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Ins1By Cindy Maike and Alexander Gray

To the average observer, the insurance industry appears to have fared well over the last few years. But in reality, as of year-end 2014, the combination of capital accumulation, competitive pricing, weak investment returns and rising loss expense has been driving down Returns on Equity (ROE) (Source: 2015 EY US Property/Casualty Insurance Outlook). Claims loss represents the largest expense item for an insurance company – regardless of whether it covers health, property casualty or life insurance – ranging from 70 to 80 percent of the operating cost.

Insurance claims leakage, or money lost through failures within existing processes, including inefficient claim processing, human error or fraud, can make up nearly 18 percent of insurance companies’ costs (Source: Accenture – Claims at a Crossroads). To combat this issue, companies use claims audit tools to discover areas of weakness within processes, such as training, business rules and automated tasks that can be optimized to minimize leakage. However, these audits have limitations: they only identify incidences that resulted in claims leakage in post mortem and only analyze a sample of the claims, as it is not commercially viable to check every claim for leakage.

The ability to help monitor and mitigate leakage occurrence during the early stages of claims processing is key to an insurance company’s profitability. The ability for an insurance firm to identify points in its process flow that pose the greatest risk for claims leakage to occur, streamline and systematize procedures and to incorporate and harness new capabilities such as machine learning will have a significant positive impact on insurance companies’ bottom line.

Through the incorporation of machine learning capabilities, insurance firms can take observations and findings from claims audits, pull those insights upstream and insert them into the investigation, evaluation and settlement steps of their claims process flow. Machine learning allows insurance firms to take action to reduce claims leakage and overpayments before money leaves the firm.

Existing Insurance Claims Process:

Machine learning can create an automated way to predict whether a claim has a high probability of resulting in leakage based on historical data. These higher risk claims can then be treated with a greater level of care or be handled by a higher skilled claims adjuster in order to minimize the aggregate leakage for the portfolio. Machine learning provides deeper insights into the mechanisms responsible for leakage, with these insights captured and leveraged systematically with future claims.

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