Accounts Payable (AP) processes continue to evolve at a frantic pace with hyper-connectivity, mobile, and closer networking between purchasing, vendors, payments and reporting. There has also been a shift from processes traditionally being a cost center to becoming a profit center. Today, Accounts Payable are instrumental in identifying and maximizing financial opportunities, enhancing productivity and ensuring focus on customer service.
According to 2014 study conducted by Institute of Financial Operations, automating the AP functions of an organization boosts the performance and productivity of the organization — resulting in lower payment processing costs, and improving cash flow, as well as efficiency of the invoice payment process. This enables the companies to focus on the core tasks that generate profits.
However, despite the best training provided to AP staff, data entry errors often occur, leading to excess payments, incorrect taxes, duplicate payments, and ultimately, profit leakage. In fact, duplicate payments are one of the most common challenges faced by the AP department of about 80 percent of the organizations surveyed. Companies usually spend between 3 - 7 days to resolve errors, impacting not only their relationship with their vendors, but also disrupting their business operations. One possible solution to the problem of duplicate payments is an amalgamation of the existing AP processes and predictive analytics.
NTT DATA Services’ BPO team has developed a Predictive De-Duplicator (PDD) solution, which uses historical data, predictive trends and transactional data from invoice processing. Our whitepaper, Payment Curiosities and Anomalies in Accounts Payable, covers this PDD solution in detail and explores the impact of predictive analytics on tackling key challenges in AP.
It will be very exciting to see how the joining of technology and analytics impacts AP in the near future.
About the whitepaper
Accounts Payable (AP) processes handles thousands of incoming documents, and the AP clerk is expected to enter, verify and match data on these documents — often non-standard, incomplete or inconsistent — to others in the system. Despite the best training and processes, data entry errors do occur, especially when the data is on disparate, incompatible systems. And in AP, this could mean excess payments, incorrect taxes and, ultimately, profit leakage. If you’ve ever tried to recover excess payments, then you know how difficult this can be. This white paper details NTT DATA’s predictive analytics approach to tackling key challenges of AP. We’ll explore a text mining-based statistical model used to identify duplicate payments and transactional errors.