counterparty credit risk interview questions

You should definitely have a solid answer ready for this question since the debt-to-equity (D/E) ratio is a key, if not the primary, financial ratio considered in evaluating a companys ability to handle its debt financing obligations.

Most of the job-specific questions an interviewee is likely to encounter revolve around these areas of knowledge.

Credit risk analysts must be experts at deciphering financial statements and evaluation metrics such as leverage and profitability ratios.

Beyond that, it should be noted that average D/E ratios vary significantly between sectors and industries. A more solid credit risk analysis includes an examination of the current state of the industry and the companys position within the industry, as well as consideration of other key financial ratios such as the interest coverage ratio or current ratio.

How you answer this type of question will display your ability to handle customer relations well and offer creative solutions for clients, while not endangering the banks position as a lender. A good answer might be something like, “I would offer a smaller loan amount I believe the bank could safely extend, and then let the client know the exact steps they could take to allow me to extend further credit, and offer to meet with them to review the situation at some appropriate point in the future to consider a larger loan.”

Top Credit Analyst Interview Questions and Answers

Below are our top credit analyst interview questions.

counterparty credit risk interview questions

Example Credit Risk Interview Questions:

If you’re considering a career in credit risk and want to prepare yourself for future interviews then take a look at some of our advice below. The interviewer may ask you specific questions about credit risk that are alluded to on the Job description, but may also focus on questions which test your analytical skills. Although it is difficult to predict the actual questions you’ll be asked, we’ve put together some useful questions and answers to guide you in the right direction!

This sounds straightforward, but this is a chance for you to really impress the interviewer with your knowledge and understanding of the sector. As a graduate, no one is expecting you to be an industry expert, but demonstrating that you have done your research helps to demonstrate your skills and enthusiasm. Do your research beforehand and brush up on credit risk related knowledge as well as why it is important to a company. Demonstrate that you know what the role of credit risk analyst entails and what the company does.

You should be able to genuinely demonstrate to the interviewer that you are passionate and enthusiastic about credit risk and why you would be suitable. You could talk about which elements of the job you find particularly interesting (which would further demonstrate your knowledge of the industry) and why credit risk motivates you. Remember to link your interest back to your own skills and experiences, interviewers like specific examples. Draw on particular modules you’ve covered at Uni, analytical techniques you’ve used, or industry relevant experience you may have gained.

Questions such as this aim to test the candidate’s analytical and logical capabilities. Employees who possess these skills help companies overcome challenges, or spot issues before they become problems. As credit risk involves gathering and analysing lots of kinds of data in detail, successful candidates need to demonstrate that they know how to solve problems efficiently. Your answer should show that you know how to plan carefully and how to use your time and resources effectively and that you can cope under pressure.

Demonstrate that you know how to gather and organise the necessary information and identify the best outcome. Use real life examples of when you’ve had to make a decision (either inside or outside a work environment) and clearly illustrate the process whereby you reached the answer, and if possible, try and relate this situation to the credit risk role.

10 Questions on Model Development and Validation

  • What is Probability of Default (PD)?
  • Average number of obligators that default in a particular rating grade in a year.
  • Estimated through logistic regression model. Where the outcome is dichotomous (1, 0 – indicating default, no default).
  • Few of the dependent variables: Current non-payment, Historical non-payment, Percentage of payment, Credit limit use, Maturity, etc.
  • What is Exposure at Default (EAD)?
  • Estimate of outstanding amount, in case the obligator defaults. Highly relevant in revolving balances
  • Focus on metrics that associate the increase in balances between reference time and date of default
  • Estimated through EADF: EADF = Balance at default / Balance at reference date
  • What is Loss Given Default (LGD)?
  • Percentage of exposure that the bank might lose if the obligator defaults. Dependent on the characteristic of the loan
  • For mortgages, collateral determines LGD. For credit card, there is no collateral, hence 3 months of cash flow post default is determined. In most of the cases, LGD is empirically drawn.
  • LGD = 1 – [Σ Payments for 3 months / max (Balancet, Balancet+1, Balancet+2, Balancet+3)]
  • What is the difference TtC and PiT PD?
  • Through the Cycle (TtC) PD: Take longer period into consideration, hence more stable.
  • Point in Time (PiT) PD: In line with recent macro-economic scenarios.
  • counterparty credit risk interview questions

  • What is Information Value (IV)?
  • IV is a very useful concept for variable selection during model development
  • IV is widely used in credit card industry
  • IV = Σ [(Distribution of Good – Distribution of Bad) x WoE
  • WOE = Log (Distribution of Good / Distribution of Bad)]
  • More the IV, more is the explanatory power of the variable
  • C Y=0Bad Y=1Good Y = 0%Bad Y = 1%Good WoE IV
    C1 2 1 0.250 0.125 -0.693 0.087
    C2 1 1 0.125 0.125 0.000 0.000
    C3 5 6 0.625 0.750 0.182 0.023
    Total 8 8 1.000 1.000 0.109
  • A typical monitoring and maintenance requires estimation of population stability index.
  • PSI = Σ [(% Actual – % Estimated) x Log (% Actual / % Estimated)]
  • PSI is checked to ensure that the model is not influenced by changes in economic conditions or changes in product offering due to internal policy changes.
  • PSI range:
    • PSI < 0.1: No action
    • Between 0.1 and 0.25: Monitor closely
    • PSI > 0.25: Need to redevelop the model
  • It is a N x N matrix, where N is the number of classes being predicted.
  • For dichotomous output N = 2.
  • Accuracy: Proportion of the total number of predictions that were correct.
  • Precision: Proportion of predicted positive cases that were correctly identified.
  • Recall / Sensitivity: Proportion of actual positive cases which are correctly identified.
  • Specificity: Proportion of actual negative cases which are correctly identified.
  • Concordant: A pair is concordant if 1 (observation with the desired outcome i.e. event) has a higher predicted probability than 0 (observation without the outcome i.e. non-event).
  • Discordant: A pair is discordant if 0 (observation without the desired outcome i.e. non-event) has a higher predicted probability than 1 (observation with the outcome i.e. event).
  • Tied: A pair is tied if 1 (observation with the desired outcome i.e. event) has same predicted probability than 0 (observation without the outcome i.e. non-event).
  • What is Gain and Lift Chart?
  • Gain and lift charts are mainly concerned to check the rank ordering of the probabilities.
  • Gain: The percentage of targets (events) covered at a given decile level.
  • Lift: It is the ratio of gain percentage to the random expectation percentage at a given decile level.
  • counterparty credit risk interview questions

  • What is KS, AUROC and Gini?
  • KS or Kolmogorov-Smirnov chart: It measures performance of classification models. The KS statistic gives the separation power of the model. It is calculated as the maximum of the absolute value of the difference between cumulative non-event and cumulative event. A good model will have a KS > 30. A high value of KS will depict over-prediction in the model.
  • AUROC curve: It is a fundamental tool for diagnostic test evaluation. It is plotted as a graph between sensitivity and 1-specificity, which we can get from the confusion matrix.
    • An ideal model will have AUROC very close to 1.
    • Lift is dependent on total response rate of the population, ROC curve on the other hand is almost independent of the response rate.
  • Gini coefficient: It is the ratio between area between the ROC curve and the diagonal line and the area of the above triangle. Gini = 2 x AUC – 1
  • FAQ

    What is counterparty credit risk example?

    Counterparty risk (also referred to as credit risk or default risk) is the risk that your counterparty in a transaction cannot honour its obligation to you. For example, you have bought a corporate bond from company XYZ, expecting to receive coupon payments and the nominal value of the bond at maturity.

    What is the difference between credit risk and counterparty risk?

    Credit risk is the risk for holding a risky bond. Counterparty risk is the risk that the counterparty will not be able to meet its contractual obligations if the credit event occur.

    How do you assess counterparty risk?

    Evaluating Counterparty Risk: Whom Can You Trust?
    1. Step 1: Prepare. …
    2. Step 2: Analyze Overall Financial Exposure. …
    3. Step 3: Identify Significant Counterparty Relationships. …
    4. Step 4: Identify Counterparties At Risk. …
    5. Step 5: Identify All Legal and Contractual Relationships with Significant Counterparties.

    Is counterparty credit risk part of market risk?

    CCR is a complex risk to assess. It is a hybrid between credit and market risk and depends on both changes in the creditworthiness of the counterparty and movements in underlying market risk factors.

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