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Pondera FraudCast

Welcome to the Pondera FraudCast, a weekly blog where we post information on fraud trends, lessons learned from client engagements, and observations from our investigators in the field. We hope you’ll check back often to stay current with our efforts to combat fraud, waste, and abuse in large government programs.

Learning from Antivirus Software

Learning from Antivirus Software

Almost everyone is familiar with antivirus software. Not everyone is familiar with how it works though. Even fewer have examined how we can apply the way antivirus software works to combat fraud. I believe that there are important lessons here which can improve our approach to fraud detection and prevention.

At a high level, antivirus software performs two important functions prior to opening a file on your computer: 1) It compares the file to known viruses and other forms of malware, and 2) It checks the file for suspicious code which may indicate a new, previously unknown virus.

The first function depends on a network of users willing to share known viruses and a system that is able to collect the virus data, design a fix, and disseminate the fix to other users prior to them being infected. The second function depends on heuristic programmers that can design systems to learn and even anticipate potential problems. Working together, this is one of the most effective ways to address the constantly changing nature of Internet malware.

Government fraud prevention, when done properly, works in a very similar manner. By examining known bad actors, bad transactions, and bad behaviors, systems can quickly compare ongoing program data to identify suspect transactions. Modern fraud detection systems also include predictive algorithms that can detect anomalies, trends, patterns, and clusters that may indicate fraud.

Unfortunately, many governments are unable, or unwilling, to share data. This limits the “network” effect that antivirus software uses so effectively. If more states and programs shared fraud schemes and findings, the library of known bad actors and methods could detect fraud and prevent it from moving from state to state and program to program.

The good news is a number of states are moving toward state-wide fraud prevention efforts and a number of government subsidy programs are moving toward cross-state fraud prevention efforts. I am confident that the future success of these efforts will promote additional sharing, leading to a larger network, and more efficient governments.
The Problem With Knowing What You Know

The Problem With Knowing What You Know

I bet you cna’t bvleiee taht you can uesdtannrd waht you are rdnaieg. Unisg the icndeblire pweor of the hmuan mnid, aocdcrnig to rseecrah at Cmabrigde Uinervtisy, it dseno't mttaer in waht oderr the lterets in a wrod are, the olny irpoamtnt tihng is taht the frsit and lsat ltteer be in the rhgit pclae. The rset can be a taotl mses and you can sitll raed it whoutit a pboerlm. Tihs is bucseae the huamn mnid deos not raed ervey ltteer by istlef, but the wrod as a wlohe.

The preceding paragraph, which has made its way around the Internet for years, can be really fun to share with friends. However, it also serves as a caution to anyone involved in fraud detection. In many ways, bad actors, knowingly or unknowingly, have depended on how the human mind works to perpetrate fraud schemes. Like the old expression goes, sometimes the best place for fraud to hide is in plain sight.

This is especially true in government programs that process massive amounts of transactions and must adhere to a staggering number of program regulations. Traditional “top down” systems can analyze large data sets and find nothing wrong (after all, the first and last letters are in the right place). “Bottom Up” systems, on the other hand, will identify individual problems (the word is scrambled) but may miss the patterns in the data (this entire paragraph is scrambled). A common example of this is the medical provider that always “flies just below the radar” by maximizing claim amounts and frequencies.

The best detection processes take both a “top down” and “bottom up” approach. They can identify individual transaction problems as well as identify patterns of bad behavior over time. In this way, you can make the old “80-20” rule work in your favor. 80% of improper payments are likely caused by 20% of program participants. If you only address each individual transaction, you’ll never run out of work but you also never really improve your program integrity efforts.

Click here for an infographic on the "80-20 rule".
Welcome to the new Pondera Blog

Welcome to the new Pondera Blog

We’re all in this together. You may work in Medicaid, Unemployment Insurance, Integrated Eligibility, SNAP, WIC, TANF, or any of the other important government programs that so many Americans depend on. Regardless of the program though, we all share the common goal of fighting fraud, waste, and abuse to make sure that our programs help those people who qualify for and truly need the assistance.

The goal of the Pondera Blog is to post and share information that is relevant to all government program integrity professionals. If we’ve learned nothing else as we work across programs and across states, it’s that bad actors don’t limit their activities to one program or one state. They follow the money wherever it leads them. For PI professionals, this means there is a lot to learn from your peers in other states and other programs.

We hope you’ll check back often for new content. Our intent is to post information on emerging fraud methods, promising detection techniques, lessons learned from our projects, and a variety of other topics. Some might question why we would share this information in a public forum where Pondera’s competitors can easily view what’s of interest to us (clearly we’ll never post anything that could help fraudsters). Our answer to that question is simple: we’re all in this together.

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Pondera leverages advanced prediction algorithms and the power of cloud computing to combat fraud, waste, and abuse in government programs.



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