Data Mining Technology Helps Insurers Detect Health Care Fraud

Specialized Software Applications

Today there are numerous specialized software applications that focus exclusively on identifying suspected instances of fraud and abuse. In general, these applications can be used to analyze a specific universe or block of claims data. This block, often referred to as a “data set,” generally consists of historical claims information for a certain period of time and a certain peer group (i.e., the application might examine the claims submitted from every chiropractor in Kentucky for the last year).

Using a combination of advanced statistics specifically designed to identify indicators of fraud or abuse, the application will allow an analyst to sort rapidly through large amounts of information and identify patterns or entities that are statistically different from their peers and potentially indicative of fraud. Because these models are fairly fixed, this process is known as a “supervised” methodology.

While these applications have been used with moderate success, a chief drawback is that they are only effective on fraud schemes that already have been successfully perpetrated on the company. While you may have the opportunity to shut down an ongoing fraud scheme, it has been proven time and again that funds already lost to fraud are nearly impossible to recoup in their entirety. In order to fully mitigate significant losses, data mining and the detection of potential fraud and abuse needs to occur prior to funds being paid.

Next Generation Applications

Currently, several payers are experimenting with technology adapted from the credit card industry. This technology has several distinct advantages. First, the technology looks at historical claim submissions to build an ad hoc model to which the current claim can be compared. Next, the application compares the current claim to the historical model and assigns a relative score that reflects the claims similarity to the model. Then, that score is used to predict the potential of fraud. Since the application is not based on a set of static rules, it is considered an “unsupervised” methodology.

One of the great advantages of this new technology is its ability to be utilized prior to payment. This way, individual claims with a high likelihood of fraud can be forwarded to an investigator who can scrutinize the claim further and evaluate the appropriateness before payment is made. Investigations prior to payment are one of the most effective means of preventing fraud.

Ultimately, data mining tools and techniques cannot automate the entire process of managing fraud and abuse. However, what these tools can do is greatly increase the effectiveness of an organizations resources by enabling individuals to sift through mountains of data quickly and focus their efforts on the pertinent information most likely to generate savings.

is a director of Fraud and Abuse Operations for Ingenix Inc., Salt Lake City.

Reproduced from National Underwriter Edition, October 14, 2004. Copyright 2004 by The National Underwriter Company in the serial publication. All rights reserved.Copyright in this article as an independent work may be held by the author.