Generating 5 percent higher profits without having to charge additional rent or find new tenants may seem like an impossible feat to most real estate investment trusts. However, in its 2018 “Report to the Nations on Occupational Fraud and Abuse” (Report), the Association of Certified Fraud Examiners (ACFE) estimates that the typical organization loses approximately 5 percent of revenues each year to fraud.
The Report is based on the results of the 2018 ACFE Global Fraud Survey, which analyzed 2,690 occupational fraud cases from around the globe. The 2,690 cases generated more than $7 billion in total losses, with 22 percent of the cases causing losses of $1 million of more.1
ACFE analyzed the reported fraud cases based on the victim organization’s industry. Among the 24 industries examined, real estate ranked 18th in frequency of occupational fraud cases. Despite having a lower frequency of fraud, the real estate industry suffered the seventh-highest median loss at $180,000.
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So what can you do? Assessing your internal controls and processes for fraud risk can help reduce fraud losses. However, many of the most damaging fraud schemes involve two or more people colluding to circumvent the controls an organization has in place. Don’t assume this won’t happen to you—it likely already has at some level. Our forensic accounting and fraud investigation team regularly sees the damage done when one or more unscrupulous employees or managers decide to abuse the trust of executives and owners. More than 88 percent of fraudsters have no criminal history, and many times the largest frauds are committed by those who are trusted the most.2 Companies are at such high risk partly due to the nature of the business. Large amounts of money regularly exchange hands for multiple properties through check, wire, ACH, etc. This drives up the opportunity and appeal of committing fraud and may help explain the real estate industry’s seventh-highest median fraud loss.
Your organization can implement many actions and processes to help reduce your fraud risk. Based on ACFE’s Report, the second most effective method of reducing fraud losses is through the use of proactive data analytics:
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Organizations that proactively used data analytics to monitor and analyze their operations reduced their fraud losses by 52 percent. The other controls listed in the table above also help reduce fraud risk, and when used together, they create an environment that will help mitigate potential fraud losses. Using data analytics can serve as the foundation for your fraud monitoring initiatives, and the following examples demonstrate ways that emerging technology and big data can help your organization address these risks and help prevent those dollars from ending up in the wrong pockets.
Example 1 – Monitoring for Personal Use of Credit/Purchase Cards
Many companies are heavy users of credit cards and purchase cards and typically have the same control in place to monitor the use of both types of cards. Companies require employees to submit receipts for the items purchased and then compare those receipts to the card statements once a month. As long as the vendor, date and amount match between the receipt and the statement, the supervisor approves the purchase. Some companies don’t even require itemized receipts (though forensic accountants don’t think this is a good practice). However, even if itemized receipts are required, the internet has dozens of websites ready and willing to help fraudsters create fictitious receipts. In the past, creating falsified documents took skill and savvy, but that’s simply no longer the case.
Example 2 – Fictitious Vendors & Conflicts of Interest
In an industry where there are many vendors providing services across a large number of properties, it’s unsurprising that vendor fraud is so prevalent. According to ACFE, almost 23 percent of all occurring frauds involve some form of billing fraud. These schemes often intertwine with corruption schemes, which are the most common fraud schemes across all industries, occurring in 39 percent of frauds—and that’s because they work.3 For example, an employee in the accounts payable group has the ability to set up new vendors and submit and approve vendor payments. This employee then sets up a fake vendor and begins sending fictitious invoices shortly thereafter. Let’s assume this is a long-term employee who’s viewed as a top performer and is known and trusted by everyone. Organizations often think this won’t happen to them because they have segregation of duties around vendor setup and vendor payment. However, this is where corruption can unravel all of the controls an organization has dutifully put in place. If an employee in purchasing colludes with an employee in accounts payable to set up, purchase and pay fictitious vendors/subs, it’s exceedingly difficult to stop them using traditional approaches. This is where BKD Big Data & Analytics can help. Using data analytics tools, an organization can find trends, patterns and matches in its transactional data that may shine a light on these types of schemes.
Example 3 – The Kickback
One of the most prevalent billing schemes is an overbilling/kickback scheme. These schemes fall into the corruption category and are popular because they’re notoriously difficult to discover. For example, in some fraud cases employees worked with existing (and real) vendors to overcharge their employers. Those working in the purchasing department have the greatest opportunity to engage in this type of fraud, sometimes carrying out these schemes with vendors who are willing to overbill or even send invoices for work not performed in exchange for receiving kickbacks. However, there’s one attribute of kickback schemes that makes them vulnerable to detection via data analytics—the tendency to start accelerating payments. In this case, accelerating payments is when the frequency and/or dollar amount of payments to the vendor significantly increases. This happens because both parties have to split the proceeds. Once they test the system and process, usually with a few lower dollar transactions, they’ll start to increase either the frequency or amount of payments, or both. Due to this tendency, organizations can use data analytics to identify changes in payments to vendors/subs over time, which helps detect anomalous spikes in activity. This test usually results in several payments to vendors/subs that show anomalous activity but can be easily investigated and explained.
For example, an organization is working with a sub on one project. The sub has done such a good job that the organization hires them to begin working on several additional projects, causing increased payment activity. Typically management can analyze the exceptions from this test very quickly, making data analytics an efficient and effective way to test for potential kickback schemes.
The Case for Data Analytics
The case studies above are a small sample of the ways data analytics can help detect fraud schemes. The power of data analytics comes from testing 100 percent of a certain population of transactions or data. In today’s resource-constrained environment where companies must find ways to do more with less, data analytics is a highly effective and efficient means to test large data sets that cover high-risk areas. Many times these tests can complement the work of internal auditors or, in some cases, are used in lieu of establishing an internal audit group in small to midsize companies. This approach sends a message to all employees that you trust them, but you’re watching. BKD Big Data & Analytics forensic accountants have found that the threat of detection is one of the best deterrents.
The 2018 ACFE Report makes it clear that fraudsters continue to find new and inventive ways to line their pockets with ill-gotten gains. They’re increasingly using technology to their advantage to conduct or cover up their schemes. BKD Big Data & Analytics can help your organization take advantage of technology to help combat the risk of significant fraud losses.
For more information, reach out to your trusted BKD advisor or use the Contact Us form below.
1 ACFE 2018 Report to the Nations on Occupational Fraud and Abuse. ↩
2 ACFE 2018 Report to the Nations on Occupational Fraud and Abuse. ↩
3 ACFE 2018 Report to the Nations on Occupational Fraud and Abuse. ↩