: Update staff/AI on new fraud tactics. 



Best practices: Multi-layer (AI + human); statistical sampling.