Is AI in claims worth the investment?
Artificial intelligence gives insurers the ability to automate manual processes, but how can they track the ROI that comes with adoption?
Artificial intelligence (AI) is seemingly everywhere these days, so it’s not a surprise to see it among the latest insurance technology (InsurTech) trends. However, it’s not just a buzzword. AI is penetrating nearly every industry that utilizes digital solutions and is quickly becoming a “must-have” instead of a “nice-to-have” for property and casualty carriers.
It’s no wonder — AI is poised to have a tremendous impact on the insurance industry. According to research by Accenture, 75% of insurance executives plan to use AI to automate more tasks within the next three years. And, by investing in AI, the research estimates U.S. insurers could increase annual profitability by as much as $20.8 billion.
The use cases are clear: Insurance carriers can do more business faster and more accurately by incorporating AI. The technology can enable insurers to streamline workflows, predict loss and fraudulent claims, and better detect the level of damage to a property. AI technology — including machine learning, image recognition and voice recognition — continue to evolve and provide policyholders with better customer service by creating a more automated, hands-off process and, in turn, increase a carrier’s customer satisfaction scores (CSAT).
Despite these benefits, however, there is still a perception among the industry that more businesses are investing in AI just for the sake of investing in it. As a result, insurance executives may be reluctant to invest in InsurTech if it fails to address a clear business or customer need — regardless of how forward-thinking that technology might be. The key for insurers is gathering the right data and identifying the workflow pain points that can be addressed with AI.
Challenges to implementing AI
Recent research by Novarica suggests more than one-quarter of insurance carriers are planning pilot programs around big data technology and predictive analytics solutions. However, not all carriers can onboard tech at the same speed. According to the Accenture survey, 70% of respondents believe AI is advancing at a pace faster than their organizations can implement it. Additionally, just 31% say they already have processes in place to use data and analytics to “drive new insight, advice, decisions and customer experiences.”
That is a critical gap for insurers. It means that, while they are eager to adopt AI technology, they may have no way to prove the benefits of those solutions. The big question facing some carriers, then, is how can they accurately track return on investment (ROI) around their implementation of AI to see success with their efforts?
Key AI measurements
How much a new solution costs is easy to determine, but to understand the true value, insurers need to identify the right metrics and successfully connect subsequent growth directly back to the technology. In other words, arm CTOs and other decision-makers with the data they need to justify an InsurTech purchase.
Understanding what business pain points exist and where there is room for improvement will help to make the case for InsurTech. By identifying crucial metrics to the organization and comparing the data pre-AI versus post-AI, insurers can then determine the true value of AI. While the exact metrics insurers look at depends largely on their organization’s goals, here are a few measurements to consider:
- Claims cycle time: What is the time from first notice of loss (FNOL) to close? Does this factor in claims following a major weather event? This evaluation should be backed up with specific data points, such as the cycle time down to the hour or minute, if available.
- Workflow changes: How has the workflow for claims processing changed? Traditionally, once the claim is received and assigned, an adjuster contacts the homeowner for an appointment, drives to the property, inspects the damage, updates and resolves the claim, and closes the claim. Technology, however, focuses on reducing the number of steps in this workflow. It’s important to look at every step involved before implementing new tech and then see which steps are still required.
- Field inspections: Insurers should track the overall cost and number of site visits required over the course of a month or year. Ideally, AI and desktop adjustment solutions should lower or even fully eliminate large portions of field inspections and costs.
- Customer satisfaction (CSAT) scores: Customer satisfaction is especially important when it comes to claims processing, but that efficiency means nothing if it is not passed onto the customer. Insurers should be able to show that they can close claims faster and more accurately, which in turn should increase CSAT scores.
Artificial intelligence and other InsurTech investments should optimize the above metrics. Shorter claims cycles, fewer steps within the workflow, and fewer field inspections will result in a better customer experience.
By proving the ROI of a new tech solution, insurers know what they can keep when it comes time to reevaluate a tech stack the following year. Demonstrating that value will help keep AI and other InsurTech solutions on that organization’s “must-have” list.
Michael Park (michael.park@eagleview.com) is the chief product and marketing officer at EagleView. You can also contact him through Twitter at @MichaelPark798.
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