Principles underpinning a fraud and abuse monitoring system

Anomaly detection is essential in monitoring human and systems interactions that are too complex or numerous for a human alone to comprehend.

Any organization delivering complex services to its customer base through intermediaries will be the subject of fraud and abuse by people in the service delivery chain.

Research demonstrates that losses incurred by the supervising organization for unnecessary payments are often in the 25-30% range.

Major causes of fraud and abuse are poor policy, rushed implementation, or an absence of active surveillance. While many of these organizations have clear operational goals and strategies, they often underinvest in a data strategy to measure the performance of internal administrators and external service delivery entities.

Fraud and abuse are rarely a single transaction. Overwhelmingly fraud and abuse are systematic behaviors identifiable with proactive data strategy and surveillance developed by a mix of data science and data engineering.

Industries that heavily rely on third parties to deliver complex services to end-users and organizations with high transaction environments, broad policy settings, complex delivery channels, and an evolving compliance regime are far more likely to be subject to these abnormal behaviors that result in unnecessary payments and resource consumption.

When designing an anomaly detection system, some guiding principles, or strategic drivers, of the design and implementation are:

Learn more about the principles underpinning a fraud and abuse monitoring system in our latest White Paper here.