Welcome to Understanding Link Analysis. The purpose of my site is to discuss the methods behind leveraging visual analytics to discover answers and patterns buried within data sets.

Visual analytics provides a proactive response to threats and risks by holistically examining information. As opposed to traditional data mining, by visualizing information, patterns of activity that run contrary to normal activity surface within very few occurances.

We can dive into thousands of insurance fraud claims to discover clusters of interrelated parties involved in a staged accident ring.

We can examine months of burglary reports to find a pattern leading back to a suspect.

With the new generation of visualization software our team is developing, we can dive into massive data sets and visually find new trends, patterns and threats that would take hours or days using conventional data mining.

The eye processes information much more rapidly when information is presented as images, this has been true since children started learning to read. As our instinct develops over time so does our ability to process complex concepts through visual identification. This is the power of visual analysis that I focus on in my site.

All information and data used in articles on this site is randomly generated with no relation to actual individuals or companies.

Using Visual Analysis to Detect Fraud in Remittance Transactions

Companies engaged in international cash remittance face numerous fraud and compliance challenges within the transaction flow. There are additional challenges faced in the remittance industry as these transactions move from in-person to eCommerce based where organized fraud rings and those engaged in AML activities can be more difficult to parse.

Online remittance transactions pose a number of risks to the company including card not present fraud, regulatory compliance issues surrounding individuals creating multiple accounts to circumvent sending limits, KYC issues and the wide variety of Anti-Money Laundering (AML) activities associated with illegal transfer of funds.

In this article I would like to discuss ways of leveraging visual analytics to detect patterns in online remittance fraud and compliance issues. Protecting and ensuring the integrity of online cash remittance transactions is a layered approach starting with fraud and compliance scoring. Fraud and compliance models, however, need to keep up with emerging and ever changing fraud and compliance trends. To address this issue, we are going to leverage visual analytics to quickly detect these emerging trends to provide timely scoring rule updates to penetrate and decision transactions.

Know Your Data

The first step in establishing effective proactive visual analytics is to know the data which is being captured in the transaction flow and how each entity in that data potentially relates or links to other activity which has occurred.

While online remittance transactions lack the identification details present in face to face transactions, in a lot of way, online transactions leave a great fingerprint of electronic discovery which should be leveraged to discover relationships between transactions.

It is also important to identify which attributes in the online transaction must be valid in order for fraud or compliance rule violations to take place. While a great deal of information in online remittance transactions can be faked, there a certain key pieces of information which must be accurate in order for the crime or violation to succeed. Using those pieces of information as your distinct identifiers for your entities in visual analytics will ensure greater accuracy in identifying potential trends or clusters of interrelated transactions, accounts and recipients.

Building Your Entities

Once the data that is going to be used for analysis has been identified, the next step is assigning that data or combination of data points to our visual entities. Lets begin with what is most often captured during online remittance transactions:

  • Sender information including names, date of birth, address, email and phone
  • Funding source information such as credit card and bank account numbers
  • Recipient information such as name, address, phone and email address
  • Recipient disbursement information such as bank account numbers, cash pickup locations, identification presented and agent names.
  • eCommerce information such as IP addresses, cookie information or device ID fingerprinting information.
All of the data contained in these five categories come together to form the infrastructure for visual analytics. As you can see, its a great deal of information that is being captured and even in sophisticated fraud schemes, the more data left behind, the greater the possibility of the attributes being reused over time.

As organized fraud and money laundering rings count on velocity of transactions and accounts to commit their activities, the greater the probability that attributes being captured in the transactions flow will be re-utilized over time leading to clusters of interrelated activity.

Building the Visualization

We clearly have a hierarchy to the data that is being captured in the remittance process. At the top of the data hierarchy we have the sender account. Attributes such as address, phone, email, IP, device and funding sources all relate to the sender. For the first section of our visualization we are going to set up our schema to reflect that first level hierarchy.

From the picture above you can see we have associated our phone, device ID, address, transfer, bank account, credit card account and email with the sender entity. Now that the sender hierarchy has been established we need to compose the recipient's hierarchy.

From the picture above you can see we have associated the recipient entity with the recipient bank account, ID, phone, email address and the transaction. As the transaction is associated with the sender and recipient, that is our linking entity between both the sender and the recipient in the remittance transaction.

Once we join the recipient entity and the recipient's associated attributes with the sender entity and the sender's associated attributes we are able to compile a complete visualization of the remittance activity between the sender and the recipient.

This visualization template forms the structure for utilizing visual analytics to discover potential fraud and compliance issues in the transaction flow.

Know the Good from the Bad and the Ugly

The key to any kind of analysis is to know the footprint left behind on good transactional activity. Like a pickpocket in a crowd, if you understand the behavior of the crowd it is easy to spot the pickpocket. The many of the crowd are behaving in a particular pattern, completely contrary to the pickpocket as the he has a different objective then the crowd does.

The same principal applies to online remittance transaction. Good remittance activity all follows a similar pattern, there is a sender who is associated with an address, phone, email address, cookie, device and payment instrument. Occasionally the sender may move or change his email address but for the most part in good activity we know the sender's attributes shouldn't be associated with other accounts.

Likewise with the recipient, a good sender may send to two or three different people on a regular basis over time. That recipient generally is only associated with that sender, or in some cases if a family is all sending to the same recipient, two or three senders. That recipient's phone number, address, email address, ID or bank account shouldn't be linked to multiple recipients in good transactions.

By knowing what good activity looks like, it makes it easy to spot signs of problematic transaction activity. Just like the pickpocket in the crowd, by identifying what my good transactional flow looks like in a visualization (above), I am going to be able to quickly identify a bad pattern that I need to focus on.

Leveraging Visual Analysis to Find The Problem

As in most types of financial analysis, the more associations between accounts, senders and recipients, the higher the probability of fraud or compliance issues. The higher number of relationships between the entities in the transaction flow, the higher the probability. This is what allows us to leverage visual analytics to look at thousands of transactions simultaneously and quickly identify those which have the highest probability of fraud or compliance issues, something that wouldn't be possible through data mining.

Lets start the analysis, as a Fraud and Compliance Analyst, I want to examine all the transactions which have occurred at my company over the past 24 hours. I start by creating a query in my visualization tool that will pull in all sender entities who have transacted since yesterday when I left work.

My results look pretty daunting although keep in mind I am looking for the pickpocket in the crowd so based on my visualization as I start the expansion process I am not going to be focusing on the good behavior or clusters, only the bad.

Visual analytics is based on levels of relationships between entities. In our analysis I am trying to find indications of multiple relationships between my senders and my recipients which are indicative of fraud or compliance issues. On my initial query I brought in all sender accounts who transacted in the last 24 hours, now I need to begin the process of expansion or finding the entities linked to my sender.

The first level of expansion is going to bring back those entities in my visualization which are directly linked to the sender. Remember from the architecture that we set up, the sender is linked to the sender's address, phone, email address, bank account, cookie and device ID. On my first expansion that is what I am going to get back.

Now I need to bring in the recipients who are linked to those transactions. The next expansion level is going to bring in the 2nd level of entities associated with the sender which includes the recipient along with any sender which is associated with any attribute my initial sender is using (linked senders).

Now I am at my second level of expansion and have all linked sender's and unique recipients. To discover bad interrelationships between senders and recipients (clusters), I need one further expansion level in order to link recipients together by their attributes and also visualized by linked senders. My next expansion level returns this information.

Now my analysis is starting to take shape. From the picture above you can see I have several clusters of interrelated transactions, senders and recipients which I am going to want to investigate for potential fraud and compliance issues. Lets start with the largest

From this cluster I have already identified a potential issue. I have four separate accounts which are all being accessed by one account holder ID. As I know from experience and from my visualization, good transaction flows never has this behavior. By leveraging visual analytics I was able to visualize over 2300 transactions over the past 24 hours and pinpoint a potential fraud issue in minutes.

From the information I learn about this activity I can leverage social networking analysis to determine which attribute in the activity needs to be integrated into my fraud and compliance rule set to disrupt the organized fraud activity.

This was a fraud specific example, but I can leverage my visual analysis of transactions to additionally detect compliance related issues. For example, I can quickly spot multiple sender accounts in different names all associated with the same address or phone number. I can find multiple recipients with different names all utilizing the same deposit account or identification card.


Visual analytics is an important part of a layered prevention approach to fraud and compliance discovery. By incorporating both fraud and compliance modeling to prevent problematic transactions from surfacing in line, and visual analytics to discover new emerging patterns or changes in known fraud and compliance patterns, I can quickly update the fraud and compliance rule engine with higher quality rule sets increasing penetration and decreasing false positives or customer friction.

The tool used in my example for visual analytics was i2 Analyst Notebook and iBase. This is an exceptional tool for visual analysis but keep in mind that regardless of the tool, the importance is on utilizing visual analysis as a principal and a complete understanding of the analyst to detect and understand the patterns shown.

Leveraging Outsourcing To Increase Fraud and Compliance Screening Ability

For those charged with fraud and compliance operations, it's always walking a tightrope between setting fraud and compliance rules and scoring to ensure suspicious transactions reach manual screening while ensuring the number queued for manual review do not overwhelm your fraud review teams.

There was a time that considering outsourcing fraud and compliance screening was unthinkable, but it is perhaps time to take another look. These are sensitive positions which require specific training and skills, however the availability of human resources, particularly in the Philippines, has risen considerably over the years making the recruitment, training and management of an outsourced fraud and compliance review unit very feasible and cost efficient.

Over the past two months I have deployed an outsourced fraud and compliance unit for an international financial transaction business and would like to share the planning, training, implementation and management process that took place and some of the lessons learned along the way.

Evaluating The Outsourcing Option

Like most financial institutions, our fraud and compliance review team had been handled for the most part in house for many years. It was a very effective unit with a high productivity rate and low loss rate, well below industry standards. So why did I consider outsourcing this manual review role when the old system was so effective?

First was flexibility of manpower, I had a very competent and able team, but with an in-house staff, growing the unit would always pose a challenge in terms of recruitment, space, resources and training. With the rate of growth the company was experiencing I found that the Fraud and Compliance Unit tended to lean towards adjusting fraud and compliance scoring models and rules to decrease the number of transactions coming in for manual review as opposed to adding staff due to the challenges involved.

The next thing I quickly learned after interacting with my in-house fraud and compliance staff, is that through years of reviewing transactions, they had become true fraud and compliance experts in our company and were working far below their abilities. I wanted to provide my in house team with new tools, training and opportunities so I could evolve the team from reactive review to a world class proactive fraud analysis and investigation unit. I looked at my in-house staff as a group who could drive, define and manage fraud proactively, making a huge impact in our fraud and compliance losses. Of course, before I could evolve this group of exceptional people, I had to migrate the day to day review process somewhere.

Our company, like most had outsourced customer service a couple of years ago. Because fraud and compliance manual review is so interlaced with customer service contacts, there was always a disconnect between customer service and the manual review team adding friction to the customer's experience. As there was no way to hand off fraud or compliance escalations which came into customer service, the process always involved at a minimum, two separate contact point with each customer. This was time consuming, costly and most important, really didn't provide a positive experience to a customer looking for resolution.

I was presented with three distinct issues, lack of staffing flexibility, a highly skilled fraud and compliance team working below their abilities with no clear way of elevating them and a high friction customer experience when interacting with my group. I needed a solution that would incorporate a global solution to all of these problems with minimal impact on the teams fraud and compliance outstanding productivity and accuracy.

Planning The Outsourcing Solution

Planning is everything when it comes to migrating the fraud and compliance manual review process to an outsourced environment. This isn't an operation with a large ramp up time to get to full speed, anything short of full speed means losses and violations which are unacceptable.

When you bring in new fraud and compliance reviewers in house, you get complacent around the process as a manager, because there is a built in knowledge transfer from experienced reviewer to new member making the transition seamless. If your company hasn't ever migrated your manual review team you can be sure there is no process documentation or training programs developed because you have always leveraged your in-house experts to train up any new personnel. Because this method of training is so intensive and hands on, the new reviewer is usually up to speed in short order with very few training related error issues.

When starting down the road on an outsourced option, you clearly realize you will not have the luxury of hands on, in house mentoring to get the team trained and productive. Even worse, because you are placing the team remotely with everyone having the same level of knowledge in your fraud and compliance review process, you have no supervisors, team leaders or managers on site who can assist with the coaching and mentoring process. Everyone is starting off in the same place and that is an uncomfortable place for a manager to be.

The most important partner you have in the outsource planning process is your customer service executive or manager. The customer service department has an established process documentation, knowledge base and training program in place. If you are integrating your fraud and compliance manual review team within the same site as customer service, which is highly recommended, you will want to format your departments process documentation and training programs to integrate completely with customer service.

The customer service management team is also going to have valuable information regarding which location would provide the best manpower, cost, facilities and management for integrating in the fraud and compliance review process. Without this partnership, the job of deploying an outsourced fraud and compliance team will be considerably more difficult.

One of the biggest advantages from a fraud and compliance management standpoint when outsourcing fraud and compliance review is, if selected properly, you have just gained a team with a unique geographic and cultural understanding of fraud and compliance scenarios in a region in the world where you do business.

I have discussed in previous articles about the distinct advantage in regionalizing your fraud and compliance analysis and investigation assets. A fraud reviewer in the Philippines is going to have enhanced knowledge of the types of fraud schemes in that country, adversely, a fraud reviewer placed in a country with a low level of fraud activity is going to have a hard time conceptualizing fraud as an act. You will be able to train that individual the review process and provide the necessary tools and information to calculate a decision but without life experience or cultural exposure to fraud and related activity, you will loose 30% of the fraud reviewing decision process which is completely based on intuitiveness.

This is by no means meant to be derogatory against Japan and from a cultural standpoint its a complement, Japan experiences very little fraud. Placing a fraud and compliance team in Japan tasked with reviewing transactions from countries with a highly developed system of organized fraud would be a monumental disaster. You will loose 30% of your fraud detection ability from a lack of reviewer intuition to discreet fraud patterns.

This does not mean when planning where to place your fraud and compliance, you only consider the countries with the highest risk in these areas, however, I am saying that wherever you do decide to place your resources, you need to ensure the people you are employing for the task can understand the concept of fraud in 360 degrees, meaning, why they do it, how they do it and what is the objective. If you happen to choose the specific country which carries the highest fraud and compliance risk for your country, more the better.

Keep in mind, you have the option to disburse your outsourced manual review teams to as many countries as you like. From a personal perspective, if you are in the business of reviewing international transactions, you should seriously consider separate Latin America and Asia Pacific teams. The regional awareness that you, as a risk manager, will gain is extremely valuable.

The next important factor in determining location for your fraud and compliance review team is to choose a place that has a manpower pool of individuals who have had exposure and experience in fraud and compliance screening. One of the reasons I selected the Philippines for our first location, other then we had a large customer service presence in that country, was an ample supply of individuals with previous fraud and compliance screening experience at other companies.

For my first outsourced fraud and compliance team, I was actually looking for a mix of experience, some with a fraud screening background and some with a customer service background and a good intuition for fraud. I have found that having people with no formal fraud or compliance screening background, incorporated with experienced individuals is a fantastic combination.

Armed with an understanding of the process documentation and training process from your customer service leadership and a location in mind which will allow you to leverage regional awareness of fraud and compliance and lend it self to recruiting experienced individuals you are ready to begin planning the transition.

Integrating The Outsourced Team

By the time you have selected the location(s) for your outsourced fraud and compliance team and have started compiling the training program and process documentation you will utilize to get the team up and running, your ready to begin the integration process between your in-house team and your new outsourced fraud and compliance team. There are actually a large number of items to ready the integration of the two teams but I will outline the primary steps from a learning perspective.

We talked earlier about your in-house fraud and compliance experts, this is where you get to leverage those years of experience to ensure the success of your teams deployment. When we were recruiting and interviewing potential candidates for the new team, I utilized our in-house team during the entire selection process. There were two main reasons, first there is no one more qualified to identify a qualified fraud and compliance reviewer with the right mindset and ambition, then someone who has done the job for years. Second, it was very important from a team perspective, that everyone feel involved and vested in the development of the new team.

In addition to my in-house team's involvement in recruitment, interviewing and selecting candidates, I began the process of elevating their experience by having each be responsible for the mentoring and coaching of the new team members. The primary problem with building an outsourced team from scratch, where no one on the team has any experience in my company specific way of performing fraud and compliance reviews, is that I lost the on-the-job knowledge transfer that used to occur with new members to the in-house team.

What I needed to establish was a virtual mentor, with the new fraud and compliance review team working in tandem with the experienced fraud and review team to facilitate and expedite the new team's abilities and proficiency just as fast as the new in-house team members.

Training the new team involved being on-site for two weeks. The first week was spent in a classroom environment with extensive training on the company's fraud and compliance screening procedures, tools and policies. With a complete understanding of the process and tool set, the team spent the next week doing hands on manual review of transaction in tandem with the in-house team. During the hands on process, each of the new team members were in constant contact with the in-house team through email, phone and instant messenger. This allowed for a virtual hands on mentor-ship and knowledge transfer that accelerated the understanding of the review process for the new team.

The mentor-ship continued for six weeks, with the mentors on the in-house team, providing QA of reviewed transactions, coaching and assistance to the new team members on a daily basis. By tracking the QA scores we were able to establish when the team was ready to be independent of the mentors.

It is important to remember that you never completely hand off the fraud and compliance process to anyone outside your company. Continuous quality assurance, metrics and management is essential for the company's fraud operations team to maintain with the outsourced review team. I make quarterly on-site visits, continually plan refresher training and updates and monitor results on a daily basis.

Elevating The Legacy Team

With the manual review process handed off to the outsourced review team, transitioning from partial to full coverage over time, the in-house team was ready to begin the transition to fraud and compliance management.

The biggest gain I received was being able to transition the talented group of experienced in-house fraud and compliance experts to world class proactive fraud analysts, investigators and managers.

As this sites name is "Understanding Link Analysis", this is when visual analysis comes into play in the project. The first step was to deploy i2 Analyst Notebook and iBase to each of the in-house fraud review team members enabling them to visualize and detect patterns of organized fraud. Each team member was also trained on SQL data mining and the concepts behind the fraud and compliance scoring models and rules.

Armed with i2 for proactive visual analysis of transactions to detect fraud, anti-money laundering and funding of illegal activity, the team is able to visually review transactions which occurred over a period of time to isolate clusters of interrelated activity indicative of fraud and compliance violations.

Once the team identifies a cluster of activity, they can utilize betweeness and closeness to determine the attributes being used by the individuals and write rules into the fraud and compliance scoring models ensuring that transactions sharing those attributes are brought in for review by the new fraud and compliance review team. Now that the team has additional fraud and compliance screening resources, we have the ability to ensure more transactions, with a higher probability of fraud, are brought in for screening.

Because we are utilizing data mining and visual analysis to detect trends and patterns, the attributes we are identifying and scoring have a much lower false positive rate. We are bringing in twice the number of transactions for screening with half the false positive rate While we haven't arrived at a point for an accurate measurement of reduced or avoided fraud versus productivity due to the newness of the team, I have high expectation in significant fraud and compliance violation reductions.

Points To Remember

There are several important items to always to keep in mind when planning on transitioning your fraud and compliance manual review process:

1. Ensure that your in-house fraud and compliance team is integrated well with your outsourced team. Even though you are outsourcing your fraud and compliance review, they are part of your fraud and compliance team and each group should communicate and interact as much as possible. Remember, the biggest benefit you gain is additional insight and understanding of fraud and compliance trends, do not ignore it, every member of the team is just as important as the other.

2. Have a complete plan to elevate your existing team to enhance your groups ability to proactively identify and investigate fraud and compliance trends. Outsourcing fraud and compliance review should never be done as a replacement to in-house review. It should be done to allow you greater access to resources and manpower allowing you to take your team to the next level.

3. Plan and acquire the tools you will need to take your in-house team from reactive to proactive fraud and compliance identification as quickly as possible. Just adding reactive fraud and compliance review to your fraud department is only half the objective. While you are planning the transitioning of manual fraud and compliance review, you need to be planning the elevation of your corporate team to proactive review. With both in place, you have just dramatically increased the value of the fraud and compliance department to your company.

4. Plan on spending plenty of time on-site with your manual fraud and compliance screening unit. For a company with large volume and over 30 review personnel, you should give real consideration to having a full time on-site presence to ensure accuracy, quality and productivity with the team.

Using Geospatial Analysis To Investigate Insurance Fraud

Many of my articles have covered the importance of visual analysis to understanding the relationships between entities in data. Visual analysis is also important in understanding the relevance of the location in where events happens to determine and discover patterns in insurance fraud behavior.

There are numerous ways we can leverage geospatial analysis to analyze and discover potentially fraudulent insurance claims. Geospatial analysis can also be key in developing investigative plans for ring activity. In this article we are going to cover the difference insurance fraud scenarios that geospatial analysis plays a key role in the strategic and tactical analytical discovery of insurance fraud.

Organized Ring Activity

In previous articles we have reviewed how to use link or association analysis to pro-actively discover organized fraud activity such as staged accident rings and medical provider fraud. Now we are going to incorporate geospatial analysis to visualize the location of the activity for the development of an investigative plan.

As an insurance fraud analyst, I have located a group of injury claims which are have interrelated participants through link analysis. All of the individuals are associated with one and other through various unique identifiers contained in my data such as address, phone or vehicle. The link analysis chart is the first part of my presentation to the investigators, now I need to incorporate a geospatial visualization to my presentation to provide investigators with the locations used by the ring to execute the fraud.

For this analysis I am going to use an analytical program called Centrifuge, which deploys a tool that allows for the integration of data with the Google Earth program, allowing for the mass visualization of geospatial data contained in my claim system.

While I know that all of the parties involved in this large group of injury claims are all interrelated, geospatial analysis is going to allow me to visualize their proximity to one and other. I can also incorporate the staged accident locations to provide a visual representation of the losses to the claimants addresses to further substantiate my theory of organized fraud activity.

I am going to download the claimants location information and loss locations from my claims database into an excel spreadsheet for upload into Centrifuge. Next, through the programs user interface I am going to select the columns that contain the claimants addresses so that Centrifuge can send the data to Google Earth for the visual representation.

From the example above you can see I selected the claimants street address, city and state. Centrifuge also allows me to bring in descriptive data for Google Earth, so I am selecting the claim number, claimants name and loss date to assist me with investigative planning.

After I have selected all of the data I wish Centrifuge to import into Google Earth, I click the show map button on the Centrifuge interface and the data is imported into the Google Earth program.

From this first perspective at the city level in Google Earth, you can see that the claimants are clustered together within the Orleans Parish. There are a couple of outliers which I am going to want to investigate further to ensure their connection with the ring, however my geospatial analysis is matching up with my link analysis, showing a cluster interrelated activity.

Another very useful feature of the Centrifuge tools import is located on the left menu of Google Earth. When I set up my import from the Centrifuge console, I also imported in the claimants name and date of loss as descriptors. On the left menu, you see that the claimants names are listed in Google Earth, if I or the investigator want to quickly locate an individual suspect in my staged accident ring, I just need to find the claimants name in the left hand menu and click on the hyperlink, Google Earth will then navigate to that location.

Lets drill down a bit into my geospatial analysis, from the visualization below you can see that we have four separate claimants all living less then 1 mile apart from each other. When I hover over one of the location, we can see the total number of claims where this address was used as the claimants residence.

Staged accident rings typically need a body shop, legal provider and medical provider to maximize their return on investment so I need to incorporate the providers locations to my analysis as well.

In addition to providing a link analysis chart to the investigators showing the interrelation between the claimants and the claims, I can now add a geospatial analysis to my presentation showing that all of the interrelated claimants live within one mile of each other and all in proximity to the legal provider, medical provider and body shop which are servicing the rings claims.

As an added benefit to using the Centrifuge tool and Google Earth, I can email or save the Google Earth file and provide it my investigators along with the location menu so they can quickly identify the location of the claimants they are going to investigate.

By also incorporating the loss locations into my geospatial analysis, I can further assist the investigators and law enforcement when developing an investigation and surveillance plan to catch the ring staging accidents.

Medical Provider Fraud

In the last example we incorporated medical providers into a geospatial analysis of staged accident activity to show the close proximity of the medical provider to the claimant. In medical provider fraud through, sometimes the opposite is a strong indicator of fraud.

We can leverage geospatial analysis to show inconsistent patterns in distance between claimants and a specific medical provider which could be an indicator of medical provider fraud.

By examining normal medical provider to claimant location records during any given period, the majority of individuals who treat with a medical provider in an accident claim, treat within five miles of the residence. The average distance changes based on location and population density so it is important to establish a clear average before performing geospatial analysis to locate potential medical fraud, but for the most part it is common sense that if you are injured in an accident, you are going to seek out a provider that is in an immediate proximity to where you live.

By incorporating geospatial analysis of a particular area, I can visualize clusters of injury claims where there is an abnormal distance between a group of treating claimants and the medical provider which would be a strong indication of organized fraud activity or medical billing fraud such as billing for services not rendered.

To look perform geospatial analysis of the distance between provider and claimants on a large scale will require an amp'ed up mapping program such as ArcGis, however on a smaller scale we can use Google Earth for specific proactive investigations.

In this example I located a medical provider through data mining who is treating a large number of vehicle injury claimants with an increased velocity of certain CPT codes which indicate fraud. I am going to utilize Centrifuge to import the claimants locations into Google Earth to show the relative distance between the provider and the claimants to substantiate my suspicion of fraud.

From this visualization, I can see that the claimants, for the most part all live in Jefferson and Orleans Parish, in and around metro New Orleans. From my medical billing database, I am going to use Centrifuge to import in the billing medical providers treating locations from their HICFA forms.

From my geospatial analysis you can see that the medical provider is located many miles away from the largest cluster of claimant addresses. To determine if there is a practical reason why so many people would travel such a distance for treatment I am going to use Centrifuge to import in all Chiropractors which have billed my company in the past six months in this region.

As you can see from the illustration, there are numerous providers in close proximity of the claimants which are not being used by this cluster of claims (represented by the red cross). Through the use of geospatial analysis, I have established that average distance between the claimants and ABC Chiropractic is over 10 miles and that there are 12 other medical providers of the same discipline in much closer proximity.

While this does not confirm fraud, it is the first step in the proactive identification of potential medical provider fraud. The geospatial analysis will be coupled with a velocity analysis of CPT codes being billed, a time line of patient treatment and a link analysis between the actual claimants to determine the probability of fraud for referral to investigation.

The analysis of the CPT codes being billed can also be accomplished in Centrifuge using the chart function after uploading my billing data into the program as illustrated below:

I can utilize the timeline function in the Centrifuge program to visualize patterns to the treatment dates of the claimants. From this visualization I can establish if the patients are all treating on the same days, pattern of days, pattern of hours and duration.

By leveraging geospatial analysis I have proactively identified a medical provider who is treating a bulk of patients outside of an average proximity of their residence. Then by leveraging the visual analytical tools in Centrifuge I have visualized the medical billing to produce a presentation of potential medical fraud for my investigation team.

This can be accomplished on a much larger scale by utilizing specialized mapping software such as ArcGis to view in bulk the geospatial relationships of thousands of medical providers to their patients.

Catastrophe Claim Analysis

Geospatial analysis is critical in the proactive identification of catastrophe claims. Catastrophe claims pose a significant challenge to insurance companies as they produce large volumes of claims which under most statutory regulations, have to be handled in a short period of time.

The penetration of SIU into potentially fraudulent catastrophe claims has to occur almost simultaneously to the time the claims are being adjusted to prevent payment and exposure to the company.

By utilizing geospatial analysis, we can overlay the claim location with geocentric weather or disaster data provided by the national weather service or noaa to pinpoint claims with a loss location outside the path of the specified catastrophe.

Lets take an example of a tornado hitting southern Louisiana. I am going to leverage geospatial analysis to import the location of catastrophe claims my company has received with the corresponding storm track information to identify those claims which were outside of the reported storm track.

In my first import, I can visualize the loss location of all CAT claims associated with this specific event. My next step is to import the storm track data from the national weather service to determine which claims were outside the path of this specific storm.

From the illustration below, you can see that the majority of the CAT claims fell within the storm track and damage geo data from the national weather service. There are several claims with a reported loss location outside this area that I will bring in for investigation.

These are a few examples of effectively incorporating geospatial analysis into insurance fraud investigation and identification. Geospatial analysis, coupled with link analysis, provides a complete analytical representation of potential fraudulent activity that can greatly assist in the planning and execution of insurance fraud investigations.

I would like to thank Centrifuge for providing access to their analytical software for this article. For more information on their products please visit Centrifuge Interactive Analytics.