The A/R Leaders’ Guide: Major Evolutions in Strategy that will Shape Your Collections Management in 2021

The unexpected economic downturn triggered due to the COVID-19 pandemic in 2020 exposed several weaknesses that have always existed in collections processes. Despite these weaknesses, collection teams worldwide fought hard to continue their operations and maintain their KPIs and goals for the year.

We studied 200+ collection teams to understand how they responded to this situation and handled the crisis. Here were some key observations from our research on the most significant trends that influenced collections in 2021, strategies and solutions used by teams to combat these trends, and how they will continue affecting the order-to-cash departments in the present decade.

Four Key Evolutions in Collection Management

With a lot of executive focus coming in on “cash,” collection efforts became a top priority across the finance department. Collectors and AR specialists tasked with collecting on invoices saw the double-pressure of balancing the customer relationships with the need to collect receivables as fast as they could. Here are some of the most exciting trends that we noticed:

• 7.1% Rise in the Average Days to Pay
• • While an increase in payment cycle time was anticipated, teams using an automated cloud-based collection process exhibited a lower increase in payment cycle time in comparison to industry benchmarks. We expected this number to be much higher due to increasing payment terms and late payments but were pleasantly surprised to see it only at 7.1%.
• 15% decline in Customers Paying Monthly
• • In our experience working with over 200+ industry-leading companies, we’ve observed a common trend where companies rely on an 80-20 approach to collections activity. This approach means organizations expend 80% effort collecting on the 20% of the most crucial accounts in their portfolio. This results in companies leaving a hefty amount of accounts receivable uncollected.
• As a result of the pandemic, collections teams switched focus towards their most critical accounts (based on the customer’s credit limit, payment history, accounts receivable balance, reference checks, etc.), thus contributing to the drop in the number of customers paying.
• Though this might have been acceptable in the short term, collection teams must prepare themselves to scale their operations and increase their collections customer portfolio coverage to 100% to survive in the new economy.
• 21.50% Decline in Payment Commitment Honoring
• • One of the most critical metrics that helped us understand the contentious relationship that exists between AR departments and their AP counterparts was the number of payment commitments honored.
• Many companies use payment commitments as input for their cash forecasting models. If these commitments are not honored, it substantially impacts their ability to forecast and manage working capital accurately.
• Potential Causes for Payment Commitments Not Being Honored:
  • Collectors were under increased pressure to deliver and were capturing payment commitments even when they were not entirely confident about those commitments being met by the buyers.
  • On the Accounts Payable side, a lot of buyers were unsure about their cash flow. They were making payment commitments to maintain business continuity and definitely did not want their lines of credit to get impacted.
  • Whether a buyer had the money to make a payment or how high a priority an AR supplier was to a buyer. If a supplier sells something non-essential, a buyer can more easily opt to keep an invoice unpaid while paying more essential suppliers.
• These causes are a clear call-to-action for a system that enables collectors to analyze the integrity of these payment commitments and truly understand the ability to pay each customer – this is far more important than an actual payment commitment, which might not have any value.
• 32.50% Spike in Collections Dunning
• • In response to market volatility, collections teams are getting more aggressive in their efforts to collect payments ASAP. The economic uncertainty has pushed collections teams to focus more aggressively on the 0-30 day aging buckets as compared to the 60+ day bucket, which used to be a typical priority.
  • This tactic may work in the short-term; however, it’s not sustainable over the long-term for two reasons:
    • It is difficult to scale this activity (correspondence, disputes, payment commitments, communications, etc) across the entire customer portfolio without a collections management system.
    • A continuous aggressive collections strategy ruins the customer experience.

A Proactive Approach to Collections is Required to Win the Game

A/R leaders are starting to embrace the reality that a new strategy is essential to ensure they win in 2021. This strategy will involve reducing the cash conversion cycle by collecting payments faster and more cost-effectively while also focusing on strengthening customer relationships.

To accomplish this, collections teams must ensure that cash from a sale is received within its due date according to payment terms to maintain a healthy DSO (Days Sales Outstanding) and meet a company’s obligations to safeguard working capital.

Historically, collections teams have depended on a reactive dunning model. This model meant waiting for customers to become past-due before signaling collectors to reach out to them. In the new economy, waiting for an invoice to go past-due before acting on it is akin to casually sipping water on the sidelines and only jumping into action after you see a runner back from the other team heading full-tilt for the 20-yard line.

While a reactive dunning model may have been adequate in the old economy, current realities have rendered it high-risk if not obsolete. With more customers falling into delinquency, greater effort and cost are expended to capture overdue payments when the AR department doesn’t take timely preemptive action.

Suppose an organization lacks advanced digital technology and an adequate workforce to follow up with overdue customers. In that case, they end up defaulting to the 80/20 pattern of expending 80% of their effort to collect on 20% of the highest value accounts. With cash-in-hand being so important, this is obviously not the most practical method to follow.

World-class organizations have proactive collections teams that embrace the advantages provided by Big Data, Machine Learning, and Artificial Intelligence. Utilizing these resources allows them to receive real-time risk signals from public financials, credit agencies, and customer payment patterns and to take preventative action to stave off a delinquency event.

Switching to a modern proactive dunning model means taking full advantage of the power of digital technology. In the game of getting paid quickly and on time, using such technology is like receiving valuable silent hand signals from the head coach before your opponent makes a move. Instead of focusing on customers who are already behind on payments, proactive collections are all about having your collections team focus on customers who have a high probability of becoming delinquent and thus can change the outcome.

AI-driven Proactive Collections

In collections management systems, AI is a prerequisite for preemptive dunning. It can assist teams in multiple capacities, such as: identifying at-risk customers before they default, generating prioritized worklists, and predicting the date on which a customer is most likely to pay.

It can also identify the appropriate actions on customers at any given point in time and then trigger them; measures such as automated dunning of customers by sending out automated emails, scheduling notices of default by mail, and making automated phone calls for “touchless” collections.

Collections payment date prediction: Collection rules and prioritization are based on static customer segments that do not change with time. Artificial Intelligence and Machine Learning algorithms can help Collections Management become proactive by predicting payment dates at a customer or account level based on past payment behavior and current open invoices.

Having this type of data enables collectors to act before invoices and customers go delinquent or past due. This in turn reduces the cost of dunning activities, and allows a 10-15% improvement on DSO, while also increasing available working capital. It additionally improves collector efficiency by letting them focus on difficult customers rather than low-risk ones.

In reactive dunning models, collectors can expand up to 30% of their work time, deciding which delinquent buyers to contact and how to contact them. In reactive AR departments, collectors rely on their intuition, skill, and experience to build worklists (prioritized lists of accounts to contact, how to contact them, and when.)

Instead of basing the analysis on the best available real-time data, collectors rely on backward-looking static indicators such as Average Days Delinquent (ADD) to prioritize customers to develop their collections strategies.

Collections teams traditionally look at static indicators, such as Average Days Delinquent (ADD), to estimate payment date for a customer and, consequently, to implement dynamic strategies for dunning. However, reliance on this metric has failed to produce optimal results.

Static data and human intuition are grossly inefficient when matched against the tactics employed by digitally transformed AR departments. Considering that a single collector can be assigned hundreds of thousands of accounts (depending on the size of a company), it’s practically impossible to expect a high level of efficiency from the process without digital technology assistance.

Automatically Generate a Prioritized List of Customer Every Day Based on AI-Predicted Payment Dates and Improvise Your Dunning Strategies With AI-Recommended Next Steps

The dynamic shift from a reactive to a proactive collections process is the most significant advantage of the AI-powered collections management process. Leveraging ML, the collections team could use accurate predictions to enhance collections output and key KPIs such as DSO and the Collections Effectiveness Index. Payment date predictions boost efficiency as the entire approach is a proactive one where collectors no longer have to wait for defaults and then request payments from them. Instead, historical data is fed into the system to proactively derive the payment date and contact only those customers who have a higher risk of default.

Conclusion

The collection operation within organizations is in dire need of innovations that could improve the overall process efficiency and help recover the cash in black swan events like the coronavirus pandemic. The collection operations will now be shaped by the adoption of technologies such as AI/ML, which will enable the development of proactive collection abilities by bringing about enhancements to traditional reactive processes.