Book 2. Credit Risk

FRM Part 2

CR 6. Credit Scoring and Rating

Presented by: Sudhanshu

Module 1. Credit Scoring and Rating Systems

Module 2. Credit Rating Agency Methodologies

Module 1. Credit Scoring and Rating Systems

Topic 1. Credit Scoring System to Credit Rating System

Topic 2. Types of Credit Rating Systems

Topic 3. Developing Credit Scoring and Rating Models

Topic 1. Credit Scoring System to Credit Rating System

  • Credit Score: A numerical score, typically in a range like 300-850, used to assess the creditworthiness of individuals and small firms. It is an automated, objective system.

  • Credit Rating: A letter-grade rating (e.g., AAA, BBB, C) used for larger firms and governments. Ratings provide a long-term, through-the-cycle view of credit risk.

  • Key Benefits of these systems:

    • Reduces subjectivity in credit appraisal.

    • Promotes transparency and consistency.

    • Reduces the time and cost of credit evaluation.

    • Enables quantitative analysis such as scenario analysis and stress testing.

Practice Questions: Q1

Q1. ABC Bank needs to conduct a risk assessment for a large manufacturing firm that is publicly traded. Which of the following statements regarding this assessment is correct? The bank should use a:
A. credit score to reduce subjectivity.
B. credit score to enhance transparency.
C. credit rating to reduce the cost of appraisal.
D. credit rating despite the limitations for stress testing.

Practice Questions: Q1 Answer

Explanation: C is correct.

Borrowers that are large firms (especially those that are publicly traded) should use credit ratings. Smaller firms and private individuals should be assessed using credit scores. The advantages to using credit scores/ratings include reduced subjectivity, reduced time/cost of appraisal, enabling analysis (e.g., scenario analysis or stress testing), and promoting transparency and consistency.

Topic 2. Types of Credit Rating Systems

  • Through-the-Cycle Approach:

    • A long-term perspective that considers data from an entire business cycle.

    • Ratings are designed to be stable, with minimal changes during short-term economic fluctuations.

    • This is the method used by credit rating agencies.

    • It is best suited for assessing long-term loans and investments.

  • Point-in-Time Approach:

    • A short-term perspective that focuses on the current economic and financial situation.

    • These assessments are more volatile as they capture real-time default risk.

    • Often used by internal bank models.

    • It is best suited for assessing short-term loans and portfolio management.

Topic 3. Developing Credit Scoring and Rating Models

  • Behavioral Scoring:

    • Assesses the historical financial behavior of existing customers.

    • The score is updated in near real-time based on factors like payment history, purchases, and probability of default.

    • It is widely used in consumer lending to manage existing accounts and debt collection.

  • Profit Scoring:

    • Focuses on the estimated profitability of a loan, rather than just default risk.

    • Includes two types:

      • Account-level: Calculates the profit for each individual account.

      • Customer-level: Aggregates profit across all accounts held by a single customer.

Practice Questions: Q2

Q2. A lender is considering using a through-the-cycle approach when assessing the creditworthiness of a potential borrower. Which of the following statements regarding this approach is correct? This method:
A. will capture default risk in the best way possible.
B. should be used if the loan is short-term in nature.
C. should be used if the loan is long-term in nature.
D. is a robust option because it relies on internal data only.

Practice Questions: Q2 Answer

Explanation: C is correct.

Through-the-cycle approaches are used by credit ratings agencies, not by internal models. They consider data from an entire business cycle, which is long-term in nature. For this reason, this method is best suited for long-term loans. Point-in time assessments use short-term data, and they capture real-time default risk better than the long-term focused through-the-cycle approach.

Practice Questions: Q3

Q3. A risk analyst is considering the best method to generate a credit score that updates in near real-time while considering the payment and purchase history of a customer. Which of the following methods for developing credit risk scoring and rating models should he use?
A. Behavioral scoring.
B. Social lending scoring.
C. Account-level profit scoring.
D. Customer-level profit scoring.

Practice Questions: Q3 Answer

Explanation: A is correct.

Behavioral scoring updates a credit score in near real-time with factors such as a customer’s payments, purchases, and probability of default. The two profit scoring approaches (account-level and customer-level) offer different vantage points on a method focused on profitability rather than on the behavior of a customer. Social lending is an emerging innovation with a credit risk scoring system still in its infancy.

Module 2. Credit Rating Agency Methodologies

Topic 1. Regulatory Requirements (Basel Committee)

Topic 2. Social Lending

Topic 3. Data Collection and Preprocessing Details

Topic 4. Model Fitting

Topic 5. Model Validation

Topic 6. Definition and Validation of Ratings

Topic 7. Implementation, Monitoring, and Review

Topic 8. Criticisms of Credit Rating Agencies

Topic 1. Regulatory Requirements (Basel Committee)

  • The Basel Committee outlines five basic requirements for credit scoring and rating systems to ensure proper governance and integrity:

    • Meaningful Differentiation of Risk:

      • ​Credit grades must be based on genuine risk differences, not designed to manage regulatory capital requirements.

      • Borrowers in the same grade may be treated differently based on transaction-level factors.

      • There must be a reasonable dispersion of borrowers across credit grades to avoid concentration.

    • Continuous Evaluation of Borrowers: Borrower ratings should be updated regularly, with annual reviews as a minimum.

    • Operational Oversight: Systems should be continuously monitored for integrity, proper functioning, and adequate controls (e.g., stress testing).

    • Correct Selection of Risk Assessment Metrics: The risk factors used must adequately predict a borrower's creditworthiness, aiming for estimates that are as close as possible to actual outcomes.

  • Collecting Substantial Data: The data must accurately represent reality and should incorporate historical data, past scores/ratings, prior default estimates, and payment history.

Topic 1. Regulatory Requirements (Basel Committee)

Topic 2. Social Lending

  • Social Lending (Peer-to-Peer Lending):

    • An emerging innovation in consumer credit where individuals lend to each other directly.

    • It operates with a credit risk scoring system that is still in its infancy compared to traditional models.

    • Often, these platforms use a combination of traditional credit data and non-traditional data (like social media profiles or online behavior) to assess risk.

Topic 3. Data Collection and Preprocessing Details

  • For Corporate Customers:

    • Financial Data: Ratios for profitability, leverage, liquidity, and cash flow.

    • Qualitative Data: Management quality, competitive position, industry risks, and corporate governance structure.

    • Market Data: Stock price and volatility for public companies.

  • For Consumer Loans:

    • Application Data: Borrower's income, assets, and liabilities.

    • Bureau Data: Payment history and credit utilization.

    • Behavioral Data: The customer's recent financial behavior with the lender.

  • Preprocessing: This involves data cleaning, normalization, and handling missing values to prepare the data for the model.

Practice Questions: Q1

Q1. Moody’s is considering a revision to their credit rating model. When they checked the model derived from model training data against out-of-sample data, they found a few issues to address. Which of the following steps in the credit rating development process were they likely on when they noticed this issue?
A. Model fitting.
B. Preprocessing.
C. Model validation.
D. Validation of the risk rating.

Practice Questions: Q1 Answer

Explanation: C is correct.

The credit score/rating process has five steps:

(1) data collection and preprocessing,

(2) model fitting,

(3) model validation,

(4) definition and validation of the risk rating, and

(5) implementation.

Step 3, model validation, involves checking the model developed in Step 2 with out-of-sample data.

Topic 4. Model Fitting

  • The second step involves fitting a model to a training dataset.

  • This is an optimization problem to find the best-fit parameters for a given model, often using a linear regression or a logistic regression approach for a binary (default/non-default) dependent variable.

  • The general formula for the model's score is:

​

  •                 are the risk attributes (e.g., income, debt).
  •                are the coefficients or weights for each attribute.
  •    is the constant term.
  • The objective is to find the optimal parameter vector     that minimizes the model's error on the training data, as defined by a loss function L:

​

 

f(x)=\beta_0+\beta_1 x_1+\beta_2 x_2+\ldots+\beta_n x_n
x_1, \ldots, x_n
\beta_1, \ldots, \beta_n
\beta_0
\beta^*
\beta^*=\arg \min _{\beta \in \mathbb{R}^n} L(\beta, X)

Topic 5. Model Validation

  • This crucial step is an out-of-sample test that checks the model's predictive power on new data it has not seen before.

  • Key Validation Techniques:

    • Backtesting: Uses historical data to simulate how the model would have performed in the past.

    • Walk-Forward Testing: A systematic process that evaluates the model's stability by moving the test period forward in time.

    • Resampling Techniques: Methods like bootstrapping and cross-validation are used to enhance the robustness of the validation.

    • Benchmarking: The model's output is compared to an external source (e.g., a credit rating agency's rating) to identify and analyze any deviations.

Topic 6. Definition and Validation of Ratings

  • After validation, the numerical credit scores are mapped to a specific risk rating class (e.g., A, B, C).

  • Each rating class is associated with an empirical probability of default (PD).

  • The goal is to ensure:

    • Meaningful Differentiation: Each rating class is statistically distinct from its neighbors.

    • Reasonable Dispersion: The distribution of borrowers is not excessively concentrated in a single risk grade.

  • This process must also account for rating migration, which is the movement of a borrower from one rating class to another over time.

Topic 7. Implementation, Monitoring, and Review

  • Implementation: The developed system is integrated into lending operations to provide real-time risk estimates for loan applications and existing accounts.

  • Monitoring: The model's performance should be continuously monitored to detect any degradation in its predictive accuracy.

  • Review and Recalibration: Periodic, comprehensive reviews are necessary to ensure the model remains accurate and relevant. If performance degrades, the model needs to be recalibrated using new data.

Topic 8. Criticisms of Credit Rating Agencies

  • Lack of Transparency: The models used are proprietary and not publicly disclosed, making it difficult to verify their fairness or accuracy.

  • Potential Conflicts of Interest: A significant conflict exists because agencies are paid by the very entities they rate, raising questions about objectivity.

  • Poor Predictive Ability: CRAs have been widely criticized for failing to predict major financial crises and corporate failures (e.g., Enron, Lehman Brothers).

  • Procyclicality: Despite their stated "through-the-cycle" approach, ratings have been shown to be overly optimistic during economic booms and overly pessimistic during downturns.

  • Promoting a Debt Explosion: By providing an illusion of risk management and reducing risk premiums, CRAs may encourage excessive borrowing and increase systemic risk.