Credit Risk Management Edinburgh Business School 1
Credit Risk Management
1. Introduction
Learning Objectives
This module introduces the key ideas for managing credit risk. Managing credit risk is a complex multidimensional problem and as a result there are a number of different approaches in use, some of which are quantitative while others involve qualitative judgements. Whatever the method used, the key element is to understand the behav-iour and predict the likelihood of particular credits defaulting on their obligations. When the amount that can be lost from a default by a particular set of firms is the same, a higher likelihood of loss is indicative of greater credit risk. In cases where the amount that can be lost is different, we need to factor in not just the probability of default but also the expected loss given default.
Determining which counterparty may default is the art and science of credit risk management. Different approaches use judgement, deterministic or relationship models, or make use of statistical modelling in order to classify credit quality and predict likely default. Once the credit evaluation process is complete, the amount of risk to be taken can then be determined.
After completing this module, you should:
understand the nature of credit risk, and in particular: what constitutes credit risk
the causes of credit risk the consequences of credit risk
understand the nature of the credit assessment problem, and that: credit risk can be viewed as a decision problem
the major problem in assessment is in misclassifying credit risk understand the different techniques used to evaluate credit risk, namely:
judgemental techniques deterministic models statistical models
be able to set up and undertake the credit review process know the basic contents of a credit policy manual.
Sections
1.1 Introduction
Credit Risk Management Edinburgh Business School 2 1.3 Expected Losses and Unexpected Losses
1.4 Controlling Credit Risk 1.5 The Credit Policy Manual
Learning Summary
This module has introduced the key concepts for managing credit risk. Credit risk arises from changes in the financial solvency of firms and individuals. An event of default occurs when the obligor fails to perform under the terms of the contract. In this case, the lender or party with the credit is exposed to a potential or actual loss. The degree of loss will depend on how much can be recovered given the credit event or default.
Many factors affect the potential exposure to credit events and hence credit-related losses. The key element in determining the acceptability of risk taking in regard to credit exposures is in assessing the probability of default. This involves analysing and assessing counterparties based on a variety of techniques. Even so, there is the potential for exposure to unexpected and – at times – catastrophic losses from credit events. For this reason, firms need to control these credit risks through setting out policies on evaluation, management and having the correct procedures in place.
Introduction
Credit risk is the risk of loss from exposure to firms that undergo credit events. This might be that the obligor defaults, but in some cases it is that adverse changes in credit quality can lead to losses. There are a great many events that can have a credit impact, which complicates the definition, analysis and man-agement of the process.
Credit risk can be seen as an informational problem. The credit giver does not know enough about the quality of the credit taker and how the obligor will per-form in the future.
As a task, credit risk management involves identifying the source of risk, selecting the appropriate evaluation method or methods and managing the pro-cess. This will mean setting an appropriate cut-off point that balances the conflicting demands of the organisation with regard to credit exposure.
Credit risk management can be seen as a decision problem. The assessment involves determining the benefit of risk taking versus the potential loss.
Decisions about extending credit are complex and subject to change, but at the same time are critical elements of risk control within most organisations.
While it is easy to outline the credit analysis decision, implementing an effective approach is more complicated. At its simplest, it requires an assessment of the likelihood that a particular counterparty will default on a contract and of the loss given default (LGD).
As a process, credit decisions usually involve some classification of creditworthi-ness into categories or classes as a precaution against credit exposure to high-risk
Credit Risk Management Edinburgh Business School 3 counterparties. This allows new credits to be analysed by comparison to pre-classified credits whose default history is known.
Credit Assessment Methods
Credit appraisal can involve a number of techniques that can be used individually but are more often combined as part of the assessment process. These tech-niques can be categorised as either qualitative or quantitative in approach. There are basically three separate methodologies: judgement, deterministic
models based on past experience or knowledge of the risks, and statistical mod-els that may be either static or dynamic, or involve monitoring behaviour over time.
Two basic methods exist for analysing credit quality: traditional quantitative– qualitative credit analysis and decision models based on deterministic or statisti-cal processes. Each offers a different insight into the credit risk problem.
Expected Losses and Unexpected Losses
In many cases, as with financial institutions, the amount of credit given by an organisation is substantial and requires steps to control the exposure in order to prevent unanticipated losses emerging.
Unanticipated losses arise due to the variability of loss rates experienced over time, for instance as a result of changes in business conditions. If the loss experi-ence in practice is above that expected, organisations will experiexperi-ence unexpected losses over and above those anticipated. This will happen as a result of variability in the actual loss rate against the expected loss rate.
In some cases losses may be catastrophic, in that they far exceed any reasonable degree of variation that historical loss experience would indicate. Such losses can have a disproportionate effect on the organisations subject to such a risk.
Controlling Credit Risk
The credit analyst or manager is required to understand the ways in which bad debts or credit losses arise and to devise methods for identifying these. This then requires that due consideration is given to how these are effectively managed. A key issue is credit control, which involves constantly managing the
credit-granting process. This can be seen as a policy that includes procedures, guide-lines and processes for managing the credit process.
Diversification can play an important role in reducing exposure to unexpected and catastrophic losses. However, spreading risks will be effective only if the principles of efficient portfolio construction are followed. There is a danger that the portfolio is ill-diversified, leading to unexpected losses.
As with all risk management processes, the exposure to credit risks has to be kept under constant review and action taken as required. Credit risk management is a dynamic process that responds to new information.
Finding the links between a firm’s financial condition, behaviour and default is the key skill required in the management of credit or counterparty risk.
Credit Risk Management Edinburgh Business School 4
The Credit Policy Manual
This process of credit risk management is formalised in most organisations in a set of procedures generally called a credit policy manual.
2. Understanding Financial Statements
Learning Objectives
Accounting numbers are the language of business. They are the means by which an organisation, firm or company records its activities (that is, the transactions it undertakes). The accounting record is not only a record of its activities. In present-ing a set of accounts, the company presents a position statement, known as the
balance sheet, which gives a snapshot of its position at a given point in time, and an income statement (also known as a profit and loss account), which provides a record of its trading activities over the reporting period.
The reported financial statements are summaries of the individual transactions made by the company over the reporting period. These are built up from the bookkeeping ledgers or accounts that track and record individual transactions. The reported financial statements can be supplemented by a cash flow statement, which shows the movement of cash within the firm.
The presentation of accounts is based on conventions and reporting standards. The recognised global method is called the International Financial Reporting Standards, but alternative ways of presenting the same underlying activity are also used. This can lead to differences in presentation that can mask the true and fair view requirement criteria for published accounts. At its extreme it can lead to creative accounting designed to mask the true underlying state of affairs of the company.
After completing this module, you should:
understand how a company’s transactions are recorded using accounting processes
understand the mechanics of double entry bookkeeping used by firms
know how transactions are recorded in accounts and how increases and decreas-es in items affect different ledgers
be able to see how financial statements provide summary statements of the underlying transactions entered into by the firm
know the elements that go into making up the balance sheet and income statement
know the elements that make up a set of financial statements, namely: the balance sheet
the income statement the cash flow statement
Credit Risk Management Edinburgh Business School 5 be able to create a simple cash flow statement from the balance sheet and
income statement
understand the principles, conventions and standards used to prepare a set of financial statements
be aware of the problems a user of financial statements might experience due to the use of different approaches in preparing the accounts, namely:
treatment of depreciation income recognition
accounting for research and development expenditure
treatment of goodwill and other issues that affect the quality of reported financial statements.
Sections
2.1 Introduction
2.2 Double Entry System
2.3 Reported Financial Statements 2.4 Problems with Financial Statements
Learning Summary
Introduction
Accounting is the language of business and records the transactions, assets and liabilities of an enterprise or organisation over a designated period.
The accounting equation, which states that assets equal capital and liabilities, is the foundation of the accounting method and dictates the presentation of ac-counting numbers. It is also important in understanding financial statements. Accounting numbers relate to two elements: stock measures, which are of a
permanent nature, and flow measures, which are transient elements. Capital is a stock measure; sales is a flow measure.
Every business transaction leads to new entries in the accounting system and hence affects the resultant financial statements. These will be recorded either in the balance sheet, which presents the operations of the enterprise at a given point in time, or in the income statement (also called the profit and loss ac-count), which presents the flow of transactions over the reporting period. A third statement, called the cash flow or flow of funds statement, can also be prepared or derived from the balance sheet and income statement.
Double Entry System
The double entry system is the way firms operate the accounting process. It involves creating ledger accounts, or T accounts, for each category of transac-tion. The key element, which gives the process its name, is that transactions are entered twice in the ledger system. That is, each transaction generates two en-tries, in different ledgers.
Credit Risk Management Edinburgh Business School 6 For each ledger account, there are two sides: the debit or left-hand side and the
credit or right-hand side. Placed side by side with the name of the account (for instance, ‘cash account’), the organisation is in a T shape, which leads to these accounts being called T accounts or ledgers.
The double entry system means that, for assets, an increase in assets is entered on the debit or left-hand side and a decrease in assets is entered on the credit or right-hand side. For liabilities and capital, an increase in liabilities is entered on the credit or right-hand side and a reduction in liabilities is entered on the debit or left-hand side. This preserves the concept of sources of funds (credits) and uses of funds (debits) for both assets and liabilities.
Given the double entry system and the existence of the relevant accounts that record the operations of the firm, at the reporting date it is possible to manipu-late these entries into a set of financial statements (that is, a balance sheet and income statement). There is normally an intermediary stage where a trial balance is created before the completion of the balance sheet. It is also possible to create a cash flow or sources and uses of funds statement.
Reported Financial Statements
For most users of accounts, what they are presented with are reported financial statements, which may or may not include additional disclosure information. The basic set of financial statements, which may be audited or unaudited, comprises a balance sheet at the reporting date and an income statement covering the report-ing period. Additionally, there may be a cash flow or sources and uses of funds statement and segmental information about the operations of the firm.
In preparing financial statements, accountants will adopt a number of principles and conventions. These are prudential treatment of reported items, neutrality, completeness, faithful representation, historical cost convention, accrual ac-counting, matching principle, no offsetting of transactions, materiality and aggregation of items, going concern concept, substance over form and con-sistency of presentation. These principles and conventions govern the way financial statements are presented and what information is provided. The key element is that they are prepared on a conservative basis according to defined rules. Any set of financial statements will have notes that explain the principles that were used in drawing up the accounts.
Financial statements and reports are prepared according to generally accepted accounting principles of the jurisdiction in which the firm operates. Increasingly, large firms in a given country will report using IFRS.
The balance sheet is a statement of the financial position of the reporting entity at a given date. It presents the total asset and liability position broken down by category. For assets, the principal categories are non-current assets and current assets. For liabilities, the principal categories are equity or shareholders’ funds, long-term liabilities and current liabilities.
Presentation of the balance sheet can be in accordance with the so-called side-by-side method, which has assets on the left-hand side and liabilities on the
Credit Risk Management Edinburgh Business School 7 right-hand side, or in accordance with the netted-down method, which shows the funds employed in the business. Both methods have their advantages. The income statement shows the revenues and expenses of the firm over the
reporting period. This is made up of three elements: a trading statement, over-head elements for supporting the business and how profit (or loss!) is allocated. The third reported financial statement is the cash flow statement, which is also
called a uses and sources of funds statement. This shows how cash was generat-ed by the firm and where that cash was usgenerat-ed. Note that, unlike the balance sheet and income statement, this statement does not allow for accrual accounting since it shows the cash movements within the firm. In principle, the net cash flow over the reporting period should equal the change in the firm’s cash position (either a decrease or increase in existing cash balances or borrowings).
Most large firms will provide additional information, breaking down operations by business activity and/or geography.
Since audited financial statements are a statutory obligation for firms, annual reports also include considerable additional information mandated by law on the operations and activities of reporting firms, which is a useful supplement to the accounting numbers themselves.
Problems with Financial Statements
A number of problems arise in the preparation and presentation of financial statements that users of such information need to be aware of. Different general-ly accepted accounting principles in use across the world can lead to differences in reported accounting numbers. The key problems relate to income recognition and the treatment of expenses.
Depreciation, which is an accounting expense to reflect the loss in economic value of assets, can be handled in different ways and hence provide different results depending on the method used. To make matters worse, accounting de-preciation is different to dede-preciation for tax purposes.
When to recognise an item as revenue, an asset or a cost can also be problematic. A major issue arises with research and development expenditure and whether to treat it as a cost (in which case it should be expensed) or as an investment (in which case it can be capitalised as an asset and subsequently depreciated). The treatment of goodwill following an acquisition can also be problematic and
differences in approach can lead to very different accounting results. The ac-counting treatment of acquisitions, namely whether it is done through acquisition (purchase) accounting or merger accounting methods, can lead to very different financial results.
At its extreme, the difference in methods allowed under generally accepted accounting principles can lead to creative accounting, or window-dressing of the financial statements, with the intent to deceive or – at the very least – present the firm’s activities in the best possible light. Where legitimate accounting treatment stops and creative accounting starts is unclear, but users of financial statements need to be aware of the potential to mislead in the way some firms report their activities.
Credit Risk Management Edinburgh Business School 8 Recent moves to International Accounting Standards are designed to minimise
the scope for creative accounting and to set a global benchmark for reporting standards, known as International Financial Reporting Standards, and which will be used by firms across the globe.
3. Ratio Analysis
Learning Objectives
Financial analysis is the process of examining the financial statements of a firm with a view to understanding the nature, activity and risks that are inherent in the business. Financial statements provide a condensed summary of a firm’s activities. The balance sheet shows the assets and liabilities that are used to make the business function. The income statement shows what revenue was earned and how it was used during the course of the reporting period. The relationships that exist between different accounting entries provide useful information on the nature of the firm’s activities. Ratio analysis is the process of using accounting and other business numbers to extract useful information for evaluation purposes. Ratios allow firms to be compared across time and against their industry or sector. Ratio analysis allows the analyst to understand the underlying risks in the business and, in particular, to ascertain the amount of credit risk.
After completing this module, you should be able to:
create common-sized financial statements for comparing two firms understand the key concepts of ratio analysis and, in particular,
understand the different types of ratio that can be created make use of available information to create ratios
know how different ratios are calculated
perform ratio analysis on any two items in a set of financial statements understand the different types of ratio that are used in ratio analysis, namely:
activity, efficiency or management ratios liquidity or solvency ratios
operating or management ratios profitability ratios
leverage or gearing ratios market-based ratios
recognise the key ratios used by most financial analysts for evaluating a set of financial statements
understand the interconnections that exist between ratios, and in particular the concept of the hierarchy of ratios that allows a detailed examination of a firm’s performance
Credit Risk Management Edinburgh Business School 9 undertake inter-company, sector or industry analysis using ratios
understand the analytic relationship model approach to using ratios for evalua-tion purposes and, in particular, the DuPont model.
Sections
3.1 Introduction 3.2 Ratio Analysis 3.3 Using Ratios
3.4 Analytic Relationship Models
Learning Summary
Introduction
The starting point for ratio analysis is the use of financial statements as discussed in Module 2. These provide a detailed summary of a firm’s activities. The balance sheet shows the position at the financial year-end, while the income statement provides an explanation as to what income was earned and how it was used dur-ing the reportdur-ing period.
Ratio analysis allows a thorough examination of a firm’s accounts to provide an understanding of the nature, activity and risks that are inherent in the business as reported in the accounting numbers.
Ratios allow firms to be examined both across time and against their industry peers or activity sector.
A key component of any risk assessment involves financial analysis. Ratio analysis allows comparisons between firms and the same firm across time by removing the size effect. One approach is to use common-sized financial state-ments, but they have limitations. The solution is to create ratios using two or more accounting numbers. This is a more flexible approach that allows the ana-lyst to understand how a firm operates.
Ratio Analysis
Ratio analysis means dividing a numerator value by a denominator value to create proportions, percentages or multiples. These facilitate comparisons be-tween items and across firms and time.
Any information available to the analyst can be used for creating ratios; this includes non-financial information (for instance, number of employees, market price of a firm’s securities and so forth), although there should be a logical case for the ratio being created.
Ratios are normally created to answer questions about a firm’s performance, risk, operations, financial leverage or other aspect of the underlying economic activity being performed.
Ratios are normally classified into different types, namely: activity, efficiency or management skill
Credit Risk Management Edinburgh Business School 10 liquidity or solvency
operational efficiency or management efficiency profitability
financial leverage or financial gearing market-based ratios
In practice a relatively small number of key ratios that address the issue of firm performance, profitability and so forth have become accepted for analytic pur-poses.
Key activity ratios include average collection period, inventory turnover and asset turnover or total asset turnover.
Key liquidity ratios include current ratio and acid test (quick) ratio.
Key operating or management performance ratios include gross profit margin, net profit margin and free cash flow (generation).
Key profitability ratios include return on capital employed, return on investment and return on equity.
Key risk ratios include debt, leverage or gearing ratios, interest cover and fixed-charge coverage.
Key market-based ratios include price to earnings multiple, market-to-book and net asset value.
Other ratios can be created to suit particular types of firms being analysed; for instance, in natural resources firms it might be profit per tonne or unit produced. In principle, there is no limit to the types and diversity of ratios that can be created. In practice, many ratios are very similar and hence only a small number are usually required to give a complete understanding of the character of the firm being analysed.
Ratios are interconnected in a logical structure, called the hierarchy of ratios, with sub-ratios feeding into higher-order ratios.
Ratios are used for trend analysis (observing the same ratio over time) or for cross-sectional analysis of the firm against other similar firms, the industry or sector.
While a useful tool, there are limitations in the approach, especially when applied across firms.
Analytic Relationship Models
Given the interconnection between ratios and the hierarchy of ratios, it is possible to create analytic relationship models using ratios for evaluating the performance and behaviour of firms.
The best-known analytic relationship model is known as the DuPont model, after the firm that first developed its use. There are two elements to this model: the underlying business performance, measured by the return on assets, and the effect of financial structure, measured by the return on equity. By using ratios, it is possible to see the contributor elements of each component when using the DuPont model.
Credit Risk Management Edinburgh Business School 11
4. Expert and Rating Systems
Learning Objectives
This module examines how credit quality is determined using expert and judgemen-tal methods. These approaches to credit risk assessment are subjective and largely rely on the experience of the analyst. As such, they represent the traditional method of credit analysis, which seeks to compare one credit with another in order to grade and rank the credit in terms of quality. How these rankings are determined involves a mixture of process and considered opinion and hence they lack transparency and rigour. That said, they are in common use as a means of credit assessment.
After completing this module, you should:
comprehend the process by which judgemental credit assessments are made be able to distinguish between formal models and expert judgement approaches
to credit risk assessment
understand how expert judgement credit evaluation is not a single technique but a set of different methods that includes:
qualitative assessments relationship models comparator credit rankings behavioural models
be able to integrate more than one expert and scoring technique
understand the template or checklist approach to subjective credit assessment and in particular know the meaning of the ‘6 Cs’ of credit; that is, a credit’s: character
capacity capital collateral conditions compliance
be able to undertake simple credit ranking procedures and be able to carry out simple credit scoring
understand the credit rating process and the meaning of the credit ratings given to firms by credit rating agencies such as Moody’s Investors Service and Stand-ard & Poor’s
comprehend the underlying rationale for behavioural scoring and in particular know the approach used in the A-score model developed by John Argenti.
Sections
4.1 Introduction 4.2 Credit Evaluation
4.3 Qualitative Credit Assessment Processes 4.4 Credit Ranking
Credit Risk Management Edinburgh Business School 12 4.5 Behavioural Ranking
Learning Summary
Introduction
Expert and ranking systems involve subjective judgements about credit quality combined with templates, processes, checklists and other decision aids in order to determine credit quality. Such systems are inherently less transparent than formal systems and subject to analytical bias and selection.
Subjective judgement based on the past experience of the assessor has been the traditional method for analysing credit and continues to be used as part of the credit assessment process. It needs to be contrasted with more formal methods that model relationships between the firm (or individual) being analysed and credit quality.
Expert judgement and ranking processes follow a logical decision-making process, although not a formal one, in that the problem is first defined and then there follows analysis in order to be able to make the appropriate decision. For credit risk assessment, the decision is whether the credit is a good one (and cred-it can be advanced) or a bad one (and credcred-it should be refused). More sophistication is achieved by ranking, where the best-quality credits are allowed more credit than poorer-quality credits.
Credit Evaluation
Credit evaluation can take a number of different forms. The major distinction is between subjective models, which include expert judgement and ranking proce-dures, and formal models, which make use of known relationships to determine credit quality.
The principal types of model that are in use include:
qualitative models or expert judgement models where the appraisal is based purely on subjective judgement
relationship models where analytic relationships are used to determine the quality of the credit
credit ranking where a credit is compared to existing credits whose quality or rank is known using a set of given criteria and analytic relationships
behavioural models, which take as their basis the actions of the firm’s manag-ers
Assessments often make use of multiple methods of analysis, and insights gained from one type of method are used to illuminate the results from other methods of evaluation.
Qualitative Credit Assessment Processes
Qualitative credit assessments involve judgements by analysts. To assist in the process and to act as a template, many such assessments use a set of elements to subjectively determine the credit quality of the entity being assessed.
Credit Risk Management Edinburgh Business School 13 A common model of credit assessment is the 6 Cs of credit. These are:
character: the personal characteristics of the borrower capacity: the past behaviour and prospects of the borrower
capital: the amount of capital, equity or own funds supporting the borrowing collateral: assets pledged or available to support the borrowing
conditions: the economic backdrop to the credit request
compliance: whether the borrowing satisfies regulatory and legal require-ments
Problems can arise with credit situations that need to be investigated as part of the credit evaluation process; in particular there may be compliance problems with types of credits and transactions that affect the credit assessment. These include ultra vires (that is, the borrowing is beyond the competency of the bor-rower to undertake), the unsuitability of the transaction and foreign counterparties.
Credit Ranking
Credit ranking is a judgemental technique that applies numerical values to elements of a credit’s financial condition, character, collateral and so on in order to score the result as part of the credit assessment process. While more formal than a simple credit assessment, the process is still nevertheless judgemental in approach as the analyst places their own estimate on the variables being evaluat-ed.
Ranking models seek to classify credits into groups that share common credit risk characteristics and that can be treated as equivalent for decision-making purposes. Credits are ranked from best to worst using a scale or criteria for de-termining the appropriate credit class for the entity being evaluated.
Ranking methods can be applied to all aspects of a business. So while it is possible to rank the financial condition of a firm (by analysing its financial statements and other financial information), it is also equally feasible to rank or score other aspects of a firm’s activities, such as its competitive position and strategy, the quality of its management, the superiority of its technology and know-how, and other qualitative aspects of the business’s activities.
In undertaking ranking procedures, qualitative elements are often given numeri-cal value using a subjective snumeri-caling system such as the Likert snumeri-cale.
Analysts will seek to reduce their judgements about the credit quality of a company down to a single number with a clear interpretation. This is called rat-ing and is a combination of objective criteria and subjective assessment of the creditworthiness of the entity being rated.
Rating agencies such as Moody’s Investors Service or Standard & Poor’s provide credit opinions and rank corporate and governmental entities into credit classes on the basis of judgement (called an opinion) that reflects their creditworthiness.
Credit Risk Management Edinburgh Business School 14
Behavioural Ranking
Behavioural ranking extends the principles behind rating methods to include observable behavioural characteristics of firms’ managers on the principle that it is the actions of managers that determine the success or failure of the firm. Causes of corporate failure include problems with a firm’s markets and products,
poor-quality management, poor investment and acquisition decisions, and poor internal management of the firm’s activities. Some of the factors are susceptible to financial evaluation (for instance, using ratio analysis) while others can be scaled, but determining the scale of the factors is a judgemental decision.
The approach looks for qualitative signs that in the view of the analyst are a cause for concern. In particular, it examines poor corporate governance (that is, how the firm is directed and how well outside parties are kept informed through financial reporting) and any signs of problems. Using the above approach, John Argenti developed an approach to corporate assessment that focused on behav-ioural aspects called the A-score.
5. Credit Scoring and Modelling Default
Learning Objectives
The previous module examined qualitative approaches to credit risk assessment. This module extends the approach and looks at a range of systematic methods that make use of statistical inference to determine credit quality for both firms and individuals. Models that are used to evaluate corporates are generally called ‘default-prediction models’, whereas models used for consumer credit assessment are usually referred to as ‘scoring models’. While the data used for the two is generally different, the aim of the model is the same: to find a statistical basis for predicting credit quality.
The principle underlying these models is that past behaviour or condition is a suitable guide for future behaviour. Statistical models seek to determine the best explanatory relationship between behaviour and a set of significant predictor variables. Such models provide a mathematical equation (which in use is called a scorecard) that provides a statistical estimate of the credit risk of the individual or firm being analysed against a known sample. A new credit is scored on the basis of the predictions of the model using the same variables that were used to develop the model.
After completing this module, you should:
understand the basis for the statistical modelling of credit
know the difference between the judgemental or expert approach and the advantages and disadvantages of the systematic approach to credit modelling be able to construct a simple scorecard using the results of a statistical model for
Credit Risk Management Edinburgh Business School 15 understand the statistical basis for credit modelling, including the decision
theoretic approach
be able to undertake a simple discriminant analysis
understand the meaning of the significance statistics used in regression analysis be able to compute the efficiency and error rate of the statistical model
be able to comment on the issues related to the statistical modelling of credit know the differences between the various models used for statistical analysis understand the issues relating to the statistical modelling of firms and individuals be familiar with the different types of consumer credit scoring as well as other
uses for the scoring approach
be aware of the issues relating to consumer credit scoring and what variables are used in a credit score
be aware of the advantages and limitations of the statistical credit appraisal and default prediction methods.
Sections
5.1 Introduction
5.2 Statistical Basis for Modelling Credit 5.3 Applying Scoring Models to Firms 5.4 Consumer Credit Scoring
5.5 Behavioural Scoring Models
5.6 Advantages and Limitations of Credit Appraisal and Default Prediction Methods
Learning Summary
Introduction
The systematic approach used for credit scoring and modelling default relies on statistical inference using a set of predictor variables to determine whether a credit is good or bad. The use of such a formal model reduces the credit evalua-tion to a data-processing exercise that makes it very suitable when there are a large number of cases, each for a relatively small amount.
Formal models avoid the problem of judgemental bias and allow the decision maker to set the cut-off point for accepting the credit risk. While profit maximi-sation is a desirable objective, it is often replaced with minimising classification errors (that is, determining a bad credit as good).
In use these models become a scorecard where a small number of key variables is used to evaluate the creditworthiness of the applicant individual or firm.
Statistical Basis for Modelling Credit
The statistical modelling of credit risk is, in essence, a discriminatory approach whereby a dataset of past cases is used to develop a model that best separates good credits (that is, credits whose track records lead the decision maker to
clas-Credit Risk Management Edinburgh Business School 16 sify them as performing credits) from bad credits (that is, credits who have de-faulted or otherwise breached the terms of the credit).
The models involve a decision theoretic approach to the credit evaluation problem, which involves developing a model whose decision rule is to minimise the expected loss from advancing credit.
A good model will successfully discriminate between a ‘good’ and ‘bad’ credit applicant by comparing key information on the applicant to the scorecard. In the context of the model, the score or result is an estimation of the credit quality of the applicant.
A linear probability model or regression model uses a linear function of predic-tor variables to explain the dependent variable. This dependent variable will be a categorical variable that usually takes a value of 1 if the case defaulted and 0 if there was no default. The predictor variable – or variables – is known infor-mation, possibly transformed in some way into a ratio or otherwise scaled (for instance, by using a log transformation), that provides the best linear explanatory equation.
A linear equation will have the general form Y = a0 +a1V1 + … +anVn, where Y is
the dependent variable (with a value of either 1 or 0) and V1 is the ith predictor
variable. The values a0… n are the coefficients of the linear model.
A statistical model will allow for various statistics to indicate the degree to which the equation provides a satisfactory degree of statistical fit and significance of the regression, namely:
regression coefficient hypothesis, which tests the significance of the coeffi-cients used in the model
analysis of variance, which tests the amount of reduction in the error from the regression equation
adjusted coefficient of determination, which measures the fit of the regres-sion taking account of the impact of all the independent variables (when there is only one predictor variable, then the coefficient of determination is not adjusted).
In practice, a user of a statistical scoring model will be concerned with how the model correctly and incorrectly classifies cases. The higher the percentage of correctly classified firms, the better the predictive power of the model. In addi-tion, the degree to which the model misclassifies bad firms as good and good firms as bad will also have an important bearing on the validity of the model. Usually, minimising the number of bad firms classified as good – and hence eligible for credit if the model is used for determining the decision – will be an important feature of the model’s effectiveness.
Usually a model will require a number of predictor variables to minimise errors and achieve the desired level of predictability (that is, it will be a multivariate-type model). Developing and testing such models becomes an important task. There are a number of practical issues that arise with the use of statistical credit
Credit Risk Management Edinburgh Business School 17 There is no theoretical basis for the selection of predictor variables, and
model developers rely on data-mining techniques.
The choice of variables may be dictated by what is available to the model developers and the need to comply with the relevant legislation on consumer credit and lending.
Many variables will be qualitative and will have to be transformed into quanti-tative variables.
Any scoring model must meet or match the credit-granting organisation’s lending policies.
Sampling and other statistical issues arise when a model is being developed, as well as an understanding of the lending and other conditions from which the sample was drawn.
There is a need to formalise many aspects of the data, including what consti-tutes a good and bad credit.
In practice, a range of models is used, including logistic regression and Probit and Tobit models.
Applying Scoring Models to Firms
There is much more information available on firms. Scoring models for firms, which are generally bankruptcy prediction models, make use of accounting and other information as part of the information set.
The principal models are known as Z-score models and make use of accounting data, which is often transformed into ratios, for their predictor variables. A key issue that arises with these models (and that is not absent from consumer credit scoring models) is the need to calibrate the model for the type and location of the company being analysed. Hence specific models need to be developed for individual countries and types of businesses.
In practice, default prediction has been most useful for analysing small and medium-sized enterprises, where there is a relatively high likelihood of firms defaulting and where lenders have a large database of prior defaulted companies that can be used to develop the model.
Consumer Credit Scoring
Consumer credit scoring makes use of information easily obtained from individ-uals at the point of application. This is now the predominant method for determining whether an individual is eligible for credit and may be the only eval-uation undertaken on small consumer credit transactions.
Factors such as an individual’s past payment history, amount outstanding with lenders, length of credit history, new credit and types of credit already advanced form part of the information used to analyse an individual’s creditworthiness. Scoring models have been developed for a range of credit situations, including
mortgage lending, collections, student loans, mobile home loans, detecting fraud, direct mail marketing and tax inspection.
Credit Risk Management Edinburgh Business School 18
Behavioural Scoring Models
Behavioural scoring models use information from an existing credit to predict future behaviour. As with application scoring, a range of information is available on the individual or firm, which is then used to predict future outcomes.
The principal difference between application scoring and behavioural scoring is that, in the latter case, there is transactional information available to develop the scorecard.
A key issue with any scoring model is the problem of reject inference. Since the data sets used to develop the model are all based on prior accepted credits, using these to predict non-performance biases the results. Hence in developing a mod-el there are major validation issues that have to be addressed.
Advantages and Limitations of Credit Appraisal and Default Prediction Models
Credit scoring has become the established means for determining consumer credit. The approach is cost-effective and allows for a formalised lending pro-cess, in which:
The criteria on which the lending decisions are made are explicit.
Decisions between cases are consistent since they rely on an objective model. Management is in control of the process and the amount of credit risk being
assumed. The cut-off point for accepting credit can be altered without major changes to procedures (although the effect will be lagged since all past deci-sions will have been made at the previous cut-off point).
While there are many advantages to statistical credit scoring, there are also some disadvantages and problems with the method, namely:
The scorecard does not take customer profitability into account; nor does it address the problem of screening bias.
Companies use creative accounting and window dressing of their financial statements in order to disguise the underlying state of affairs.
The score is developed at one point in the business cycle and may not be appropriate or may mislead in different economic circumstances.
The sample is based on past cases that may not be a good guide to new cred-its (for instance, if existing customers are used to score a new credit card). There is no good underlying theory that supports scoring models and the
variables used to develop the scorecard.
Credit Risk Management Edinburgh Business School 19
6. Market-Based Credit Models
Learning Objectives
This module extends the credit risk analytic techniques to those that incorporate or make use of the prices in traded securities markets to assess creditworthiness. As such, the approach in this module extends firm-specific analysis in that the models make use of the prices at which transactions take place in the financial markets. Prices in these markets reflect the market’s best estimate of the value of the securi-ties and the underlying obligor’s credit risk. That is, they incorporate the market’s collective judgement about the security’s credit risk that is embedded within the security price. The analytic tools in this – and the next – module have been devel-oped to reveal this information and make use of it in determining credit risk and the probability of default.
The models can be considered to fall into two kinds: those that aim to measure all credit risk (that is, any change from a firm’s current credit status), which are covered in this module, and those models that measure only a credit’s default risk. The module starts with an explanation as to how market prices can be used to reveal default expectations. This is extended into the option-theoretic approach in the next module. Differences in methodology and what is being modelled explain why there is a multiplicity of market-based approaches in use by credit assessors.
It should be added that these techniques are both relatively recent and, because of their purpose, relatively complex in operation.
After completing this module, you should:
understand how the market price of financial securities is affected by changes in credit quality
be able to recognise how the market credit risk models determine the credit risk from market information
know how to calculate the default probability implied by the market prices of pure discount bonds
be able to undertake simple calculations of default probabilities given bond prices and yields and recovery rates
have the ability to calculate the spot or zero-coupon interest rates from financial market data
be able to compute the implied forward interest rates embedded in zero-coupon interest rates
know how to compute the forward prices of bonds for different credit classes as part of the estimation procedure for calculating a bond’s credit value at risk be able to calculate the expected value and variance in value for a particular bond
due to changes in its creditworthiness at a given future time horizon
understand how to adjust the variance of the bond value for uncertainty in the distribution of future values from credit effects
know how to calculate the credit value at risk of a simple two-asset portfolio, that is:
Credit Risk Management Edinburgh Business School 20 be able to compute the expected value of such a portfolio
be able to calculate the variance and standard deviation of such a portfolio under the assumption of no correlation and in the circumstances when there is correlation between the two constituents
be able to determine the credit value at risk of the portfolio at a given confidence limit
understand the problems of using parametric methods with credit risk, given its asymmetric characteristics
understand the problems in modelling credit risk by using ratings transition matrices
be aware of and be able to calculate the benefits of portfolio diversification understand how market credit risk models may be adapted to take account of
economic factors
know the differences in rating philosophy and credit risk assessment between the point-in-time and the through-the-cycle approaches.
Sections
6.1 Introduction
6.2 Credit Risk Portfolio Model
6.3 The Economic Factors Model: CreditPortfolioView
Learning Summary
Introduction
Market-based models are a recent development in credit risk management techniques and rely on insights from financial theory and practice. These new models are ‘market’ in the sense that they rely on the information that is em-bedded in the transaction price of securities over time.
The market prices of securities contain useful information about the prospects of the underlying credits. Among the values that are reflected in the price is the market’s consensus estimate about the likelihood that the underlying credit will default or suffer a credit downgrade.
Among the useful information that a market-based credit model can reveal from securities prices are hazard rates (i.e. the likelihood of a credit event occurring) and conditional default probabilities over time.
For certain kinds of securities, such as bonds, it is possible to determine the market’s estimates of future credit risk with some degree of precision. In order to do so accurately, one needs to know what the loss or recovery from the credit event will be. So the evaluation requires us to know not just the market prices (or yields) on securities, but also the estimated recovery rate involved. Hence, credit spreads include two unseen and changing elements.
Credit Risk Management Edinburgh Business School 21
Credit Risk Portfolio Model
The credit risk portfolio model is an extension of the market risk portfolio model. In similar fashion to the market risk model, it aims to compute the obli-gation’s value at risk (VaR). A security’s risk is made up of two elements: market risk and credit risk. Credit VaR (CVaR) aims to compute the credit risk element of a security, while market VaR computes the sensitivity of the security to mar-ket effects (exchange rates, interest rates, commodity prices and so on).
For a single obligation or security, in order to compute CVaR, we need to know
migration probabilities for the security between risk classes as well as its de-fault probabilities. Given that, we also need to determine a time horizon over which the credit risk is to be estimated.
The Economic Factors Model: CreditPortfolioView
CreditPortfolioView adds to the approach used by CreditMetrics by making adjust-ments to the default probabilities for obligors by country and – if reasonable estimates are available – at the industry level for the different points in the eco-nomic cycle.
The advantage of this model is that it provides a way of adding in macroeco-nomic conditions and the state of the economy that are known to influence the default rate and, based on predictions concerning the economy as a whole, ob-tain better estimates of the expected defaults over the prediction horizon. This is particularly useful if the portfolio consists of lesser-rated credits that are very
pro-cyclical in their default behaviour.
The model is calibrated for each country (which may have differences in default experience given the nature of local bankruptcy laws) and by industry, if available.
The model uses simulation techniques to derive default probabilities that are conditional on economic conditions. Depending on how much change in migra-tion there is in credit quality as a result of economic condimigra-tions, to ensure an accurate estimate of the credit risk it may be necessary to use a through-the-cycle model rather than a point-in-time approach.
7. Market Default Models
Learning Objectives
Market-based credit risk assessment models can be considered to fall into two kinds: those that aim to measure all credit risk (that is, any change from a firm’s current credit status) and those that seek to model default. These differences explain why there is a multiplicity of approaches in use by credit assessors. This module extends the market-based credit risk analytic techniques to models that estimate default risk. These models are designed to estimate accurately a probability of default and hence the loss given default for a particular obligor or portfolio.
Credit Risk Management Edinburgh Business School 22 The models make use of market information such as the prices of a firm’s securi-ties as traded in the financial markets. In addition, they make use of the historical default experience to estimate probable default rates in the future for similar-risk obligors. A particular attraction of these models is that they seek to include firm-specific information.
The module concludes by examining credit derivatives, which are financial in-struments that, for those entities on which these contracts are traded, allow a direct estimation of the credit risk of the obligor. It should be added that the techniques for credit risk assessment discussed in this module involve a complex methodologi-cal approach that requires a lot of market data to implement operationally. That said, the underlying concepts that lead to these models are relatively straightforward.
After completing this module, you should:
understand the various credit events that trigger a credit default
understand how the value of the firm can evolve over time with the influence of macroeconomic and firm-specific factors
understand how borrowing creates an option to default
be able to calculate the value of the embedded default option created when a firm takes on debt
understand the optional structure of a firm’s balance sheet when the firm’s liabilities are debt and equity
understand the contingent claim valuation approach used to determine the value of the default option
know the inputs used in the option pricing models that underlie the market default models
understand how the Moody’s KMV model works
be able to price a default option using the Black–Scholes–Merton option pricing model
understand the insurance approach to default risk modelling be able to calculate the marginal mortality rate for a group of loans understand the underlying probabilities of credit downgrades understand how credit default swaps work.
Sections
7.1 Introduction
7.2 Debt and the Option to Default 7.3 The Insurance Approach: CreditRisk+ 7.4 The Differences between the Models 7.5 Credit Derivatives
Learning Summary
This module has expanded on the market-based approaches to credit risk evalua-tion. In particular, it has examined models that seek to model the probability of
Credit Risk Management Edinburgh Business School 23 default. These models – like those of the previous module – make use of infor-mation derived from financial markets. As the bad news for the firm piles up (e.g. cancelled orders, problems with staff and difficulties in raising money), this is reflected in the value of the securities in the market. This is because the markets process this information and act accordingly. Hence the prices of securities in these markets include the market’s best guess as to the default risks involved. A key element of this module is how an analytic model can extract this information from quoted securities prices as part of the credit evaluation process.
Introduction
Default is an option an obligor creates when money has been borrowed. Conceptually, it will rationally take place if the value of the firm is less than the value of the monies borrowed; if the value of the firm exceeds the borrowing, the firm will repay the amounts due.
A credit event leading to default is not a single event but can take many forms. Typical credit events are a firm entering bankruptcy, a credit downgrade, failure to pay on a debt, declaring a moratorium on debt interest and principal pay-ments, and the repudiation of debts.
Default is a consequence of a fall in the value of the firm – the obligor – over time after the borrowing has been taken out.
Economic and industry- and firm-specific factors will affect the value of the firm. While it is impossible to observe the value of the firm directly, it is possible to observe a good proxy for the firm’s value, namely the firm’s traded securities.
Debt and the Option to Default
When a firm borrows, it creates the option to default; that is, it may elect not to repay the debt. The borrower has discretion whether it repays the obligation when it is due or defaults. Lenders, on the other hand, have granted this option to the borrower.
Options can best be evaluated using a contingent claim valuation approach. Option pricing models provide a tractable approach to deriving the value of the option to default and the probability that default will take place.
By using the contingent claim valuation approach, it is possible to determine the expected default frequency (EDF) for a particular borrower. This is the market’s best-guess estimate of the likelihood of default by the obligor over a given time frame. Typically, the models consider the one-year probability.
The model requires five inputs: the value of the underlying firm, the amount of debt, the term to maturity of the debt, the risk-free interest rate, and the volatility of the value of the firm. Some of these inputs to the model are difficult to esti-mate. In particular, the value of the firm and the volatility of firm value are hard-to-estimate variables.
Some of the assumptions that underpin the option pricing models are problem-atic when applied to default put valuation. In particular, the firm is not traded in an active, liquid market.
Credit Risk Management Edinburgh Business School 24 There is a relation between the probability of default and the default spread (that
is, the price discount or additional yield over the risk-free interest rate).
An operational model that applies the contingent claim valuation approach has been implemented by Moody’s KMV. In putting it into practice, a number of simplifications have had to be introduced. In particular, assumptions about the amount of debt due past the analytic period (typically one year) have had to be made. Usually half of the total long-term debt is included in the analysis.
The model proceeds in three steps. It uses the market prices of a firm’s debt and equity to estimate the value of the firm. A distance to default (in terms of stand-ard deviations on a normal distribution) is computed, and this, in turn, is used to calculate the expected default frequency.
Problems with the model are reduced by comparing the model’s output to a data set of similar firms to derive the empirical EDF.
The Insurance Approach: CreditRisk+
CreditRisk+ uses an actuarial modelling approach to determine the default risk of
debt portfolios. A portfolio of loans is similar to many insurance products since default is a stochastic event and can be modelled using one of the many different distributions available. By combining data on the frequency of defaults with data on the severity of losses given default, the distribution of default losses can be worked out.
The expected loss is simply the loss given default times the number of expected defaults in a portfolio. The unexpected loss is the difference between the ex-pected loss and the losses at some level of confidence limit (say, 90 per cent). The model is designed to reflect the fact that a given portfolio can experience
any rate of defaults from none (zero) to the whole of the portfolio. However, there will be an expected number of defaults by obligors. By using a Poisson distribution for the default process, and knowing the mean number of defaults for the risk class of the portfolio, a probability distribution for the losses can be obtained. When combined with information on the severity of the defaults (that is, the loss given default for the class of obligation), the unexpected losses at the appropriate confidence level (say, 90 per cent) can be calculated.
Each class or risk category of the portfolio needs to be modelled separately. These can then be combined to give the total loss rate for the lending book across all classes.
The Differences between the Models
While the models in this module and the preceding module share a number of similarities in that they all to a greater or lesser extent make use of market data, they do have a number of differences. All the models, since they seek to deal with future uncertainty, include the volatility of value in the modelling, either as a direct input or indirectly via varying default rates.
The principal differences relate to whether they adjust for the economic cycle and the effects of macroeconomic factors on default rates.
Credit Risk Management Edinburgh Business School 25 In practice, for the same obligations, the models will provide different results. In
the case of the insurance approach, the model allows analysis only at the portfo-lio level.
Credit Derivatives
A market for exchanging credit risk has evolved that allows participants to insure or speculate on credit events. These instruments are called credit derivatives and the principal type is the credit default swap. This allows a protection buyer, who wants to remove the credit risk in a particular obligation, to buy insurance against a credit event from the protection seller for a fee.
The evolution and depth of the credit derivatives market is such that the prices for credit protection are good indicators of how the market views the probability of default. As such, the market provides price discovery; that is, the prices for protection provide information on how the market views a particular credit’s default risk.
8. Managing Credit Risk in a Corporate Environment
Learning Objectives
This module examines the processes and evaluation that an industrial and commer-cial firm will apply to providing trade credit. Trade credit is provided by firms in the course of their normal business to purchasers of a firm’s goods and services. This creates a potential for the purchaser to default if it is buying on account. Hence the risk and profitability of such short-term granting of credit and assuming of credit risk needs to be managed. International transactions add a complicating layer since there is not only the normal trade credit risk but also that of dealing in a foreign country, with all that this entails.
After completing this module, you should:
understand the motives for trade credit and the factors that influence the decision to offer trade credit
understand the processes by which a corporate firm manages its credit process be able to undertake simple profitability evaluations of the trade credit decision be able to evaluate changes in a corporation’s credit policy
understand how the trade credit cycle is monitored and collections are made be aware of the complications that arise from international transactions.
Sections
8.1 Introduction
8.2 Credit Administration 8.3 Determining a Line of Credit 8.4 Evaluating Changes in Credit Policy
Credit Risk Management Edinburgh Business School 26 8.5 Monitoring and Collections
8.6 Collection Procedures
8.7 International Credit Risk Management
Learning Summary
Introduction
This module covers the credit risk management of industrial and commercial firms. Firms assume credit risk as a result of the decision to offer trade credit to customers. This presents special problems since offering trade credit is a com-mercial decision that helps boost sales, but can lead to delinquency or late payment by customers.
Motives for granting trade credit are primarily financial in that the company benefits from increased turnover and hence profitability, but can also be opera-tional in that customers with a line of credit may buy in larger quantities, helping to reduce contracting costs and allowing more flexibility in pricing.
Credit Administration
Credit administration or credit policy is the way firms administer the process of offering trade credit. It involves processes and systems, such as the credit decision, maintaining accounting records on each customer, ordering sys-tems,and credit limit and receivables management.
The key element of a credit policy will be determining the credit standards, namely which customers are eligible for a line of credit, the credit terms being offered, and how much credit is allowed for each customer and overall for the firm (that is, the credit limit and the collection procedures that will be fol-lowed).
Determining a Line of Credit
How a line of credit is determined is a key element in the credit policy. Which firms and how they are assessed will be at the core of the firm’s credit philoso-phy, and no two firms will have the same view on which customers are acceptable for a line of credit. This is the amount of money that the company implicitly lends by offering delayed payment terms to buyers, and will consist of two elements: the amount of credit and the normal payment delay that is permit-ted.
Deciding on which firms are acceptable (or not) for a line of credit is an exercise in credit risk appraisal. The company’s views on a particular credit will be deter-mined by its credit philosophy. But, so long as it offers trade credit, it will need to establish a view of the risk involved. This often relies on an analysis of the customer’s financial statements and ancillary information, such as credit bu-reau referrals and the firm’s character and reputation.
Customers who are being assessed for a line of credit will be grouped into risk classes or given a risk score, which will determine whether they are eligible and,
