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.
A recent arrest in New York City illustrates a common fraud method that Pondera has been talking about for years: falsifying an identity (of an individual or business) and using it across multiple states, or in this particular case, across multiple subsidy programs within a state.
In February of this year, the New York State Attorney announced the arrest of several individuals allegedly involved with a fraudulent medical supply company. The company’s owner operated under a false social security number and billed the State Medicaid system for an expensive nutritional formula required by patients with feeding tubes. In actuality, when they delivered the service at all, they dispensed lower-priced Pediasure to dramatically increase their profits—apparently ignoring the health consequences to the patient.
But, as is often the case with bad actors, they didn’t stop there. In addition to their fraudulently obtained Medicaid profits, the fraudsters also used their fake socials and claimed income of less than $800 per month in order to qualify for Welfare payments. This despite the fact their medical “business” incomes were over $180,000 per year. It would not surprise me to learn that these same people were operating in other subsidy programs or in neighboring states.
This is a disturbing, but somewhat logical, pattern that we see again and again. When someone goes to the trouble of creating a fake identity or business, they use it to generate as much income as possible. They “fly below the radar” of each individual program (or state) to avoid detection, but the fraud can be very lucrative in aggregate.
The obvious solution to this is increased cooperation and data sharing across programs within a state and across states. The federal government has made significant efforts to support data sharing including the List of Excluded Individuals and Entities (LEIE), the Death Master File, and the Prisoner Update Processing System (PUPS) which can help identify claims that are fraudulently made by ineligible, deceased, or incarcerated identities.
Our hope is that these efforts expand, including at the state level, where multiple agencies cooperate to identify cross-program fraud schemes. It is not enough to detect and then stop individual incidents of fraud. Many of these incidents are too small, when viewed as discrete occurrences, to warrant prosecution. Knowing this, enterprising fraudsters “sprinkle” their claims across multiple jurisdictions to avoid attention.
Unfortunately, as was the case in New York, even these smaller, distributed fraud efforts can have an impact on patient health. The good news is that New York detected and put an end to this incident. But we all know there are thousands of similar cases each year.
A few months ago, I wrote an article offering our support to the USDA Food and Nutrition Service (FNS) as it rolls out a new program offering online access to groceries for Supplemental Nutrition Assistance Program (SNAP) recipients. My main concern with the new initiative was that FNS cannot provide an accurate SNAP fraud rate because of unreliable data coming in from the states. And we all know that offering goods and services online presents even more opportunities for fraud.
Now Congress is asking FNS additional questions in a letter sent to them on February 8th. Outlining the lawmakers’ concerns, the letter points out that as many as 10% of retailers who accept SNAP EBT cards participate in illegal trafficking schemes. These schemes pay recipients a discounted amount of cash or unapproved grocery items in exchange for their cards. They go on to point out that total annual fraud in the program is over $858 million.
The massive size of the SNAP program is one of the major reasons, historically at least, it is so difficult to detect fraud. In 2016, the program distributed $67 billion in benefits to 44 million Americans through 260,000 authorized retailers. Interestingly though, as much as 85% of the retailer fraud is committed by small grocery and convenience stores, or even flea markets like the one in Opa-Locka, FL that we recently wrote about.
With the advent of cloud computing and advanced analytics solutions, FNS now has access to the tools required to make a real difference in their fight against fraud. And by addressing the retailer side of the equation, they will also find, through association, many of the fraudulent individuals in the system as well. It would certainly make sense for FNS to leverage modern fraud detection technologies at the same time that they offer online access to groceries.
It is also important to note that the number of SNAP program retailers and recipients, while large, is very manageable. Consider that at Pondera we’ve performed equally complex fraud analytics on Medicaid programs with as many as 200,000 providers and Unemployment Insurance systems with over 1,000,000 employers. And when one considers that the overwhelming majority of SNAP trafficking fraud occurs in a concentrated subsection of small and medium retailers, the problem becomes even more manageable.
I read with great interest the story this month about a woman who cheated her way to a second-place finish in the Fort Lauderdale half marathon. After posting a time of 1 hour and 21 minutes, the website www.marathoninvestigation.com revealed several problems with the woman’s results including: the race statistics she posted to a website were manually entered (versus those calculated by her GPS), a second set of results she posted seemed more consistent with a bike ride, and a zoomed photo of her post race wristwatch revealed that she ran only 11.65 miles of the 13.1 mile race. This evidence led to an admission and apology by the runner.
What I find interesting about this incident is how indicative it is of the ever-increasing power of data. While runners collect data to help them train and perform better, it can also be used to uncover cheating and fraud. This is no different in government subsidy programs, like Medicaid and welfare systems. Governments collect data to help them improve service delivery to their constituents, and with modern technologies, the data can also reveal fraudulent anomalies and patterns.
Of course, bad actors who want to defraud programs are aware of the increased use of data to catch them. Gone are the days when they can blatantly abuse government systems knowing that the size and complexity of the programs would make it nearly impossible to catch the cheats. In running, who would dare to repeat Rosie Ruiz’s 1980 Boston Marathon “victory” where she was spotted riding the subway with her runner’s bib?
Instead, bad actors often “fly under the radar” – stealing smaller amounts over longer periods of time to avoid being noticed. Second place in the Fort Lauderdale Marathon is certainly “under the radar” compared to a victory in the Boston Marathon.
So, now that our fraud detection capabilities can catch bad actors who boldly fly above the radar and those who strategically fly below the radar, one would hope that it would lead to decreases in fraud attempts. But I also know that making fraud harder to commit rarely turns fraudsters into honest and contributing members of society. It just makes them work harder. This simple fact provides us with the incentive to continually improve on our technologies and approaches. This is one war we fully intend to win.
In a recent Texas senate hearing, it was revealed that in 2015, the state’s 22 Managed Care Organizations (MCOs) had recovered only $2.5 million of fraudulent payments out of $12.5 billion in claims. That’s about two-hundredths of a percent. Not one of the MCOs recovered even 1% of payments and most reported less than $20,000 in recoveries per full time investigative resource.
These numbers are stunningly low considering the actual amount of managed care fraud, estimated by the American Bar Association to be over $17.5 billion per year. There are dozens of ways to commit fraud in managed care programs including enrolling ineligible, deceased, or incarcerated individuals, collusion and kickback schemes among providers, and billing across MCOs.
In fact, many instances of managed care fraud can be even more insidious than the fraud found in fee-for-service programs. For example, rather than billing for unnecessary services which is common in fee-for service, fraudulent managed care providers are more apt to deny necessary procedures to increase their profits. They also recruit healthy members to bill capitation fees while incurring smaller expenses than those for less healthy members.
As states move more of their Medicaid populations into managed care, it is critical to not pass the responsibility of fraud detection to the MCOs. The current situation in Texas, whatever the causes, should not be tolerated. It is clear that not all MCOs will “play by the rules” and this will inevitably lead to higher capitation rates and less effective care. This is pretty ironic considering that lower costs and improved care were two of the main drivers behind moving to managed care in the first place.
One of my colleagues recently returned from a conference on government program integrity with an interesting anecdote. He recounted a vendor presentation where the speaker was touting a 52% accuracy rate in their fraud lead generation system. So… nearly half of the system’s leads generated false positives. Not so sure I’d brag about that.
High false positive rates lead to wasted investigative time and money and unwarranted intrusions into the lives of legitimate program beneficiaries and service providers. Ultimately, they lead to a lack of confidence in the system itself and investigators revert back to more manual detection methods. When one considers all the important services governments deliver and the immense political pressure they endure, this is obviously not acceptable.
Shortly after hearing this story, we were asked to respond to a question about false positive rates and any existing industry standards or even benchmarks. While every vendor, including Pondera, makes claims about our system efficacy, very few standards actually exist. Conversely, our clients (the government program administrators) generally are subject to improper payment standards placed on them by the federal government.
I think there is a great opportunity, even responsibility, for governments to create these standards. Fraud detection standards would challenge the vendor community to “put up or shut up”, leading to more innovation. They could also be adjusted as the standards are met and surpassed leading to constant improvement. And they would provide governments with a uniform method for measuring vendor performance.
It is true that fraud detection systems still rely on quality program data and can suffer from the old adage "garbage in, garbage out”. So government would still share in the responsibility of meeting any new standards. But clearly, there is more we can do. And this would benefit all parties involved… except, of course, the fraudsters.
A few weeks ago, I published a blog post titled “Money Obtained Fraudulently is Rarely Used for Good Purposes”. In it, I made the argument that government fraud is a serious, and at times very ugly problem. Now I no longer have to make that argument because the United States Justice Department is making the argument for me.
Last week, the Justice Department announced the largest health care fraud case it’s ever prosecuted; one that defrauded over $1 billion over the past 14 years. The alleged perpetrators of the fraud are said to have leased private jets and chauffeured limousines. One even bought a $600,000 watch! Remember, this is your tax money we’re talking about. The system ran on a complex network of bribes and kickbacks.
And if that’s not enough, here is one of the schemes they allegedly ran. They “treated” seemingly healthy, elderly people with medications they did not need in order to create addictions which would lead to further treatments. Pure evil. Unfortunately, fraudsters are most active where large amounts of money meet vulnerable populations. This is yet another example of that and more reason for us to do what we do.
Last year, 60 Minutes did a segment on the impact of errors in the Social Security Administration’s Master Death File—a database that stores dates of death for Americans. The system stores 86 million records, and despite all best efforts, it still has some issues.
60 Minutes pointed out that errors in the system contribute to millions of dollars in improper payments each year. After all, the system would seem to indicate over 6.5 million Americans over the age of 111 when, in fact, there are probably fewer than 100. On the other hand, false positives where people find themselves mistakenly placed on the list, lead to nightmarish scenarios for obtaining loans, opening bank accounts, and other everyday tasks.
This story demonstrates one of the largest challenges for government agencies: how to use imperfect data sources to minimize fraud, waste, and abuse while also not “harassing” legitimate people and businesses. And unlike a private business that may view a false positive as an inconvenience (who hasn’t had to call their credit card company to say that “yes, that large ice cream purchase was legitimate”), government officials are severely criticized when they act on false positives. In effect, they are criticized for not acting and they are criticized for acting.
Pondera suggests that governments mitigate the effects of false positives by using composite indicators that draw information from multiple sources—both simple data matches like the Master Death File and more complex behavioral sources. For example, a Medicaid investigator would feel much more confident looking into a person who not only shows up on the Master Death File, but also appears to be traveling 100 miles for 20-minute doctor appointments, receives highly unusual (and expensive) procedures for their apparent diagnosis, and often sees two doctors in distant cities on the same day.
Anyone who has worked for or with government program integrity units understands the unique pressures they face. Combining available data sources with intelligent analytics can go a long way toward helping them investigate the right cases while not interfering with program delivery.
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.