In this chapter, credit risk in retail banking is examined. We study the different nature of retail and commercial credit risk including the demerits of the retail credit business. We also study credit scoring which is also a popular procedure in all sectors with the need for analysis of consumer credit standing or the approximation of the probability by a client to file a claim.
Some of the common aspects in this chapter are:
- Home mortgages: The residential property financed by the loan is used as security for fixed-rate mortgages and adjustable-rate mortgages with the loan-to-value (LTV) ratio, which is a key risk variable, representing the percentage of the value of the property the loan is financing.
- Home equity loans: They are a hybrid of a consumer and mortgage loan secured by residential properties and are sometimes referred to as home equity line of cred loans (HELOC).
- Installment loans: They are comprised of revolving loans like personal credit lines repeatedly used to a given limit. The loan is usually secured by residential properties or financial assets.
- Credit card revolving loans: This is an arrangement which allows for the loan amount to be withdrawn, repaid, and redrawn again in any manner and any number of times, until the arrangement expires.
- Small business loans: These are loans secured by business assets or the owner’s personal guarantees.
The Nature of Retail Credit Risk
Default by a single client does not pose a danger to the bank as they are less expensive, with their defining feature being that they arrive in bite-sized pieces.
The loss likely to be suffered in the event of a future default and the proportion of the bank’s portfolio likely to default are easily approximated as retail credit portfolios in normal markets tend to behave like well-diversified portfolios.
The implication of this ease of predictability is that the rate of expected loss can be built into the price the client is charged.
Another characteristic is that a change in the client’s behavior is an advance signal to the rise of default. To reduce the risk, the bank can, therefore, take pre-emptive actions based on these warning signals. However, it is usually the case that these early signals may be ignored by the banks since the bank can be steered away from the apparently lucrative business.
The idea of the relative predictability of the retail credit risk has been accepted by regulators, including the idea that the safety in mortgage loans can be attributed to the specific real estate asset the loan is being backed with.
Under Basel II and III, it is a requirement that a relatively small amount is set aside by retail banks as compared to that for corporate loans. However, for clearly distinguished segments of their portfolios, statistics on probability of default, LGD, and exposure should be provided to the regulators. Credit scores or other similar measures should be the basis of this segmentation.
Credit Scoring: Cost Consistency and Better Credit Decisions
A credit scoring model is always applicable when one is applying for a credit card, opening an account with a telephone company, submitting a medical claim, or even applying for auto insurance.
The credit applicant’s data is converted by a statistical procedure into numbers that will be combined for the score to be computed. The individual in question’s likelihood to repay is indicated by this score. High score implies lower risk.
Due to credit scoring, risky clients can be avoided by institutions. Moreover, through the comparison of the remaining profit margin (by taking gross revenues less operating and approximated costs of default), the assessment of the profitability of certain business types is possible.
Furthermore, the automation of the process of adjudication is possible for small credits and credit cards, by financial institutions through the application of credit scoring.
The data items taken from bureau reports and applications are weighted and treated by credit risk scorecards. The items correspond to credit card application questions or credit bureau report entries and are referred to as characteristics by the credit industry.
In addition to assessing whether the attribute is positive or negative, credit scoring will also assess by how much it is positive or negative. The ratio of a good event’s probability to the likelihood of a bad event is called the population odds.
Types of Credit Scoring Models that are Available
There are three types of models listed and explained below consumer credit applications to be scored:
- Credit bureau scores: They are popularly called FICO scores and are generic scores with low costs, easily installed, and an applicant’s creditworthiness is offered.
- Pooled models: By applying information gathered from a wide range of lenders having credit portfolios that are related, the creation of these models by outside vendors is possible. Even though they are not as expensive as custom models, these models are more costly than generic scores.
- Custom models: By applying information gathered from a lender’s credit applications’ unique population, the creation of these models is usually in-house and tailored to screen for a specified application profile for a given lender’s product.
Each individual with accrediting history has their information contained in the credit bureau data. The types of data contained in each credit files are:
- Identifying information: It is not usually considered much of credit information and, hence, not applied in scoring models. The nature of the data is dictated by laws that can be collected and set by the local jurisdictions.
- Public records: This data emanating from records of civil courts and is composed of tax lines, bankruptcies, and judgments.
- Collateral information: It is information reported by agencies tasked with debt collection or credit granting entities.
- Trade line/account information: It is from a compilation of monthly receivables information sent by credit grantors to credit bureaus.
- Inquiries: An inquiry must be placed on the file every time a credit is accessed. The inquiries placed are only viewed for new credit’s extension by credit grantors.
Individuals are allowed to obtain their own scores by some credit bureaus and be given some explanation of how their current scores can be improved. Based on a series of some key variables, a more all-encompassing credit score can be derived by the application of a bureau score.
From Cutoff Scores to Default Rates and Loss Rates
Credit scoring models during their earlier development days were designed to put applicants in rank order based on their relative risk, due to the fact that lenders used the models to choose an appropriate cutoff score.
With respect to the bank’s actual experience, determination of the loss rate and the retail product’s profitability is possible given the cutoff score. For each product’s profit margin to be optimized, adjustment of the cutoff score by the bank will also be essential to reduce the false goods and false bads.
The retail portfolios of banks should be segmented into sub-portfolios with similar loss features as required by the Basel Capital Accord. For these portfolios, both PD and LGD will have to be established by banks by segmenting each retail portfolio by score-bands each corresponding to a risk level.
Measuring and Monitoring the Performance of a Scorecard
Predicting applications that will turn out to be good or bad future risks is the purpose of credit scoring. By assigning high scores to good credits and poor scores to bad credits, distinguishing between the two is possible for the scorecard.
How a scorecard’s performance can be measured and knowing the time to adjust, recreate, or change the policy of operation of scorecards are the practical challenges risk managers are interested in.
The accuracy ratio (AR), cumulative accuracy profile (CAP), and its summary statistic are the traditionally applied validation methods. The CAP curve can be used to monitor a scoring model’s performance, and only when its performance deteriorates will the model be replaced.
The change in the scoring system’s performance is never abrupt but, due to some reasons, it deteriorates. Finally, if a financial institution changes the nature of products offered to clients, then the scoring model should be replaced.
From Default Risk to Customer Value
Data on a client’s current behavior is applied by the bank to determine the risk of default over a time horizon that is fixed. There is no restriction to the approximation of default in this risk modeling type. New areas have seen the application of credit scoring techniques with a number of different scores describing each customer.
Between customers’ profitability and their creditworthiness, a trade-off has to be made since issuing expensive credit cards to creditworthy clients is useless if they are never going to use them.
The sophistication in the interplay of risk and reward is being experimented, by leading banks, on how they can be accounted for. Traditional credit default scoring is being discarded and banks are slowly adopting customer profit scoring, where credit limits, interest margins, and other product characteristics can be adopted for the client’s profitability to be maximized.
Risk-based pricing is particularly becoming popular in the market for credit products. Market initiatives involve new clients being targeted and existing ones introduced to new products. Deciding the applications to be considered or rejected based on the scorecards is called screening applicants. By managing the account, decisions based on past behaviors or activities that were observed, can be made. These decisions range from: modification of credit line, product pricing, authorization of a temporary excess in the use of credit line, credit card renewal, and collection of past due interest. The loop of customer relation cycle is summed up by cross-selling initiatives.
Emerging trends in the market after the 2007 financial crisis are:
- The impact of changes in macroeconomic factors to a given score bands’ behavior for the adjustment of predicted defaults to account for the current stage in the cycle of the economy.
- A closer look at how variations in product offering can be tested for their responses and the early performance of those taking up retail offers are monitored.
The Basel Regulatory Approach
Features of retail loans’ whole portfolios are what interest lenders currently, and are with respect to the emphasis on internal ratings-based modeling in Basel II and III, allowing the use of either a standardized or advanced approach to computing the amount of regulatory capital required by the banks.
The three retail subsectors considered in the accord are residential mortgages, revolving credit, and other retail exposures. For the risk of the risk-weighted assets to be captured, three distinct formulas are applied as per the accord, hence the need for default probabilities’ accurate estimates to be developed and the loans portfolios to be segmented.
Securitization and Market Reforms
A bank can gain a principal payment upfront by selling loans to investors via some securitization, instead of the money trickling in over the retail product’s life. The selling of the securities might be to third parties or they can be issued in the public marketplace as tranched bonds.
Based on their legal frameworks, securitization can assume a multitude of forms.
Furthermore, structured with regulatory rules in mind, the securitization will reduce the amount of risk capital required by regulators to be set aside for a given client.
The well being of mortgage securitization markets was undermined by three key trends prior to the 2007-2009 financial crisis. These trends were:
- Lightly capitalized and regulated firms with no long-term interest in controlling the underlying loans’ quality started doing lending that was subprime and equivalently risky.
- There was wrapping up of subprime credit into complex securities, and they later were offered high ratings which again turned out to having assumptions that were fragilely based.
- The failure to distribute securitized loans with large amounts of securitized risks was, directly or through various kinds of investment vehicles, held by banks instead.
Risk-Based Pricing (RBP)
The implication of risk-based pricing to financial services is the explicit incorporation of risk-driven economics into the interest rate that is annualized and charged at the account level to the client.
Risk-based pricing adoption by many leading financial institutions has already taken place for their credit card loans, auto loans, and home mortgages business lines acquisitions. Through risk-based pricing, raising a bank’s shareholder value is made possible by achieving management objectives, while considering multiple constraints like: trade-offs among profit, market share, and risk.
The examination of bad accounts’ rates by retail banks will, in a risk-adjusted manner, increase market share. This examination will be as a function of the overall population’s percentage of acceptance rate.
Lastly, when credit is offered by nonbanks to clients and small businesses, the application of risk-based pricing should be mandatory. But logistical and operational infrastructures are a requirement in this arrangement, which is lacking by most retailers.
1) FICO Scores, or the credit scores most lenders use to determine your credit risk, most likely come from:
- A wide range of lenders having credit portfolios that are related
- Outside vendors
- A credit bureau
- A custom model
The correct answer is C.
FICO Scores are the credit scores most lenders use to determine your credit risk. You have FICO Scores from each of the three credit bureaus—Experian, Equifax and TransUnion. Each score is based on information the credit bureau keeps on file about you.
They are generic scores with low costs and are easy to install.