Viewpoints

Internal fraud cost this city $50 million. How to keep all your tax revenue

Dec 05, 2016

By Kevin Ebi, Smart Cities Council

If you’re like most cities, you pay a lot of attention to how much tax revenue you collect. But if you stop there, you could end up in serious budget trouble.

A Washington, D.C., tax manager stole nearly $50 million from the city over a 15-year period. She’s in prison now, but told investigators it was incredibly easy to do — others in her department were “asleep at the switch” and she could easily steal more and pay off investigators if they just gave her a little more time.

As easy as it was for her to steal the cash, you can identify and stop fraud. A new paper from SAS Institute explains how, using this case as an example. We share a few of the tips below, but check out the full report for great advice your city can use to protect your precious tax dollars.
 

1. Watch your blind spots
Try not to get laser-focused on any one thing. If you devote all of your resources to one thing, such as identify theft, you won’t be paying attention to anything else, like internal fraud. And it’s in those blind spots where rampant fraud can go unchecked for long periods of time.
 

2. Give your investigators analytics
Giving your investigators the ability to generate their own analytics can help stop fraud sooner. They sometimes run into anomalies, so let them run their own reports to fully explore them. Don’t make them wait for automated reports that may or may not help.
 

3. Look for relationships
Basic analytics that compare returns only to defined rules are better than nothing, but they tend to miss fraud cases. The tax manager in this case started small and gradually started stealing more when she saw where the thresholds were. But she had a network of friends and associates who were in on the fraud. Deeper analytics that look for relationships between people could have helped stop it earlier.
 

4. Predict and compare
Deep analytics can tell you what’s normal for a given type of business or resident in a particular area. Once you know what normal patterns look like, anomaly detection can quickly alert you to unusual cases, allowing you to quickly focus on and check out unusual cases where the fraud is much more likely to occur.

 

Kevin Ebi is editor of the Smart Cities Council’s publications. The Council publishes the free Smart Cities Readiness Guide, which provides help and advice for crafting a smart cities vision, plan of action and method of tracking progress.

Please log in or register to post comments.