When will AI arrive in life insurance?

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When it comes to the use of artificial intelligence (AI) in the insurance industry, the focus is primarily on property and casualty insurance. AI does not seem to have really arrived in life insurance yet, even though cost efficiency and data quality are considered success factors – and the sector would therefore be predestined for the use of AI.

When people talk about artificial intelligence (AI) in the insurance industry, they almost always refer to property and casualty insurance. Automated claims processing, dark processing, fraud detection – there is one use case after another. In life insurance, on the other hand, there is rather a radio silence, even though the conditions for AI use would be ideal here.

Three factors influence the outcome in life insurance: capital investment, life expectancy, and costs. The first two are not controllable, but administrative costs are. Nevertheless, the use of AI has been very limited so far. Yet this is precisely where enormous potential could be tapped—with positive effects for underwriting, performance, portfolio, and product development.

Risk assessment: From application to data set

AI can not only analyze health information, doctor's letters, and questionnaires more quickly, but also store them in a structured manner – for each contract, over its entire term. This creates a completely new database for pricing, risk assessment, portfolio development, and forecasts. It also opens up new ways of applying medical findings to the portfolio, for example by identifying new risk groups—such as when the next pandemic threatens.

Life insurers are “sitting” on complex portfolios with decades of product history. AI can automatically evaluate and cluster rates, addenda, and options.

Customer communication: Making complex things understandable

Whether for consulting or portfolio communication, generative AI can help explain products in an understandable way, answer questions, and provide individual support to advisors or customers. This not only improves the client journey, but also the closing rate.

Product development: faster, more targeted, data-driven

AI can analyze market needs, evaluate reasons for cancellations, and identify trends early on. This shortens product cycles and increases accuracy.

Knowledge management: securing experience, shortening training periods

AI agents can be used to answer questions about processes, rates, and cases in a context-based manner—ideal when knowledge is at risk of being lost or new colleagues need to be trained.

Conclusion: Life insurance offers ideal conditions for AI – it just isn't using them yet. That's because it combines three levers: automation potential across the entire value chain, reduction of the “loss ratio” through better underwriting and more efficient benefit assessment, and the “leapfrog effect” through data-driven portfolio management and product optimization.

Perhaps this is precisely where the quiet playing field for the second wave of AI in the insurance industry lies: quiet but effective.

German Version: Wann kommt KI in der Lebensversicherung an?

Read more: Wann kommt KI in der Lebensversicherung an?

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