Casting a Net(work) to Crack Down on Corruption

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Jeremy Clopton, CFE, CPA, ACDA
Senior Managing Consultant, Forensics and Valuation Services, BKD, LLP

In nearly every industry in the 2014 Report to the Nations, corruption was the most common fraud scheme. And while this means there is likely plenty of attention given to corruption, it remains one of the more difficult types of fraud to detect and try to prevent. Much of the basis of corruption is power and influence, neither of which typically show up in the financial aspects of an organization. However, we do have something in our organizations we can use to take a more aggressive approach to detecting corruption — data.

Data Types
We have more data in our organizations now than we have ever had. Organizations continue to generate more data each day — data with great variety. It is the variety of data that provides us a means by which to take a more aggressive approach to addressing corruption. We need to analyze all of the data we have in our organizations, both structured and unstructured. Structured data comes in columns and rows, neatly organized. This is the data traditionally analyzed in examinations. Unstructured data is everything else, which in many organizations accounts for 80 percent of the data. This has typically been left to the digital forensics specialists for analysis.

Analysis Methods
When it comes to analyzing data sets to address corruption, it takes more than just structured data. That said, the structured data is a great place to start. An analysis of attributes of vendors, customers, employees and others an organization is doing business with can provide insights into potential relationships and conflicts of interest. The results of the attribute analysis becomes the foundation for further analysis that includes unstructured data. 

Incorporating all of the available unstructured data is a critical next step when addressing corruption in an organization. One of the key data sets for expanding the relationship network related to individuals in your organization is communications data. Email, phone logs, text messages and other means of electronic communication provide context around the relationships between individuals inside and outside the organization. Not only the entities and individuals in the email, but the actual content of the messages and nature (tone) of the communications.

In addition to communications data, other useful information includes social media postings, business documents and information regarding an individual’s role in a particular business process (such as purchasing/contracts). Using this information, coupled with the communications data and relationships from attributes, allows you to build an enhanced relationship network that provides insights not otherwise available in the normal course of business. This network may provide the information you need to identify signs of corruption in your organization.

Additional Information
To learn more about this topic, head over to Fraud-Magazine.com and check out the Fraud EDge column. A colleague and I recently wrote a six-article series on the topic of integrating data analytics and digital forensics for more effective analysis of all data in an organization. If you’re attending the upcoming ACFE Global Fraud Conference, I will be presenting on this very topic. I hope to see you there!

CFE Uses Data Skills to Bridge the Gap Between Two Career Dreams

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Jeremy Clopton, CFE, CPA, ACDA
Senior Managing Consultant at BKD Forensics

Jeremy Clopton, CFE, CPA, is a Senior Managing Consultant at BKD Forensics in the forensics and valuation services division, and is a recurring author of the Fraud EDge column in Fraud Magazine. Clopton focuses on data analytics for fraud prevention and detection, and he works with other CFEs in his division to use data analytics for investigative purposes. “Data analytics can be used to enhance the perception of detection,” Clopton says. “Organizations that use analytics have the opportunity to focus their efforts on transactions of higher risk and do so more quickly than organizations without analytics. I am a firm believer that the fight against fraud is much more effective when it’s fought with preventive measures within an organization rather than after it’s occurred.”

How did you become passionate about fighting fraud?

Growing up I wanted to be an attorney, but after discovering accounting, I decided it was the career I wanted pursue. In high school, I shadowed a CFE for job experience, and it became clear to me that being a CFE would give me the chance to enjoy the best parts of being an accountant while still working with attorneys. After entering the profession, I’ve been amazed at the lengths to which people will go just to commit fraud. Fighting fraud is definitely challenging, because fraudsters are always using new methods — or variations on old methods — to commit fraud. The constant innovation and creativity required to keep up with these schemes drives my passion.

What steps led you to your current position?

When I started in the forensics division of BKD, I had a knack for Excel and an interest in statistics. This led me to some interesting investigation projects and the chance to learn more about data mining and analytics. About a year into my career, I started using ACL software and focused on the use of data mining in fraud examinations. Since then I’ve expanded my toolset to include other analytics products, and I now work with organizations to use data analytics for investigations, prevention and fraud risk management.

What’s one tip you have for people working in data analytics?

Don’t be afraid to try. Data analytics, especially with large data sets, can be intimidating. To get started, you must be willing to accept that you might not succeed on your first effort. The key is to learn from that experience and make the next even better. Failure is an option as long as you are willing to use that failure as a springboard for success.

Why is using data analytics important in the fight against fraud?

Many fraud schemes affect a very small number of transactions in an organization, especially when compared to the total population of transactions. Pulling a sample of items as a preventive measure is no longer enough. Data analytics allows an organization to have 100 percent coverage when testing their transactions. Rather than pulling a sample of items from the full populations, we can now pull transactions that are exceptions to normal business expectations for follow-up and research. Using a dashboard of fraud risk indicators, organizations can quickly see where their highest risks exist.

What hobbies or activities do you like to do outside of work?

I enjoy spending my time with my wife and kids. We tend to be an active family, spending time running, enjoying the outdoors, playing sports or, on occasion, just relaxing on the deck with friends and family.

Read Jeremy's full profile in the Career Center on ACFE.com.

How to Tame ‘Data in the Wild’

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Misty Carter, CFE
ACFE Research Specialist

Emails, social media posts, blogs, instant messages — what do they all have in common? For one, they are tools used by millions of people each day to communicate with the rest of the world. What else do they have in common? They help detect fraud. You might be wondering, “Why and how is a Facebook update relevant to fraud detection?” Consider how much new data is created every second. Think about how many posts, emails or text messages you personally send each day. Now think about how much of this data is never touched. 

To put it into perspective, a study conducted by International Data Corporation (IDC), a U.S. market research firm, estimated that text, also known as unstructured data, will account for 90 percent of all data created in the next decade. Unstructured data, sometimes referred to as “data in the wild,” is basically free-form data that has not been put into a structured format. Since unstructured data is a relatively unexploited resource for fraud examiners, it makes sense to use it in a way that can provide more insight into areas prone to fraud that might have been previously untouched.

Before coming to the ACFE, I spent 10 years working in the audit field. I found mining through text data during fraud investigations to be one of the most useful tools in my auditing toolkit. Today, many fraud examiners are using a similar data analysis method to help explain, understand, or interpret a situation or a person’s actions or thoughts. This type of non-traditional analysis is referred to as textual analytics. In fact, the FBI and Ernst and Young’s Fraud Investigation and Dispute Services Practice have used textual analysis on email communications from past corporate investigations to determine the most common words used by employees engaged in rogue trading and fraud. As a result of their analysis, they identified the top 15 keywords and phrases used by fraud perpetrators. This list of keywords can be used proactively to prevent fraud from occurring or spot it early in the process.

The use of keywords, however, is only one facet of analyzing textual data. The ACFE’s new online course, Textual Analytics, identifies various techniques that can help fraud examiners, including examples of how data from free-text fields, email, social media sites and other sources can be used to uncover fraud. This course provides an overview of different types of data and how it should be managed prior to being analyzed. It also explains how textual data can be used to assess fraud risk in areas that might not be on management’s radar. 

If you are looking for new and innovative ways to add value to your organization, this course will provide you with the tools necessary to effectively reduce fraud risk exposure while enhancing your fraud detection skills.

Read more about the new course.  

Adding Structure to the Use of Unstructured Data

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Jeremy Clopton, CFE, CPA, ACDA
Senior Managing Consultant, Forensic and Valuation Services
BKD, LLP

In the age of big data, it should come as no surprise the ACFE’s 2014 Report to the Nations ranks proactive monitoring and analysis of data as the most effective anti-fraud control, with respect to both duration and median loss. What may be surprising is the type of data being monitored and analyzed.

Unstructured data (things like external emails and social media) is becoming a larger portion of the big data pie every year. In a 2005 report, Gartner Research indicated that unstructured data comprised about 80 percent of all available data in an organization. Fast forward a few years, and that percentage is likely much higher. The challenge we face as investigators is how to best use this unstructured data in our investigations. The solution begins with the collaboration between data analytics and digital forensics, as referenced in my post in February on that topic.

As highlighted in a recent article posted on venturebeat.com, the Detroit Crime Commission (DCC) has embraced this collaboration as well. The article and accompanying video cover the general framework of how the DCC is using analytics of both structured and unstructured data for fighting crime. While the article is focused on a specific software solution, it contains valuable information about the DCC’s mindset and reasoning behind the use of what they call big data analytics. This information is applicable regardless of software choice, industry or location. Some key conceptual takeaways from the article include:

• Network analysis and relationship mapping. Using information gathered from online sources, the DCC identifies criminal enterprises and their members, as well as how the various organizations interact. Applying this to occupational fraud, identifying the network and relationship map for key vendors, employees and customers may help in uncovering corruption and kickback schemes.
• Analyzing both unstructured and structured data. Rather than relying solely on criminal databases and arrest records, DCC uses information from online posts to supplement their structured information and gather intelligence not otherwise available. Applied to occupational fraud, the analysis of email communications, text messages and chat sessions may provide information regarding unknown relationships or activities not identifiable in the transaction detail.
• Data visualization. The video accompanying this article shows a great example of using data visualization to uncover relationships and “see the data” more quickly than traditional methods. The old saying that “a picture is worth a thousand words” is truer than ever in data analytics. Using data visualization helps identify trends, patterns and relationships not readily identifiable in reading through large volumes of data. This technology can truly help an investigator see the issues in the data.

The application of data analytics in law enforcement is a great example of leveraging big data. The DCC’s success using these concepts underscores the importance of proactive monitoring and analysis of data for fraud detection and prevention. 

Follow Jeremy on Twitter @j313 or at BKDForensics.com.

New Ways to Incorporate Analytics Into Your Next Investigation

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Jeremy Clopton, CFE, CPA, ACDA
Managing Consultant, Forensics and Valuation Services, BKD, LLP

As an investigator, much of the data I analyze on a day-to-day basis is standard financial data – accounts payable, payroll and general ledgers. For those of you using data analytics, you likely see these data sets as well. However, a recent presentation by a colleague of mine and an article on insurance fraud reminded me of the many other data sets that are useful in investigations.    

Email Analytics

My colleague and I work closely together, but we do not always have the chance to discuss the behind-the-scenes details of what we do. Always looking for a new way to apply data analytics, I was definitely intrigued by his presentation on predictive coding and analytics surrounding “unstructured” data like email. I quickly realized that email, much like financial data, is just as ripe for mining as accounts payable data. It takes some prepping and cleansing, but at the end of the day, it contains the same types of information: names, addresses, dates, times and keywords. Considering these documents as data can open your eyes to a completely new set of potential procedures in your next investigation.

Visual Analytics

This recent article on insurance fraud did not specifically cover data analytics, though it did highlight some potential applications and red flags. Applying data analytics to claims and employee data can help identify many of these red flags (new employee, poor attendance, late Friday/early Monday claims, unusual location, etc.). For example, an attendee at a recent presentation I gave talked to me about using visual analytics to map insurance claims and identify “hot spots” in a city. Plotting all claim addresses on a map allowed them to focus on streets and neighborhoods with multiple claims during a specific period. This visual analytic helped them discover fraudulent claims.

Social Media Analytics

The beginning of the article also described how an individual who filed for workers’ compensation got busted after appearing on the game show, “The Price is Right.” I’m not advocating watching hours of game shows to identify potential workers’ compensation fraud; however, analyzing social media data could be just as helpful. With Twitter, Facebook, Instagram, Pinterest, LinkedIn and the many other sites out there, individuals create enormous amounts of data every day (much of it about what they are doing). Analyzing this data for keywords, events, times and locations can effectively supplement an investigation. Again, once obtained, this information is much like the financial data we are already accustomed to analyzing. 

You may be aware of these data sources and their applicability, but if you aren’t, I hope this article has you thinking of new ways to incorporate analytics into your next investigation.