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A Statistical Approach to Receivables Risk

by Helen Sanders, Editor

The evolution in the role of treasury is a perennial feature in industry conference programmes and seminars. For a number of years now, we have seen a gradual expansion of treasurers’ responsibility as they influence the elements that contribute to the financial supply chain, such as payables, trade finance and supply chain finance. In some cases, such as payables, the actual payment processing may take place in a separate shared service centre, but treasury takes ownership of issues such as bank relationships, bank connectivity and payment timing as part of a wider working capital responsibility. However, the area that is more difficult for treasurers to influence is collections, which is typically closer to the business and often less centralised. Achieving oversight and influence over collections at a strategic level, if not necessarily taking full operational responsibility, potentially has a far greater impact on the treasurer’s ability to manage working capital than payables. This article looks at some of the most recent developments in credit and collections management, specifically risk-based collections, and how these techniques contribute to an effective liquidity and working capital strategy.

Receivables metrics

The trade receivables portfolio is, for many companies, the first or second largest asset on their balance sheets and in the same way as any other asset, it makes sense to take care of it. Collections delinquency has a major impact on companies’ ability to forecast cash and manage working capital effectively. Uncertainty about when a customer will pay an invoice results in treasurers having to create large working capital ‘cushions’ which sit on the balance sheet providing little value to the company. Companies have developed sophisticated models to determine the probability of customer payment based on customer segmentation and historical data which they use to calculate the size of these cash cushions. In addition, many organisations have developed efficient collection procedures with automated payment reminders and dedicated collections professionals calling customers to remind them to pay. Despite the automation and sophistication of these techniques, being proactive in chasing payment is only one part of a credible collections management strategy. Assigning probability of non-payment for cash flow forecasting is also important, but neither technique contributes to reducing the incidence of late payment.

Invoice ageing is typically the metric used most frequently to determine receivables risk, with specific actions such as chasing invoices or referring amounts to a collection agency. However, using ageing alone as a way of measuring and addressing collections is effectively closing the stable door after the horse has bolted. Far better is to allocate credit limits, and decide on appropriate collection processes based on intelligence about customer behaviour. Indeed, it seems ludicrous that pre- and post-sale processes are often so poorly aligned. For example, having a reliable understanding of the customer risk profile can inform the way that the sales team work with a customer, and how they prioritise their focus on different customer groups. At the other end of the process, automated, timely reconciliation and posting of collections is vital to free up credit limits for lower risk customers to enable the company to do more business with them.

As companies start to centralise their collections and standardise their use of technology, they are increasingly able to leverage the benefits of proactive credit scoring techniques.

Anticipating late payment

Treasurers and finance managers should therefore be looking at how to anticipate and prevent late payment. This does not mean taking a draconian attitude to payment terms, but simply to set payment terms according to the risk associated with each customer and their behaviour. To do this requires a proactive and regular approach to credit scoring the receivables portfolio to adjust the way that customer credit terms are determined, and how invoices are followed up. However, according to a recent study undertaken by SunGard, while 34% of the study participants reported using their receivables as part of their overall capital structure, 46% never credit score their portfolio, and only 19% are performing monthly risk analysis. This has major implications on how a company is evaluating one of the largest assets on the balance sheet, and the decisions that are made accordingly.

But what approaches could a company take to credit scoring? Essentially, there are three general types. Bureau scoring, that uses bureau data and allocates statistical scores, is typically used for evaluating credit risk to new customers. Judgmental or rules-based scoring models combine internal and bureau data in a scorecard in order to make decisions on new accounts and credit lines. The scorecard variables, weights and score ranges are subjectively determined, with a ranking of customers as opposed to quantifiable risk. Finally, statistical portfolio or collection scoring models take into account internal A/R and performance data but can also include bureau or other external data, with definable scorecard variables, risk categories and weightings.

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