The Credit Limit
If you work under the assumption that 50% of all trade activities are business-to-business (B2B) related, the global volume is close to US$6 trillion per annum.1 An analysis of trade credit shows that the percentage of companies’ current assets differs depending on the industry: in retail and wholesale, for example, it is kept at a very high level (about 70%). Even in manufacturing companies, the proportion is about 40% of total assets.
With this in mind, it is interesting to understand how a company allots trade credits when risk is not transferred to a bank or credit insurer.
In credit limit data modelling, known as the credit limit proposal, there are generally two basic approaches:
The credit analyst assigns a maximum limit that corresponds to the limit request.
If the application limit is the same as the maximum limit, this is arrived at by one of four methods:
In this approach, the credit analyst sets the maximum acceptable limit regardless of the current credit requirements. The advantage of this method is that the sales unit can respond immediately to new credit demand. However, there is no credit check done in the cases where the client buys significantly more.
To calculate a credit limit regardless of the application limit, methods 2 and 3 above can be used.
One ‘constraint’, in terms of the individual loan limit, is the consideration of risk concentrations. It is important to avoid a situation where a single change in market conditions leads to a late or no payment, which endangers the solvency of the supplier.3 This relates to a large single exposure, which could be a company, sector or country exposure.
Whether the final credit limit is higher than the financial-oriented credit limit or not, commercial conisderations have to be taken into consideration. But these are judged differently. For additional ‘marketing limits’, there are always exposures that will not be supported by the credit analyst. For example, if a company is expanding into a new region, there isn’t meaningful data and experience to obtain a credit analysis. A separate marketing limit at least exposes these risks and, as a result, can be better managed.
The total ‘marketing limits’ should be known, since there is likely a much higher risk of default in situations such as the one described above.
The aim for all corporates is to make sensible decisions about whether to give a trade credit to a company or not. Such decisions are commonly based on information about the transaction and customer. The most interesting transaction data is regarding the profit margin and nominal profit of sales. The credit risk nominal value (based on the probability of default) should always be lower than the profit of the sale. Otherwise, the total transaction makes a loss.
The customer data should be used to define the relevant probability of default. To calculate this, many models have been developed. The most well known model is Altmann Z-score4, which is also the basis for a credit default swap (CDS) credit derivatives calculation.
If the default rate is converted into a nominal value by considering the payment terms and invoice amount, and the resulting amount is lower than the nominal profit amount, then the trade credit makes sense. But is there a maximal exposure the seller wants to hold on a customer? Tthere is not yet a common formula, such as the Z-score, because the limit is not the same for all sellers.
First, the seller and trade obligor have to determine what is the maximum credit limit of one customer or group that the company is willing to assign. Here the seller will probably relate this to the equity of their company, where the default of a customer that is greater than the equity leads automatically to a seller’s insolvency. For many large companies, such concentration risk in one client is not acceptable.
But even then the seller may look to ensure that the open receivables are not too high for the buyer, in the sense that they might not be able to pay back the debt. In practice, a credit limit is given for each B2B customer over a threshold. But how should one decide such a limit?
Many B2B credit analysts talk about the ‘art’ of determining a credit limit because there are so many ways to do it. Despite this, it is common for a B2B credit manager to set the credit limit. Therefore, I conducted a practical benchmark study, which was split into two parts. First I researched the criteria commonly viewed as important in defining a credit limit with help from 25 experienced credit analysts. In total, we were able to name 30 different criteria. Many of these are part of an annual report, but we also included payment behaviour, some soft factors and further information about the company.
All participants were given the 30 criteria, which then sorted as to the ones that were used by the majority of the 25 credit analysts. The results were less then 10 criteria.
Second, we performed an analysis as to how the criteria selected by the majority were used in practice. The result came from 15 credit analysts, who delivered 91 typical data constellations from trade debtors all over the world.
The 91 results can be summarised as follows:
It is interesting to compare the results from this analysis with the creditworthiness theory’s determining factors: liquidity, solvency, efficiency and profitability of a firm.5
“Liquidity refers to the availability of company resources to meet short-term cash needs”.6 Credit analysts incorporate this by evaluating the working capital ratio and payment behaviour. In feedback from the research, credit analysts viewed companies with credit limits over €10m as quite strong, as these customers had nearly no overdues and a positive working capital ratio. For companies with credit limits between €1m and €10m, liquidity data was not as good as for those with the higher exposures. At this level, overdues of 19 days are acceptable. The working capital ratio shows that in one third the current liability is as high as current assets. And for those with limits below €1m, the liquidity figures seem to be even less important. The overall trend shows that liquidity must be higher than the credit limit.
Solvency is focused on bankruptcy risk. Bankruptcy occurs when the net worth of a company become negative. Interestingly, net worth was not a common criteria used by the research expert group. This came as a surprise, as many participants explained that, in addition to specific delivered data, their way of determining a credit limit is to combine creditworthiness analysis with a final score for probability of default and a percentage of the net worth of the debtor (i.e. 20% of net worth for a medium-rated company and 30% of net worth for a firm with a low default score-result).
It seems that the credit analysts assess solvency by determining how long the company has been in the market. The analysts consider the age of the firm, as well as whether the buyer is able to handle market conditions and stay ‘alive’. This is confirmed by the fact that the credit limit increases in relation to the company’s age. The business relationship time is an even stronger element when looking at solvency. A long and stable relationship is obviously a security against the risk of bankruptcy. Therefore, it is not surprising that the credit limit increases in relation to the duration of the relationship between seller and buyer.
The research data shows no overt hint concerning solvency. The answer might be that the participating companies mainly have invoice to cash times of less than 90 days, and see the product order as a further point for the companies’ short-term creditworthiness. Therefore, the seller does not fear its customer’s bankruptcy until the invoice is paid.
To become more efficient, it is important to evaluate management. But this task is more difficult since it is evaluating soft factors, which are difficult to translate into a mathematical formula, where another analyst can come to the same result. Not surprisingly, this criterion was not one of the 30 criteria in round one, nor round two.
Perhaps the gap between theory and practice is based on the fact that credit analysts are the wrong people to ask. In the seller’s firm, the ones who might be able to answer this question are the sales people. In practice, this means that the credit analyst should not be the only person to define the credit limit. Otherwise, the efficiency aspects (25% of the theoretical focus) are not part of the limit calculation.
Profitability stems from self-financing. From the risk management viewpoint, profit is directly related to assets, and must be higher than a risk-free bank loan. As credit analysts have a clear focus on controlling data, it was surprisingly to find that in the first step of the credit limit research the majority of the credit analysts preferred the profit to turnover ratio, not the profit to net worth ratio. This might be because the profit to turnover ratio gives a short-term view, which is sufficient to make further sales and will keep the company alive. But even this ratio does not give a clear behaviour signal, when checking the operational data.
The basic data does not help provide a formula to calculate a credit limit. But there seems to be a common understanding that certain constellations of data lead to a common agreeable credit limit. Over time, many other features can influence the common understanding, for example insolvency experience, high inflation or a sector crisis. Therefore, the process must be updated from time to time.
This empirical study shows that such data can be part of a scoring system to determine the credit limit. It will reduce the work needed to ascertain all the limits reaching beyond the data constellation. Finally, it will help new credit analysts learn how to assign credit limits, as well as convince the internal and external business partners that these limits are conforming to market practice.
1 World Trade Report 2009: Trade Policy Commitments and Contingency Measures, World Trade Organization.
2 Wells, Ron. 2003. Global Credit Management, p65. UK.
3 Thomson Financials. “€176bn Punishment for the largest European companies for giving poor customer service”. Survey of 230 credit managers, June-September 2007. Published in CFO Europe 11/2007.
4 Altmann, EI. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 23 (1968) 9, p. 589-609.
5 Subramanyam, KR. 2010. Financial Statement Analysis, 10ed, p. 526, New York.
6 ibid.
The views expressed in this article are the author’s only and do not necessarily reflect that of the company.