Regular readers of “Pushing the Envelope” know this OIG is a firm believer in the power of data. We use data analytics to identify the root causes of problems or inefficiencies, and then develop solutions that get to the heart of the issue.
Data analytics include data mining, risk assessments, and predictive analytics — tools that allow us to synthesize data in ways that help us better detect fraud and misconduct. Our results often equate to major financial savings and recoveries, improved efficiency, or mitigated risk. Leads from our specialized data analytics often help us fulfill our mission of ensuring efficiency, accountability, and integrity in the U.S. Postal Service through independent investigations, audits, and white papers.
One critical data tool is known as a Tripwire, which lets us focus on one or two data points to identify activity that indicates a high likelihood of fraud, criminal behavior, or critical control weaknesses. Tripwires and other tools point our agents and auditors in the right direction, so they chase fewer false leads. This is true whether we are investigating contract fraud or looking at best practices among postal retail units.
In fact, data analytics play a major role in our audits of financial controls at retail facilities. These audits are designed to provide Postal Service management with timely information on potential financial control risks at USPS locations.
For example, a recent audit looked at No Sale transactions at the Bloomfield, NJ, Main Office in the Northern New Jersey District. Postal Service retail associates can use the retail software’s No Sale administrative function to open the cash drawer, usually to exchange higher value currency for lower denominations. The selection of the No Sale option is not accidental and thus draws suspicion when done frequently.
We analyzed Postal Service Enterprise Warehouse Data and reviewed related documents to determine if No Sale transactions were managed effectively at the Bloomfield, NJ, Main Office, and to provide information to USPS to help it improve its monitoring and management.
How do you think data analytics could be used to measure other post office operations, whether financial or performance?