Using data analytics for fraud prevention

As data volumes increase it’s becoming harder and harder to spot fraud with traditional methodologies. For most businesses, it’s like trying to find a needle in an ever-growing haystack. Long gone are the days of glancing over a report and easily spotting the anomalies. Some of these reports generate thousands of lines of data—an amount of data that no human can process in a reasonable timeframe. Regardless, the answer sits within your data. The issue is simply that the data isn’t in a manner that’s conducive to identifying trends, patterns, anomalies, and exceptions. With the continuous advancements of technology, it has become critical that organizations utilize technology to maintain a competitive edge for managing risks and understand the usage of analytics tools to support fraud detection and prevention efforts.

The rise of new technology tools has made it easier for fraudsters to commit traditional fraud schemes that impact businesses across industries. It is imperative that organizations invest in keeping ahead with technology advancements to be better prepared in the event that fraud does occur. Data analytics software allows organizations to implement automated controls to aid in fraud prevention. Controls can be placed throughout the organization varying from access to systems, physical controls over IT hardware, system changes, and much more.

Cloud technology solutions enable organizations to secure their data more easily compared to the cost factors associated with data center management. Policies and procedures should be documented to manage IT operations and prevent loss. Moreover, controls should be clearly defined when setting up access for internal and external system users. This starts from identity management to access controls. It is imperative that organizations establish separation of duties to prevent access to confidential data that is not related to the user’s role. It is also important for organizations to establish a formal process for system changes to prevent unauthorized changes and reduce the chance for fraudulent activity to take place. While these are just a few examples, there are many controls your organization can rollout to deter malicious activity.

More and more businesses are embracing a true analytics approach in order to manage their data. This approach allows them to consolidate information from multiple sources and transform their data into a way that makes sense to the business users. With analytics comes a ton of traditional benefits such as increased reporting performance and ease of use associated with report design. However, one of the more overlooked benefits is the fact that analytics enables businesses to detect fraud much more easily. Instead of taking a manual reporting approach and having someone pour over spreadsheets all day, business can instead visualize their data and have the anomalies jump out at them.

One example of this would be to leverage tools such as Power BI which have built-in anomaly detection. Power BI can look through large amounts of data and then visually represent anomalies to the business users that may otherwise be missed. Also, being able to look at this data visually, using charts and graphs, as opposed to just analyzing the raw data can make it much easier to spot trends and take a proactive approach to mitigating risk within the organization.

To summarize, analytics is designed to analyze large volumes of data quickly and enable users to spot trends, patterns, anomalies, and exceptions. While this effort can be used to spot opportunity for the business, it can also be used to highlight fraud within your business.

Data analysis software allows you to gain insight to your entire database to detect fraudulent activity and to validate your internal controls. A few types of fraud to be mindful of when assessing your data are:

• Payroll Fraud
• Altered Invoices
• Duplicate Invoices
• Duplicate Payments
• Fictitious Vendors
• Financial Statement Fraud

One example of detecting internal fraud could be a report or dashboard that is run periodically that returns a list of all sales orders or invoices that had either a zero-dollar amount or a negative profit margin. This can help to identify documents where an employee may be creating orders that ship to a friend or relatives house with a 100% discount. This may be done in order to redirect company inventory into their own hands without physically removing that inventory from a warehouse where there is a higher likelihood that it would be noticed during inventory cycle counts as missing inventory.

It is important to know how to properly analyze your data and understand techniques in order to detect fraudulent activity. By implementing a proper data analysis tool, you are able to reach data from multiple data sources to fully comprehend how your business is running. Taking a subset of your data can often hinder your chances of detecting fraud while it is happening.

Once your data is moved into an analytics format, it opens a world of possibilities for fraud prevention. Some of the most popular include:

• Data Visualization – Create dashboards that use powerful visuals to showcase anomalies.
• Artificial Intelligence – Classify, cluster, and segment the data to automatically find associations and rules in the data that may signify interesting patterns.
• Machine Learning – Use historical information to ‘teach’ the machine to flag and rank suspicious activity within the data set. This approach becomes more and more accurate as time passes and data volumes grow.

If any of these activities are interesting, let us know and we’d be happy to discuss further! For more information on the most effective system controls to help prevent fraud, watch our webinar led by Amber Culbreath, VP of ERP, and Brian Petersen, VP of Data Sciences.