Our client is a Fortune 500 company, providing life and disability insurance to more than 100 million customers across 50 countries.

The client wanted to improve sales practice fraud detection using a predictive analytics framework. The current process is limited to the compliance team reviewing the policies and agents based on official complaints. This reactionary approach resulted in higher losses and increased investigative labor to address the occurrence. Our client wanted to proactively identify fraud, thus enabling the compliance team to focus on identified outlier cases. The objective was to:

— Develop a predictive framework to flag possible sales fraud practices and detect agent and policy-level anomalies
— Build an interactive dashboard to speed up identification, evaluation, and interpretation of the drivers of anomalies for the Special Investigative Unit (SIU)

• Lack of labeled fraud data – No recorded historical frauds known to take the conventional route of building a predictive model using supervised learning methods
• Inconsistent data – In-silo data sources with limited information, no data dictionaries, and minimal business context