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LO 34.4: Describe the different types and structures o f credit derivatives including credit default swaps (C D S), first-to-default put, total return swaps (TRS), asset- backed credit-linked note (CLN ), and their applications.
One critique of traditional credit risk mitigation techniques is that they do not unbundle the credit risk from the underlying asset. Credit derivative products, such as credit default swaps, first-to-default puts, total return swaps, and asset-backed credit-linked notes, are over-the-counter financial contracts that respond directly to this critique. Payoffs for these instruments are contingent on changes in the credit performance or quality of a specific underlying issuer. Therefore, these tools directly enable one party to transfer the credit risk
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of a reference asset to another party without ever selling the asset itself. In doing so, credit derivative products also aid in price discovery aimed at isolating the economic value of default risk.
Consider an example of the usefulness of credit derivatives. Bank A specializes in lending to the airline industry, while Bank B specializes in lending to energy firms. When energy prices are high the energy firms tend to do well, while airlines languish. The reverse is true when energy prices are low. Bank A and Bank B could mitigate their credit risk by either directly selling 30% of their loans to each other, or they could use credit derivatives to more cost effectively meet the same need.
Investors and financial institutions can use credit derivatives to accomplish several different goals. Credit derivatives can provide access to specialized risk factors for both risk mitigation and for speculation. As previously mentioned, these credit products also unbundle credit risk from ownership of an underlying asset, effectively creating two unique tradable assets. Credit derivatives also provide yield enhancement and a mechanism to hedge industry- specific and country-specific risks borne by an investor or institution. Hedge funds, and other speculative investors, also use credit derivatives to exploit arbitrage opportunities.
Credit Default Swaps
The most popular form of credit derivative is the credit default swap (CDS). A CDS is customizable insurance against the default of some underlying asset. It is a de facto put option on the underlying asset. The protection buyer is trying to outsource credit risk, while the protection seller is actually buying credit risk by providing the insurance. Think of the protection buyer like a put buyer and the protection seller like a put seller. The protection buyer will make pre-specified payments to the protection seller over a pre-specified time period and the protection seller is liable for making the protection buyer whole if a credit event occurs. Hence, a single-name CDS operates essentially as an insurance contract but a key difference is that the protection buyer need not actually own the underlying asset. Figure 1 illustrates the mechanics of the single-name CDS.
Figure 1: CDS Structure
For example, if an institutional investor owned $100 million of a certain companys debt and wanted to protect himself from a potential default, the investor could enter into a CDS contract with 130 basis points with quarterly payments. That means that the investor would pay $375,000 [(1.5% x $100,000,000) / 4] every quarter for the length of the contract. The length of a CDS contract is usually much less than the duration of the underlying asset. If this debt instrument matured in 15 years, then the CDS contract might have a 23 year tenure. If this debt issuance has a yield of 4.5% then this protection buyer has enabled a 3.0% annual return without any default risk for the tenure of the CDS contract.
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Professors Note: According to the Bank for International Settlements (BIS), credit default swaps were virtually non-existent in 1997, ballooned to $62.2 trillion in 2007, and subsequently fell to approximately $16 trillion of notional value by the end o f 2014.
Why would an investor pay for credit default protection using a CDS contract? Obviously, they are concerned about a negative credit event impacting the value of the underlying asset. Payment to the protection buyer is triggered with several potential credit events. The first potential trigger event is bankruptcy. Payment can also be triggered by a specified drop in the value of the underlying asset that does not include bankruptcy. Downgrade below a certain threshold and unfavorable debt restructure can also result in a payment. The International Swaps and Derivatives Association (ISDA) has the final say on whether a credit event has officially triggered a CDS payment.
If a payment is triggered, the payment could be made in one of three ways. The first payment option is the par value of the underlying asset minus the post-default price. This is essentially a make me whole payment. The second payment option is par value minus a stipulated recovery factor. Most corporate bonds have a contractually stipulated 40% recovery rate. This means that the CDS was priced so that the most that a protection buyer could recover is 60% of the notional principal. The third payment option is the full payment of the par value, without any subtractions, but the protection buyer must also physically deliver the underlying asset to the protection seller. This is also a make me whole payment.
Credit default swaps can be used for risk management, but they can also be used for direct speculation. During the months and years leading up to the 20072009 financial crisis, some savvy investors who saw trouble on the horizon purchased CDS contracts against the default of various companies. In this way, these investors were using CDS contracts to replicate put options. However, the CDS contract is only as good as the creditworthiness of the counterparty. This risk should be considered as well. Lehman Brothers and American International Group (AIG) were two significant counterparties that did not weather the financial crisis very well.
First-to-Default Puts
A variation of the CDS is known as a first-to-default put. To explain this innovation, it will be easiest to consider a bank that holds four different B-rated high yield loans. Each loan has notional principal of $100 million, a five-year maturity, and an annual coupon of LIBOR plus 250 basis points. The idea is that this pool of loans has very low default correlations. The bank could purchase a first-to-default put for two years at perhaps 400 basis points. This means that the bank would pay $4 million annually for two years. In return for this insurance premium, the bank would be made whole in the event that any one of the four bonds defaulted. If two bonds default, then they still are only paid for the first bond and the second becomes a loss event. This is a much more cost effective option for the bank if the loans truly have uncorrelated default risks. The cost of the first-to-default put will lie between the cost of a CDS on the riskiest bond and the total cost for a CDS on all bonds.
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Total Return Swaps
While a CDS contract outsources only the credit risk, a total return swap (TRS) outsources both credit risk and market risk. A TRS is designed to mirror the return on an underlying investment, such as a loan, a floating rate note, a coupon bond, a stock, or a basket of assets. There are two parties to a TRS contract. The payer is the owner of the underlying asset. The payer will agree to pay the underlying assets total return, including any price appreciation and coupon or dividend payments, to a receiver. The receiver is responsible to pay the payer for any depreciation in the asset. The receiver will also pay LIBOR plus a predetermined spread to the payer regardless of what happens with the reference asset.
Figure 2: Total Return Swap Structure
LIBOR +/- Spread + price depreciation
In essence, the receiver is taking a synthetic long position in the underlying asset without actually owning the underlying. Tremendous leverage can be applied by the receiver because they do not need to invest the notional principal. They only need enough money to make payments when due. Hedge funds are one common receiver counterparty in TRS contracts. Banks are usually the payer counterparties because they have access to capital to purchase the underlying assets but are looking to remove credit and market risk exposure from their balance sheets.
Asset-Backed Credit-Linked Notes
A further innovation called an asset-backed credit-linked note (CLN) embeds a default swap into a debt issuance. A CLN is a debt instrument with its coupon and principal risk tied to an underlying debt instrument, like a bond, a loan, or a government obligation. Unlike a CDS contract, principal is exchanged when a CLN is sold to an investor, although the CLN seller retains ownership of the underlying debt instrument.
A CLN is best understood with an example. Assume there is a hedge fund that wants to capture $123 million of exposure to a fixed income instrument, but it only wants to invest $25 million as collateral. A bank agrees to help in the process. The bank will borrow $123 million at LIBOR and purchase the reference asset, which is currently yielding LIBOR plus 250 basis points. The reference asset is placed in a trust, which then issues a CLN to the hedge fund. The hedge fund will then give the bank $25 million, which is invested in a risk- free U.S. government obligation yielding 4%. This investment now represents the collateral
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on the banks borrowing. That means there is 20% ($23 million / $ 123 million) of collateral and a leverage multiple for the hedge fund of 5 times ($125 million / $25 million).
Remember that the reference asset is yielding LIBOR plus 250 basis points. The bank will keep LIBOR plus 100 basis points, using the LIBOR return to fund the cost of acquiring the reference asset and the additional 100 basis points as compensation for assuming the risk of default above the collateral of $25 million. The hedge fund will receive the 4% earned on the $25 million of collateral ($ 1 million) and will also receive the additional 150 basis point spread over the funding cost on the full $125 million ($1,875 million in additional return). This means that the hedge fund has captured an 11.5% leveraged return [($1 million + $1,875 million)/$25 million initial investment]!
There are no margin calls with a CLN, but there is still risk on the table for the hedge fund and for the bank. Should the reference asset lose more value than the $25 million in collateral, the hedge fund will default on its CLN obligation and lose the full $25 million investment. The bank will be responsible for any losses greater than the $25 million of hedge fund collateral. Thus, the bank will likely look to outsource this risk using a CDS contract.
T h e C r e d i t R i s k S e c u r i t i z a t i o n P r o c e s s

LO 34.3: Describe the originate-to-distribute model o f credit risk transfer and discuss the two ways o f managing a bank credit portfolio.
Over the last two decades, portfolios of loans in the banking industry have become much more concentrated in less creditworthy borrowers. During periods of crisis, default rates increase and this creates significant losses for debtholders. In 1990, defaults cost approximately $20 billion, while in 2009 they cost $628 billion. At the same time, banks have developed strong relationships with large corporate borrowers and robust distribution networks for securitized loan transactions. These realities coupled with Basel capital adequacy standards have led financial institutions to switch from traditional, originate-to- hold, lending models to the portfolio-based, originate-to-distribute (OTD), model.
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OTD models have produced three primary benefits. The first benefit is that loan originators enjoy increased capital efficiency and decreased earnings volatility because the credit risks have been largely outsourced. The second benefit is that investors have a wider array of diversification options for the fixed income portion of their portfolios. The third benefit is that borrowers have expanded access to credit and lowered borrowing costs. From one vantage point, the 20072009 financial crisis occurred partly because banks did not pursue these three benefits through a pure OTD model, but instead held very concentrated risks on their balance sheets through SIVs.
Professors Note: According to the Federal Reserve Bank o f New York, in 1988, 21% o f term loans originated by a lead syndicator were held on their balance sheet. This percentage declined to 6.7% in 2007 and 3.4% in 2010.
Under the traditional originate-to-hold (OTH) lending model, credit assets are retained at the business unit level. Loans are originated using a binary (accept or reject) approval process. At loan origination, risk is measured based on notional value and then left unmonitored thereafter. The compensation structure in the OTH system is based on loan volume. More loans mean more profit potential.
At its core, the OTD model involves dividing loans into two groups: core loans and non- core loans. The bank will typically hold the core loans and either securitize or outright sell the non-core loans it has originated. Here, loan origination focuses on charging a sufficient risk-adjusted spread over the banks hurdle rate. After origination, the OTD model transfers credit assets to the credit portfolio management group who monitors the apparent risks until the asset is transferred off the originators books. One key function of the credit portfolio management group is to monitor credit risk concentrations and to outsource any risks that could potentially threaten bank solvency. Some credit portfolio strategies are therefore based on defensive risk mitigation and not solely on a profit motive. Ultimately, all returns are measured in a risk-adj listed format and compensation is based on risk- adjusted performance.
The OTD model enables financial institutions to provide access to capital for less creditworthy borrowers and then subsequently sell or hedge their lending risks through the use of credit risk mitigation techniques and credit derivatives.
C r e d i t D e r i v a t i v e s

LO 34.2: Identify and explain the different techniques used to mitigate credit risk, and describe how some o f these techniques are changing the bank credit function.
When conducted properly, the credit risk transfer process can be revolutionary for financial institutions. Proper techniques can include bond insurance, collateralization, termination, reassignment, netting, marking to market, syndication of loan origination, or outright selling of a loan portfolio in the secondary market.
As its name suggests, bond insurance is a formal process of purchasing insurance against the potential default of an issuer. In the corporate debt market, it is the lender who needs to purchase default protection. However, in the municipal bond market, insurance is purchased by the issuer of the municipal obligation. Approximately one third of all municipal bond offerings are covered by insurance. This insurance coverage enables these municipalities to issue debt at reduced rates. Some issuers also need to utilize guarantees and letters of credit, which are both de facto forms of insurance. These tools involve a third party with a higher credit rating than the issuer agreeing to make good on any deficiencies should the issuer default.
The use of collateralization is perhaps the longest running form of credit risk mitigation. The losses sustained by the lender will be offset by the value of the collateral in the event of a default. One issue that needs to be considered is liquidity of the collateral. Liquidity is the ability to turn an asset into cash quickly while retaining reasonable value. In the 20072009 financial crisis, we learned that mortgage lending, while secured with real estate as collateral, is riskier than it sounds because the same risk event that triggered the default of securitized loans also caused the value of the collateral to deteriorate.
.Another risk mitigation option is termination. Using this tool means that the debt obligation is canceled prior to maturity at a mid-market quote. Usually, this means that a certain trigger event, such as a downgrade, has occurred and the issuer was obligated to repay the loan early. An alternative to outright termination is known as reassignment, which gives the right to assign ones position as a counterparty to a third party if a trigger event occurs.
Sometimes a counterparty will enter into numerous derivative transactions with the same financial institution. Some of these derivatives will have a positive replacement value while others will have a negative replacement value. All over-the-counter (OTC) derivatives transactions between common counterparties can be aggregated together so that the net replacement value represents the true credit risk exposure. This process is known as netting.
Marking to market is another option. This tool involves periodically acknowledging the true market value of a transaction. This transparency will result in immediately transferring value from the losing side to the winning side of the trade. This process is extremely efficient, but it does require sophisticated monitoring technology and back-office systems for implementation.
Some financial institutions also utilize loan syndication when an issuer needs to raise a significant amount of capital or perhaps the credit risk is more than a single lender would
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choose to absorb. The syndication process will involve multiple lenders all working together as a team to provide funding to a given borrower. The lead syndicator will receive a fee from the issuer for arranging the syndicate.
Professors Note: According to the Federal Reserve Bank o f New York, the syndicated loan market grew from $339 billion in 1988 to $2.2 trillion in 2007.
Typically, syndicated loans end up being traded on the secondary markets, and there is an emerging trend for certain entities (e.g., insurance companies) to match the duration of their long-term liabilities with loan pools purchased from banks. These are secondary market transactions. This innovation can mitigate risk for the loan-originating bank and also the insurance company at the same time. The risk reduction is not limited to insurance companies. Secondary market transactions are being used for risk mitigation at all financial institutions.
The traditional model of the bank credit function is changing as a result of these credit risk mitigation techniques. Traditionally, banks would hold credit assets, like loans, until they matured. As such, banks focused on estimating the expected loan losses using the notional value of the loan and the probability of default (PD). These expected losses formed the basis of loan loss reserves held on the banks balance sheet and were explicitly priced into the spread charged on the loan over the banks funding costs. However, the real world has proven that there are many unexpected events that are not captured by normal probability estimates.
Sometimes unexpected events materialize because of credit concentration in loan pools. Many banks become highly concentrated either in their geographic dispersion of loans, their industry exposure, or even in a small number of issuers due to a special relationship with the bank. One of the credit departments key responsibilities is to increase the velocity of capital by using lower cost borrowing to facilitate more profitable opportunities. Credit mitigation techniques enable banks to continue extending credit to riskier borrowers and to more concentrated pools of issuers and then transfer the credit risk to keep overall risk within an acceptable margin.
T h e O r i g i n a t e -t o – D i s t r i b u t e M o d e l o f C r e d i t R i s k

LO 34.1: Discuss the flaws in the securitization o f subprime mortgages prior to the financial crisis o f 2007.
The financial credit crisis of 2007 is thought by some to have been caused by the process of transferring credit risk. However, during the credit crisis, the credit risk transfer mechanism did perform its intended function. The true issue was not the credit risk transfer process itself, but rather the underlying flaws in the pre-crisis securitization process.
Securitization in its most basic form is simply using financial engineering to repackage a pool of assets into a new asset that can be sold to investors. This innovation enables banks to transfer the credit risk inherent in mortgage lending to investors through mortgage- backed securities and similar investments. This process enhances the availability of loanable funds for borrowers, expands the pool of diversification options for investors, and minimizes the borrowing costs for a given risk-class of borrowers.
The securitization process enabled an active originate-to-distribute model where banks could originate a loan for the sole purpose of turning a quick profit and selling the securitized product to investors. This creates a conflict of interest because every link in the securitization chain, from the originator to the lender to the investment banker to the credit rating agency, had the potential to earn a relatively quick, short-term profit through securitization without retaining any of the risk, which was ultimately outsourced to investors. The gains in the securitization supply chain were linked solely to deal completion and not to the potential risk of the borrowers. In this process, U.S. financial institutions
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misjudged the liquidity and credit concentration risks inherent in mortgage lending. This paradoxical deviation from the traditional risk-reward tradeoff created less incentive to monitor the creditworthiness of borrowers and reduced accountability. It is one of the key flaws in the securitization process.
Professors Note: According to the Joint Center for Housing Studies at Harvard University, from 2001 to 2006, conventional mortgages (30-year, fixed rate mortgages) fell from 57.1% o f all loan originations to 33.1% . Subprime loans rose from 7.2% to 18.8% ! Approximately 40% o f all mortgages purchased by Fannie Mae and Freddie Mac, from 2005 to 2007, were subprime loans. Demand increased substantially for loans built with adjustable interest rates, zero down payments, and no documentation o f income sources.
Another flaw in the securitization process is the opaqueness of the end product. Neither investors nor the rating agencies, whom the investors relied upon, fully understood how to evaluate the multilayered securitized products. Investors did not fully understand either the credit quality of the underlying loans or the potential correlation within the loan pool should an unexpected shock occur. This created a very fragile system based on trust, which was later broken when the expected low default rates exceeded a margin of safety.
A third flaw is that as time progressed without any initial problems, many banks elected to start retaining the risk of structured products. In fact, in mid-2007, U.S. financial institutions directly held $900 billion of subprime mortgage-backed products on their books. They used rolling short-term debts to finance purchases of long-term mortgage- backed structured products in off-balance-sheet entities known as structured investment vehicles (SIVs) partly because mortgages earned much higher returns than corporate bonds. Banks thought that they were earning a riskless spread over corporate bonds with comparable ratings. By using a leveraged SIV instead of holding the actual loans on their balance sheets, banks were able to use far less capital to hold a pool of mortgages. On one hand, this flaw appears to be a partial correction for the first flaw. Banks did start to retain some risk, but they used a mechanism that still prevented investors from evaluating the full extent of the risk.
In fairness, banks sometimes used SIVs as a warehouse for unsold structured products that were waiting to be matched with a willing buyer, but more often, the securitized products were seen as a sound investment by themselves. Some of the largest buyers of securitized U.S. subprime loans were European banks. For example, in 2006, subprime securities accounted for 90% of the profits of Sachsen Landesbank in Leipzig, Germany. This bank probably does not sound familiar, because it is no longer in existence. The bank ceased operations in 2007 due to its excessive level of leveraged risk exposure.
When structured with transparency, the credit risk transfer mechanism should assist the price discovery process for credit risk. If the three flaws identified can be adequately addressed, then financial institutions can once again use the credit transfer process to effectively manage risk.
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C r e d it Ris k M it ig a t io n Te c h n iq u e s

LO 33.8: Discuss the benefits o f risk-based pricing o f financial services.
Recognizing that charging a single price for a product to all customers regardless of risk levels may lead to adverse selection (i.e., high-risk customers attracted to a relatively low price relative to their risk profile and low-risk customers pushed away by the higher price relative to their risk profile), lenders have been moving toward risk-based pricing (RBP). RBP involves lenders charging different customers different prices based on their associated risks. Although RBP is still in the early stages of implementation in the financial retail sector, it has been utilized more frequently in credit card, home mortgage, and auto loan lines.
Key external and internal factors which account for risk and play into the interest rates and prices charged by lenders include:
The probability of take-up (i.e., acceptance by the customer of the offered product). The probability of default (PD). The loss given default (LGD). The exposure at default (EAD). The cost of equity capital to the lender. Capital allocated to the transaction. Operating expenses of the lender.
Prices may be set on a tiered level based on score bands allocating risks from high to low. The lender can then map pricing strategies to metrics such as profit/loss, revenue, market share, and risk-adjusted return at the various score bands. Utilizing RBP effectively allows management to evaluate the inevitable tradeoffs among profitability, market share, and risk with the short and long-term goal of increasing shareholder value.
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K e y C o n c e p t s
LO 33.1
Retail banking involves the acceptance of deposits and lending of money to customers. Credit risk (the probability that a borrower will default on debt obligations) represents the biggest risk in retail banking. Other risks include operational risks, business risks, reputation risks, interest-rate risks, and asset valuation risk.
LO 33.2
Retail credit risk differs from corporate credit risk in the following significant ways:

Retail exposures are relatively small such that one default has minimal impact, whereas commercial exposures are much larger and single defaults can have a significant impact. Losses exceeding expected thresholds can have a much greater impact for corporate portfolios than retail portfolios. Lenders can take preemptive actions to reduce retail credit risks, whereas commercial portfolios often send warning signals after it is too late.

LO 33.3
The dark side of retail credit risk occurs when a large scale risk factor causes a decline in asset values coupled with an increase in default rates. The end result is losses which exceed an estimated threshold. Lenders offering mortgage loans must evaluate customers ability to pay as well as determining whether a mortgage is qualified. In addition, banks must segment their portfolios and set aside risk capital as well as assess exposures and probabilities of default along with potential losses.
LO 33.4
A credit risk scoring model assigns (to each credit applicant) a score which serves as a measure of borrower risk; the higher (lower) the score, the lower (higher) the risk that the borrower wont be able to pay the debt obligation. Models include credit bureau scores, pooled models, and custom models which all use applicant data and weight them based on their historical relationship to potential defaults.
LO 33.3
Key variables associated with mortgage credit applications include FICO scores, loan-to- value ratios, debt-to-income ratios, payment types, and documentation types. Cutoff scores are thresholds set by lenders which dictate whether credit will or will not be extended, as well as terms associated with the loans. Probability of default and loss given default metrics are critical to assessing the risk associated with various lender portfolios.
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LO 33.6
The cumulative accuracy profile (CAP) and the accuracy ratio (AR) are used to assess the performance of a credit scorecard. The closer the accuracy ratio is to 1, the more accurate the CAP model is at predicting the distribution of defaults relative to the risk levels of the associated population.
LO 33.7
For new and existing credit applicants, lenders may use a variety of scorecards to evaluate both creditworthiness and potential profitability. The customer relationship cycle involves marketing products, screening applicants, managing customer accounts, and eventual cross- selling to an existing customer base.
LO 33.8
Risk-based pricing (RBP) involves charging different prices for the same product such that higher (lower) prices can be charged to higher (lower) risk customers. Several external and internal factors are used to determine the prices charged, which are then evaluated in conjunction with various key performance metrics at each score (risk) band in order to maximize the tradeoff between risk and profitability.
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C o n c e p t C h e c k e r s
1.
2.
3.
4.
5.
Which of the following statements is most accurate regarding risks incurred by retail lenders? A. Reputation risk is more of a concern for the borrower rather than the lender. B. Business risk relates to the day-to-day operational risks of the business. C. Credit risk relates to the potential for a lender to default on their obligation. D. Refinancing a mortgage when rates decrease is an example of asset valuation risk.
The dark side of retail credit risk is perpetuated by all of the following factors except: A. capital set aside to protect a bank in the event of default. B. process flaws resulting in high risk applicants receiving credit. C. new products which do not have sufficient historical loss data. D. a social acceptance of bankruptcy and borrowers walking away from their
obligations.
Which of the following statements is correct regarding credit risk scoring models? A. A pooled model will result in scores ranging from 300 to 830. B. A custom model is cheaper to implement than credit bureau scores. C. Multiple requests for new credit will reduce an applicants credit score. D. An example of a characteristic in a scoring model is the applicants current gross
salary of $30,000.
In assessing the key variables associated with a potential mortgage loan, a bank will charge a higher interest rate if the borrower has a relatively: A. high FICO score. B. high loan-to-value ratio. C. low debt-to-assets ratio. D. low debt-to-income ratio.
By implementing risk-based pricing on its mortgage products, a bank will likely charge a: A. higher interest rate to a customer with a higher FICO score. B. C. higher interest rate to a customer with a higher probability of default. D. lower interest rate to a customer positioned on a lower relative score band.
lower interest rate to a customer with a lower credit bureau score.
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C o n c e p t C h e c k e r An s w e r s
1. D Refinancing a mortgage is considered a prepayment risk to the lender, which is a component
of asset valuation risk. When rates decrease, borrowers are more likely to refinance their existing (higher rate) mortgage into a lower rate obligation. The lender then earns less in interest on the debt obligation than they would have previously. Reputation risk is primarily a concern for the lender. Business risk relates to strategic risks tied to new products and volume, while credit risk is the risk that the borrower (rather than the lender) will default.
2. A Capital must be set aside to protect banks in the event of default, but this is a response to the
dark side of retail credit risk rather than a perpetuating factor. A process flaw which grants credit to high risk individuals, a new product which doesnt have historical loss data, and the social acceptance of failing to meet debt payments are all considered perpetuating factors of retail credit risk.
3. C An individuals credit file will show a history of credit requests, with multiple requests
causing an applicants credit score to decline. A credit bureau score model (rather than pooled model) will result in scores ranging from 300 to 850. A custom model is more expensive to implement than credit bureau scores. Gross salary with current employer is an example of a characteristic, with the actual salary number itself representing an attribute.
4. B The loan-to-value ratio represents the amount of the mortgage versus the appraised value of the property. The higher this ratio is for a property and an associated borrower, the more risk there is to the lender. In order to protect their position, a lender will charge a higher interest rate. Each of the other scenarios will result in a lower interest rate.
5. C The more likely it is that a customer will default, the higher the interest rate the bank will charge. A customer with a higher (lower) FICO/credit bureau score will be offered a lower (higher) interest rate. A customer positioned on a lower relative score band will be offered a higher interest rate.
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The following is a review of the Credit Risk Measurement and Management principles designed to address the learning objectives set forth by GARP. This topic is also covered in:
T h e C r e d i t T r a n s f e r M a r k e t s a n d T h e i r Im p l ic a t io n s
Topic 34
E x a m F o c u s
Securitized financial products became very popular prior to the 20072009 financial crisis. Although it is important for investors to understand the inner workings and risk potential inherent in any investment before adding it to a portfolio, more complex assets such as securitized products demand even more scrutiny. For the exam, be able to identify flaws in the securitization of subprime mortgages, and be able to explain the different techniques used to mitigate credit risk. Also, be able to describe the different types and structures of credit derivatives, including credit default swaps (CDSs), first-to-default puts, total return swaps (TRSs), and asset-backed credit-linked notes (CLNs). It is also important to be familiar with the structures of collateralized debt obligations (CD Os), synthetic CD Os, and single-tranche CDOs.
F l a w s i n t h e S e c u r i t i z a t i o n o f S u b p r i m e M o r t g a g e s

LO 33.7: Describe the customer relationship cycle and discuss the trade-off between creditworthiness and profitability.
Entities in the business of loaning money do not focus entirely on risk and creditworthiness; they also have to evaluate customers from the perspective of profitability. If a credit card is issued to a customer with a very high FICO score who pays their bill in full every month, the bank will not earn any interest from that customer on borrowed funds. At the same time, issuing that same credit card to a customer with a low FICO score is a greater risk because the customer may be unable to pay back loaned funds. Along with credit default scoring, lenders are using product and customer profit scoring measures to evaluate the potential profitability of a specific product and the potential profitability of a specific customer.
In utilizing scorecards to evaluate customers, there are several variations beyond just the credit bureau (FICO) scores. These additional scorecards can be used to evaluate both creditworthiness and profitability. They include:
Revenue scores’, used to evaluate existing customers on potential profitability. Application scores: used to support the decision to extend credit to a new applicant. Response scores’, assign a probability to whether a customer is likely to respond to an offer.
Insurance scores: assign a probability to potential claims by the insured.
Behavior scores: assess existing customer credit usage and historical delinquencies.
Tax authority scores: predict where potential audits may be needed for revenue collection. Attrition scores: assign a probability to the reduction or elimination of outstanding debt
by existing customers.
The customer relationship cycle involves the process a lender goes through to market its products/services, screen applications from customers, manage customer accounts, and then cross-sell to those customers. Marketing efforts will focus on selling new or tailoring existing products to meet the needs of both new and existing customers. Applicant screening involves the acceptance or rejection of an application based on scorecards noted previously,
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as well as ultimately determining the appropriate price to charge for accepted applicants. Managing the customer account will primarily involve product pricing, credit line authorizations, modifications, renewals, and principal or interest collections. Cross-selling efforts will target existing customers by offering other lender products to meet their needs.
R i s k -B a s e d P r i c i n g

LO 33.6: Discuss the measurement and monitoring o f a scorecard performance including the use o f cumulative accuracy profile (CAP) and the accuracy ratio (AR) techniques.
Credit scoring is used as a means of predicting default risk, such that high (low) scores on the scorecard are assigned to strong (weak) credits. In assessing the performance of the scorecard, a cumulative accuracy profile (CAP) and the accuracy ratio (AR) are often used. The CAP shows the population distribution based on credit scores (and therefore risk) versus the percentage of actual defaults.
Figure 1: Cumulative Accuracy Profile and Accuracy Ratio
Perfect Model (5% default rate)
Actual Rating Model (cumulative % defaults)
(cid:31)—————- Population Distribution Based on Credit Scores —————-(cid:31)
High Risk
Low Risk
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Lines plotted on the graph include the perfect model line, random model line, and observed cumulative default percentage line defined as follows:

In a perfect model, if the bank predicts, for example, 3% of its accounts will default over a specific period, 100% of those defaults will come from the riskiest 3% of the population.
A random model will assume 5% of the defaults will come from the riskiest 5%, 20%
will come from the riskiest 20%, etc.
The observed cumulative default line represents the actual defaults observed by the bank.
The area between the perfect model and the random model is represented by Ap, while the area between the observed cumulative default percentage line and the random line is represented by AR. The accuracy ratio (AR) is defined as AR/Ap, with a ratio close to 1 implying a more accurate model.
A scoring model must be monitored on a regular basis due to underlying changes in the population as well as potential product changes.
T r a d e o f f b e t w e e n C r e d i t w o r t h i n e s s a n d P r o f i t a b i l i t y

LO 33.5: Discuss the key variables in a mortgage credit assessment and describe the use o f cutoff scores, default rates, and loss rates in a credit scoring model.
In assessing an application for mortgage credit, the key variables include:

FICO score: a numerical score serving as a measure of default risk tied to the borrowers credit history. Loan-to-value (LTV) ratio: the amount of the mortgage divided by the associated propertys total appraised value.
Debt-to-income (DTI) ratio: the ratio of monthly debt payments (mortgage, auto, etc.)
to the monthly gross income of the borrower. Payment (pmt) type: dictates the type of mortgage (adjustable rate, fixed, etc.)
Documentation (doc) types, which include:
(cid:31) Full doc. a loan which requires evidence of assets and income. (cid:31) (cid:31) No ratio: similar to stated income, employment is documented but income is not.
Stated income, employment is verified but borrower income is not.
The debt-to-income ratio is not calculated.
(cid:31) No income/no asset. Income and assets are provided on the loan application but are
not lender verified (other than the source of income).
(cid:31) No doc. no documentation of income or assets is provided.
C u t o f f S c o r e s
Cutoff scores represent thresholds where lenders determine whether they will or will not lend money (and the terms of the loan) to a particular borrower. As noted earlier, the higher the score, the lower the risk to the lending institution. Setting the cutoff score too low presents a higher risk of default to the lender. Setting the cutoff score too high may limit potential profitable opportunities by unintentionally eliminating low risk borrowers.
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Once the cutoff score is established, historical experience can be used to establish the estimated profitability for a specific product line and the associated loss rate. As estimates are made from longer time horizons (which hopefully capture a full economic cycle), a bank may adjust its cutoff score to maximize the appropriate balance between risk and profitability.
Banks are required by the Basel Accord to group their portfolios into subgroups that share similar loss attributes, with score bands used to differentiate the groups by risk levels. For each of these subgroups, banks are required to estimate the PD and the LGD. The implied PD is a by-product of the historical loss rate and the LGD such that if a portfolio has a loss rate of 3% with a 73% LGD, then the PD is 4% (i.e., 3%/75% = 4%).
S c o r e c a r d P e r f o r m a n c e

LO 33.4: Define and describe credit risk scoring model types, key variables, and applications.
A credit risk scoring model takes information about an applicant and converts it into a number for the purpose of assessing risk; the higher the number, the higher the probability of repayment by the borrower and the lower the overall risk. Credit scoring models facilitate the gathering of an enormous amount of information into a single automated process.
A credit risk scorecard will gather information from applications and credit bureau reports and weight it depending on the type of questions answered. The question/entry will ask for a specific characteristic like number of years with current employer, and the attribute will be the response (e.g., 10 years). Credit scoring models will determine positive and negative values and weight each attribute according to past history and the associated probability of repayment.
Three model types exist in regard to scoring applications for consumer credit:
Credit bureau scores: this refers to an applicants FI CO score, and is very fast, easy, and
cost effective to implement and evaluate. Scores will typically range from a low of 300 to a high of 830, with higher scores associated with lower risk to the lender and lower interest rates for the borrower.
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Pooled model: this model, built by outside parties, is more costly than implementing a credit bureau score model; however, it offers the advantage of flexibility to tailor it to a specific industry.
Custom model: created by the lender itself using data specifically pulled from the lenders own credit application pool. This model type allows a lender to evaluate applicants for their own specific products.
Every individual with a credit history will have credit files containing the following information:
Personal (identifying) information which doesnt factor into scoring models.
Records of credit inquiries when a file is accessed. Requests for new credit will be visible
to credit grantors.
Data on collections, reported by entities that provide credit or agencies that collect
outstanding debts. Legal (public) records on bankruptcies, tax liens, and judgments.
Account and trade line information gathered from receivables information sent to credit
bureaus by grantors.
M o r t g a g e C r e d i t A s s e s s m e n t

LO 33.3: Discuss the dark side o f retail credit risk and the measures that attempt to address the problem.
An unexpected, systematic risk factor may cause losses to rise beyond an estimated threshold, damaging a banks retail portfolio through declines in asset and collateral values and increases in the default rate. This represents the dark side of retail credit risk.
Primary causes include:
The lack of historical loss data due to the relative newness of specific products. An across the board increase in risk factors impacting the economy overall that causes
retail credit products to behave unexpectedly.
An evolving social and legal system which may inadvertently encourage defaults. An operational flaw in the credit process due to its semi-automated structure that results
in credit granted to higher risk individuals.
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The Consumer Financial Protection Act (CFPA), in an attempt to manage the dark side of retail credit risk, requires credit originators to evaluate qualified mortgages and ability to repay.
A borrower with a qualified mortgage is assumed to have the capacity to repay. A qualified mortgage will put a limit on the amount of income allocated to debt repayments (e.g., debt- to-income ratio < 43%). A qualified mortgage cannot have excess upfront fees and points, may not be balloon payment loans or interest-only loans, may not be for longer than 30 years, and may not be negative amortization loans. When a lender is evaluating a customers ability to repay, the following underwriting standards must be considered: Credit history. Current income and assets. Current employment status. Mortgage monthly payments. Monthly payments on mortgage-related items such as insurance and property taxes. Monthly payments on other associated property loans. Additional debt obligations of the borrower. The monthly debt-to-income ratio resulting from the mortgage. Due to the predictable and relative safety of retail credit, banks must set aside a relatively small amount of risk capital compared to requirements associated with corporate loans. Banks must provide regulators with specific statistics associated with differentiated segments of their portfolios. These statistics include: probability of default (PD), exposure at default (EAD), and loss given default (LGD). C r e d i t R i s k S c o r i n g M o d e l s