As the year draws to a close, everyone is making predictions about payments in 2017. Unfortunately, most of what’s making headlines are the same topics that have been talked about all year—fraud, blockchain, partnerships between banks and fintechs, etc. While these are all necessary and valid discussions, they’re nothing Nostradamus is going to get excited about.
The prediction likely to have a significant impact in 2017 is that predictive analytics could revolutionize electronic payments. Here’s how.
Easing supplier onboarding
For years, check usage has been in decline. While conversion to e-payments is on the rise, a barrier to truly achieving full payment automation has been the challenge of supplier onboarding. According to the 2015 RPMG Electronic Accounts Payable Benchmark Survey, 61 percent of organizations rely on their banks or AP card providers to perform supplier enablement—but only 24 percent of them are satisfied with the overall level of supplier acceptance on AP card programs.
Now factor in the 63 percent of organizations that still make more than half of their payments by check. That’s a huge number of businesses that need to switch to e-payments that either won’t try or will try and will come up short because of the challenges they’ll face with onboarding. Predictive analytics will eliminate this burden for everyone by forecasting which suppliers are most likely to be receptive to e-payments and inform the priority order in which suppliers are approached. Faster supplier adoption means accelerated payment automation, savings and security—wins for everyone.
Providing analytics to enable cash flow forecasting
Accurate cash flow forecasting is a requirement for any organization that wants to grow its business and compete effectively in today’s business landscape. Predictive analytics will help organizations gain a degree of detail on the status of their cash positions that was not possible before. It’s all about the power of data. Organizations that understand when they’ll be paid or those that can better predict the impact of AP payments timing on their cash positions are better able to make smart investing, borrowing or payment timing decisions.
Leveraging historical invoice and payment information to inform decisions about extending credit
Banks today may use an approved invoice model to decide whether or not to extend financing to a supplier. It’s a system that works fine, but it requires a lot of upfront configuration with the supplier’s payers and often results in no credit being given.
Predictive analytics will make it simpler for banks to quantify the risks of extending credit by also assessing data about the invoices and payments exchanged between trading partners. Knowing when an organization is likely to be paid provides insight into its ability to pay loans back. Having access to this data, along with other streams of information available about trading partnerships, will enable banks to make more informed decisions than ever before. Plus, assessing risk also reduces the payer’s engagement in the process, making it more streamlined; the approved invoice method is contingent upon the payer’s participation, which adds a level of complexity.
You don’t need a crystal ball or tea leaves to know that the payments industry is an exciting place to be right now. There’s a lot going on. As you’re thinking about your 2017 plans, definitely have payment fraud protection at the top of the list and give some thought to the other headline-worthy issues everyone is talking about. But make sure predictive analytics is on your list as well.
Jessica Moran is general manager, cloud payment solutions, for Bottomline Technologies.