Tag Archives: analytics

Crime Hot Spots and Risk Terrain Modeling

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A typical ‘hot spot’ in crime analytics

By Tyler Wood, Operations Manager at Crime Tech Solutions.
One of the many functions crime analysis performs is the identification of “hot spots”, or geographical areas that seem to be hubs for criminal activity. Identifying these hot spots through best practices in geospatial crime mapping allows law enforcement to focus their efforts in areas that need them most. The trouble that law enforcement and crime analysts have encountered is displacement – the fact that once a hot spot is “cleared”, crime seems to pop up again in a different location. The good news is that the displacement is never 100%, so policing hot spots is important – it’s just not a magic bullet.
To solve this problem, a team at Rutgers University’s School of Criminal Justice set out to develop new methodologies that would result in peaceful outcomes that are built to last instead of merely temporary.
The difference between the old approach and the new approach is stark. Where police and analysts used to focus solely on geographical concentration of crimes, Risk Terrain Modeling examines the factors that contribute to such dense concentrations to begin with. Rutgers team have identified several characteristics of any given geographical location which may attract or generate crime. Their technology takes these characteristics, which include socioeconomic data, physical layout, types of local businesses, etc… and uses them to calculate the likelihood crime occurring in the area. This allows law enforcement to be proactive in the prevention of crime in these areas.
CrimeMapLite
Advanced crime analytics show statistically significant risk factors.

The technique seems to be highly effective. After a trial run in New Haven, CT, police were able to identify sixteen “statistically significant risk factors that underlie violent crime occurrences.” A high percentage of violent crime in New Haven during the test period occurred in locations already identified by the concept of risk terrain modeling. Though the technology is still new, it is clearly showing impressive results already.
Shutting down hot spots is important policing, and risk terrain modeling technology allows analysts and law enforcement officers to be even more proactive in their prevention of crime.
The author, Tyler Wood, is head of operations at Austin, TX based Crime Tech Solutions – an innovator in crime analytics and law enforcement crime-fighting software. The clear price/performance leader for crime fighting software, the company’s offerings include sophisticated Case Closed™ investigative case management and major case management, GangBuster™ gang intelligence software, powerful link analysis software, evidence management, mobile applications for law enforcement, comprehensive crime analytics with mapping and predictive policing, and 28 CFR Part 23 compliant criminal intelligence database management systems.)

Crime Tech Solutions Acquires Case Closed Software

June 1, 2016 (Austin, TX)   Crime Tech Solutions, LLC, a leading provider of analytics and investigation software for law enforcement and commercial markets, today announced that it has acquired Cleveland, TN based Case Closed Software in a cash transaction. The terms of the deal were not released, but according to Crime Tech Solutions’ founder and president Douglas Wood, the acquisition brings together two dynamic and fast-growing software companies with an unparalleled complement of technologies.
For Crime Tech Solutions, the opportunity to add Case Closed Software into the fold was too good to pass up” said Mr. Wood. “We think that the technology offered by Case Closed helps to further differentiate us in the market as the price performance leader for this type of investigative solution.PNG
Crime Tech Solutions, based in the city of Leander, TX, delivers advanced analytics and investigation software to commercial investigators and law enforcement agencies across the globe. Their solution suite includes criminal intelligence software, sophisticated crime analytics with geospatial mapping, and powerful link analysis and visualization software. The company says that the addition of Case Closed Software expands those offerings even further.
Case Closed Software develops and markets investigative case management software specifically designed for law enforcement agencies. The suite is built around four primary software products including best-in-class investigative case management software, property and evidence tracking, a gang database tool, and an integrated link analysis and data visualization tool. The company also plans to release the solution as Case Closed Cloud for cloud-based access.
Case Closed couldn’t be happier than to be joining Crime Tech Solutions,” said Keith Weigand, the company’s founder. “The blending of our technologies creates a suite that will add tremendous value to our mutual customers, and will be hard for others to duplicate.
According to both Mr. Weigand and Mr. Wood, the name Case Closed will continue on as the product brand, given its widespread popularity and loyal customer base. Crime Tech Solutions is expected to retain all Case Closed employees, with Mr. Weigand joining as the company’s chief technical officer.
Crime Tech Solutions says it expects continued growth via ongoing software sales and strategic acquisitions.
About Crime Tech Solutions
(NOTE: Crime Tech Solutions is an Austin, TX based provider of crime and fraud analytics software for commercial and law enforcement groups. Our offerings include sophisticated Case Closed™ investigative case management and major case management, GangBuster™ gang intelligence software, powerful link analysis software, evidence management, mobile applications for law enforcement, comprehensive crime analytics with mapping and predictive policing, and 28 CFR Part 23 compliant criminal intelligence database management systems.)

Uncovering Fraud means Uncovering Non-Obvious Relationships

Posted by Tyler Wood, Operations Manager at Crime Tech Solutions
Although no fraud prevention measures are ever 100% foolproof, significant progress can be achieved by looking beyond the individual data points to the relationships between them. This is the science of link analysis.
Looking at data relationships isn’t straightforward and doesn’t necessarily mean gathering new or more data. The key to battling financial crimes it is to look at the existing data in a new way – namely, in a way that makes underlying connections and patterns using powerful but proven tools such as the Sentinel Visualizer software offered by Crime Tech Solutions.
Unlike most other ways of looking at data, link analysis charts are designed to exploit relationships in data. That means they can uncover patterns difficult to detect using traditional representations such as tables.
Now, we all know that there are various types of fraud – first-party, insurance, and e-commerce fraud, for instance. What they all have in common is the layers of dishonesty to hide the crime. In each of these types of fraud, link analysis from Crime Tech Solutions offers a significant opportunity to augment existing methods of fraud detection, making evasion substantially more difficult.
Let’s take a look at first-party fraud. This type of fraud involves criminals who apply for loans or credit cards but who have no intention of ever paying the money back. It’s a serious problem for banks, who lose tens of billions of dollars every year to this form of fraud. It’s hard to detect and the fraudsters are good at impersonating good customers until the moment they do their ‘Bust-Out,’ i.e. cleaning out all their accounts and disappearing.
Another factor is the nature of the relationships between the participants in the fraud ring. While these characteristics make these schemes very damaging, it also renders them especially vulnerable to link analysis methods of fraud detection.
That’s because a first-party fraud ring involves a group of people sharing a subset of legitimate contact information and bogus information, and then combining them to create a number of synthetic identities. With these fake identities, fraudsters open new accounts for new forms of loans.
The fraudsters’ accounts are used in a normal manner with regular purchases and timely payments so that the banks gain confidence and slowly increase credit over time. Then, one day… Poof! The credit cards are maxed out and everyone has disappeared. The fraudsters are long gone and ready to hit the next bank down the road.
Gartner Group believes in a layered model for fraud prevention that starts with simple discrete methods but progresses to more elaborate types of analysis. The final layer, Layer 5, is called  “Entity Link Analysis” and is designed to leverage connections in data in order to detect organized fraud.
In other words, Gartner believes that running appropriate entity link analysis queries can help organizations identify probable fraud rings during or even before the fraud occurs.
 

IBM Crime Analytics: Missing the mark? CTS Hits the bullseye!

quote-one-may-miss-the-mark-by-aiming-too-high-as-too-low-thomas-fuller-10-41-63Posted by Crime Tech Solutions – Your source for analytics in the fight against crime and fraud.
September 7, 2015. IBM announced this week a major update to its IBM i2 Safer Planet intelligence portfolio that includes a major overhaul of the widely used Analyst’s Notebook product. The product, which has become increasingly abandoned by its user base over the past five years, is now being positioned as ‘slicker‘ than previous versions.
IBM suggests that the new version scales from one to 1,000 users and can ingest petabytes of information to visualize. (A single petabyte roughly translates to 20,000,000 four-drawer filing cabinets completely filled with text).
That’s a lot of data. Seems to me that analysts are already inundated with data… now they need more?
This all begs the question: “Where is IBM headed with this product?”
The evidence seems to point to the fact that IBM wants this suite of products to compete head-to-head with money-raising machine and media darling Palantir Technologies. If I’m IBM, that makes sense. Palantir has been eating Big Blue’s lunch for a few years now, particularly at the lucrative US Federal market level. Worse yet, for IBM i2, is the recent news of a new competitor with even more powerful technology.
If I’m a crime or fraud analyst, however, I have to view this as IBM moving further and further away from my reality.
The reality? Nobody has ever yelled “Help! I’ve been robbed. Call the petabytes of ‘slick’ data!”  No, this tiring ‘big data’ discussion is not really part of the day to day work for the vast majority of analysts. Smart people using appropriate data with intuitive and flexible crime technology solutions… that’s the reality for most of us.
So, as IBM moves their market-leading tool higher and higher into the stratosphere, where does the industry turn for more practical desktop solutions with realistic pricing? For more and more customers around the world, the answer is a crime and fraud link analytics tool from Crime Tech Solutions.
No, it won’t ingest 20,000,000 four-drawer filing cabinets of data, and it’s more ‘efficient‘ than ‘slick‘. Still, the product has been around for decades as a strong competitor to Analysts Notebook, and is well-supported by a network of strategic partners around the world. Importantly, it is the only American made and supported alternative. Period. It’s also, seemingly, the last man standing in the market for efficient and cost-effective tools that can be used by real people doing their real jobs.

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.

SEC taking stock of analytics (and a cool use case for stock exchanges)

Image Posted by Douglas Wood, Editor.
I read with interest today an article about the SEC’s use of analytics in the ongoing fight against financial crimes. The link to that article is below. It reminded me of some work I once did with a major stock exchange around insider trading.
As a passionate anti-fraud technologist, I was thrilled with the challenge of helping the stock exchange better recognize cases of illegal insider trading. The results of the work we did was pretty cool.
The stock exchange – as do all exchanges – had a great deal of data at their disposal. They knew, for example, the names of each and every ‘insider’ within every company listed on their exchange. Insiders include senior executives, board members, legal counsel, auditors, and so on.  Basically, everyone who knew – or ought to have known – about an upcoming event that would likely cause a significant change in stock price.
They also knew, of course, the identity of investors who traded profitably prior to the public release of that information. The problem was exposing the hidden relationships that might exist between Insiders and investors.
Here is an actual example… Joe Blow was an associate partner at the independent accounting firm responsible for auditing the quarterly financial results of publicly traded Company A. By definition, Joe is an insider. He knows Company A’s financials. Jane Doe dumped her entire position in Company A mere days ahead of what turned out to be very poor results. The stock plummeted, and Jane was saved from significant losses.  She was seemingly a complete outsider. So, did she somehow know Joe Blow (or any other insider)? Or was she just one lucky gal.
Using link analysis, crime mapping, and behavioral analytics, we set about the challenge of finding out. Here’s what the analytics exposed:
Joe Blow, the insider by way of being employed at Company A’s auditing firm, shared an address with… oh, let’s call him “Rich Quick”. Rich held no positions with Company A whatsoever.  He did, however, own a pet food store with a lovely young lady.  Can you guess her name?  Yep.. Jane Doe. So, the analytics exposed that Company A had an insider relationship with Joe Blow. Joe lived with Rich Quick. Rich owned a business with Jane Doe. Coincidence? Not likely.
Without the ability to draw out hidden links between individuals and organizations, this case may never have been discovered.  It’s like Six Degrees of Kevin Bacon, only with much higher stakes. All of the suspects were investigated and prosecuted.
(Note: All the names in this example are fictitious, but the case is not. If your name happens to be Jane Doe, Joe Blow, Rich Quick… or if you work for an organization called Company A, rest assured that I’m not talking about you.)
Here is the link to the SEC article.  http://fcw.com/articles/2013/09/18/sec-taps-analytics-to-predict-risk.aspx?s=fcwdaily_190913 .