Can you imagine finding a million dollars of surplus funds in your ledger?
That’s exactly what happened to us when we ran our reinsurance contracts through a popular data-driven audit analytics program.
Here’s a little-known fact: Most reinsurance agreements have error correction features that have no statute of limitations (meaning you can dig back throughout the years to get your money back!) This makes it a very opportune area for a deep-data audit.
But seriously, who has the time, right?
Well, from our experience, time is money, and a lot of money can be found with just a small investment of time — if it’s spent wisely and the right tools are used.
Our company is a Life and Property & Casualty (P&C) multi-line insurer with annual premiums totaling about $800 million. Over the last 25 years, our internal audit department has made a conscious effort to audit reinsurance contracts both for premiums ceded and losses recovered.
Due to our mid-size status, reinsurance contract administration is not centralized, touches several departments and is a small part of many employees’ jobs. In our risk assessment process we ask ourselves: “Who is getting graded on this?” Our risk ratings inflate when we can’t answer that question with just one or two names.
At a large company there might be a dedicated reinsurance manager or director who oversees the function, but even with this structure in place, it would still pay to audit reinsurance contracts for potential profit.
Reinsurance is not just another contract audit
How do you go about a reinsurance audit? Before you can even begin, you’ll need to obtain, read and understand your contracts. You might need to get data from your policy and claims administration systems as well, because reinsurance is not just another contract audit.
Our P&C reinsurance contracts are industry standard: catastrophe cover, property per risk, equipment breakdown and excess of loss. However, our antiquated P&C systems were code-driven. To apply reinsurance correctly, you had to know and enter a code, and be able to maintain it (fancy drop-down menus didn’t exist yet). In fact, you couldn’t even extract a full description of loss because the field only kept the first 15 characters.
As our systems improved, we were able to get full loss descriptions. However, the loss cause code-driven environment that fed reinsurance reporting still applied. As a result, we found that the available codes often failed to specifically support a subsequent reinsurance agreement, or in many cases, what was keyed just didn’t make sense.
Keyword matching was key
To rectify the potential disparities between the actual claim and the submitted code, we applied a simple keyword matching search through the auditing software we use, ACL™ GRC. Here are some examples of what we found:
- One loss cause code in our system was “Fire and Lightning.” A kid playing with matches who accidentally started a fire didn’t qualify for catastrophe reinsurance. However, a lightning strike that started a fire did! But we only had one code. It was incredibly helpful to extract the description-of-loss field for the entire population of fire claims.
- Loss cause codes could be keyed as “theft,” which by itself is not reimbursable under the catastrophe agreement. A closer look at one loss cause code description revealed that a storm was responsible: “Wind knocked out power to alarm system and theft occurred.” This would be covered under the agreement.
- Similar problems were noted in our Equipment Breakdown (EBK) contract. Eligible claims have a specific EBK loss cause code. Claims without the EBK code but with certain keywords in the loss description field, like lightning, broken, failed or cracked, were run through ACL keyword matching, yielding hundreds of thousands of dollars in recoveries.
We found $1 million!
Overall, we found that under normal operations, the description of loss was often not being matched to the code that drove the automated portion of catastrophe loss recoveries. Upon review, we uncovered a small number of losses that had already been submitted to the reinsurer for reimbursement that were not eligible based on loss description.
Of course, we returned our reimbursement to the reinsurer for these claims. However, we discovered a large number of claims that were eligible for reimbursement for which we had failed to bill the reinsurer. We subsequently did bill the reinsurer and received payment for the claims.
Using audit analytics programs like ACL’s, small and mid-size firms like ours can complete a reinsurance audit rather easily, quickly and with little upfront cost — especially considering the return on investment.
By conducting a thorough, retroactive audit using modern data analytics tools, we were able to uncover more than $1 million in eligible claims that were previously overlooked. How many millions of dollars are waiting for you?
Steve Hummer is the senior audit manager for the Farm Bureau Insurance of Michigan. He can be reached by phone at 517-679-4722 or by email at firstname.lastname@example.org. Opinions expressed are the author’s own.