During last year’s lockdowns, a 35-year-old New Orleans resident went online, and in the time it took to finish his freshly brewed coffee, he secured a $750,000 life insurance policy. Until recently, the same policy took over a month, requiring a battery of medical tests. The life policy was purchased from a new age life insurtech, which relies on algorithms and augments quote information with data from such sources as prescription drug databases, DMV records to gain required information to underwrite.
In a not too distant future, underwriting as we know it today would have shaken up most personal and small-business lines across life and property and casualty insurance. The process of underwriting, reduced to mere seconds, would be automated and supported by algorithms straddling data accessed through APIs. Devices provided by carriers, reinsurers, manufacturers and product distributors would push data to a variety of repositories and streams.
Brief History of Underwriting
Edward Mores, founder of Society for Equitable Assurances, pioneered life premium calculations based singularly on age in 1762. Those life tables prevailed until the 1940s, when gender was added as an underwriting criteria, based on gender mortality statistics. In the 70s, following FTC’s warnings on smoking, insurers started rating smokers separate from non-smokers. Yet again in the 80s, the emergence of HIV/AIDS got underwriters to mandate blood routines. The reports lead to criteria expanding to weight, blood pressure, heart rate, family history, cholesterol and hazardous activities.
Underwriting changes. Carriers are better informed about risks and selection continues to improve. Accelerated changes have one common driver: technology. Data has become ubiquitous, being generated from multiple sources including wearables, social media and telemetric devices. Electronic health records are available through partnerships with ecosystem specialists. An example is New York Life that partnered with Cerner to facilitate EHR retrieval, reducing processing time.
The New Workbench
Leading reinsurers and technology vendors have built underwriting platforms wherein automated rules replicate underwriting manuals. Differentiating such platforms are workbenches that support workflows, APIs for third-party data, visualization and reporting aids. Using modern standards, 90%+ applications are processed within minutes.
An underwriting workbench is used to assess information from variegated sources, including third-parties, while leveraging predictive models for decision-making. It facilitates application submission, collaboration, renewal handling, third-party data integration, diary/notes, quotes and documents.
Workbenches are central to insurer workflows, monitoring and controlling a large part of the book of business. Well-designed workbenches allow underwriters to leverage the potential offered by improved data analytics, access IoT technology, and plug into marketplaces. Integration with dynamic data sources allows better-priced risk and proactive risk management.
Haven Life is a digital life insurance agency that uses algorithms to assess applicants’ historical lab results and medical claims data to generate instant coverage quotes without the need for in-person medical exams. Life insurers are capitalizing on alternate data sources for continuous underwriting. AXA has launched a life insurance policy for senior citizens that comes with an intelligent medical wristband to continuously monitor policyholders’ vital health parameters. Using alternate data, insurers identify emerging risks and are betting on personalized services using individual risk profiles.
Underwriting is a powerful differentiator when it comes to superior customer experience. In life insurance, the concept of “pay as you live” has never been more relevant as now, as consumers conduct life transactions online. Carriers are seeking ways to accelerate underwriting transformation. By automating the many routine tasks and empowering teams with workbenches and alternate data sources, underwriters are taking strides towards newer skillsets and possibilities.
In Part 2, latest trends in algorithmic underwriting and how they are gathering force in various P&C lines are discussed. In Part 3, the focus will shift to the impact from abundance of new alternate data sources.
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