Detecting And Preventing Banking Application Fraud

This white paper from SAS addresses the rising threat of synthetic identity fraud in banking applications, where fraudsters fabricate convincing digital identities to obtain and abuse credit. It outlines how these synthetic profiles are built using stolen or falsified Social Security numbers and gradually groomed through credit-building tactics like authorized user piggybacking and collusion with sham businesses. The paper categorizes fraud into three phases—make up, pump up, and cash out—each requiring distinct detection strategies. Through machine learning, network link analysis, anomaly detection, and behavioral analytics, financial institutions can proactively identify and prevent bust-out schemes. Real-world case studies show the potential to multiply fraud detection efficacy and speed up investigations. The paper also highlights how analytics improve customer experience by accelerating legitimate approvals while stopping fraud in its tracks.