A chessboard, with SARS-CoV-2 virions COVID-19 has changed the game board. (Credit: iStock; NIH)

Many insurance organizations worldwide have turned to artificial intelligence (AI) technology in recent months to help them address the disruptive changes in their business due to COVID-19. Their historical data and business intuition is no longer reliable nor relevant, as this New Normal has affected customer behavior and emergency responses that have dramatically shifted insurance workloads and approaches.

(Related: Will the Customers Come Back?)

In today’s climate, data accrued in the last couple months are of more value than data accumulated across the last five years. This means that all AI-based models need to be rebuilt to address the new trends we currently see. What’s more, scaling analytical processes and deploying automatically-updating AI models is critical now, more than ever.

In this article we will first highlight key struggles facing the industry by line of business, then we will provide examples for how AI can aid to shed more light as the situation develops.

The Long-Term Effects of COVID-19 on the Insurance Industry

Most of the insurance industry is relatively resilient to the pandemic. However, different business lines are affected in different way:

  • Travel, trade credit, business interruption, and event cancellation insurance: Issuers of these lines were, of course, severely affected, some with long-term implications.
  • Life and health insurance: At least some issuers of these products are likely to face more claims and a higher cost per claim. An additional, upcoming spike in health claims is expected to offset a decrease in spending resulting from postponed elective procedures. As millions of workers lose employer-sponsored health benefits, they may enroll in Medicaid, buy Marketplace coverage, or become uninsured.
  • Auto insurance: Issuers of this product experienced a short-term decrease in claims as a result of a reduction in driving and a corresponding decrease in the number of accidents. In the coming months, many workers’ transition from relying on public transportation to using cars may lead to an increased number of claims.
  • Cyber and fraud insurance: Claims against this coverage spiked in parallel to the real-world pandemic, as cyberattackers took advantage of work-from-home policies.
  • Reinsurance: Reinsurers are likely to suffer significant material damages.

Moreover, for issuers of many lines of insurance, court litigation may become more aggressive, as plaintiffs’ lawyers try to compensate for revenue lost during the COVID-19 emergency.

From an operational point of view, we see a leap forward in adoption of digital service channels. A transition to online servicing that was expected to take a few years has been accomplished in a few weeks. This shift, however, imposes many challenges, especially for the non-online players that need to adjust quickly to the new normal.

How Can AI Provide More Visibility, and Aid Decision-Making Processes?

The heterogeneity we have discussed above is also true in the granularity of each specific line of business and use case. Risk, policies, customer populations and regulatory requirements are affected in different ways.

For example, looking at term life insurance, clearly the risk (currently and looking forward) has sharply increased for some populations, including people in certain age groups and people with certain travel patterns. This has critical implications in three areas that a good AI process would need to address: risk model performance, granularity, and external information.

  • Performance: COVID-19 effects require updating the risk models, as many of the underlying assumptions are no longer valid, or need to be updated with new information or variables, including, for example, states’ new regulations.
  • Granularity: Old underwriting approaches that are based on actuarial calculations based on population averages are limited in their ability to assess the risk of an individual. AI and machine learning (ML) approaches, on the other hand, give insurers the opportunity to assess risk on the individual level, based on the unique patterns of an individual.
  • External information: External factors play an increasingly important role in determining the risk level. For example, proximity to well-funded health care services may become even more critical in emergency situations.

To emerge stronger in the wake of COVID-19, an AI analytic platform is essential to build new, relevant and reliable decision support systems. Companies like my own, SparkBeyond, are designed to adapt quickly to rapid change and uncertainty, while offering a way to navigate the new normal with confidence.

— Connect with ThinkAdvisor Life/Health on Facebook and Twitter.


Guy Zinman (Credit: SparkBeyond)Guy Zinman is general manager of solutions at SparkBeyond.