Building a Successful Insurance AI Business Case

Artificial intelligence can have a powerful impact on a carrier’s business and management needs to have a clear idea of ​​what they want to accomplish with its implementation. (Photo: THANANIT / Adobe Stock)

The pandemic has not been favorable to the insurance industry. This unexpected global disruption caught insurers off guard, with 87% of insurance operations managers saying it revealed gaps in their organization’s digital capabilities, according to Deloitte Insurance Outlook 2021. In addition, the financial impact of the pandemic has been significant. Expectations are that for 2020, global non-life premiums will have been stable, with a drop of 1% in advanced markets.

But long-term trends have been difficult, even without the pandemic. Investment returns have fallen sharply while combined ratios have steadily increased in the last three years. Insurance executives are eager to find ways to become more efficient, and in response 95% of insurance executives say they accelerate their digital transformation efforts.

Develop the use of AI

Artificial intelligence (AI) plays an important role in these digital transformation projects. In Europe and Asia-Pacific, expanding the use of AI in underwriting is their number 2 priority, although North America gave much lower importance, ranking it 8th. This is not necessarily surprising, because while AI has the potential to powerfully transform and optimize insurance processes, its effective deployment and operation requires a careful approach with a lot of planning and forethought. After all, 80% of companies report their AI projects are at a standstill, according to dimensional research.

That said, as long as IT works closely with operations and the business in a deliberate and thoughtful way, AI can have a powerful impact on a carrier’s business. To ensure that a carrier’s initiatives meet and exceed expectations, insurance executives should make sure to incorporate the following into their AI planning process.

Identify business goals

Management needs to have a clear idea of ​​what they hope to accomplish early in the process. Deploying AI just because everyone else is doing it is a recipe for failure. Typically, AI is used to gain efficiency by automating and optimizing one or more of the following:

  • Reduce leaks: Loss is the biggest expense item for insurers, and premium losses are estimated at billions of dollars per year. The number one expense item for insurers is loss. Verisk believes that auto insurance leaks alone are a $ 29 billion problem. AI can help plug these leaks by detecting fraud patterns, excessive or unintentional exposures, and misalignments.
  • Increase in subscription: Risk assessment is generally a manual process using third party reports. AI can quickly extract risk factors to produce an evidence-based score, allowing risk engineers to focus their time on complex cases. As a result, underwriters can produce quotes more quickly, providing a competitive advantage.
  • Acceleration of complaints: AI can automate the review of claims files to determine their level of complexity and route them appropriately. They can pull data from claims to help determine liability and provide suggestions based on that data, automatically review claim files to assess complexity and route them accordingly. The result is an optimized and streamlined workflow that empowers claims managers to make faster, more informed decisions, which can dramatically improve customer satisfaction.

While it is possible to work towards more than one goal, be careful. It’s easy to get too ambitious, especially given the powerful impact AI can have. The safest strategy is to focus on a single purpose and use case, learn from that experience, and then, once proven, expand the use of AI across the board. within the organization.

Find the right use case and the right KPIs

Once the business goal has been identified, the next step is to determine where in the organization AI can deliver these benefits. For example, if the goal is to reduce the cost of the service, analyze your lines of business to determine which one suffers the most and offers the greatest opportunity for AI to generate value.

As a guideline, consider the following two metrics in your assessment: 1) How many transactions or cases, such as claims or policies, does this particular function handle, and 2) How much efficiency gains , such as hours saved per claim, are likely?

Follow the numbers. Political pressure and other factors within an organization can push IT to implement AI in less than optimal use cases. Again, giving in to this pressure when the data points in another direction is likely to lead to disappointing results. Especially with relatively new technologies such as AI, it is essential to be successful early in order to gain confidence and, just as importantly, experience so that larger applications do not only enjoy widespread support; they will be more likely to achieve the desired results.

And, speaking of results, management will need to establish tangible KPIs to measure and demonstrate success, which can be difficult for AI to do. That said, there are certain best practices that carriers need to follow.

Look at the reduced exposure. Leaks are a pressing issue for most carriers, and through the use of AI in risk engineering, risk assessments can take place faster and the scoring system can become more consistent. Efficiency is another good metric to assess, especially for underwriting processes. AI can dramatically reduce the time it takes to process documents like policies or risk assessments, which can increase production and increase capacity. Process efficiency does not just reduce costs; it can improve and accelerate customer acquisition and growth, so this is another good KPI to follow. Faster policy turnarounds put an organization in a much better position to do business.

With the above items done, it’s time to map the infrastructure. AI is not a plug-and-play technology. It must be closely integrated with existing processes. The AI ​​implementation team will therefore need to carefully determine the expertise, data, and other systems needed.

Insurance faces a difficult future, and AI can help businesses meet these challenges, but only if they approach AI strategically and thoughtfully. With targeted deployment that produces rapid success, carriers can position themselves for greater gains in the future with this exciting new technology.

Pamela Negosanti is Sales and Industry Strategy Manager for Financial Services and Insurance at expert.ai. Contact her at [email protected].

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