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:

1. The maximum limit is equal to the contracted limit application

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:

  1. The previous year’s limit and the failure score determine the limit. This method is based on the assumption that whoever was able to avoid a bankruptcy last year is also capable of avoiding it this year.
  2. The limit is determined by fixed criteria and the credit score, in particular the balance sheet equity, as the owner has the greatest impact on the company and should therefore contribute the highest risk. Other criteria include the total value of the assets and the importance of the customer for the supplier.
  3. Each individual score value is assigned a risk amount and the total amount of risk is equal to the credit limit. For example, if the equity is €1m, the amount at risk is €200,000. Added to this is a risk amount of €100,000, based onthe profit margin of 5%. If there is no other information available, then this translates to a credit limit of €300,000.2
  4. The limit is based on the expected profit from the sale of goods and the insolvency risk. The relevant ‘recovery period’ is one year and, until then, the sum of the sales profits has to be at least as high as the credit limit. For example, if the profit margin is 20% and each month sales are the same amount (i.e. the maximum), then days sales outstanding (DSO) is 72 days. This should ensure that the limit is high enough. This method is in use at a high probability of default and should be based on a portfolio management (see below).
2. The maximum limit is independent of the contracted limit application

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.

Constraints in Lending

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.

Evaluating Marketing Limits

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.

An Operative Analysis of the Theoretical Credit Limit

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:

  • For most of the items, the profit margin of relevant sales is unknown. Therefore, we must assume that there is no product-specific margin-oriented limit given. Credit analysts work with the company’s average profit margin when they assess the credit limit.
  • Credit limits over €10m are usually paid on time. On average the debtor has been in the market for many years with a long sales relationship between seller and buyer. The debtor’s balance sheet shows 11% or more equity of total assets. The current assets are higher than the current liabilities, which is successful working capital management (WCM) in practice. The customer’s turnover is €250m and above.
  • Credit limits between €1m and €10m showed the following: 86% of credit limits were paid less than 20 days late. Nearly all debtors have been in business for a significant amount of time, and the customer/buyer relationships are usually longer than six years. Eighty percent of the debtors had a balance sheet where the equity was at least 11% of the total assets. In 65% of the credit limits, the working capital ratio is greater than one. For more than 50% of the debtors, the return of sales is over 1%. Finally, many have customers with annual turnover of between €10m and €250m.
  • Companies with credit limits between €300,000 and €1m are not so strongly focused on punctual payments, as 50% of them had overdues of more than 20 days. These firms have been more than six years in the market, and the relationship between obligor and debtor is usually longer than three years. Only 45% of debtors have an acceptable level of equity (11% and better) and only 42% have a positive working capital result (better than one). The 75% of the buyers have a turnover of less than €250m, or it is unknown.
  • Those with a credit limit below €300,000 do not fare worse in nearly all of the criteria compared to those companies with credit limits of up to €1m. It is only the turnover of the debtor that shows that such a limit is strongly provided for companies with a turnover below €250,000 per annum. Only 12% had a turnover over €250,000.

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 and Net Worth

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.


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