Case Study
Fortune 500 Global Data Center Provider saves 10 FTEs with AR Automation
Results
10FTEs
10 FTEs saved annually
75%
75% of unapplied payments automated, up from 30%
Speed
Faster payment posting
Company Profile
Publicly-listed global data center provider operating a worldwide network of carrier-neutral colocation, interconnection, and cloud-adjacent facilities across dozens of countries. Customers include hyperscalers, network carriers, financial exchanges, and large enterprises. Operationally complex at global scale with massive customer-interconnection volumes and continuous AI-ready capacity expansion.
Challenge
Manual cash application across multiple geos
Cash application is a tricky function of every Accounts Receivable accounting department. Not every customer remits details with their payments. For example, we might know the customer but not the invoice number, or we might not have either.
In these cases, AR credit analyst must post the payment manually. The process entails scanning the prior-day and intraday banking reports and searching Oracle for clues. A customer cross-reference cheat sheet is also used to find customer name/number in Oracle. Management wanted to automate payment application for these enigmatic remittances.
Solution
Custom RPA Development
To meet the customer’s AR payment application needs across global markets Custom RPA Development was needed. Automation begins by identifying unapplied payments for processing and examines each remittance relative to 15 geo-specific user-defined matching rules. It integrates with the customer’s systems extracting information from Oracle and bank reports.
In this AR automation solution, a balance is needed between automated cash application and exception handling by AR accounting professionals. The rules are applied as a loop from most precise to likely match. Each region determines the minimum required confidence score before cash is applied in AR system. If confidence is high, the cash is applied. If confidence is low, the automation alerts the credit analyst with its findings and recommendations.
We were able to increase the number of payments applied by automation to 75% from the previous 30%. The automated application combined with the recommendations made for the AR credit analysts saved the company 10 FTEs annually.
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