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How to break the DSO deadlock
May 04, 2017
Collections models are largely the same today as a century ago. This despite new technologies, revolutionising the way that corporations produce and sell their goods and services, communicate with customers and suppliers, and conduct their internal operations. In most cases, collections teams approach their list of outstanding debtors and take action according to the age of the invoice and its value – indeed, 41 percent and 35 percent of participants respectively in the 2017 FIS Credit and Collections Market Study identified these as their top method. This appears logical, in that successful collections of the largest, oldest invoices has the greatest impact on DSO and past due metrics.
However, there is a strong argument for a complementary approach that advocates pre-empting and avoiding overdue collections by focusing on the customers that are most likely to pay late, or not pay at all. This requires a different way of assessing and prioritizing due and overdue invoices, and ideally brings together internal payment behavior data to help predict the invoices and customers that pose the greatest risk. While many companies currently lack the analytical tools – often referred to as predictive analytics or statistical scoring – to achieve this, in most cases they already have the data that underpins this analysis. For example, they have their own data on customer payment history.
The incentive for doing so can be considerable. Over a third of credit and collections managers report that more than 11 percent of their receivables portfolio is overdue. And a third of that group has more than 20 percent of accounts overdue. Furthermore, the same study shows that DSO remains one of senior managers’ top challenges, a trend that we have seen for many years. This suggests that traditional collections are not sufficient.
Instead, a new approach could help to reduce pressure on DSO, and allow more time to focus on qualitative, as well as quantitative metrics. Using statistical scoring models to identify customers in your portfolio that are going to go delinquent and queue them up for immediate contact in the proper order of risk will help to prevent those delinquencies from happening time after time. This also can have significant positive impacts on DSO.