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Ways to optimize credit decisions for B2B transactions

By Herbert Broens
Herbert Broens is currently the Head of Export Department at Bayer AG. Prior to his current assignment in 2009, Mr. Broens was the Head of Credit Management of the Bayer Group responsible for credit risk management, credit insurance arrangement, credit workshop to the group since 2000. He is also leading the German Chemical Credit Manager Association since 1999.

1. Introduction

In B2B transactions, suppliers are increasingly having vendor credits reviewed by credit analysts in an effort to prevent payment defaults caused by customer insolvencies and ensure that credit is only granted if it is expected to lead to an improvement in overall business performance (ref. 20). A two-step method is generally used: first the probability of default is calculated, then an individual credit limit is set. In many B2B transactions, vendor credit is extended on the basis of sparse data about the customer. And yet the granting of credit is often a precondition for the transaction.

The following analysis contains proposals designed to promote a more systematic approach that could replace the variety of methods currently employed by credit analysts. Such an approach could help to avoid individual credit errors in the future and improve the efficiency of current processes.

2. Basic framework

A company normally sets a basic framework for credit risk assessment by issuing a credit directive. This may contain mandatory regulations and set forth best-practice rules, which are then evaluated subjectively by individual credit analysts.

3. Definitions

3.1 B2B

B2B (business to business) transactions are transactions between (at least two) companies, as opposed to business relationships between companies and other customer groups (e.g. B2C = business to consumer) or government agencies.

3.2 Credit scoring

A B2B credit score is a numerical value derived from a statistical analysis and indicates the creditworthiness of a company. Credit scoring is a predefined, standardized procedure used by a company to assess the creditworthiness of customers.

The advantages of credit scoring are:

The disadvantages of credit scoring are:

3.3 Insolvency forecasting

Insolvency forecasting methods analyze indicators considered to be relevant in assessing whether a company is a going concern. These can be subdivided into indicators that give clear signals and those that are more diffuse.

There are many insolvency forecasting methods, reflecting the fact that economic conditions and thus confidence in a company’s ability to service its debt change continually.

Insolvency forecasting methods are often based on historical business data. When conditions are changing rapidly, economic forecasts become more important. However, economic forecasts are not a particularly reliable indicator on their own.

A distinction needs to be made between probability of default and risks arising from unexpected events. Probability of default is derived from historical insolvency trends, which can be extrapolated to the future. The risk associated with unexpected events also needs to be taken into account. This can be modelled, for example, using risk buffers or stress scenarios (refs.12 and 11).

Reliable insolvency forecasting data are very important in determining default rates, which are used to define credit limits, set risk-based prices and optimize processes. Efficient insolvency forecasting also encourages borrowers to adopt appropriate, risk-aware practices.

3.4 Default rates

The probability of default is the likelihood that a receivable (in this case an unpaid trade account) will not be paid because the debtor does not have the necessary funds. The probability of default affects the theoretical interest rate for vendor credit that forms part of product pricing.

Historical data that provide an insight into the probability of default in B2B transactions are increasingly available. These data are shown on a per annum basis, so they have to be converted for pricing, which is based on the term on the receivable, i.e. days sales outstanding (DSO). As a result of recent IAS accounting requirements, international corporations, in particular, now publish details of default rates. Based on these, historical default rates for manufacturers with a broad customer base were around 0.2% of sales in 2008.

Default rates which can be used to assess credit risks in B2B transactions may be derived from data on payment performance as well as the normal financial data available to banks. Less research has been done on stress scenarios (ref. 12). These look at extreme scenarios for the development of various underlying data constellations and the potential default rates. Such scenarios have gained in importance as a result of the financial crisis because of the risk of default affecting entire sectors (e.g. the automotive sector). This places suppliers in a position where they have to weigh up whether to accept the attendant risks.

3.5 Credit limit

The credit limit defines a maximum level of receivables up to which deliveries will be made to a specific customer. The credit limit includes open orders if the goods are produced specifically for the customer. In addition to individual limits per customer, credit limits may be set for groups of companies or for entire countries or sectors.

3.6 Marketing limit

Suppliers may be prepared to grant trade credit even where the default rate is above the financially viable limit determined for granting credit to individual customers, for instance, to generate additional sales. From a business viewpoint, this is part of the marketing mix (refs. 24 and 11). The purpose is to increase overall profit.

4. Basic data used in credit decisions

In B2B credit management, credit decisions are based on observable actual data. Such data can be subdivided into general data, financial data, external economic trends, secondary data, soft factors and forecast data.

General data

The basic data is the expected exposure pattern (required credit limit). This is normally derived from days sales outstanding (DSO), the level of payments and the date on which a receivable is initiated. This information can be obtained from the company’s sales and accounting departments. Late payments in the past twelve months are regarded as particularly important in assessing the likelihood of insolvency (ref. 15).

Another factor that needs to be checked is whether the legal status of the purchaser (= borrower) allows the supplier to enforce payment via the legal system. In Germany, the commercial register is the basic source of information on such matters for B2B transactions. In some cases, the commercial register also shows the customer’s ownership structure. If a company’s owners have a strong capital base, they will be in a position to inject capital and liquidity.

Finally, general data also include negative information obtained from the courts. Publicly available data of this type includes disputed bills of exchange and insolvency proceedings.
Financial data

It has been shown that annual financial statements provide a sound basis for forecasting the probability of default (ref. 21). A variety of ratios and absolute data are analyzed.

The most important are, for example:

These data provide an insight into the company’s assets and financial condition. They can be used to extract information on past profitability and as a basis for forecasting. Such data also provide information on the extent to which a company has set aside reserves to cover both foreseeable risks and unexpected events. Normally the data are analyzed as absolute figures and in comparison with previous periods and the market.
Forecast economic and sector trends

Key economic data include a country's inflation rate, currency reserves and balance of payments (refs. 3 and 26). If the seller operates internationally, such factors have to be evaluated for all relevant countries. - Sector influences include supply and demand and expected changes in products (ref. 16).
Secondary indicators

Experts consider that a range of other data sources can provide information on the credit standing of B2B customers. These include the age of the company (ref. 15) and the length of the business relationship. The length of the business relationship in turn forms the basis for two further criteria: a) how punctually the customer pays and b) how accurate its corporate forecasting tends to be.

There are also analyses of the importance of data on dispute management and customer relationship management, and the distance between the lender and borrower (supplier and customer) when assessing the probability of insolvency (ref. 9). Further observations relate to the development of order size and cross-company evaluations of payment behaviour.

Evaluation of the customer’s dependence on the supplier and the customer’s credit management in its own sales also have to be taken into account.
Soft factors

Soft factors are criteria and assessments based on what is often called “gut feeling”. Assessments of the management and the state of business premises are recognized soft factors, even though they are difficult to quantify for credit scoring purposes.
Forecast data

Forecasts are based on actual data and help in anticipating future changes that are likely to affect the credit limit and risk class. Relevant changes include sales, method of payment and country and sector risks. Changes affecting the customer, for example, the impact of market entry by a new competitor should be taken into consideration.

Due consideration also needs to be given to changes in the customer’s financing conditions, such as higher interest on loans. However, such aspects do not have to be considered separately for each customer. Their impact can be examined with respect to the entire customer base or homogeneous segments within the portfolio using a uniform correction factor.

Assessing the customer’s business model is particularly important. This focuses on an evaluation of how the customer’s business model and service/product are likely to fare on the market in the future, rather than an analysis of additional financial indicators. This can be supported by looking at product lifecycles and the strengths and weaknesses of individual customers.

Finally, two metrics are used to calculate the acceptable overall risk of default: the confidence range and stress scenarios (ref. 11) – see section 3.3.

5. Data gathering

Data may be obtained from internal o external sources.

Internal data can be obtained from accounting and order processing departments. Sales data may also be useful, and may already have been prepared for evaluation by customer relationship management software. Further sources of information include personal observation by credit managers and sales staff.

External sources of data include information provided by customers and their tax accountants/auditors, on-site inspection of facilities, publicly accessible electronic data sources such as the electronic Federal Gazette in Germany and registers kept by other official bodies (for example, the commercial registers held by local courts). These data sources are supported by legislation on the disclosure of data to help prevent defaults.

Further sources are commercial data agencies, which can provide information on the business condition and legal situation of a company. The fact that these data are available in standardized electronic form considerably expedites their processing by companies that use credit management software. Commercial data providers and debt collection agencies may also be able to provide cross-company analyses of corporate payment behaviors.

Informative pointers can also be derived from daily newspapers or electronic media, e.g. via the Internet. However, neither provide a sound basis for credit decisions.

So far data gathering has been limited by the fact that companies other than stock corporations are reluctant to disclose details of their business condition for a variety of reasons. Limited data disclosures that do not jeopardize the borrower's competitive position may be of help in such cases. For example, such companies could present their statement of financial position (balance sheet) but withhold their income statement (see below).

6. Data interpretation

Two methods can be used to interpret data. Along with verbal/qualitative methods such as a conventional credit report or summary, quantitative methods such as scoring models are increasingly being used (ref. 19).

a) Verbal/qualitative evaluation focuses on a credit report based on traditional balance sheet analysis. This takes account of ratios and absolute figures, which are presented in a standardized form to help the reader form an opinion quickly. An evaluation of historical financial data is supplemented by an analysis of future expectations based on planning data. Finally, importance is placed on an overall evaluation of all risk factors on a case-by-case basis. The overall assessment is summarized in a recommendation.

b) The rating or scoring method employs a grading system. B2B scoring methods with proven default rates are based on balance sheet and income state statement data. Examples of the main types of information are:

Commercial data providers have developed methods based on other data, with payment behaviour as a key indicator. Indicators of insolvency are amounts that are more than 30 days overdue (refs. 15 and 22) or repeated failure to adhere to payment schedules.

There are also methods based on a range of other customer information. For example, customer relationship data. Surveys have shown that the weighting accorded to the various factors varies greatly.

A further insight into the risk of insolvency can be obtained by looking at the sector in which the customer operates. There is a risk of insolvency if there is a general drop in demand in the sector, but also in the event of strong sector growth (ref. 8). The country where the customer is based may play a critical role if market conditions there are difficult, for example, as a result of state intervention or high inflation. Finally, ownership structure or membership of a group can be important if exposure is above the level that would be justifiable on the basis of a company's stand-alone creditworthiness.

In the first step, the individual criteria should be evaluated systematically with a view to their significance for possible default. In the second step, the findings are aggregated to produce an overall conclusion. Weightings are applied to the various factors when calculating the overall score, since not all criteria are equally important when assessing the possibility of default. The correlation between factors also needs to be considered when aggregating the findings.

The data used in this standardized process need to be reviewed regularly to check that the defined default criteria are still applicable and that defaults are still being detected. Similarly, the underlying economic assumptions, for example, as regards inflation and trends in demand, have to be adjusted. Fundamental analysis and backtesting of historical data are performed for this purpose.

7. Credit limit

Two basic data modeling approaches are used to set the (proposed) credit limit.

The credit analyst sets a maximum acceptable credit limit without reference to current credit requirements. This enables the sales function to respond immediately to any further request for credit.

Alternatively, the credit limit may reflect a requested limit. This allows more effective credit monitoring because any unusual increase in credit uptake should be obvious.

As a "secondary condition" for individual credit limits, possible cluster risks have to be taken into account. It is important to ensure that individual changes in market conditions do not result in a level of defaults that puts the supplier at risk of insolvency (ref. 23). That would be the case if, for example, the supplier had a single major exposure to one large corporation, or to a single sector or country. Suitable limits therefore have to be set.

There are essentially three methods for calculating suitable limits:

A. The limit is determined based on the previous year's limit and the default score. This method assumes that a good track record in the previous year will be maintained in the coming year.

B. The limit is derived from fixed reference values and a credit score (ref. 21). Fixed reference values may include, in particular, the company’s equity as stated on the balance sheet, since the owners have the greatest influence on the company and are therefore expected to bear the highest risk. There are also other suitable reference data, such as total assets or the customer's importance for the supplier.

C. A profit/risk factor can be determined and used to calculate how long the company will be exposed to the risk, i.e. the period until net profit matches the exposure. This method is used where there is a high probability of default and should be based on a portfolio management approach (see below). As a guide, the maximum risk period should be one year. The exposure is derived from the amount invoiced and the days between invoicing and payment. In the case of recurrent deliveries to the same customer, exposure may be reduced by a shorter DSO.

Opinions vary on whether it is permissible to set the final credit limit at a level above that indicated by financial factors in order to take marketing criteria into account. One argument in favor of such “marketing limits” is that in practice there are always exposures that credit analysts would not consider acceptable. One example is if a company is expanding into a new region for which no reliable empirical data are available. In such cases a separate marketing limit at least provides an opportunity to quantify the risks and thus improve their management. - If the data used to set the marketing limit are sufficient for a risk assessment, a risk assessment should be performed to provide an appropriate basis for measuring the success of the portfolio or in order to take a more risk-aware approach to entering high-risk markets or market segments.

The total value of such "marketing limits" should be known as they may involve a significantly higher risk of default.

8. Ways of reducing credit requirements

If the proposed limit is below the expected exposure, ways of reducing risk need to be examined.

Various types of collateral may be obtained to reduce credit limits, for example, guarantees from owners, retention of title to goods, land charges, mortgages, etc. and third party guarantees. These normally comprise bank guarantees or sureties or, in international trade, letters of credit. For some years, banks have also traded credit default swaps, a form of hedge in which the buyer receives a fixed sum if a borrower ceases to service a bond. The bank receives a premium for assuming the attendant risk. This enables receivables from issuers of listed bonds to be hedged against insolvency by means of a “natural hedge”.

Another alternative is credit insurance. Credit insurers are normally prepared to insure portfolios of trade receivables if the insuree is prepared to assume a certain low level of risk itself. With the traditional form of credit insurance, the insurer reimburses individual defaults less the insuree's deductible. As in the case of the financial derivatives used by banks, credit insurers now offer a wide range of alternatives for the insuree's risk assumption, including option strategies (ref. 22). The insuree should first consider which risks really should be insured and which it would be better to assume himself. This applies both to risks relating to individual customers and to the overall minimum level of risk to be borne. Companies can normally negotiate freely with private credit insurers regarding the events to be insured.

By contrast, state export credit insurers are less flexible in the shaping of policies, although the differences have narrowed in recent years. However, state credit insurance agencies assign higher credit limits in their core area of operation – the emerging markets – than do private insurers.

9. From individual analysis to portfolio management

If customer risk is assessed using a uniform credit scoring method, this can be used to indicate the overall risk. Overall risk is calculated by aggregating risk classes and exposure.

Example of a risk class model and the determination of total default risk

* = Collateral including credit insurance and bank guarantees

Ref. 19

Establishing and evaluating portfolios in this way allows the acceptance of higher risks than could be assumed if risk were assessed on an individual basis. This is because a certain level of default is accepted in portfolio-based risk management, while risk management that focuses on individual risks is geared to complete avoidance of default (see section 7). The portfolio approach therefore allows a higher turnover of goods than if credit is granted on the basis of individual risk assessment, and thus increases overall earnings.

10. Standardized credit policy to improve the results of credit management

If the standardized process is based on proven relationships or a decision by a group of experts, the default rate will normally be lower than where decisions are taken by a single credit analyst because spontaneous decisions are not possible and decisions taken collectively by experts tend to be superior to those taken by each expert individually.

A uniform, standardized process essentially permits a company to grant trade credit wherever the contribution margin (marginal income) from the products exceeds the probable default rates. It shows which risks and limits are acceptable and which are not.

Individual assessment by a credit analyst is thus only required for receivables for which credit scoring does not produce a clear outcome, or where special factors are important in the credit assessment.

A standardized credit limit is also a better way of achieving an objective. The underlying risk exposure is derived from the maximum acceptable level of default and the required maximum volatility of defaults in the reporting period.

A standardized method allows the use of automated computer-based techniques which speed up processing and decision-making so subsequent process steps can also be initiated more quickly. It also reduces costs, for example, the costs involved in obtaining information (e.g. annual financial statements or data from information agencies). Similarly, internal costs are reduced because fewer staff are required for on-site credit negotiations and in-house processing is faster.

To a certain extent, portfolio management also improves controlling, even of cluster risks. This is because cluster risks are broken down into risk classes and transactions involving a negligible risk of default are excluded from the process.

The portfolio approach is beneficial in planning sales and pricing because there is a clear relationship between the credit standing of a customer, acceptable price levels and procurement trends.

11. Outlook

Over the years, B2B credit management at many companies has shifted from credit decisions focusing on individual customers to a standardized, portfolio-based method. Risks are entered into and managed more consciously.

Methods are increasingly computer-assisted and have a clear process orientation. (refs. 4 and 5). That is also true for mid-sized enterprises, providing they have a sufficiently large customer portfolio (at least 50 relevant exposures). Major corporations are extending this approach in the direction of Group-wide portfolio optimization by using "credit factories”, central hedging and insurance strategies and data storage. The age in which corporations use a combination of their own historical data, regression analysis and artificial intelligence (ref. 18) for B2B credit management has already dawned.

Further best practices for evaluation need to be developed by academia. This applies specifically to the treatment and risk evaluation of various non-financial scoring criteria.

Risk exposure levels are still extremely relevant in the corporate sector. Companies are still being forced into insolvency by late payment or default by customers. The present financial crisis has highlighted the significance of risk exposure for national economies as well (ref. 8). Ultimately, a delivery is a gift until payment is received.


The article represents solely the personal opinion of the author.

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Created on 01-Apr, 2011 by HKCCMA.

Last Edited on 09-Apr, 2011 by HKCCMA.