Editor's Note: This is another article in a multi-part series that examines the use of AI in the claims space.
According to the National Oceanic and Atmospheric Administration (NOAA) the United States endured
Since science has not yet figured out how to control the weather, these natural disasters will continue to occur. And for the insurance industry, these events will continue to result in
For example, can you automate "normal course of business" claims, allowing your adjusters to focus on more complex scenarios that may require personalized attention while also ensuring that policyholders' claims not impacted by the event are settled as quickly as possible? Are you able to efficiently and accurately identify and separate legitimate from suspicious claims, ensuring you do not pay out for opportunistic or even organized fraud related to the event? And finally, can you spot and respond to claims subject to subrogation and recovery before the window of opportunity closes?
Advances in artificial intelligence (AI) and generative AI (Gen AI) are serving as the foundation for new approaches to mitigating the impact of catastrophes on the claims process. These technologies allow insurers to
Finding and eliminating fraud
Unfortunately, we know that catastrophic events create perfect opportunities for bad actors to take advantage of the situation. In the chaotic aftermath of a disaster, the surge in claims volume can overwhelm insurers, creating vulnerabilities that both opportunistic and organized networks of fraudsters can exploit. Fraudulent claims may range from exaggerating damages to entirely fabricated losses attributed to the event. On the surface, these claims may look legitimate. Reported damages may be in line with accepted industry norms. Supporting documentation and accompanying images may appear to support the claim's veracity. Even under normal circumstances, these claims may be difficult to identify as suspicious. However, there are often telltale signs of malfeasance hiding just below the surface, waiting to be discovered.
AI and Gen AI have emerged as important tools in this regard. Take for example the opportunistic fraudster who decides to exaggerate their claim following a catastrophe and declare totally unrelated or previously existing damages. Maybe they are working alone, or perhaps they are working in collusion with a dishonest contractor or other service provider. The claim may be supported by photographic evidence and other documentation. In the past, these claims may have slipped through the cracks and been considered a "cost of doing business" when dealing with claims during a catastrophe. AI and Gen AI completely change how to deal with these types of fraudulent claims.
Let us look first at how AI handles the issue of photographic evidence. AI can analyze the meta data attached to a photo and quickly determine if time and date stamps align to when the event is reported to have taken place. AI can also be useful in determining whether a photo includes previous damage/disrepair that may have been exposed by the catastrophic event but not caused by it, such as rot impacting a sill plate. Any identified discrepancies raise an alert for further investigation.
AI and Gen AI are also useful in accurately matching photographic evidence to the narrative provided by the policyholder at FNOL. If details do not match up, for example the damage was described as impacting the kitchen, but the photo appears to show a different room, the claim is flagged for further review. Finally, AI is proving adept at analyzing images and determining if those images were created using Gen AI, possible evidence the claim may be fraudulent.
AI and Gen AI are also helpful in understanding the relationships between parties involved in a claim. The ability to see which contractors or other service providers may be part of an organized fraud ring or working in collusion with a policyholder to exaggerate a claim is useful in separating legitimate claims from those that may be fraudulent in the deluge following a catastrophic event. AI's ability to analyze claims data along with relevant third-party data accurately identifies suspicious connections and anomalous behaviors that may indicate fraud, and that often remain overlooked in the increased claims volume following an event.
Claims automation and the customer experience
Despite the often localized direct impact of a catastrophic event, it is important to remember that its effects can be felt across the entire business district. The rapid influx of claims, and their increased complexity and severity, put considerable stress on the claims organization. And while claims professionals' focus may be diverted to responding to the hurricane, tornado, wildfire or other disaster which has just occurred, normal course of business claims continue to happen across the country. All policyholders want their claims settled quickly, accurately, and fairly. If this doesn't happen—if customers feel that they are not being taken care of—there is higher risk of customer churn.
AI again can play a crucial role in helping insurers deliver amazing customer experiences in the midst of a disaster. The ability to accurately weed out suspicious claims gives claims professionals more time to focus on those that are legitimate. As important, knowing exactly what is happening with a claim supports claims automation initiatives. If insurers can automate appropriate claims with confidence, or even automate certain parts of the claims process, claims are settled faster and with significantly less friction.
For those claims where automation may not be suitable, Gen AI can remove many of the roadblocks that stand in the way of settling claims quickly. Gen AI is able to analyze and summarize vast amounts of data, removing that burden from the claims professional. Gen AI is also able to offer critical guidance on next steps based on its analysis of claims and related data. This is crucial in situations where more experienced claims handlers and adjusters are assigned to activities directly related to the catastrophe, leaving more junior staff to deal with the normal course of business claims. AI and Gen AI level the playing field and help ensure all policyholders receive exceptional customer experiences.
Disasters and subrogation
On the surface, the relationship between subrogation and catastrophic events may not be readily evident. In the case of a natural disaster, it is rare that a third-party, and not the event itself, is responsible for reported damage. However, that does not mean that subrogation and recovery strategies are not influenced by these events, nor that applying AI to this process is not beneficial.
Take for example a heavy rain event. Local officials may have a responsibility to enact certain protocols to reduce the potential for flooding in a particular area. What happens if those protocols are not implemented? We can see this scenario play out in a hypothetical water damage claim. AI can help insurers spot anomalies that may indicate the claim needs more investigation.
Is the insurer seeing water damage claims in an area where they typically do not? Is there evidence in the claim narrative that something that should have been done was not, resulting in unnecessary damage? In these situations, AI can be useful in helping insurers understand what is truly happening in a claim and accurately assessing responsibility.
We also know that policyholders outside of the impacted locality are incurring everyday losses, some of which may be the responsibility of a third-party. In many jurisdictions, the window of opportunity to begin subrogation and recovery efforts is limited. In an "all hands-on deck" situation it becomes difficult to spend the time with an individual claim to spot hidden subrogation and recovery opportunities. In these cases, AI analyzes the claim, determines if a subrogation and recovery opportunity exists, alerts the subrogation specialist and highlights the probability of success all within an acceptable timeframe that allows them to take appropriate action.
Unfortunately, we live and work in an environment where disaster can strike at any time. For insurers, the ability to react quickly to address policyholder needs while keeping an eye on the bottom line remains paramount. AI and Gen AI are playing an increasingly important role in shaping how the claims process responds to the pressures introduced by a catastrophic event. From identifying fraud and subrogation opportunities to ensuring an exceptional policyholder experience, these technologies are changing the way insurers respond to any event that drives claims severity and volume.
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