With a renewed focus on analytical methods for detecting fraud, insurers now have the opportunity to achieve desirable results. There are several key areas to consider when implementing an analytical fraud detection program. Here are five steps insurers need to take:

Don't underestimate data management. With any modeling exercise, the results are wholly dependent upon the type and quality of source information. In many cases, more than half of the work for a fraud detection project can occur during the data preparation phase. This step may involve integrating data from multiple internal and external sources, addressing data quality issues like misspellings and missing values, and correctly matching entities from different systems.
Incorporate unstructured text sources. Within core systems, a huge percentage of information is stored as text. Especially on long-tail injury claims, claim notes and customer service logs contain crucial information for accurate fraud detection. In some models, up to half of the variables might come from these sources. Furthermore, as insurers gain access to information sourced from social media, text analytics, content categorization and semantic analysis are essential to derive value from comments and posts.
Use multiple detection techniques to identify both known and emerging schemes. Don't rely on just one method of detection. Business rules and supervised modeling are good at identifying known fraud schemes, while anomaly detection and network analysis are better suited to detecting emerging exposures. Using a combination of these methods can help cover all the bases. Separate detection scenarios can be built for different points in the claim life cycle to yield the best results. And additional detection scenarios can be focused on identifying suspicious organized networks to produce leads for complex case teams, as opposed to singular claim models that can support traditional SIU teams.
Think about how users will consume the model results. Building a model is only half the battle. Consideration must be given to how users will receive and review the results of the model. Robust implementations will provide an alert queue for evaluation by a triage team, along with functionality to review the details of associated claims and entities. The system should also allow users to share intelligence and document the disposition of the alert, as this information can be useful for monitoring model performance. More advanced implementations can involve integration directly back into the core processing system.
Maintain the models. Predictive modeling is not a one-time exercise. Models experience decay over time. In order to maintain satisfactory results, models need to be tuned and periodically updated. Source-system modifications, access to new information, changes in underwriting risk appetite, legal and regulatory developments, and shifting fraud patterns can all influence model results. It is important to routinely monitor the performance of the fraud detection system and plan for periodic updates. Most models can be updated every 12-18 months but in frequently changing environments, six-month updates might be more appropriate.

The old days of simply relying on claims adjusters to notice red flags are over. Adjusters will always remain an integral part of the fraud defense system. However, given adjusters' increasing caseloads, less experience and greater performance demands, it is unrealistic to expect them to adequately identify increasingly complex fraud exposures. Predictive analytical solutions have evolved and the insurance industry has reached a tipping point where manual methods are no longer sufficient. Those insurers who embrace an analytical approach will be ahead of their peers. Those who lag behind risk becoming soft targets.

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Five Steps to a Better Fraud Detection Program

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