Unlocking Big Data Benefits: Driving Claims Efficiency and Customer Satisfaction

Today’s claims executives face tremendous financial pressures and must make better use of information and automation to respond faster and more effectively to an ever changing landscape. Many have focused their attention on data and analytics for help with loss management, resource planning, process improvement and policyholder satisfaction only to discover their datasets do not provide the level of detail or marketplace insight to create competitive advantage. As such, there is an evident market need for comprehensive real-time information that provides actionable insights, especially for insurers who do not have access to large amounts of historical claims data.

Proper and fully comprehensive datasets should provide the following benefits through predictive analytics:

• Streamlined workflows;
• Cost driver evaluation;
• Improved fraud identification and investigation;
• Enhanced adjuster access to information.

After a collision, facts from claimant statements and information mined from claim handler diary notes can significantly increase the accuracy of the medical severity prediction. This information helps to identify with a high degree of confidence, which claims can be “fast tracked” and which require more in-depth analysis. The ability to predict medical severity in this manner can streamline the auto casualty claim assignment process by ensuring the appropriate claim handling expertise required for accurate claim resolution is assigned to the claim.

Claim organizations are continually forced to do more with less while maintaining unsurpassed customer service and claim outcomes. Having the incorrect resource assigned to a claim places an unnecessary strain on an organization. While an experienced claim handler can certainly handle a simple treat-and-release medical claim, wouldn’t their time and expertise be better used investigating and resolving challenges associated with a more complex claim? Having the correct resource available and assigned to handle a claim commensurate with their expertise will improve loss adjustment expense results (LAE) while ensuring excellent claim handling results and customer satisfaction.

Comprehensive datasets that can be mined for insight across the physical damage, auto casualty and workers’ compensation industries can be leveraged in a number of different ways. With the right combination of data, a claims executive could easily investigate the relationship between property damage estimates and corresponding medical treatment. By matching property damage estimate data with auto casualty medical bill data, baselines can be established around the typical medical treatments sought for each type of collision. Establishing this baseline makes it much easier to identify fraud. For this reason, it is critical that claims organizations of all sizes utilize tools that can analyze auto property damage estimate data, point of impact and injury information to predict the medical claim severity.

Helpful tools to identify fraud and buildup include MedRadar, the Opioid Risk Score and Mitchell’s own Claim Triage. Claims Triage utilizes auto property damage estimate data, point of impact and injury information to predict the medical claim severity; MedRadar identifies areas where medical services are being utilized in greater concentrations than are expected when compared with national utilization; and the Opioid Risk Score identifies claims with a higher propensity for opioid drug abuse, a factor know to increase medical claim cost by a factor of six.

Data analysis can also advance medical charge and claim severity monitoring. Tools that continually monitor the charges submitted for medical services in both auto casualty and workers compensation markets to proactively identify procedure code trends at the national, state and county levels could provide early insight into shifting medical costs. This would allow claims examiners to better monitor overall claim cost and utilization.

With an eye towards continually improving the insurance marketplace and customer experience, Big Data and predictive analytics will be top of mind for insurers. An increased comfort level and understanding of their business requirements and available data sources will move the industry towards platforms that not only provide reporting capabilities but also make advanced analytics possible and easily accessible. The best platforms will provide a better understanding of injury mechanics, relatedness, necessary medical treatments and quality outcomes by combining property damage data and casualty data with external data sources.

As business intelligence tools advance the understanding of challenges faced by the industry, they will be increasingly asked to deliver even more complex and comprehensive knowledge, all designed to ensure accurate and efficient claim settlement to add value to the existing claims process. By 2020 the model claim organization will use advanced algorithms and prescriptive analytics to mitigate manual intervention in upwards of 70 percent of claims allowing claim handlers to focus on meaningful and rewarding investigative tasks. The end result will certainly be improved claim outcomes and customer and employee job satisfaction.

Ed Olsen, DC, CPCU is a senior business process consultant at Mitchell Auto Casualty Solutions.