How to Automate P-Card Reviews to Reduce Misuse, Fraud

May 25, 2016

At the first breakout session at the IDEA User Conference, I decided to attend Alexander Muina’s session on automating purchase card (P-Card) reviews. Muina is the Assistant Director, IT Audit at University of Miami and has significant experience in reducing misuse and fraud of P-Cards.

The University of Miami has 1,200 credit card holders, three categories of P-Cards and $25 million in spend. For the university, the benefits of issuing purchase cards included cost-effectiveness since there was less processing by reducing manual labor, and quick payments because it automated procurements.

There is also a cost issue that applies to the P-Card industry in general: with P-Cards, cost per transaction is around $20 compared to $90 for standard AP process. There was also a 70% reduction in cycle time per transaction.

But according to Muina, there are also risks which include:

  • Employees may not be dealing with approved vendors
  • Lack of review and approval of vendors until after the fact
  • P-Card purchases provide limited documentation and traceability of purchases
  • Lack of proper review of purchases due to complexity and high-volume transactions

There is also the issue of fraud. An ACFE report says that 5% of revenue is lost due to fraud, of which 58% is not recoverable. Adding to the problem, South Florida has one of the highest rates of fraud in the country.

But for the University of Miami, rather than continuing to operate as they were, they decided to fight the situation with data analytics. The previous process at the University of Miami included:

  • Review 20% of P-Card transactions nine months of the year (which was not done consistently)
  • Review 100% of transactions, three months of the year

When automating the P-Card process, the university:

  • Identified data sources (i.e., AMEX data files)
  • Identified data analytics queries needed to monitor P-Card transactions
  • Identified exception reports
  • Automated queries and reports
  • Ran the P-Card review process periodically

The monitoring queries created to highlight atypical or possible abuse transactions include:

  • Round dollar transactions, which can indicate purchases of unapproved gift cards and fraud
  • Specific identified merchants – Macy’s, Nordstrom, Walmart, Pottery Barn, Staples
  • Unusual vendors – Keyword search on vendor name such as “Toy”, “Baby”, “Cash”, “Advance”
  • Weekend purchases
  • Holiday purchases
  • Abused MCC codes (merchant codes)
  • Purchases in excess of policy limits
  • Multiple purchases to circumvent single item or monthly card limits (splitting)
  • Taxes that should not be charged for not-for-profits organizations (AMEX field shows taxes – makes it easy!)
  • Duplicate/multiple cards to same cardholder

According to Muina, the benefits of automating the process include:

  • 100% review of transactions
  • Reduced admin overhead with exception reports rather than manual reviews
  • Timely reviews
  • Better customer service – staff is doing less monitoring and more customer service

So how much money was saved or fraud identified? Since the University of Miami is a private organization, Muina was not able to provide any specific numbers but he did say that there has been cost savings for the organization, which made the process highly worthwhile.

About Anu Sood 

Anu Sood (LinkedIn | Twitter) is the Director Marketing at CaseWare RCM and is responsible for the company’s global marketing strategy. She has over 20 years of experience in product development, product management, product marketing, corporate communications, demand generation, content marketing and strategic marketing in high-tech industries.

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