Tag Archives: Anti Money Laundering

Using Link Analysis to untangle fraud webs

Posted by Douglas Wood, Editor.

NOTE: This article originally appeared HERE by Jane Antonio. I think it’s a great read…

Link analysis has become an important technique for discovering hidden relationships involved in healthcare fraud. An excellent online source, FierceHealthPayer:AntiFraud, recently spoke to Vincent Boyd Bryant about the value of this tool for payer special investigations units.

A former biometric scientist for the U.S. Department of Defense, Bryant has 30 years of experience in law enforcement and intelligence analysis. He’s an internationally-experienced investigations and forensics expert who’s worked for a leading health insurer on government business fraud and abuse cases.

How does interactive link analysis help insurers prevent healthcare fraud? Can you share an example of how the tool works?

Boyd Bryant: Link analysis is most often used to piece together different kinds of data from multiple sources–to identify key players, connections between those players and patterns of behavior frequently missed. It can simplify an understanding of the level of involvement of individuals and criminal organizational hierarchies and can greatly simplify visualizing and communicating the operations of complex criminal enterprises.

One thing criminals do best is hide pots of money in different places. As a small criminal operation becomes successful, it will often expand its revenue streams through associated businesses. Link analysis is about trying to figure out where all those different baskets of revenue may be. Insurers are drowning in a sea of theft. Here’s where link analysis becomes beneficial. Once insurers discover a small basket of money lost to a criminal enterprise, then serious research needs to go into finding out who owns the company, who they’re associated with, what kinds of business they’re doing and if there are claims associated with it.

You may find a clinic, for example, connected to and working near a pharmacy, a medical equipment supplier, a home healthcare services provider and a construction company. Diving into those companies and what they do, you find that they’re serving older patients for whom multiple claims from many providers exist. The construction company may be building wheelchair ramps on homes. And you may find that the providers are claiming payment for dead people. Overall, using this tool requires significant curiosity and a willingness to look beyond the obvious.

Any investigation consists of aggregating facts, generating impressions and creating a theory about what happened. Then you work to confirm or disconfirm your theory. It’s important to have tools that let you take large masses of facts and visualize them in ways that cue you to look closer.

Let’s say you investigate a large medical practice and interview “Doctor Jones.” The day after the interview, you learn through link analysis that he transferred $11 million from his primary bank account to the Cayman Islands. And in looking at Dr. Jones’ phone records, you see he called six people, each of whom was the head of another individual practice on whose board Dr. Jones sits. Now the investigation expands, since the timing of those phone calls was contemporaneous to the money taking flight.

Why are tight clusters of similar entities possible indicators of fraud, waste or abuse?

Bryant: When you find a business engaged in dishonest practices and see its different relationships with providers working out of the same building, this gives rise to reasonable suspicion. The case merits a closer look. Examining claims and talking to members served by those companies will give you an indication of how legitimate the operation is.

What are the advantages of link analysis to payer special investigation units, and how are SIUs using its results?

Bryant:  Link analysis can define relationships through data insurers haven’t always had, data that traditionally belonged to law enforcement.

Link analysis results in a visual reference that can take many forms: It can look like a family tree, an organizational chart or a time line. This reference helps investigators assess large masses of data for clustering and helps them arrive at a conclusion more rapidly.

Using link analysis, an investigator can dump in large amounts of data–such as patient lists from multiple practices–and see who’s serving the same patient. This can identify those who doctor shop for pain medication, for example. Link analysis can chart where this person was and when, showing the total amount of medication prescribed and giving you an idea of how the person is operating.

What types of data does link analysis integrate?

Bryant: Any type of data that can be sorted and tied together can be loaded into the tool. Examples include telephone records, addresses, vehicle information, corporate records that list individuals serving on boards and banking and financial information. Larger supporting documents can be loaded and linked to the charts, making cases easier to present to a jury.

Linked analysis can pull in data from state government agencies, county tax records or police records from state departments of correction and make those available in one bucket. In most cases, this is more efficient than the hours of labor needed to dig up these types of public records through site visits.

Is there anything else payers should know about link analysis that wasn’t covered in the above questions?

Bryant: The critical thing is remembering that you don’t know what you don’t know. If a provider or member is stealing from the plan in what looks like dribs and drabs, insurers may never discover the true extent of the losses. But if–as a part of any fraud allegation that arises–you look at what and who is associated with the subject of the complaint, what started as a $100,000 questionable claims allegation can expose millions of dollars in inappropriate billings spread across different entities.

Perhaps a nice change at NICE Actimize?

Posted by Douglas Wood, Editor.  http://www.linkedin.com/in/dougwood

Though not publicly released, news out of NICE Actimize is that long-time CEO Amir Orad is leaving the company effective May 1. Indicative of the ‘what a small world this is’ nature of the financial crimes technology marketplace, former Pegasystems co-founder and head of Americas for BAE Systems Detica, Joe Friscia, will be taking over the helm at that time.

Mr. Orad led NICE Actimize’s product and strategy functions prior to his five year tenure as CEO. During his tenure, he scaled the business size over six-fold. He is also a founding board member at BillGuard the venture backed personal finance analytics and security mobile app company.

Prior to Actimize, Orad was co-founder and CMO of Cyota a cyber security and payment fraud cloud company protecting over 100 million online users, acquired by RSA Security for $145M. Following the acquisition, he was VP Marketing at RSA.

I’ve known both Amir and Joe for several years, and have a tremendous amount of respect for both gentlemen. While it’s sad to see Amir leave the organization, I know that his rather large shoes will be more than adequately filled by Mr. Friscia.

Joe’s background is well-suited to this new position, and all of us here at FightFinancialCrimes wish him well. Joe joined Detica when BAE Systems acquired Norkom Technologies in early 2011, where he served as General Manager and Executive Vice President of the Americas. Joe led the rapid growth of Norkom in the Americas, with direct responsibility for sales, revenue and profit as well as managing multi-discipline teams based in North America. Prior to Norkom, Joe helped start Pegasystems Inc in 1984, a successful Business Process Management software company that went public in 1996.

Best of luck to Amir in his new ventures, and to Joe as he guides Actimize into it’s next phase.

To 314(b) or not to 314(b)?

Posted by Douglas Wood, Editor.  http://www.linkedin.com/in/dougwood

FinCEN today (November 1, 2013) released a fact sheet regarding data sharing between financial institutions under the Section 314(b) of the US Patriot Act.

314(b) provides financial institutions with the ability to share information with one another, under a safe harbor that offers protections from liability, in order to better identify and report potential money laundering or terrorist activities.  314(b) information sharing is a voluntary program, and FinCEN has always encouraged its use.

A few years ago, I spent considerable time looking at the overall 314(b) program. I interviewed dozens of Chief Compliance Officers (CCO) and AML/Fraud experts. I found that, despite the benefits to financial institutions – reduction of fraud loss, more complete SARs filings, shedding light on financial trails, etc – the program was not particularly well-utilized. The system, for all it’s good intentions, is very manual.

Imagine you are a 314(b) officer at a financial institution. Your job is to facilitate the data sharing amongst the community. So, much of your time is spent interacting with your CCO on which specific cases should be shared, and with whom. When you get that information, you open up you financial crimes investigation tools, and begin contacting your counterparts across the U.S. and asking them “Hey, do you know anything about Douglas Wood?” You’re calling the other officers completely blind with no idea whatsoever if they know Doug. In the meantime, your voicemail inbox is being flooded with other calls from other institutions asking if you know a bunch of other people (or entities).

Finding the institutions that know Douglas Wood is a lot like looking for a needle in a haystack… except you don’t know which haystacks to look in. The system too often grinds to a halt, despite some excellent work being done by 314(b) officers across the country. There has to be a better way, and some have proposed a data contribution system where financial institutions upload their bad guy data into one large third-party haystack, making the needle a little easier to find. As an advocate for the use of technology in the fight against financial crimes, I hope that model finds some success. The problem, of course, is that banks are LOATHED to put their data in the hands of a third party. Also, it’s typically up to each individual bank to decide if and when they choose to upload their data to be inter-mingled with other institutions. Far too often, it is not entirely reliable and not particularly current.

There is a better way. Several years ago, working with some tech-savvy employees, I envisioned a member-based 314(b) program where each institution maintained total control of their data. The model does not require individual banks to contribute their data for inter-mingling.  All ‘bad guy’ data sits and remains securely behind the banks’ respective firewalls. When an individual bank sends out a request to find out who, if anyone, may have information about a suspicious entity, the request is systematically sent out to all members using a secure network such as SWIFT, for example. That electronic search returns to the querying bank only a risk score which indicates the likelihood that another member is investigating the same entity.

No personally identifiable information (PII) is ever shared, yet the search is productive. The enquiring bank now knows that the person of interest was found in the bad guy data from other participating institutions. With this information in hand, the respective 314(b) officers can move their voicemail exchanges from “Have you ever heard of Douglas Wood” to “We’re both investigating Douglas Wood… let’s do it together.” The time-consuming, manual efforts are dramatically reduced and more bad guys are put away.

So if the question is to 314(b) or not to 314(b), perhaps the answer lies in data privacy compliant technology.