Once thought of as highly traditional and reluctant to change, insurers today are rethinking everything. The rise of digital-first carriers, with an ethos borrowed from their ecommerce and ebanking cousins, has generated an intense interest from incumbent carriers to focus on the needs of the customer and use new technology to deliver a superlative customer experience. We see this most specifically in efforts to introduce Straight Through Processing (STP) with the ultimate goal of true claims automation.
And it makes sense. What better way to exceed customer expectations than to have filing and getting compensated for a claim take the same amount of time – or less – than buying something from your favorite online retailer? But, despite the promise of high customer satisfaction and improved customer loyalty resulting from various forms of claims automation, it does not come without unique risks. Principle among these is an increased opportunity to commit fraud. And while fraud has always been present in the insurance industry, conventional wisdom indicates that fraudulent claims may increase by up to 300 percent when automation is introduced into the equation. Therefore, while fraud detection has always been an important part of claims management, the increased adoption of STP and the drive to claims automation makes it critical.
What STP tells us about the relationship between fraud and automation
As insurers move toward deploying AI-based claim-handling bots to process straightforward, low‑cost claims, it’s only natural that those intent on making an easy buck will look for new ways to defraud insurers. All it takes is a little knowledge of the core rules underlying the process to significantly increase the possibilities of fraud. So, to begin our examination of the potential impact claims automation could have on fraud rates, let’s start at the beginning with STP.
Many P&C insurance companies have started setting up STP, also known as “One-touch” or “Fast-track” processing to streamline a significant portion of their claims volume. Such processes usually consist of handlers paying claims directly without the involvement of loss adjusters and sometimes even without requiring invoices and quotations. Typically, insurers would apply STP logic to claims falling under a certain monetary threshold, where the pay-out would be less expensive than the costs associated with expert analysis. Some concrete examples include baggage claims under $500 within travel policies and water damage under $1,500 for homeowners’ insurance.
Despite still requiring a human touch, STP can be viewed as an important first step toward broader claims automation. And the way STP expedites the payment process sheds light on the ways claim automation may heighten the temptation for some consumers to behave fraudulently.
STP has already given birth to new fraud trends
As STP becomes more prevalent, two types of fraudulent behaviors quickly emerge and take center stage. The first centers on policyholders filing multiple, yet completely fake, claims for amounts just under the STP monetary threshold set by their policies. Our own data, specifically related to travel insurance, bears this out with a significant spike in policyholders filing claims for approximately $400 happening in the months following the implementation of STP. Similarly, in the realm of homeowner’s insurance, policyholders began faking multiple water damage incidents across several policies, each time providing nearly identical descriptions within the claim statements.
In the second type of fraud related to STP, policyholders with meritorious damages are starting to exaggerate the costs for reimbursement up to the given threshold amount. This has been especially rampant where repair shops are aware of insurers’ limits and deliberately encourage customers to fabricate the gravity of the damages. For example, a home repair normally quoted at $500 might change to $1,500 following collusion between the two parties. Generally, the service provider will offer to do the work “for free” or at a reduced cost if the policyholder agrees to the scheme. Both groups have quickly figured out that if they keep their claims under the insurers’ magic numbers compensation will come without any questions.
The allure to commit fraud is compounded by the relatively low risk for legal repercussions. If an insurer decides to investigate one of these claims further, the policyholder simply drops the claim, while the insurer generally will not take legal action for such low financial stakes.
Putting New Rules in Place
To combat this phenomenon some insurers have decided to employ a set of basic rules to limit their exposure to risk, such as a maximum of two STP claims per policyholder per year. Unfortunately, this type of rule is very easy to circumvent. Whether through networks of professional fraudsters or communication between friends and family who can then do the same on their own policies, the fraud will continue.
Clearly, this in untenable as insurers cannot afford to payout two fraudulent claims per policyholder per year. At the same time, this is a legitimate, albeit bleak, scenario for insurers that do not remain vigilant as they turn more and more towards automation.
Looking at claims data, it’s clear that when fraudsters understand the rules behind STP, such as monetary thresholds or the maximum number of claims per year, fraud attempts quickly spread among policyholders eager to “take a little back” from the insurance companies. In just one example, four months after an insurance company set up STP for all glass breakage claims under $1000, a sudden spike in the rate of glass breakage claims, concentrated in pockets of communities across the insurers’ geographical coverage zone, occurred.
And unfortunately, fraud schemes are also more likely to spread during natural disasters for which the associated claims are often treated using STP. Policyholders trying to game the system will capitalize on the insurers’ limited resources to send loss adjusters. During a recent flood in France, large numbers of policyholders who clearly lived clearly outside of the impact zone, used photos of their friends’ flooded basement to make fraudulent claims.
What We can Expect with the Rise of Claims Automation – and the Need for AI
Despite the tremendous benefit to the customer experience associated with claims automation, the potential for fraud becomes even more dangerous. Based on both research and experience, we can predict that instances of fraud to at least double depending on the safeguards insurers have put in place as they turn to bots to handle their claims. Thinking they can outsmart a bot, a policyholder might feel more at ease testing several different versions of a claim statement until finding the perfect circumstances that warrant coverage and a quick, tidy payout.
Insurers should be very cautious as they apply deterministic sets of rules to their claim-handling bots. If the claim handling bot is programmed to process claims individually and does not have the capacity to detect trends, once fraudsters succeeds in cracking the system, they’ll know they can repeat the process. The fraudsters will inevitably share information with their networks, encouraging them to claim exact same accident, with similar circumstances and documents, knowing the bot will pay.
For these reasons, it is crucial for a claim-handling bot to integrate a powerful and comprehensive fraud detection solution with the intelligence to compare multiple seemingly unrelated claims to detect unusual similarity in circumstances or invoices, and detect those statistically unlikely trends that would to stop the automated process and raise the red flag for human investigation.
Claims automation faces several complex challenges. In practice, can it:
- Reach a precise estimate of the amount of the claim?
- Drive good customer experiences and relationships?
- Understand the context and the amount of damage with enough precision to propose an appropriate settlement method?
Furthermore, while bots will always have the recourse to redirect claims back to a human handler at the customer’s request, customers are unlikely to voluntarily notify insurers of fraud detection deficiencies, allowing for fraud schemes to run rampant and millions lost before the insurer catches on. Thus, the biggest challenge facing insurers wishing to automate claims is not producing a bot capable of going through steps as one might expect. Rather, insurers must first and foremost protect themselves from their increased vulnerabilities through integrating an adequately robust combination of fraud detection tools to safeguard the billions at stake.
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