AI's role in mitigating risk

As the adoption of artificial intelligence (AI) grows, its role in helping insurers mitigate the impact of catastrophic claims is becoming increasingly clear. AI effectively detects both opportunistic and organized fraud attempts while also streamlining case management for the SIU when fraud is found. In addition, AI uncovers subrogation opportunities that might otherwise be overlooked during the surge in claims following natural disasters. By automating routine, time-consuming tasks, AI reduces employee burnout and fatigue, accelerates the overall claims process, and enhances customer satisfaction.

The benefits of AI in the claims process in the immediate aftermath of a catastrophe is clear. But what about before disaster strikes? Can insurers use AI to better protect themselves from the impacts of future events? The answer is a resounding yes. However, current thinking about AI's role in risk mitigation for catastrophic events may be too limited.

Today, insurers primarily use AI to analyze patterns related to natural disasters and weather-related incidents, helping them better manage their exposure. By examining a large number of historical events, AI can predict future occurrences and enable insurers to model risk more accurately. This leads to more effective underwriting in catastrophe-prone areas. While this approach helps ensure premiums align with exposure, there are even greater opportunities to leverage AI for mitigating catastrophe risk throughout the underwriting process.

From claims to underwriting

If we think about claims and underwriting as being part of an integrated system rather than two separate and distinct processes, we begin to see how each can influence the other. What AI uncovers during the claims process can have a direct, and powerful, impact on how insurers think about writing policies in CAT-heavy zip codes. Take fraud, for instance. Once a suspicious claim is identified, investigated, and confirmed as fraudulent, the policyholder is typically no longer desired as a customer.

However, if underwriting and claims operate in isolation, this undesirable applicant might still obtain a new policy. Slight alterations to personally identifiable information (PII)—like shortening a first name, adding a middle name or initial, or including a generational suffix—are often enough to obscure a past policyholder's identity. AI-based entity resolution, which integrates claims data with underwriting, can circumvent this. By recognizing that "John Smith" is the same as "John A. Smith" or "Jonathan A. Smith, Jr.," AI provides underwriters with better visibility into an applicant's true identity and their history with the company. This immediate access to information enables them to make more informed decisions regarding the risk associated with writing a policy.

AI-powered analysis of claims data can also create a profile of an applicant, revealing their associations and potential links to suspicious activities. In the context of natural disasters and catastrophes, these connections could highlight contractors, remediation specialists, or other service providers with a history of involvement in exaggerated or falsified claims. Providing underwriters with this network information during the application process offers crucial data to assess the risk of future fraud an applicant might pose to the business.

Rooting out misrepresentation, policy hijacking and hyperendorsement

In areas frequently affected by catastrophic (CAT) events, applicants may struggle to find policies with what they consider reasonable premiums. In such scenarios, potential policyholders might resort to questionable tactics to secure coverage. AI-powered risk mitigation is ideal for these situations. This could involve applicants deliberately providing false, incomplete, or misleading information during the insurance application, only to alter those facts after endorsement. Even in cases where post-application checks are limited, AI-based underwriting risk mitigation can flag these changes for further review and investigation.

In CAT-prone locations, applicants may attempt to misuse or abuse their policy through hyperendorsement, adding endorsements that increase coverage or transfer risk in ways not originally underwritten or approved. This exposes the insurer to unforeseen liabilities. Similar to misrepresentation, AI can effectively identify these activities.

Finally, insurers may encounter bad actors attempting to hijack existing policies. While less common than misrepresentation and hyperendorsement, this still poses a significant risk for insurers operating in areas prone to natural disasters and other catastrophic events. Through identity theft or other fraudulent methods, a bad actor can assume control of a policy or modify it without proper authorization, increasing the insurer's risk exposure without adequate underwriting review. Again, AI can detect these attempts.

While catastrophic events often have their most significant impact on the claims process, the insurance industry is increasingly recognizing the benefits of applying AI-driven risk mitigation during underwriting. A more holistic approach, connecting underwriting and claims, can significantly alter how these incidents affect insurers. By understanding an applicant's history and network affiliations, insurers can better assess who they are doing business with and whether that business represents an acceptable risk. AI can help detect misrepresentation, hyperendorsement, and policy hijacking, ultimately ensuring a healthy book of business well-prepared for any natural challenges.