We just released a member Point of view which summarizes AFP’s recently completed annual event, which, like all other industry business gatherings in 2020, was hosted via a virtual event experience. In this preview of the event, we discuss some of the key trends in payment tracking sessions, of which there were a few dozen.
Interestingly, about 20% of payment follow-up sessions had something to do with receivables, which has traditionally been the somewhat overlooked segment of cash cycle operations. This has started to change over the past couple of years and now debt technology is getting as much if not more attention focused as other parts of financial operations.
This referenced article can be found in Cision PR Newswire and was provided through receivables automation fintech Billtrust, which is in the process of going public through a stock purchase transaction with South Mountain Merger Corp. He talks about one of the key developments in the AR space, which is the growing use of machine learning (typically included in umbrella AI technologies).
‘Billtrust, the leader in B2B accounts receivable automation and integrated payments, recently upgraded its Cash application the software’s advanced machine learning capability, dramatically improving match rates and reducing manual processing while converting payments to cash as quickly as possibleâ¦ Billtrust’s Cash application quickly adapts to a vendor’s ERP system without being explicitly programmed, delivering a tailored experience based on how accounts receivable teams work with their systems and data. Modeling from remittances and data, match rates improve over time as the model learns usage while accommodating any change in invoice structure. Higher match rates allow users to efficiently navigate their worklist with fewer exceptions, which means faster access to money. ‘
In AR processes, companies want to optimize the match rates between remittance / payment data and associated invoices. The faster this can be done, the faster the money can be applied to the right accounts in the GL. Like everything else over the past 9 months, improving the DSO here can be critical to working capital efficiency.
This matching process has been further complicated (ironically enough) by the increase in various forms of electronic payments, which often arrive disassociated from remittance data. Machine learning (ML) is used to improve automated match rates over time as patterns emerge and strengthen algorithms. This is an area where Billtrust has added capacity.
âSince the July 2020 upgrade, Billtrust customers have seen a 12.4% increase in overall match rates and an 18% increase in electronic payments. A customer of Billtrust, heavy equipment dealer Gregory Poole Equipment Company, switched to enhanced machine learning in July 2020 and reported sharp increases in match rate and an increase in automatically matched envelopes. âWe’re a complex organization, so improving our match rates was a challenge,â said Mary Stumpf, Accounts Receivable Supervisor. âBilltrust has more than risen to the challenge and the transition to a new machine learning-based platform has gone smoothly. We continue to see great results with strong match rate performance, which is more important than ever with a remote workforce. It’s really amazing how machine learning actually adapts to our systems for continuous improvement.“‘
Insight by Steve Murphy, Director, Commercial and Corporate Payments Advisory Services at Mercator Advisory Group