Automation, Predictive Analytics Allow AR Teams To Manage ‘Unthinkable Workloads’

The accounts receivable (AR) space is changing rapidly as the benefits of digitization become more and more evident, with new automation and dispute management technologies gaining popularity among accounting teams. Many companies are finding that their AR staff are handling workloads that would have been unthinkable in the past because smart programs allow accountants to punch far above their weight.

AR teams still face several notable challenges, however, especially if their companies continue to rely heavily on manual processes. YayPay reported in March that 93 percent of companies experience late payments from customers, with the average payment period lasting 34 days as compared to expected terms of 27 days. What’s more, the average organization writes off 1.5 percent of its receivables as bad debt, resulting in massive expenses each year.

The good news is that the rapidly growing trends of automation and predictive analytics can help AR departments solve these problems by improving the timeliness of payments to prevent funds from vanishing into the ether. The following Deep Dive explores various AR challenges companies face on a daily basis as well as the technologies that could make them a thing of the past.

The Challenges AR Departments Face

Delayed payments represent one of the most notable pain points for AR teams. Many organizations’ financial plans rely on money being in the right place at the right time, and any deviations can have massive ripple effects if delayed payments persist. Nearly 17 percent of small businesses require either a deposit or a payment in full before they begin work, for example, and 36 percent of businesses will wait anywhere from 30 to 90 days to be paid, according to a Tide Business data survey. These delayed payments have strained relations between vendors and merchants. In fact, 73 percent of procurement professionals in a study claimed that late payments had negatively affected their supplier relationships. Fifty-nine percent said that their vendors had ended discounts due to late invoices, and 55 percent said at least one vendor refused to work with them again due to the issue.

Many delays occur due to a lack of AR digitization, with up to 40 percent of firms still leveraging paper checks and postal mail to make B2B payments. Reliance on traditional payments means that payments must be manually matched to open invoices, which can be time-consuming. Physical payments are also vulnerable to mail delays and reconciliation errors, creating countless avenues for payment delays. According to Transcard, 60 percent of corporate treasurers report that manual processes are their biggest challenge.

Such a difficult problem requires a multifaceted solution, and some technologies stand out from the rest as being particularly noteworthy. Automation and predictive analytics have achieved significant results when it comes to smoothing over AR obstacles.

How Automation And Predictive Analytics Make AR More Efficient

One of the best ways to improve AR teams’ timeliness and efficiency is to eliminate as much human error as possible through  automation. PYMNTS research finds that 87 percent of companies that leverage AR automation have reported decreased processing times, while 79 percent said their teams’ efficiency had improved and 75 percent reported “superior customer experiences.”

One key metric when measuring AR effectiveness is days sales outstanding (DSO), with a shorter term generally leading to a more reliable cash flow. Eighty-eight percent of energy and advertising companies that harness AR automation report shorter DSO cycles. This contrasts with businesses that do not use AR automation, which saw DSO increases of up to 20 percent last year.

The most advanced AR automation systems leverage predictive analytics, which uses artificial intelligence (AI) and machine learning (ML) techniques to analyze AR data and outline potential trends. These systems can help AR staff dynamically predict which clients and vendors will be most inclined to make late payments, fall behind on past-due invoices or other risks. The technology has allowed staff to devote more time to such issues and subsequently, to lower the odds of delayed payments or other inefficiencies. YayPay’s predictive analytics system, for example, was found to improve productivity among AR teams by a factor of three, lowering DSO cycles and freeing up funds tied up in AR.

Automation and predictive analytics can help AR teams tackle delayed payments, missing invoices as well as other expensive and time-consuming obstacles. Reliance on this technology can ultimately save teams money which can then be spent on identifying and solving other AR challenges, creating a more productive and positive experiences for AR staff and vendors alike.