Making the case for AI in commercial insurance

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Last time, we discussed some of the potential benefits of AI in commercial insurance. Now, let's talk making the business case.

Many insurers are hesitant to invest in AI without proof that these theoretically smart systems will yield real-world returns. A mature AI vendor will have the foresight to develop a team within its organization that’s dedicated to value analytics. This team — made up of data scientists and actuarial experts — will use the company’s own AI solution to run a simulation that can quantify potential savings the solution could provide.

This capability is crucial, as insurers don’t want to wait three or four years to realize a return. The value analytics team will take an insurer’s historical data and run the simulation. It might conclude that if the insurer had implemented this AI solution two years ago, it could have saved a certain amount — such as 5 to 10 percent — on claims costs. This percentage of savings might be based on a specific action, such as transitioning injured workers from low-ranked providers to high-ranked providers — or doing the same for attorneys. Or, the savings might encompass claims that could have avoided certain scenarios, such as surgery or litigation.

Once the AI solution is deployed against live data, the models continue to run every month (or quarter) based on a pre-defined set of performance metrics. Every month (or quarter), the calculations become more accurate, moving from a rough estimate to a tighter range and eventually to a precise calculation of savings achieved.

Traditional models were challenged by the fact that claims are long-term transactions that can take as much as 18 to 24 months to close, but AI — with its machine learning — is able to handle this complexity with a high degree of accuracy.

A Holistic Approach, Not a Silver Bullet

In folklore, it’s the silver bullet that kills the wolf. This bullet has come to signify a simple solution that magically resolves an insurmountable problem. However, an important part of making AI real is understanding that while it is powerful, it’s no silver bullet.

At the end of the day, AI is most effective when it’s part of a holistic approach. All the pieces of the puzzle must be put in place. At a high level, these pieces include the AI technology itself, operational tweaks, and metrics to gauge results. Impact follows when all these components work in harmony. When these conditions are there, we’ll begin to see the needle move on costs and outcomes. For example, insurers can use AI insights to create new, more efficient workflows; they can facilitate more effective hiring and training practices that enable human resources to apply their expertise at precisely the right moment in the claims process. It’s iterative, with machine learning driving change in a continuous cycle.

Although immediate savings can be achieved, an enduring competitive advantage can only be realized when the application of AI is seen as a journey. It requires ongoing effort and investment. Strategic players understand it can take a few years of making ongoing improvements to truly redefine their cost structure, customer experience, and position in the market. The organizations that start early on the AI path with an iterative mindset will be well equipped to succeed in the future. We’re looking forward to an exciting decade ahead.

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Artificial intelligence Machine learning