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Case Study on Using ML to Revolutionize Retail AP Operations

Case Study on Using ML to Revolutionize Retail AP Operations

August 25, 2023


Market leader in retail and wholesale in North America, with a high volume Accounts Payable operation of >6M invoices a year of which 35% were non-EDI invoices totaling a volume of ~2.1M. The existing AP implementation had taken two years to get to a 49% straight though processing for non-EDI invoices, after starting at 4% straight through processing. Automation was therefore not yielding the required benefits, and there was a high manual intervention needed to resolve the 51% of unresolved invoices. Cost of manual processing was $4.5 per invoice against industry averages of $2.5 for Retail and Wholesale sector. Invoice cycle times and paid on time metrics were also compromised.


Machine learning extraction and matching models were trained on 3 months of non-EDI data. The system was implemented in shadow mode so as to not disrupt the existing pipeline and to provide a comparative view of the efficiency of the heuristic system vs the machine learning system.


Against an existing straight through processing efficiency of 49%, Sapper APaaS was able to deliver straight through processing of 97%. Cost of manual processing was reduced from $4.5 to $2.5 due to automation of extraction and lesser exceptions. This savings of upwards of $6M dollars in manual processing costs, and vastly improved cycle times.