Here’s the bad news: No organization is safe from fraud. It can happen in any industry, to companies of all sizes. The good news? Being proactive and aware of what to look for can offer some protection. And here’s another bit of good news: One key way to more quickly detect fraud and reduce the associated financial loss is something you’re already doing on a regular basis – monitoring and analyzing your organization’s financial data.
A quick refresher on fraud types and impact
The three most common types of occupational fraud are asset misappropriation, corruption and financial statement fraud. The four tactics most often used to commit fraud are billing schemes, check tampering, skimming and payroll/ expense reimbursements. Billing schemes can take many different forms, including the use of shell companies and incorrect payments to request returned checks. Check tampering involves any type of misuse of checks, such as concealed checks and forged endorsements. Skimming involves misrepresenting account totals in order to pocket money, and payroll/expense reimbursements includes falsified or overstated documents such as time sheets or expense reports.
According to the 2020 ACFE Report to the Nations, a typical fraud case causes a loss of $8,300 per month and lasts 14 months before being uncovered. This report also estimates that organizations lose 5 percent of their revenue to fraud every year.
How to start using data analytics for fraud detection
Start by monitoring and reviewing common financial data sources for your organization, which include bank statements, payroll detail reports, financial reports, inventory records, credit card statements and accounting systems. Look for reports in your company’s accounting software, such as the general ledger, check register, vendor detail and audit trail, to uncover trends and anomalies. Having a backlog of data gives you historical information to compare recent statements to and can help you identify any unusual entries or patterns.
Analytical techniques to combat fraud
Many types of fraud are detectable through data analytics. Armed with the necessary data and an understanding of both your business and red flags for various fraud schemes, you can identify fraudulent transactions in data sets. Data preparation is both a difficult and crucial step before performing any analytics technique. Taking the time to read and understand the data you are working with is crucial to data interpretation.
Be sure to make a copy of the original data file. This will give you a file to check your work against, as well as a backup in case your working file becomes corrupted. You will also want to document the method you use to clean and organize your data so that you can interpret your results correctly. Once your data is ready, you can use different analytical techniques to search for anomalies. Common methods include:
- Comparing vendor and employee information to look for any potential relationships
- Looking for any unusual vendor attributes
- Searching for checks issued on holidays or weekends
- Looking for unusual journal entry amounts
Tools to help organize and visualize data
Keeping your data organized is an essential step of the process. Use Microsoft Excel for the initial step of tracking your data and findings. With your data sorted and organized, other, more sophisticated tools can help conceptualize it. The tool you use will depend on what type of data you are working with and what you are trying to convey. Data visualization platforms, such as Power BI, Tableau and Qlik, can identify trends in your data sets and help communicate your findings to a broader audience. Software like IDEA and JMP can conduct predictive testing and outlier and distribution analysis.
Implementing these practices at your organization can help protect you against large-scale fraud that can cost a fortune. RKL’s fraud prevention specialists can help you get started using data analytics. Reach out to your advisor, contact one of our local offices or use the form below to get started.