Life insurers are using alternative data sources like electronic health records, biometrics, and genomics to boost underwriting efficiency and accuracy.
The traditional underwriting process to buy life insurance is often painful for customers and costly for insurers.
It involves multiple interviews, both in person and over the phone, as well as a paramedical exam to collect fluid samples. But even after all of that, customers could still be denied coverage without a clear explanation as to why.
Although a tedious process, it has been a necessary one to help insurers gather enough data to estimate applicant morbidity and mortality rates, and thus price their policies appropriately.
Now, a number of life insurers are looking to use alternative data sources — such as electronic health records (EHRs), biometrics, and genomics — to replace or supplement the data collected in the traditional process, and more accurately underwrite life insurance.
- Covid-19 has accelerated the use of alternative data in underwriting for life insurance. Government shutdown orders halted the traditional in-person underwriting process for many insurers at a time when life insurance applications surged. Some insurers quickly partnered with alternative data providers in order to continue underwriting and issuing new policies.
- Alternative datasets could allow for more streamlined & accurate data collection. Through API connections, insurers can instantly access alternative data to underwrite policies much faster, and often at a much cheaper price. Alt data can also provide a more personalized, longer-term view of applicant health and uncover new health behaviors and traits.
- Currently, insurers are predominantly using alternative data to offer instant simple term products. Startups and incumbents are using alternative data to accurately underwrite simple products that customers can purchase online, without the need of a medical exam.
Why life insurance underwriting?
At its core, underwriting for life insurance is the attempt to statistically classify a person’s mortality risk — or chance they will die in a given time frame — and price policies accordingly so the insurance company’s expected return is positive. While individual results will fluctuate, the law of large numbers should lead to a positive result for the insurance company.
Traditional factors that life insurance underwriters look at to price policies include age, gender, height, weight, motor vehicle records, and prescription records. Underwriters traditionally also require a medical exam to gather data from a blood test, drug test, and blood pressure measurements.
Alternative data sources simply offer insurers another input for their underwriting models. While this data could be used to replace the need for a medical exam, for instance, it could also be used to supplement traditional methods to create more accurate underwriting results. While there are many data sources available for insurers, we look at 3 emerging sources on which insurers are focusing — EHRs, biometrics, and genomics — below.
Electronic health records
In recent years, hospitals have invested in electronic health record (EHR) systems, which organize patient data more effectively and efficiently than paper records. Life insurers are working on accessing this data for underwriting purposes, either through direct integration with these EHR system vendors (e.g. Cerner and Epic) or with other EHR aggregation systems (e.g. Human API and Clareto).