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Insurers explore new frontiers in claim management

Emerging data sources and advanced analytics provide a universe full of promise for insurers — if they can navigate it effectively.

Insurance Consulting and Technology|Insurance Linked Securities|Investments|Reinsurance|Risk Control and Claims Advocacy

By Katey Walker | July 25, 2019

In a Willis Towers Watson survey of the U.S. property-casualty industry, insurers emphasized claim management as a high-impact area for analytics applications within the next two years.

Advanced analytics has the potential to transform an insurance companies’ operations. Many insurers have made considerable efforts to leverage analytics to create better segmentation and more accurate premium calculations but are now changing the focus to the other side of the loss ratio: how to identify, manage and mitigate losses. In the recent Willis Towers Watson survey of the U.S. property-casualty industry, insurers emphasized claim management as a high-impact area for analytics applications within the next two years. The investment in claim analytics is not limited to personal lines but includes commercial LOBs such as workers’ compensation, commercial auto, general liability, business owners and medical malpractice. Detecting potential fraud ranks among the top use cases for analytics in claims (Figure 1).

Figure 1. How advanced analytics will transform claim management

Now Two years
Evaluation of claims for fraud potential 26% 82%
Claim triage (identify complex claims to triage workflow) 26% 80%
Evaluation of claims for litigation potential 15% 74%
Evaluation of claims for subrogation potential 13% 62%

Reducing insurance fraud

At a cost of more than $30 billion annually, insurance fraud is the second costliest white-collar crime in the U.S., according to the National Insurance Crime Bureau. The Association of Certified Fraud Examiners Inc. notes that internal fraud alone costs the typical organization five percent of annual revenue. Fraud identification is challenging, as claim handling can be complex and involve multiple third parties, introducing opportunities for disparate fraud schemes. Also, the volume and speed of transactions limit a company’s ability to monitor and identify potential fraud. Impactful use of data including predictive modeling and business intelligence are essential tools to help adjusters uncover sophisticated and complex fraud schemes.

Data analytics can be used for more than just fraud; applications include appropriate adjuster assignment, subrogation, litigation management, settlement evaluation, loss reserving, fast-track identification, and claim service strategies and prioritization.

Impactful use of data including predictive modeling and business intelligence are essential tools to help adjusters uncover sophisticated and complex fraud schemes.

Workers’ compensation

The Willis Towers Watson industry survey notes that in commercial lines, claim analytics usage is highest in workers’ comp (27 percent) and is expected to grow to 65 percent in two years as more companies invest in claim triage, severity propensity and fraud identification modeling.

Workers’ comp is conducive to claim analytics applications. It has a longer claim duration than other commercial lines, with many claims remaining open years after they were first reported. Additionally, workers’ comp claims often have extensive data for the adjuster to consider (e.g., medical reports, interviews, diagnostic/procedure codes, litigation status and injured worker demographics), since they frequently involve a serious injury and may coexist with sensitive employer-employee dynamics. As a result, there is an opportunity for analytics to be used to help adjusters recognize which claims may become complex and how severe those claims are likely to be.

Guiding principles

Each company’s journey will be different, but our experience has continually reinforced four guiding principles to advance claim operations:

  1. 01

    Data first

    New analytical methods, including AI and machine learning, are justifiably getting a lot of attention, but quality experience data, predictors and customer response information will outperform new methods. Companies can also improve their models by augmenting their data with third-party information.

  2. 02

    The endgame is implementation

    Once the analytics is done, the product is only valuable when the business can understand, implement and monitor it. Otherwise, the work done is simply a technical modeling exercise.

  3. 03

    Stay on top of tech

    Legacy systems and networks make it increasingly difficult to extract the full benefits of big data and advanced analytics. New technologies that enhance analytical capability and system connectivity, including those offered by new insurtech companies, will have a greater role to play.

  4. 04

    Build a dedicated fraud management team

    It's important to deploy specific resources to prevent fraud internally and externally.

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Katey Walker

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