Harnessing Predictive Analytics for Increased Cash Flow

How statistical modeling technologies are helping companies revamp their credit and collections functions.

Big data” is widely discussed these days, among both businesses and consumer groups. As the volume of data that corporations compile increases exponentially, more and more organizations are leveraging predictive analytics technologies to transform that information into intelligence—and, ultimately, value—for the company. A familiar example is the way in which certain online stores suggest items a particular consumer might be interested in purchasing, based on an analysis of his or her prior buying behavior.

Today, many companies are grappling with how to harness their vast data stores, exploring the potential of predictive analytics to support a variety of corporate functions. One department that may soon be revolutionized by predictive analytics solutions is credit and collections. Analytics software can help accounts receivable (A/R) teams increase cash flow by not only prioritizing which customers are contacted by collections staff, and when, but also recommending a method of contact that is most likely to help the organization get its invoices paid fast.

Predictive analytics applications can apply the same principles even when invoices are not past-due. Suppose you’ve had a customer for three years that has always cut checks once a month to clear out all its outstanding invoices. The customer currently has 50 invoices outstanding: 40 due in a few days and 10 due two weeks later. In the past, the customer would have sent a check for all 50 at once, but instead this month it pays only the 40 that are due now. The other 10 invoices are not yet due, and they may well be paid by their due date. But if this shift in payment behavior continues, it may indicate that the customer has started to manage its cash more carefully, and a predictive analytics solution may elevate the priority of the collections team’s contact with this customer.

 

Sophisticated companies take this customer segmentation a step further, prioritizing collections activities based not only on the probability of delinquency, as determined by collection-risk scores, but also by the amount of cash at risk with each customer. “Cash at risk” is simply the probability of delinquency multiplied by the dollar value of outstanding invoices.

Suppose a supplier has two customers with unpaid invoices that are a few days past their due date. Customer A owes $20,000, and its collection-risk score indicates an 80 percent probability that the invoice will pass 30 days past-due, so the supplier’s cash at risk with this customer is $16,000 ($20,000 × 80%). Now, suppose Customer B owes the same supplier $100,000, but its collection-risk score gives it only a 5 percent probability of becoming 30 days past-due. The supplier’s cash at risk with Customer B is only $5,000 ($100,000 × 5%). The company certainly should proceed with collections activities on both customers, but to get the most out of its limited resources, it will want to prioritize and collect differently from Customer A than from Customer B. 

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