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How to identify potential payment problems with customers

There are various possibilities to preventively monitor or forecast the payment behaviour of customers even in times of crisis.
Blog post
03.09.2020

Identifying potential payment difficulties

Here are four aspects which can provide you with indications that customers could get into payment difficulties.

Your own payment records

One important source of information is one's own experience with the customer in relation to payments. The classical method of waiting until overdue payments exist in order to identify them is, however, unsatisfactory because at this point in time the problem has already occurred. With IT solutions that perform automated analyses, "early warnings" can be obtained much sooner and with less effort. Which customers have started to pay later? Who has stopped taking advantage of early-payment discounts? In which areas is there deterioration in payment behaviour? When abnormalities occur, the situation needs to be investigated further.

External payment records

In addition to your own payment records, external payment record pools can be helpful. These can also be evaluated automatically with the help of IT. The pools are based on reciprocal cooperation: a company submits its payment records for the customer and as soon as there is sufficient information about this customer in the pool the company receives information about the payment records submitted by other suppliers. Illuminating information here could be, for example, when one's own A-customer, who is still exhibiting good payment behaviour, is much worse in terms of payments to other, less important, suppliers – an alarm signal.

Balance sheet simulation

The published balance sheet from 2018 is yesterday's news. It can, however, be used as a starting point for balance sheet simulation if one establishes that there has been continuity in the balance sheets over recent years. The target here is to find out whether the financial stability of the company is sufficient to survive a shortfall in turnover over multiple months – also taking into account the short-time working allowances from the government. The simulation of many variants is possible, e.g. also in relation to a higher level of debts due to new loans. This analysis is of particular interest in the case of good regular customers. There are also tools available for this purpose which reduce the workload.

Behaviour of the customer

The fourth aspect derives from actions performed by the customer himself. He asks for payments to be delayed or a new payment plan. Fundamentally, in such cases one should check the exact economic situation of the customer – and then, for example, also perform an appropriate annual report prognosis. If the customer cannot regain the lost turnover, it will probably be difficult to make the payments that have been delayed at a later date – unless the use of the supplied goods or services only takes place when the customer himself has received new orders. Here also, one's own payment records and external payment information can provide further clues.

Conclusion

There are a number of possibilities to monitor or predict the payment behaviour of customers preventively, even in difficult times. With large customer bases, however, the work needed to do this is often not practically possible if the activities have to be performed manually.

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Industry, wholesale & energy companies

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