USPS Office of Inspector General

You have likely heard the term “big data” frequently these days. From health care to marketing to law enforcement techniques, data analytics are driving decisions and improving outcomes.

At the OIG, data analytics — which include data mining, risk assessments, and predictive analytics — allow us to synthesize data in ways that help us better detect fraud and misconduct. Data analytics also help us identify the root causes of problems or inefficiencies, and then develop solutions.

In 2001 First-Class Mail (FCM) hit an all-time peak of 104 billion pieces. Fast forward to today and it is down more than 40 percent.

Well, if you’re a reader of this blog, you also know that despite this, First-Class Mail remains the biggest provider of revenue and contribution (profit that goes to paying the Postal Service’s institutional costs).

Imagine receiving a text on your phone saying, “The robot has just arrived with your delivery.” Thanks to on-demand delivery services like Postmates and Deliv, which are making robotic deliveries of food and convenience items in San Francisco and D.C., meeting a package bearing robot at your front door could become routine.

The U.S. Postal Service manages about 30,000 contract actions (awards and modifications), and spends roughly $13 billion on contracts annually. The sheer volume of contracts and the huge dollar amounts involved make it an area to regularly monitor for fraud, waste, and abuse.

It also means USPS contract officers (COs) have a big responsibility given the essential role they play in the contracting process.

Every U.S. Postal Service-owned vehicle is assigned a Voyager credit card, which operators use to pay for fuel, oil, and routine vehicle maintenance. With over 227,000 vehicles, that’s a lot of plastic to manage.

U.S. Bank manages the Voyager Fleet Systems credit card program for USPS and monitors transactions for potential fraud. Each month, USPS site managers are responsible for reconciling the Voyager card transactions identified as high-risk, such as purchases that exceed a fuel spending limit.