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How to Use Investigative Data to Uncover and Prevent Fraud

How to Use Investigative Data to Uncover and Prevent Fraud

Don’t just “set it and forget it”. Consistent and continuous data analysis will take your anti-fraud program to the next level.

When you're trying to prevent internal fraud, you can't just hop right in. In order to create an effective, efficient anti-fraud program, you need to know your company's areas of risk.

Using data for fraud prevention can help you detect patterns of fraud you'd have missed otherwise and implement the right controls for your needs. In just four steps, you'll improve your efforts at preventing financial losses, lawsuits and reputation damage.

Are your fraud investigations effective? Here's how to tell.

In order to be as effective as possible, your fraud investigation process must be timely, thorough, well-documented and fair. Download our free checklist to make sure you're taking all the right steps for better detection, investigation and prevention.

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Step 1: Design Your Fraud Risk Assessment

To effectively use data for fraud prevention, you must first create a risk assessment program suited to your needs. The techniques you use will depend on your industry, organization's size and goals.

"An effective fraud risk assessment program should contain programs and controls related to fraud deterrence, detection, response and remediation," says Paul Zikmund, adjunct professor at Rider University's Masters of Accounting program. "It is critical for organizations to adopt a holistic view on fraud risk management and not only focus on one or two elements of the program."

Some techniques you could use include:

  • Rule-based analysis: Uses your historic data to spot patterns of fraudulent behavior you already know
  • Anomaly-detection analysis: Helps you spot uncommon behaviors that could indicate fraud or another problem. For instance, if your monthly bill to a vendor is normally $1000 and it jumps to $5000, you'd catch it and investigate to see if it was a mistake or attempted accounts payable fraud.
  • Predictive analytics: Based on people, places or behaviors that were involved in fraud cases in the past, this predicts if new subjects or behaviors could be involved with fraud
  • Link analysis: Makes connections between data involved with past fraud (e.g. names, email addresses, credit card numbers, job titles, etc.) for faster detection and better prevention
  • Text analysis: Analyzes documents, emails and other text-based evidence for words and patterns of speech commonly used when committing fraud

In addition, you'll need to determine who will be responsible for executing the program and how you will collect the data you want to analyze.

RELATED: How to Conduct a Fraud Investigation: The Complete Guide

Step 2: Analyze the Data

Next, start in on the actual data analysis. This will be an ongoing process, as you'll have new data coming in all the time. Pull historic and current data from your internal systems, ensuring that you stay compliant with data privacy and protection laws in your area.

You might find that your programs don't work perfectly at first. They might take some tweaking to generate accurate, pertinent analysis. Take the time to feed the programs relevant historic fraud case data, then review and refine them until they are effective for your needs.

RELATED: 41 Types of Employee Fraud and How to Detect and Prevent Them

Step 3: Report Your Findings

Next, you need to study the results of your data analysis and report them to stakeholders (such as upper management). Manually combing through the data takes hours, time that you could be spending on an investigation. Instead, consider using software that analyzes your data and generates reports in minutes.

Angelo Rigakis, reporting analyst at i-Sight (now Case IQ), outlines three ways you can use i-Sight (now Case IQ)'s award-winning reporting tool to detect and prevent fraud.

First, "HR administrators are able to track employees' leaves of absence (LOA) using start, expected return and end dates," then extract this data to "find trends in employees’ LOA that could suggest that the employees are abusing the system for personal benefit." This can help you detect time theft and misuse of paid time off.

Similarly, data analysis can uncover benefits and expense fraud. "Reports can be run to analyze claims from people who are trying to game the system by submitting inflated dollar amounts," says Rigakis.

"Investigators can see patterns in the person’s claims and create alert reports that send information immediately to the investigator when certain conditions have been met," such as numerous claims of the same amount for different expenses/providers.

If you're in manufacturing, the reporting function can help you detect and prevent fraud and theft, too. "Police bust a warehouse that holds products that have gone missing from a factory. The product data, like serial numbers and tracking numbers can be logged in i-Sight (now Case IQ) and this information can be used to build investigations by tagging it to factories. Reports can be used to list all the factories, their missing products, people assigned to the factories and even any interviews that have happened."

RELATED: Here's What the World's Most Ethical Companies are Doing to Combat Fraud

Step 4: Evaluate and Adapt

Finally, evaluate your current fraud controls based on your findings. What do you need to do differently to prevent internal fraud? Use the reports you generated as evidence when creating a corrective and preventive action (CAPA) plan.

But the process doesn't stop there.

After you implement the corrective and preventive actions, continue analyzing your data. This will show which controls are working and which areas of risk still need attention. Then, do the whole process over again. The more you study your data and address the trends you find, the safer your organization will be.

Having a "set it and forget it" fraud prevention program isn't effective. You also have to "elevate awareness, [and] ensure accountability and consequences for those who fail to follow the program requirements," Zikmund says. Data analysis is a continual process that refines your fraud prevention strategy or program more and more as you repeat it consistently.

Case management software helps you track and manage your fraud investigations to boost your risk management and preventive efforts. To learn how, download our free eBook.