There are two main risks for any accounts receivable manager. The first risk is that of default. Most businesses have processes in place to make sure that there are adequate credit limits in place to ensure that any exposure is limited. Some go as far as insuring their accounts receivable balances. This all helps to minimise the value of bad debt written off and mitigate against the risk that a customer becomes insolvent. To do this properly will entail employing a team of people to constantly monitor customer account positions, review credit limits on a regular basis and block orders when that credit limit is set to be exceeded. This will often involve purchasing risk data to support the process of reviewing account positions. But these activities do not necessarily guard against the risk of late payment. There are many companies that have extremely robust credit risk processes and still have considerable values of overdue receivables. Many assume that customers with a high credit risk are more likely to be late payers and that this pattern of late payment is likely to be a guide to a possible later default. We have heard this argument many times and we decided to put that hypothesis to the test.

The Comparison

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We compared the two approaches using a real client’s data. If the approaches were compatible we would expect that low risk would be aligned with good customer payment behaviour and that high risk would correlate with poor payment behaviour. The boxes highlighted in yellow would show the highest percentage of customers in this group if the hypothesis was true. But this is not what we found. In only two segments did the level of risk and the payment behaviour seem to match in any way. In the large good payer segment, 52% of customers were deemed to be low risk while 48% of customers were deemed to be medium or high risk. In the large poor payers, only 22% were deemed to be high risk and a whopping 56% were deemed low risk. The least level of correlation was least amongst small customers. The comparison would suggest that in the majority of cases with small new customers that there is more to be learned from trading on a limited basis so that payment history can be understood rather than basing an approach purely on credit risk assessments. Some of this also applies to large customers. Some of the largest companies in the world are private companies. In many credit risk assessments that would automatically make them a high risk customer which may not necessarily reflect reality.

Conclusion

Does this analysis suggest that the credit risk scores are wrong? Does the analysis mean that analysing payment behaviour is a better methodology than credit risk? The answers to both of these questions is a resounding no. They are different methodologies that are trying to achieve different things. Minimising bad debt written off does not necessarily mean you will have low levels of overdue. Equally having low levels of overdue doesn’t protect you from bad debt write off. The data simply shows that minimising bad debt is not a compatible goal with minimising overdues and promoting good payment behaviour. This does not invalidate either approach and only becomes a problem where the methodologies are confused. Many companies focus on credit risk to the detriment of collection calling and vice versa. Neither are right. The balance of activity will be different in different industries since the risk of default can be so much higher e.g. construction subcontractors. The best scenario is to have both effective credit risk procedures and good collection process