SCOR recently conducted a mortality analysis using LexisNexis® Risk Solutions data to assess the effectiveness of the LexisNexis Risk Classifier with Medical Data scores in stratifying mortality risk when compared to the LexisNexis Risk Classifier scores and ExamOne HealthPiQture scores separately.
This article from The Actuary discusses results of an RGA study on the relationship between medical expenses and lifestyle factors that wearable devices can measure, such as physical activity, sleep, and heart rate. The conclusion: Lifestyle data can indeed help health insurers better assess risk, improve underwriting, and develop propositions that incentivize healthy lifestyles.
As digital data becomes more available and accessible, life insurers are exploring how to use this alternative data to assess applicants.
More data means more insights, and a data-sharing partnership between two industry leaders confirms that the challenges facing the industry can best be overcome together, through companies and organizations with aligned incentives collaborating and sharing resources.
Recent announcements in Colorado, Louisiana and New York addressing the use of not only big data but also more traditional “scoring” models signal a continued focus on this area by state insurance regulators.
After looking at Disparate Impact Testing in our previous blog, where we provided background on the concepts of algorithmic accountability and proxy discrimination, this blog will describe the current, evolving regulatory environment and make some projections about the future.
Risk aggregation techniques such as these are useful for a range of underwriting applications; however, a significant degree of subjectivity may still be required in making a decision. Even the most experienced underwriters can find this challenging. In such cases, data analytics offers a potential solution.
Probabilistic and statistical modeling have long been central to life insurers’ efforts to responsibly manage and accurately price risk. However, over the past two decades, technological advances have transformed these businesses’ use of data and algorithms.
Supplementing traditional insurance data with different types of third-party data can provide deeper insights to improve the underwriting process, enhance operational efficiency, and gain a better understanding of historical mortality, morbidity, and lapsation experience. Realizing the potential of third-party data integration requires appropriate evaluations, both analytical and actuarial.
As data ethics continues to garner increased attention in the (re)insurance industry, it is critical for organizations to have a strategic and measured approach to data utilization.