Fraudulent Life Claims

Fraud not only hurts an insurer’s bottom-line; it can also have a direct effect on customer experience. While fraud losses continue to mount, most insurers still rely on statistical models to identify fraudulent claims within their anti-fraud programs. The result is higher premium costs for customers making it difficult for life insurers to retain existing customers and obtain new customers in a highly competitive market.


Fraud Can Also Impact Honest Customers

In addition to identifying fraudulent claims that should not be paid, insurers are increasingly concerned how fraud can impact honest customers. Identifying both legitimate and fraudulent claims requires insurers to be able to make decisions quickly in order to provide a positive customer experience for their honest customers.

With many life insurers depending on a manual process to identify potential fraudulent claims, it has become increasingly difficult to identify fraudulent claims. Claim adjusters and SIU resources must search for data that is spread across multiple policy and claims databases. Many of these core systems are legacy systems that are difficult to access and provide outdated reporting capabilities.


Identify Fraud Before It Happens

The Aureus approach is to predict potentially fraudulent claims before they happen - with the large amount of data that insurers already possess.

The insurance process generates data at every step of the policy lifecycle from lead identification to quote, issue and bind. With these data points that exist in policy administration and underwriting systems, a real time fraud framework that can predict instances of possible fraud can be built. Using behavior analysis and interaction data points, the system can throw severity prompts to tackle fraud.

For example, at the point-of-sale of a new life insurance policy, the data gathered from the POS of a new life insurance policy is sent to CRUX. In real-time, CRUX will return an early claims risk assessment that is made available to the agent or broker.

Expected Results

Improved Accuracy of Fraud In Less Time and Less Cost

Using predictive models, CRUX can identify somewhere between 10-13% probable fraud cases in significantly less time than manual identification processes. This is a substantial improvement over manual identification of fraud cases with less time spent on finding data and more time spent on the investigation itself.

Reduced costs can be realized by focusing the efforts of SIU resources investigating claims that have a higher probably of fraud as opposed to those that are not. For insurers that outsource the SIU function, this can be a substantial savings alone.

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