The Fraud Examiner

Big Data Analytics are Necessary to Fight Unique Fraud

Katherine Larilee Moore, J.D., M.S.

President, Vector Analytics, Inc.               


When natural disasters, mass tragedies or class action settlements occur, victim compensation funds are often created contemporaneously. These funds are created to benefit victims, but they are also an attractive target for fraud. When large corporations, agencies or insurance companies establish relief funds, fraudsters rationalize their thefts because they believe that these large entities can absorb the costs. Since huge settlements involve numerous transactions and claims, fraudsters often hope that their actions will be lost in an enormous sea of data. The allure of a big payout can be a draw for fraudsters. Luckily, there is an answer: big data analytics.

Analytics streamline the review process for both intake assessors and upper-level reviewers and investigative teams. These analytics are particularly useful in identifying two types of fraud: overstatement/exaggeration of loss and “ghost” claimants.

The BP oil spill is a paramount example of how big data analytics is used as a tool to fight fraud. The oil spill was the largest settlement of its kind in history. The data analytics and statistical trend regressions that I created during my time investigating claimant fraud are some of the most advanced that I’ve ever had the opportunity to employ.


Lost tips and overstatement fraud due to the BP oil spill

Overstatement or exaggeration of loss is common to anyone who has ever conducted a fraud review or audit of any kind. Overclaiming loss is probably as old as the first insurance claim itself. A claimant-friendly review recognizes that everyone is different and as such, injury can differ in type and magnitude across a population despite exposure to the same event. While reasonable variation is to be expected, extraordinary variation is not.

Typically, overstatement fraud is discovered when one juxtaposes similarly situated claimant files. Effective big data analytics can prescribe an objective, numerical standard of loss and define acceptable variation for a database. There is no replacement for a pair of knowledgeable human eyes conducting a thorough qualitative review, but an initial analytic review can detect anomalous submissions and isolate outlier populations before the reviewer even receives the file — thereby replacing a number of time-consuming assessments.  

One example of how overstatement fraud occurs in compensation funds is in settlements that provide compensation for loss of income. Inherent in these income claims are what I call “soft” claims. Soft claims are typically self-reported income that does not comport with general accounting principles and may lack any significant history of documentation. This type of claimant is often employed in a service industry where the bulk of income is derived from tips. It can also be a small business that does their own books and submits their own profit and loss statements. Both of these types of claimants are vital to any economy and they are often the first and most severely damaged when a natural or man-made disaster befalls an area. Settlements recognize this fact and make it a point to pay these claims expeditiously. This situation is ripe for the claimant who wants to exaggerate loss and overstate income. 

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