Industry Issues | ERM & Emerging Risks

Emerging Risk – Big Data/Predictive Analytics

Big Data/Predictive Analytics

Best's Special Report: Predictive Analytics Aids Performance, Balances Underwriting Cycles for Commercial Lines Insurers

While the presence of predictive analytics in the U.S. property/casualty industry has become increasingly prevalent, its specific use has varied by company and line of business, according to a A.M. Best special report.

The Best's report, titled, "Predictive Analytics Aids Performance, Balances Underwriting Cycles for Commercial Lines Insurers," states predictive analytics and related tools such as artificial intelligence, machine learning, big data and predictive modeling often focus on the same concept - using statistics and probabilities to predict outcomes. Predictive analytics has the potential to help organizations improve their effectiveness and bottom line results. A.M. Best observes that one initial obstacle to its widespread use was the limited ability to obtain enough data to create truly effective models.

This market research reports that predictive analytics within the P/C industry started with personal lines applications because there was much more data available on individuals. The commercial side of the industry has presented greater challenges given its size, complexity, diversity of risks, and overall lack of quality data historically. Nevertheless, over the past decade or more, the rating agency has witnessed changes within the commercial lines segment that have enabled companies to be more responsive to shifts in market dynamics. Enhanced enterprise risk management (ERM) processes have also helped companies improve decision-making throughout different disciplines. As an example, the reports highlights that despite catastrophe losses within the commercial lines segment nearly doubling in 2017 from multiple events, the losses were most often within stated risk tolerances and fell within company catastrophe retentions, reflecting the progression within and appropriateness of commercial lines insurers' ERM programs. A.M. Best finds that greater utilization of data and analytics has led to better insights into risk selection - when and where to grow or shrink - and the establishment of technical pricing while expanding into other areas, such as claims management.

The workers' compensation line appears to be the furthest along in terms of the impact of predictive analytics. Leveraging a volume of available statistical data, the workers' compensation line ventured into the adoption and evolution of data analytics, which A.M. Best concludes allowed for better management of claims, the application of resources to deal with them, the detection of fraud, and the prediction of the types of claims with elevated levels of frequency or severity

A.M. Best also believes a company's ability to be innovative in developing better products and services for their clients and providing them in a faster and more efficient manner will become more critical in the future. Gauging these capabilities within the evaluation of a company's overall financial strength is becoming increasingly important. Insurers that are nimble when it comes to technology and can respond quickly to changing market dynamics will be better-positioned for future trends and developments.

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