The LP Endgame: Using Data Analytics To Combat Fraud Schemes

While using analytics to combat fraud isn’t as dangerous as leveling the Infinity Stone-fueled powers of Thanos –anticipating fraudulent behavior before it happens can feel daunting, defeating or just plain impossible if you don’t have the right attack plan in place. The good news is there is a path to victory and it starts by evaluating your various transactional data sources.

According to Association of Certified Fraud Examiners, organizations that undertake proactive data analysis techniques experience fraud are 52% less costly and 58% shorter than organizations that do not monitor and analyze data for signs of fraud. As these stats indicate, consolidating your business data will have a significant impact on your efforts to detect, prevent, and eliminate fraud.

Unfortunately, stopping fraud in its tracks isn’t something you can just throw up a “bat signal” to solve (I know that’s a different comic book universe but stay with me). To combat fraud through analytics, you need to look for the solution that best aligns with your department’s unique needs and your strategic corporate objectives. Look for the solution that best ties together all of the stories you are trying to or hope to tell with your data.

Data Cleansing…with the Snap of your Fingers

Before constructing or refining any analytics-based fraud technique, first focus on how your data is being prepared. According to Gartner the average financial impact of poor data quality on organizations is $9.7 million per year.” While cleaning your data is a little more daunting than the snap of your fingers, it’s the most critical step in ensuring you are dealing in facts vs. fiction.

The initial step to a data cleanse is making sure you have made a copy of the original data set. This original data is your infinity gauntlet – everything hinges on this data being safe from corruption as it will power your quest for data perfection. The next step is having a deep understanding of your organization’s data. It’s crucial that the person(s) involved in cleaning your data understands the location, structure and granular details of your data sets. Failing to understand the minutiae can cause confusion around data definitions or worse lead to inaccurate analysis that spreads across your entire corporate universe. One technique to eliminating confusion is by documenting the steps to your clean-up by developing a data dictionary – this will not only help your current team but will be invaluable to the new hires & vendors that may assist you in data processes at a later time. As a follow-on to the initial creation, it’s also important that there you have ongoing iterations of your dictionary, if the dictionary is dated so is your analysis.

The second step to this process is consulting with your analytics users on what they ideally need from analysis. Each department or level of management may have different requirements for what they need from “clean” data, you will want to take their input into consideration especially once you start provisioning reports or dashboards to suit their needs. The other importance of defining needs is it helps establish a process so that you or your team are the key characters involved in analysis and there aren’t supplemental characters being tasked with similar tasks. The worst occurrence is when you’ve expended resources to reevaluate a process or a data source, only to find out another department was doing the same job – you need to possess that analytics stone.

The Hardest Choices Require the Strongest Wills

When it comes to fraud, it attacks your company in three ways: it depletes revenue, it increases your operating expenses and it impacts your overall profitability. But armed with clean data and the right analytics solution in place, you can fight fraud head on and generate a strong ROI on fraud detection.

With the right anti-fraud solution in place your investigators with be able to drive operational efficiency and decrease labor costs. Benefits of having the right fraud battle plan in place include the following:

• Detecting incidents of potential fraud by using risk indexes to improve investigative precision and reduce the likelihood of false positives.
• Optimizing the allocation of resources by quickly prioritizing the investigations with the largest impact on revenue through real-time alerting on potentially fraudulent transactions happening at the register or within the walls of your organization.
• Improving collaboration between your investigators and management by having one centralized source of truth to review. This helps cut down on “going with the gut” initiatives and helps make better informed decisions ultimately increasing the efficiency and effectiveness of your team of investigators.

The End is Near

To effectively communicate the results of your fraud analysis, you need to be able to visualize it. While a simple .CSV output may get the job done amongst your team, it’s important to be armed with the ability to deliver the “truth & beauty” of your analysis. Visualizing your findings helps amplify your message in order for the layman (or the busy Executive) to see the impact of your fraud investigations. By giving them clear comparative visuals, it’s easy for leaders within your team and organization to make quicker decisions too. And the final benefit of being able to visualize your findings is allowing the data tell the story – unbiased and sometimes even ugly – it’s important that your team is highlighting the reality behind your investigations.

Anti-fraud analytics are incredibly powerful and can have drastic consequences across your enterprise – whether they’re positive or negative is up to you. A poor analytics game plan can taint your decision-making and water down the impact your investigators have on your business. But when applied correctly, clean data and powerful visualizations can help avenge your business and take down threats of fraud...and Thanos too, but only if he was committing some type of fraud.