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.
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.
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.
Automate invoicing, collections, deduction, and credit risk management with our AI-powered AR suite and experience enhanced cash flow and lower DSO & bad debtTalk to our experts