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.
Recent allegations of racial bias and discrimination in marketing, rating, underwriting, and claims practices by insurance companies and other licensees have come to light nationwide.
Interview with Samantha Chow on "the state of life insurance underwriting now, in the wake of all of the changes brought on by advances in underwriting data sources and technology."
Life insurers are embracing the use of machine learning (ML) and artificial intelligence (AI) models and techniques in all areas of their business.
From voice assistants to personalized recommendations on streaming services, machine learning is a branch of artificial intelligence that many of us encounter daily and makes our lives easier.