Identify business objectives

Management must have a clear idea of what it hopes to accomplish at the very beginning of the process. Deploying AI simply because everyone else is doing it is a recipe for failure. Typically, AI is used to gain efficiencies by automating and optimizing one or more of the following:

Reducing leakage: Loss is the top expense item for insurers, and premium leakage is estimated to amount to billions of dollars annually. The number-one expense item for insurers is loss. Verisk estimates that personal auto insurance leakage alone amounts to a $29 billion problem. AI can help plug these leaks by detecting fraud patterns, over or unintended exposures and misalignments.
Augmenting underwriting: Risk evaluation is typically a manual process using third-party reports. AI can rapidly extract risk factors to produce an evidence-based grade, which enables risk engineers to focus their time on complex cases. As a result, underwriters can produce quotes faster, providing a competitive advantage.
Accelerating claims: AI can automate the review of claims packages to determine their level of complexity and route them appropriately. They can extract data from claims to assist with liability determination and provide suggestions based on that data, automatically review claim packages to evaluate complexity and route them accordingly. The result is an optimized, streamlined workflow that enables claims handlers to make faster, more informed decisions, which can significantly improve customer satisfaction.

While working towards multiple objectives is possible, be careful. It's easy to become too ambitious, especially given the powerful impact that AI can have. The safer strategy is to focus on a single objective and use case, learn from that experience, and then, once it has proven a success, expand the use of AI within the organization.

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Building a successful AI business case in insurance

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