Very Risky Business: The Pros and Cons of Insurance Companies Embracing Artificial Intelligence
It’s a new day not very far in the future. You wake up; your wristwatch has recorded how long you’ve slept, and monitored your heartbeat and breathing. You drive to work; car sensors track your speed and braking. You pick up some breakfast on your way, paying electronically; the transaction and the calorie content of your meal are recorded.
Better Underwriting Decisions are Just a Heartbeat Away
Technological advances in biosensors and increasing amounts of heart rate data from wearable devices and electronic health records are leading to the development of more sophisticated underwriting algorithms. This data, when coupled with robust epidemiological evidence about the prognostic value of heart rate, may improve insurer understanding of cardiovascular risk and ultimately allow underwriters to better predict morbidity and mortality risk.
Evolution of the Role of the Predictive Modeler
As data mushrooms, models become more complex, roles become more specialized, and terminology becomes more confusing (and over-hyped) – we need to be honest with ourselves, honest with stakeholders and not allow hubris in our models to displace common sense.
Don’t Share Your Health Data with Insurance Companies Just for the Perks
Insurers are today capable of and are, in fact, gathering ever-more-detailed information about us, using publicly available and purchasable information like shopping records, household details, and social-media profiles to inform decisions.
Health Insurers Are Vacuuming Up Details About You — And It Could Raise Your Rates
Without any public scrutiny, insurers and data brokers are predicting your health costs based on data about things like race, marital status, how much TV you watch, whether you pay your bills on time or even buy plus-size clothing.
Unveiling Black Box Models - Interpretability and Trust
In most fields, domain-specific data analysis and generalized linear models (GLMs) have been routinely used to extract insights from the data. The underlying mathematics of such analyses are rather straightforward, and practitioners as well as non-technical project members are experienced in how to interpret the results, and thus are adept at applying them in the context of business.
Milliman IntelliScript Underwriting with Rx Based Models (Slides)
Slides from this presentation, given at the 2018 ACSW Spring Meeting, have been posted at the Actuaries’ Club of the Southwest website.
LexisNexis Risk Classifier – Stratifying Mortality Risk Using Alternative Data Sources
Munich Re assessed LexisNexis Risk Classifier, a predictive modeling tool developed and owned by LexisNexis Risk Solutions, Inc. that accurately stratifies mortality risk using public records, consumer credit history and motor vehicle history. Insurers considering alternative data-based mortality scores should begin with a retrospective validation study on their own experience data.
Classification Model Performance (Gen Re Risk Insights)
Insurers are increasingly developing prediction models to use in their insurance processes. Often these models are using traditional techniques, but more and more we see machine learning techniques being applied.
Big Data Sharing and Predictive Modeling: 5 Things for Regulators to Consider
The U.S. market is ready to realize this potential from these expansive technologies and networks but a regulatory pathway needs to be established to safeguard adherence to our core insurance principles and an individual’s right of privacy.