The Credit Decision

After completing this reading, you should be able to:

• Define credit risk and use examples to explain how it arises.
• Explain the components of credit risk evaluation.
• Describe, compare, and contrast various credit risk mitigants and their role in credit analysis.
• Compare and contrast quantitative and qualitative techniques of credit risk evaluation.
• Compare the credit analysis of consumers, corporations, financial institutions, and sovereigns.
• Describe quantitative measurements and factors of credit risk, including probability of default, loss given default, exposure at default, expected loss, and time horizon.
• Compare bank failure and bank insolvency.

Introduction to Credit Risk

The word “credit” is derived from the ancient Latin word credere, which means “to believe” or “to entrust.” In the world of finance and investment, credit refers to a contractual agreement in which a borrower receives something of value now and agrees to repay the lender at a later date, generally with interest. Credit is built upon a foundation of trust. This implies that a borrower can either meet obligations as set out in the contract or fail to do so, in which case, the lender incurs a loss. As such, a lender must assess the likelihood that a borrower will pay back the loan in accordance with the terms of the agreement.

Credit risk is the probability that a borrower will not pay back a loan in accordance with the terms of agreement. It is the risk of loss caused by consumer default on credit products, including amortized loans, credit cards, revolving credits, and residential mortgages. Credit risk can arise in different ways. For example, it can emerge when:

• One party offers services to another and then sends an invoice to the recipient of the services.
• One party makes an advance payment to the other and awaits delivery of the items purchased or a scenario in which one party has advanced the items purchased and awaits payment.
• A retailer or producer offers a product on credit, i.e., trade credit. For example, a computer manufacturer might allow customers to buy now and pay later. The terms of credit could specify the number of days the customer has to make the required payments.

For retail banks, credit risk is simply unavoidable. Banks receive deposits from customers and then lend the money to individuals and firms. In this process, every borrower is associated with some level of credit risk. However, the incremental risk of any single risk exposure is small. A single obligor is not big enough to threaten the operations of the bank.

To manage credit risk, banks invest heavily in credit analysis models that estimate future default rates and related losses. They also use a range of risk mitigants such as loan guarantees and collateral.

The variables most directly affecting relative credit risk include:

• The capacity and willingness of the obligor to meet their obligations.
• The external environment (e.g., business climate and country risk) affects the probability of default and the expected loss occasioned by default.
• The characteristics of the relevant credit instrument (credit terms, disbursement schedule, repayment schedule, etc).
• The quality of credit risk mitigants utilized.
• The length of time over which exposure exists.

Components of Credit Risk Evaluation

The four primary components of credit risk evaluation are as follows:

1. The borrower’s (or obligor’s) capacity and willingness to repay the loan: The lender must answer the following questions:
   • What is the capacity of the obligor to service their financial obligations?
   • How likely will the obligatory fulfill the debt obligation through maturity?
   • Of what type is the obligor and what are the usual credit risk characteristics associated with their business niche?
   • What are the outside forces that may affect the borrower’s willingness and ability to repay? For example, how is the organizational structure at the firm? How many similar obligations does the borrower have?
2. The External Environment: This has much to do with factors such as the prevailing business climate, the political conditions in the host country, and operation conditions relevant to the lender. The goal of this scrutiny is to establish how such factors impinge upon the credit risk to which the obligee is exposed. The lender also has to establish whether there are any cyclical changes that will affect the level of credit risk.
3. Characteristics of the Credit Instrument: The attributes of a credit instrument have a major bearing on credit risk. The following are some of the concerns that should be addressed by the lender:
   • What are the inherent risk characteristics of the obligation? Save for the general legal risk in the relevant jurisdiction, is the obligation subject to any specific legal risk?
   • What is the maturity of the product?
   • Is the obligation secured?
   • What priority is assigned to the creditor (obligee) in the event of winding-up?
   • What is the denominated currency of the obligation?
   • Are there any contingent risks?
   • Are there specific covenants and terms that benefit each party, and do they increase or decrease the credit risk to which the obligee is exposed?
4. The quality and adequacy of risk mitigants such as collateral, credit enhancements, and loan guarantees: Risk mitigants are the strategies put in place to reduce the extent of exposure to a risk and/or the likelihood of its occurrence. To check on credit risk, obligees often settle for secured lending which ensures that if the obligor fails to meet contractual obligations, the obligee can seize the items designated as collateral and sell them. Collateral serves to reduce the extent of loss in the event of default. In addition, it also lowers the probability of default because the obligee typically does everything possible to avoid losing the collateral.

   Risk mitigants guide the obligee’s decision to either grant the obligor a facility or not. The obligee will often determine the market value of collateral so as to determine whether it is sufficient to cover potential losses. To do this, they might engage the services of a professional asset valuer. Key questions under this component are therefore as follows:

   • Has the collateral been used to secure another loan?
   • Has the collateral been subjected to valuation?
   • Is there a loan guarantor? Has enough credit analysis been undertaken to determine whether the obligee and guarantor have the ability (and will) to meet contractual obligations?

Quantitative vs. Qualitative Techniques of Credit Risk Evaluation

Qualitative credit risk analysis uses subjective judgment based on non-quantifiable information, such as management expertise, industry cycles, and the amount (and strength) of research and development. Qualitative analysis is used to gauge an obligor’s willingness to pay, which is a subjective attribute.

Qualitative analysis can be described by the acronym 6Cs derived from the first letters of the areas taken into account in the analysis:

• Character, which gauges the obligor’s level of responsibility, honesty, and whether there is a serious intention to repay the loan in time.
• Capacity, which assesses the financial condition of the obligor and their ability to properly repay the loan.
• Capital or cash, which assesses the ability of the obligor to generate enough money to repay the loan.
• Collateral, as a real cover for the loan.
• Conditions i.e., the macroeconomic or industrial circumstances that affect the obligor’s ability to repay the loan in time.
• Control, i.e., the assessment of whether changes in regulations can adversely affect the creditworthiness of the obligor.

Some of the qualitative techniques often used include:

• Face-to-face meetings with the potential obligor to assess their character.
• Extensive information gathering from parties with first-hand knowledge of the obligor and/or their business. This may involve talking to customers, suppliers, and business partners.
• “Name lending,” which involves lending to an individual based on the perceived status of the individual in the business community.
• Extrapolating past performance into the future. Obligees often assume that a pattern of borrowing and repaying in the past will continue in the future.

In general, qualitative credit risk analysis looks at three key things about a company:

• Ownership structure.
• Historical development.
• Core business.

Quantitative credit risk analysis primarily involves consideration of past, current, and forecasted financial statements of the prospective obligor. These include the balance sheet, income statement, and cash flow statement. Qualitative analysis is used to gauge the obligor’s ability to pay.

While scrutinizing financial statements, the obligee assesses the obligor’s liquidity, revenue streams, receivables, existing assets, liabilities, and all outstanding obligations to various stakeholders such as suppliers.

Quantitative analysis, however, has several limitations:

• The financial statements readily available for scrutiny are usually historical in scope and never entirely up to date. For this reason, such figures are mere estimates of the obligor’s future ability to pay because the past cannot be extrapolated into the future with any certainty.
• Financial statements are highly abbreviated reports that undoubtedly leave out certain pieces of information that are crucial in determining a borrower’s ability to pay. Financial reporting guidelines are developed by multiple parties with diverse interests. Furthermore, firms enjoy some discretion regarding what and how they report financial information, subject to established accounting rules. In short, financial statements are mere summaries that are subject to diverse interpretations depending on the needs, perspectives, and experiences of the various analysts.

Bottom line: The best credit analysis must combine quantitative techniques and qualitative judgments. Financial statements have their shortcomings and credit analysis must, therefore, be a synthesis of quantitative tools and sound qualitative judgments.

Credit Analysis of Consumers, Corporations, Financial Institutions, and Sovereigns

There are four main categories of borrowers, which give rise to four types of credit analysis:

Consumer Credit Analysis

Consumer credit analysis is the evaluation of the creditworthiness of individual consumers.

The most basic measure of an individual’s ability to honor their obligations involves assessing their net worth. The other important indicator of the ability to pay is the borrower’s cash flow stream – both inflows and outflows such as rent and salaries receivable, respectively. Since for most individuals, earnings and cash flow are generally equivalent, income and net worth provide the fundamental criteria for measuring their capacity to meet financial obligations.

Consumer credit analysis is a fairly simple process and it is not uncommon to find lenders using statistical tools to correlate risk to a fairly limited number of variables. Furthermore, extensive credit analysis does not often make economic sense because the amounts advanced are comparatively small.

Corporate Credit Analysis

Here, an analyst evaluates the creditworthiness of nonfinancial firms. Businesses are typically more difficult to analyze than individuals, although the process is similar. The income streams, asset, and liability values of businesses are more volatile.

The credit analysis process is usually detailed because the amounts advanced are relatively large. While evaluating the creditworthiness of a nonfinancial obligor, the lender is particularly concerned with the company’s liquidity, cash flows, near-term earnings capacity and profitability, and solvency position. Each of these attributes is also relevant to the analysis of financial companies.

Financial Institution Credit Analysis

Financial institution credit analysis is the evaluation of financial companies including banks and nonbank financial institutions (NBFIs), such as insurance companies and investment funds. As with corporate credit analysis, liquidity, solvency, earnings capacity, the quality of management, the state of the economy, and the industry environment are vital factors in evaluating financial company creditworthiness. However, financial institution credit analysis differs from corporate credit analysis in two main ways:

• There’s more focus on asset quality.
• Cash flows are omitted from credit analysis.

An institution’s earnings capacity over time is a more relevant indicator of creditworthiness than cash flow.

Sovereign/Municipal Credit Analysis

Sovereign/municipal credit analysis is the evaluation of the credit risk associated with the financial obligations of sovereign states and subnational governments. The creditworthiness of a state has implications for the creditworthiness of non-state entities operating in specific jurisdictions.

Quantitative Measurements and Factors of Credit Risk

There are several measures used to assess the creditworthiness of a firm:

Probability of Default – PD

The probability of default measures the likelihood of the obligor defaulting on the terms of a contract.

While highly relevant to what constitutes “good credit” and “bad credit”, the probability of default is often not a creditor’s central concern. That’s because a borrower may briefly default and then quickly correct the situation by making a payment, going as far as to pay up interest charges or penalties for missed payments. Non-payment for a limited period of time can cause a lender some financial strain particularly if the lender happens to be relying on the cash flows to meet their own obligations, otherwise, the tangible harm would be negligible. As a result, creditors tend to rely on other measures of risk in addition to PD.

PD is expressed as a percentage.

Loss Given Default – LGD

Loss given default (LGD): LGD represents the likely percentage loss if a borrower defaults. The severity of default is equally as important to a creditor as the probability of default itself.

A default event can be brief such that a borrower immediately makes the required payments resulting in a negligible loss for the lender. In such a scenario, the creditor would not be too alarmed. On the other hand, if payment ceases and no further revenue is ever received by the creditor, a substantial loss is incurred.

The probability of default and the severity of the loss as indicated by the loss given default are crucial to a creditor in the determination of the tangible expected loss.

Like the PD, LGD is expressed as a percentage.

Exposure at Default – EAD

Exposure at default is the total value a creditor is exposed to when a default event occurs. EAD may be expressed either as a dollar amount (i.e., outstanding loan balance) or as a percentage of the nominal amount.

Expected Loss – EL

Expected loss for a given time horizon is calculated as the product of the PD, LGD, and EAD \(\left( \text{i.e.,}\text{PD}\times \text{LGD}\times \text{EAD} \right) \).

Time Horizon

The longer the time horizon (i.e., tenor of the loan), the more likely it is that a default will occur. Both the EAD and LGD change with time. The former increases as the borrower draw on a credit line loan is fully drawn and decreases as it is gradually repaid. LGD can change over time, where the nature of the change is contingent on the specific terms and structure of the loan.

Example: Calculating Expected Loss

Prime Bank has examined its loan portfolio over the past 1 year and arrived at the following conclusions:

• The probability of default was 5%, adjusted for the size of the exposure.
• The loss given default over the period was 70%.
• The exposure at default was 60% of the potential exposure.

Calculate the expected loss given a one-year time horizon.

Solution

$$ \text{EL}=\text{PD}\times \text{LGD}\times \text{EAD}=\text{5}\%\times \text{70}\%\times \text{60}\%=\text{2.1}\% $$

Bank Failure vs. Bank Insolvency

Bank insolvency can be defined as a bank’s inability to pay its debts. This can happen in two ways:

1. If a bank’s liabilities exceed its assets, i.e., the bank owes more than it owns or is owed. This can happen if a bank loses too much of its investments, possibly a large amount in one investment vehicle.
2. If the bank has severe liquidity problems such that it cannot pay its debts as they fall due, even if its assets may be worth more than its liabilities.

Bank failure is the closure/collapse of a bank plagued by insolvency by a federal or state regulator. It results in significant losses to depositors and creditors.

In the 21^st century, banks regularly run into solvency issues but very few fail. The main reason behind this is that the failure of a bank often has huge implications on the economy as a whole and regulators/governments do everything possible to prevent this from happening. As a result, regulators find it more convenient and less expensive to simply fold an insolvent bank through a merger or acquisition, resulting in a stronger bank. This is especially true for systematically important banks (SIBs) that are considered too big to fail.

Statistics seem to back up the claim that banks rarely fail. In the United States, for example, only 50 banks failed between 2001 and 2008, half of which failed in 2008 at the height of the financial crisis. This equates to a rate of approximately 0.1% per year during the period. In the aftermath of the crisis, approximately 2% of banks failed in both 2009 and 2010. An additional 1.2% of banks failed in 2011. Furthermore, research has shown that nonfinancial institutions fail more often compared to banks.

In a bid to avoid financial stress and possible failure, banking regulators around the globe have come up with a range of preventative measures. For example, the Financial Stability Board has crafted additional capital requirements to improve the loss absorption capacity of some 29 systematically important financial institutions. This is informed by the fact that the failure of a SIB has a spillover effect that causes severe financial strain to other financial institutions.

Apart from mergers and acquisitions, banks are often bailed out by governments. The Federal Reserve, for example, acts a lender of last resort and often injects capital into banks under financial strain to forestall impending failure.

Although bank failures are rare, continuous credit analysis is important:

• By evaluating the default risk exposure of a particular bank, a counterparty credit analyst working for the bank is able to come up with reliable (quantitative) risk estimates that help in pricing and capital allocation by the bank.
• Even though the risk of default is low, the possibility of such an event is itself a worrisome thought for parties with credit exposure to such a bank. Consequently, parties have an incentive to avoid default-prone institutions.
• Although outright failure is of utmost concern, events that fall short of an outright failure can also be quite draining, both financially and psychologically. Reports of financial strain, for example, can trigger a drastic fall in the price of a stock, resulting in massive portfolio losses. Such a stock can take months or years to “crawl back” to its original position.

Practice Question

Caroline, a credit risk analyst at a major global bank, is reviewing a complex credit portfolio. The portfolio contains numerous financial instruments, including bonds, derivatives, and loans extended to various corporate entities. She observes that for a particular corporate loan, the borrower has an increased chance of defaulting due to recent economic conditions. The bank expects to recover only 40% of the outstanding balance if a default occurs. The outstanding balance of the loan stands at $5 million. Caroline plans to factor in a 1-year time horizon for her analysis.

Given this scenario, Caroline wants to calculate the expected loss on the loan. Which of the following formulas given by her team correctly calculates the expected loss based on the provided information?

A. Expected Loss = Probability of Default × Loss Given Default × Exposure at Default × Time Horizon

B. Expected Loss = Probability of Default × (1 – Loss Given Default) × Exposure at Default

C. Expected Loss = Probability of Default × Loss Given Default × Exposure at Default

D. Expected Loss = Probability of Default × Exposure at Default / Loss Given Default

Solution

The correct answer is C.

The Expected Loss (EL) on a credit exposure can be computed using the formula: EL = Probability of Default (PD) × Loss Given Default (LGD) × Exposure at Default (EAD). The PD represents the likelihood of the borrower defaulting, the LGD indicates the proportion of the exposure that will be lost if a default occurs, and the EAD represents the amount the bank is exposed to at the time of default. The formula captures the quantitative measurement of credit risk for the loan in question.

A is incorrect. The inclusion of Time Horizon in the formula for expected loss is not standard practice. While time horizon is an important factor in credit risk, particularly when considering the term structure of credit risk or PD term structures, it’s not directly multiplied in the expected loss formula as suggested.

B is incorrect. The formula incorrectly uses (1 – Loss Given Default). This would represent the recovery rate (the amount expected to be recovered in the event of a default), not the amount expected to be lost. While recovery rate is the complement of LGD, it’s the LGD that should be used in the expected loss formula.

D is incorrect. Dividing the Exposure at Default by the Loss Given Default does not make sense in the context of calculating expected loss. The expected loss is about multiplying the likelihood, the amount exposed, and the proportion lost – not dividing any of these factors.

Things to Remember

• Expected Loss (EL) quantifies the potential average loss a bank might face due to credit risk over a certain period.
• While Probability of Default (PD) gauges the default risk, the Loss Given Default (LGD) assesses the severity of the loss. The Exposure at Default (EAD) captures the potential outstanding amount when default occurs.
• EL forms a cornerstone in credit risk modeling, informing decisions on loan pricing, capital allocation, and risk management strategies.
• Recovery Rate is the flip side of LGD. If LGD indicates a 40% loss, the recovery rate would be 60%. It’s essential to use the correct metric in EL computations.
• EL doesn’t factor in extreme, unlikely losses. For such assessments, concepts like Unexpected Loss (UL) or Credit Value at Risk (CVaR) might be employed.