Fraud Detection Framework

Context:One of the world's largest pharmaceutical companies with 2,000+ active clinical studies spanning 35,000 sites and medical centers globally.

Challenge: Required a robust fraud detection framework to identify data fabrication, ensure data integrity, and support regulatory submissions and patient safety decisions.

Solution: Designed and executed a comprehensive statistical fraud detection framework applying multiple methods:

• Demographic distribution analysis
• Birthdate test for unrealistic patient data
• Cluster analysis to detect unusual grouping patterns
• Perfect schedule of attendance detection
• Study visit pattern analysis
• Constant findings and duplicate records detection
• Multivariate outlier and inlier analysis
• Adverse event summary pattern recognition

Outcome: Flagged statistically significant anomalies across 2,000+ studies, with findings escalated through regulatory compliance processes. Directly informed regulatory submissions and patient safety decisions.