Predictive Models – Again
Indirect use of discrimination factors that are outlawed is inexcusable and needs to be avoided by due diligence by insurer and indeed the firm supplying the data. Any decent carrier would not want to cross those red lines anyway – and that is even if the data company involved is not itself subject to regulatory oversight and/or consumer protection laws. Moral: act with integrity and choose your business partners carefully.
Why the Life-Insurance Industry Wants to Creep on Your Instagram
Insurers are using customers’ social-media posts to determine premiums, inviting the potential for our digital lives to become disingenuous performances.
Predictive Modeling – a View from New York
On 19 January 2019, the New York State Department for Financial Services (DFS) issued a circular letter concerning the use of external consumer data and information sources for life insurance underwriting. This followed a prior notice sent to insurers that the Department was investigating the use of such data for potentially unfair or discriminatory practices.
Why the Future of Life Insurance May Depend on your Online Presence
As the use of algorithms and public data to inform insurance premiums becomes more common, we’ll need to decide what is and isn’t okay
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.