Credit Risk Modelling: What and How does it Matter?

Risk modelling is the process of analyzing historical risk events and quantifying risk using mathematical and statistical methodologies. Risk modelling enables businesses in practically every industry to better analyze, manage, and mitigate their unique risks.

Risk models are used in commercial and consumer finance to evaluate the risk of loss due to loan default or prepayment. Credit risk modelling is critical for lenders to sustain profitability.

In this post, we’ll walk you through a high-level look at credit risk modelling, as one shop-stop solution.

What is Credit Risk?

Credit Risk is referred to as the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual commitments. It usually refers to the risk that a lender will not receive the owed principal and interest, resulting in a disruption in cash flow and higher collection costs.

Excess cash flows can be issued to provide extra credit risk protection. When a lender faces increased credit risk, a higher coupon rate can mitigate the risk by providing more cash flow.

While it’s hard to predict who will default on their commitments, correctly analyzing and managing credit risk can help to mitigate the severity of a loss. Interest payments from a debt obligation’s borrower or issuer are a lender’s or investor’s incentive for engaging in credit risk.

Types of Credit Risks


  1. Credit Default Risk

Credit default risk comes into the picture when a borrower is unable to pay the loan in full or has already missed the loan payback deadline by 90 days. There is a credit default risk associated with all credit-sensitive financial transactions, such as loans, bonds, securities, and derivatives.

A broader economic shift can influence the level of default risk. It could also be due to a change in the borrower’s economic status, such as greater competition or a recession, which affects the company’s capacity to set aside principal and interest payments on the loan.

  1. Country Risk

Country risk is the possibility that a country will default on its debts if it freezes its foreign currency payments obligations. The risk is linked to the country’s political unrest and poor macroeconomic performance, both of which could have a negative impact on the value of its assets and operating profitability. Changes in the business environment will have an impact on all businesses in a given country.

  1. Concentration Risk

Concentration risk refers to the risk posed by exposure to a single counterparty or sector, and it has the potential to result in huge losses that could jeopardize the lender’s main operations. The danger arises from the fact that highly concentrated portfolios lack diversification, resulting in more linked returns on the underlying assets.

A business borrower who relies on one major buyer for its primary products, for example, faces a high level of concentration risk and could suffer significant losses if that buyer stops buying their products.

What is Credit Risk Modelling?

Credit risk modelling is applying risk models to lender operations in order to develop methods that maximize return (interest) while minimizing risk (defaults).

Credit risk models help in estimating the likelihood of a loan default or prepayment. The lender faces a loss of interest revenue in the event of either default or prepayment.

Factors Affecting Credit Risk Models

Meisenzahl, et al., validate a long-held view that auto loan credit performance is connected to unemployment levels in their research on consumer auto loans. They calculated that the “unemployment rate explained about 55 % and 66 % of the loss performance in prime and subprime auto loan losses, respectively,” and that a “100% increase in unemployment rate resulted in a 119 % increase in prime loan losses during economic downturns with rising unemployment rates.”

When calculating credit risk, there are a few important aspects to consider. From the borrower’s financial health to the repercussions of default for both the borrower and the creditor, there are a number of macroeconomic factors to consider. A borrower’s credit risk is influenced by three primary elements.

1. The Probability of Default (PD)

This is the most essential aspect of a credit risk model since it refers to the chance of a borrower defaulting on their loans. This score is calculated for individuals based on their debt-to-income ratio and current credit score.

Rating organizations such as Moody’s and Standard & Poor’s determine this probability for entities that issue bonds. In most cases, the PD sets the interest rate and required down payment.

2. Loss Given Default (LGD)

The amount of loss a lender will incur if a borrower defaults on the loan is referred to as loss-given default (LGD). Consider two debtors, A and B, who have the same debt-to-income ratio and a similar credit score. Borrower A takes out a $10,000 loan, whereas Borrower B takes out a $200,000 loan.

The two borrowers have distinct credit profiles, and because Borrower B owes a higher amount, the lender stands to lose more money if he defaults. Despite the fact that there is no standard method for determining LGD, lenders look at the complete portfolio of loans to calculate the total risk of loss.

3. Exposure at Default (EAD)

When calculating credit risk, there are a few important aspects to consider. From the borrower’s financial health to the repercussions of default for both the borrower and the creditor, there are a number of macroeconomic factors to consider. A borrower’s credit risk is influenced by three primary elements.

Credit Risk Modelling Algorithms

It usually comes down to assessing a probability or possibility that a risk event will occur to measure risk. Companies can reduce risk by modifying the factors they can control or hedging their losses for factors they can’t.

1. Prediction using Risk Models and Algorithms

Choosing and creating a prediction model is a common part of estimating a probability. Prediction’s purpose is to correctly predict a target variable.

As previously stated, the goal variable in credit risk models is frequently the event of a loan default or prepayment. The risk model is built or “fit” using historical data on actual loans, and the fitted model then delivers estimated probability for different values of the components.

Prediction methods and models come in a variety of shapes and sizes. Popular predictive models for binary outcomes like default/non-default or prepayment/non-repayment include logistic regression, decision trees, neural networks, the Naive Bayes classifier, and others.

Factor values on a fresh loan application can be “run through” a fitted predictive model to estimate the likelihood of default or prepayment. This is an element of the “credit scoring” procedure.

2. Dimension Reduction and Variable Selection

There are several approaches for determining the most predictive collection of elements from a vast set of prospective components. Exploratory tools like charts and summary statistics to advanced dimension reduction approach like principal components and factor analysis are among these strategies.

Finding the best set of variables (or factors) results in lower measurement and data storage costs, more accurate predictions, and a better understanding of the risk-risk factor relationship.

3. Credit Risk Modelling Optimization

The term “mathematical optimization” refers to a group of techniques that are frequently employed in conjunction with predictive models. While predictive models evaluate the likelihood of default or prepayment in credit risk modelling, optimization approaches can be used to discover the ideal mix of credit products in a portfolio to minimize risk while maximizing expected return.

Your Takeaway

Credit risk modelling still has a variety of methodologies, and different approaches function better in certain lending conditions. Of course, credit risk modelling has progressed as well, especially with the introduction of modern analytics tools.

The ease and accuracy of credit risk modelling have substantially improved due to the use of R, Python, and other analytics-friendly computer languages. Credit risk modelling is indeed a small field, but it offers excellent career opportunities for those with a strong understanding of analytics and the financial sector.

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Author: Sravani Kinjarapu


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