Innovation across the order-to-cash process in the B2B space has allowed businesses to weather the ever-evolving complexities of the last few years. From allowing teams to make smarter credit decisions, deliver invoices electronically and make payments remotely, each step of the process is relying on technology in new ways with an increased sense of urgency. What about collections?
Today, we find ourselves at a new crossroads with the potential for a recession looming over many industries. With the inevitability of increased days sales outstanding (DSO) that this type of economic uncertainty brings forth, it’s crucial that collections becomes even more of a priority for organizations. Of course, a streamlined collections process is imperative no matter the economic circumstances. But with data suggesting that late payments have increased due to the financial challenges of the last few years, it’s fair to say that the role of the collector has intensified, too. For example, according to an Atradius study, late payments affected 47% of the total value of all B2B credit sales in the second half of 2021. Meanwhile, as recently as the end of last year, the trade gap – which is measured in outstanding receivables – stood at $3.1 trillion.
Of course, these are figures which are sure to cause anxiety for AR teams tasked with maintaining their organizations’ cash flow during yet another financial crisis. Late payments and follow-up delays already had a detrimental impact on many businesses during the pandemic, with the average DSO increased from 39.7 days to 42.6 days. And now there is a chance that this number will grow during a recession that although some experts predict would be shallower than 2008, will still be impactful. As they prepare, it’s safe to say that collections has become more important than it’s ever been – and that artificial intelligence could be the solution to streamline the collections process. Here’s how:
Injecting a Much Needed Dose of Predictability Into Cash Flow
Lagging DSO may not be an imminent threat in ordinary times, but in a landscape rife with uncertainty, predictable cash flow is imperative. A fact that makes the ability to predict when an invoice will be paid by a customer all the more alluring for collections and AR teams, with access to this type of information also crucially enabling suppliers to influence what their customers pay as well as when.
One of the advantages of AI is that it’s always learning and improving with time. An algorithm can predict that a payment will arrive tomorrow. But if the payment is not received tomorrow, the algorithm learns and will no longer take this feature into account – ultimately taking cash forecasting to the next level.
To be able to forecast cash flow like this, however, it’s important to track and monitor the payment behavior of customers. That is, the speed with which they pay invoices in relation to the agreed payments terms. So, for example, if a debtor typically pays an invoice 7 days after its due date, we speak of a payment behavior of +7 days.
But a lot of parameters influence and impact the payment behavior of a customer. Some of these influencers include the amount an invoice is worth, the date of an invoice, the date of payment, the risk profile of the customer, and their likelihood to dispute an invoice. Payment behavior says a lot about a debtor, but a change in payment behavior is also an important determinant. Recognizing all these payment patterns is no guarantee for the future, but you can derive a number of things from them.
With this ability to analyze data from a variety of parameters, teams can gain a powerful, real-time window into their cash flow and identity where their receivables are. This allows them to stay ahead of potential cash flow issues by using large amounts of available data in every platform to optimize all aspects of collections. Moreover, for companies that rely on their credit revolver to meet obligations, cash forecasting helps their treasury department know how much money they need to borrow. This is especially important for seasonal customers who have dips in revenue based on the nature of their business.
Indeed, AI’s power not only enables teams to harness the power of insights from the past but also leverage the power of foresight.
Making the Right Decision at the Right Time
A successful collections strategy is a proactive one and involves taking actions at the right time to avoid issues turning into bigger problems. Admittedly, this can be hard to do at scale when you have a multitude of customers with a wide variety of factors impacting how and when they pay. And unfortunately there is no crystal ball that can tell us when a customer will pay. But what if you could determine the optimal collection procedures and give collectors insights into the results of their actions? Thanks to the power of AI, you can.
Take a customer with an 80% chance of paying a bill on time. Although this may seem like a dependable customer, data shows that the longer an invoice goes unpaid, the harder it is to retrieve the payment in full. Therefore, an additional, prompting action could prove to have a positive impact on his/her payment behavior, and potentially increase the likelihood of payment by 20%.
What AI’s power also gives AR and collections teams is an incredible opportunity to more easily improve relationships with customers in a way that facilitates faster payments. For example, it enables collections professionals to prioritize the parts of the job that they are best at, whether it be contacting customers personally in the first phase of a collections process or perhaps in the later phases. By leveraging AI to both predict payment behavior and handle more cumbersome tasks, it frees collectors up to focus on portfolio responsibilities that deserve more of a human touch and therefore, make a much bigger impact.
Taking all this into account with AI can further optimize the collections process. In the end, the algorithm will learn what the most efficient procedure is, depending on the match between the collections team and the customer, and the workload of the controller.
With AI analysis, you can foresee payment problems, generate a plan, and get step-by-step advice to resolve it. You can also simulate collections scenarios and project likely success.
Elevating the Customer and Employee Experiences
It’s no secret that late payments strain business relationships. A collections process guided by AI can bolster CX and strengthen relationships by getting ahead of issues and creating a customized approach that fits the needs of any given customer.
Indeed, AI in the collections process can help organizations strengthen relationships when they matter most. And in this highly competitive labor market, this very much includes businesses relationships with their employees who are also increasingly prioritizing great work experiences.
The truth is, in the finance world, collections professionals are often overlooked because they are forced into a reactive and uncomfortable role. In reality, however, they are responsible for bringing money into the organization and should be treated with the same level of importance as other teams such as sales and marketing. Yet, they often lack the technology and support that their cross-departmental colleagues have to execute their workflows strategically.
For example, today’s sales teams leverage AI for a wide variety of reasons, from automating workflows, determining things like the highest probability of prospects to convert, and identifying when and how to reach out to prospects. With these types of tools, collections teams can take a much more strategic approach. After all, the best time to collect is not when the invoice is past due.
Supporting Collections Teams in Every Economic Environment
No matter the economic circumstances, it’s clear that AI continues to have a big impact on the collections space and credit management. Although it will never replace the invaluable work of a collector, it has the potential to make them much more effective and efficient by boosting their ability to maintain their organizations’ cash flow at a time when external challenges pose enormous threats.
Cash flow is the lifeblood of every B2B company. Poor cash flow, on the other hand, can prevent B2B companies from meeting their financial obligations, limit profitability, and inhibit growth. Elevating the role of AI in collections not only contributes to the financial health of a company in a more efficient way, but enables businesses to strengthen relationships with their two most important stakeholders – their buyers and their employees. Both of which will be critical for survival in any market downturn.