PRESS RELEASE

Purchase Card Analytics

While Federal Government audit reports consistently reveal significant and repetitive failure to comply with existing law and policies on purchase card misuse, the private sector has been far more active in benchmarking and implementing preventative measures and best practices.

According to a 2013 report by PayStream, the top three fraud-prevention measures that companies employ are the following:

  • Requirement of receipts for purchases (83%)
  • Spending limits or individual transaction limits defined (81%)
  • Audits of compliance conducted with card usage policies and procedures (75%)

However, simple prevention methods are not sufficient to eliminate fraud and abuse. When you factor in that annual purchase card spending in North America was $229 billion in 2013, the motivation exists that much more needs to be done. Better data analysis can help.

One of the ways to improve data analysis is to incorporate information from multiple systems such as card provider information, accounts payable systems, third-party expense management applications, and HR systems. Employing the application of algorithms and statistical analysis techniques to these diverse information sources can identify misuse such as improper spending by disgruntled employees or frequent buys of the same amount to circumvent per order purchase limits.

Analysis is enhanced when data is harmonized (normalized) and evaluated against predefined checks. Examples could be searching transactions to uncover those related to inactive employees or suspicious product codes. Multidimensional analysis (comparison of multiple data sets, e.g., comparison of performance indicators of all organizations for all years) can uncover variations in names used by card uses (middle initial versus middle name, familiar versions of first names like Mike versus Michael, etc.). Best practices in multidimensional analysis will track patterns of variation over time, using cloud-based resources.

Benchmarking by industry as to what is typical or atypical facilitates analysis and can help set guidelines for policies that are most relevant to a particular enterprise.

nGAP’s OAS procurement system uses built-in spending controls and collection of extensive data to support a wide range of fraud solutions. OAS provides the top three fraud prevention measures noted above and does so with real time data so fraud can be detected quickly and damage can be limited. OAS data includes numerical, text, notes, spreadsheets, and other forms of data that are stored electronically for easy retrieval or can be made accessible to comprehensive data analytics solutions.