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.
Last month CNN published a horrifying report on sexual abuse in America’s nursing homes and assisted living facilities. The report provided details on dozens of assaults, rapes, and other incidents that, quite frankly, were extremely difficult to read. In my opinion, however, this level of detail is probably necessary to shock people into taking action against what CNN rightly labelled “an unchecked epidemic”.
The numbers themselves are devastating. Approximately one million senior citizens are currently residing in 15,000 government-regulated long term care facilities. Since 2000, it appears that over 16,000 cases of sexual abuse have been reported, but the number is probably higher because of complex reporting systems and processes. And it’s impossible to determine the number of unreported cases.
Between 2013 – 2016, CNN found that 1,000 government-regulated facilities had been cited for mishandling or failing to prevent sexual assaults. 100 of the facilities had been cited numerous times. And despite this, only 226 facilities were fined just $9 million. Only 16 of the facilities were cut off from Medicaid and Medicare!
What is equally disturbing to the actual cases of abuse is the blatant disregard of safeguards and even the intentional impeding of investigations. Consider a case here in California where the employer allowed a nurse to continue working for weeks after reports of him kissing and fondling a female resident. This crime, by the way, resulted in only a $27,000 fine.
At Pondera, we often say that fraud and abuse is most prevalent at the intersection of large amounts of money and vulnerable populations. This makes nursing homes “ground zero” for abuse because it is here that the escalating costs of long term care combine with dementia and other health issues that can make senior citizens problematic witnesses.
Among several recommendations made by CNN was a call for improved reporting systems. We agree that this is an important piece of the solution. It will provide greater transparency and help regulators identify trends and clusters of abuse. But clearly, stricter oversite and enforcement are needed. So too is the type of no-nonsense reporting that CNN did for this report.
It’s April, which every year brings more news about tax fraud scandals. The news this year, however, is even more disturbing than expected. IBM’s X-Force threat intelligence group released a report last week that showed a 6,000% increase in spam emails designed to steal information from W-2s and other tax documents. Last year, these criminals “earned” over $3 billion through similar scams. And if you were one of the victims, then you are already familiar with the hassles of having your return stolen or a completely false one filed using your identity.
The continuing use of the Dark Web is a major factor behind the acceleration in this form of cybercrime. Stolen identities that include tax information are currently fetching around $40 on illicit marketplaces. While this may not seem like much, it is extremely lucrative when a fishing scam succeeds at stealing thousands of identities. So lucrative, in fact, that would-be scammers can even visit the Dark Web to buy online tutorials on how to perpetrate tax fraud.
Popular scams this year include sending emails that appear to be sent from TurboTax and other tax preparation companies. The hope is that you respond because you use that tax service. So-called spearfishing scams are also targeting corporate human resource departments. They will often send an email to an HR manager, seemingly from a member of the company’s executive staff, requesting W-2 and other tax information on the company’s employees.
Cybercriminals will continue to hone their skills resulting in more convincing emails and websites. They will continue to take advantage of technologies that allow them to increase the number of outbound messages. And they will continue to learn and share new techniques on the Dark Web. This means that all of us, as businesses and as private citizens, need to step up our efforts to protect data. These days, it’s no longer just “a fool and his money” who are soon parted.
At Pondera, we are often asked whether fraud detection algorithms will ever completely replace human investigators. And while I can’t address the “ever” part of the question, I can confidently state that it will not happen in the foreseeable future. One of the major reasons for this? Prediction models, like many people, struggle to distinguish between cause and effect.
A Stanford University professor recently shared her studies on this topic which support many of our own findings. She noted that while prediction algorithms are excellent at finding patterns in large data sets, their effectiveness is limited because they struggle with determining causation. An example she used is that algorithms have been shown to help identify patients who should not receive hip surgery because they would likely die of other causes. However, the algorithms are unable to prioritize those patients who should receive the surgery.
In several cases, the professor notes that correlation can be as low as 50%. And she properly notes that while this may be fine in certain situations, governments simply cannot conduct such high-risk experiments with social welfare, economic policies, and other important matters. And unlike controlled environments, such as those that use placebos to test medications, the real world is simply too messy and unpredictable to control all factors.
This problem of causation identifies an important intersection between human reasoning and prediction algorithms. We believe that in complex, rapidly changing environments like fraud detection, effective detection systems combine the power of modern detection algorithms with experienced human reasoning.
By leveraging the individual strengths of both machine and human learning, we can analyze massive data sets and make sense of the findings. We regularly use the system to find the problem and ask the human experts to help explain the problem. This makes the results actionable, which ultimately is what our government partners require.