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Life Insurance Underwriting

What is Life Insurance Underwriting?

Life insurance underwriting is the act of accepting liability under a life insurance policy. Insurers increasingly use machine learning to identify risk categories and recommend policies, faster and more accurately than humans alone.

In these times of lockdowns, these programs become especially important as people are more interested in life insurance but may only be reached remotely.


Why Is It Important to Have a Good Life Insurance Underwriting Program?

In addition to greater pressures to reach potential clients remotely—and, consequently, to reduce the number of doctor assessments required by applicants—insurance companies turn to artificial intelligence.

These programs don’t just improve company speed but service, as well: they take the masses of data already recorded by companies to identify new risk categories. They analyze the data leagues faster than human workers. They identify potential fraud better than people can by making connections that humans typically do not. In short, clients and potential clients both enjoy faster service and more personalized coverage.

What Internal Data Should I Have for a Good Life Insurance Underwriting Program ?

Life insurance companies should create an AI underwriting program using their current policies and onboarding requirements.

Additionally, they should use client payment and demographics data. Payment data can train the AI to predict payment lapses; demographics can identify a population the company can reach out to in the future for onboarding.

What External Data Is Essential for a Good Life Insurance Underwriting Program?

External client data that insurers must have are, of course, medical history, profession, credit score, and behavior data—especially whether the client smokes.

Other essential data include industry standards, medicine, and location-based data. This location data should indicate whether the area the client lives in has a high crime rate or environmental hazards. If possible, insurance companies should also use client EMR data.

What External Data May Prove Useful for a Good Program?

Additional external data that insurance companies may find useful tend to fall under behavioral data. However, this data goes further than asking whether the client smokes. Pet ownership, fitness level, even a history of charitable giving can impact the client’s risk category.

What Are the Main Challenges of this Use Case?

Machine learning programs can scan massive amounts of information and find surprising connections between behavior and longevity. Yet, while AI for life insurance underwriting is supposed to make the process easier and faster for both sides, it can be difficult to know what information should be gathered. Compounding this, the insurance industry as a whole does not have standard guidelines on how to incorporate artificial intelligence into underwriting.

An additional challenge, of course, is security. Data breaches can severely damage a life insurance provider’s reputation, so maintaining total safety and secrecy of client data is paramount.

Interesting Case Studies and Blogs to Look Into

McKinsey & Company: Rewriting the rules: Digital and AI-powered underwriting in life insurance
Accenture: Machine Learning in Insurance

Tangible Examples of Impact

Many major carriers approve low-risk applicants based on big data and then require medical exams for everyone else, says Jeremy Hallett, CEO of Quotacy, a life insurance broker. On average, it takes nine days for an insurer to reach a final decision using accelerated underwriting instead of the traditional 27

Minot Daily News: COVID-19 accelerates no-exam trend in life insurance

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