In Insurance, the Policy Review is one of the most valuable areas for adopting practical AI solutions. This critical activity is owned by the Underwriting function, and it is an important aspect of very specific steps in the typical insurance workflow. Policy Review is important for the following scenarios:
- When the carrier receives a new submission from the broker. In this scenario, underwriters must ensure that coverage and exclusions are in line with the written documents (i.e., binder or instruction vs policy).
- At renewal time, when brokers send a summary of the renewal and the underwriter must verify this against what was expected.
- For international programs, to ensure that, in the process of localizing a policy or a contract, the changes required do not introduce critical, risk profile impacting changes to the terms of the global policies.
- In response to unexpected and rare events, as many carriers have experienced with COVID-19. In this scenario, underwriters might be required to analyze all existing policies to evaluate the potential exposures of claims related to these rare events. In this scenario, underwriters might be required to analyze all existing policies to evaluate the potential exposures to claims related to these rare events.
Policy Review is also important for brokers. Brokers face similar requirements when, for example, they receive a binder from the carrier and need to make sure that binder and policy are aligned.
Policy Review is not straightforward. The review process typically involves multiple team members, it happens more than once in the lifetime of the policy and could take anywhere from 2 to 6 hours, or longer for certain lines of business like Reinsurance. This makes Policy Review probably the first and most valuable target area for AI-driven automation.
When we think about the value of automation in Policy Review, there are different aspects that contribute, often in a compounded way, to create value. Let’s look at three such areas.
- To mitigate the carrier’s exposure to risk. Automating the policy review can ensure that every aspect of coverage is reviewed, even details that might be easily missed. For example, it can uncover if an event that is not intended to be covered in the policy is either included in the coverage or worded in a way that doesn’t comply with the carrier’s risk profile. Automation can also ensure that 100% of policies are reviewed before being executed, even at times when resources are scarce (i.e., during renewal peaks), and it can ensure that the review is based on standard and consistent criteria. While this may seem obvious, in the traditional, pre-AI world, policies are often reviewed by multiple people at different times, and therefore, those reviews might vary.
- Automation creates value by increasing capacity, possibly in an unlimited way. By nature, the Underwriting workload is seasonal. There are peaks, at renewal time for example, that limit the resources that can be allocated in managing new submissions. Because the speed in which a carrier responds to a new submission is an important factor in winning the new contract, delays or a lack of response can result in losing business that could be avoided through automation.
- The third driver of value is the one that is most often associated with automation: Efficiency. In underwriting, “efficiency” means less time dedicated to reviewing each policy without compromising the quality and accuracy of the review. Therefore, efficiency is also about optimizing costs.
While the value of automation in underwriting is indisputable, the adoption of AI in this area is still in the early phase. Carriers are showing an appetite for AI technologies that drive automation because they understand that any improvement in activities at the beginning of the Insurance value chain can generate a positive impact across the organization.
However, to enable automation (and fully maximize the value of AI-driven solutions), carriers have to think about process design from the very beginning. In Insurance, and underwriting specifically, the amount of manual work required even today is astounding. While this means that the value automation can add is significant, many processes are designed around manual tasks, which prevents automation from coming in.
Going back to the example of automating Policy Review, while AI solutions that ensure the deep understanding of content are not 100% accurate, neither are humans. That’s why many processes include human review at the end for reviewing the outcome of AI-driven comparison. This is a persuasive use case for those in the underwriting community—a position that has traditionally been at the center of value creation in insurance—who are still skeptical of the value of AI.
However, if insurers cannot overcome this resistance to innovation, the alternative is being left behind and unable to compete. For those who are still unsure, there are lots of use cases that not only show the value that can be achieved, but also demonstrate that implementations do not have to be complex. Underwriting is the perfect area to take innovation to the next step where the full potential of technologies like AI-based automation can be realized.
Global VP of Insurance
EVP Strategy & Business Development