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Credit Risk Analytics

Table of Contents

Introduction

The rising number of credit risks and frauds in the finance sector has become an important point of concern for professionals and institutions across the globe. As a result, stepping into the credit market without any research and analysis is a blind shot that shall be avoided at every possible stage of credit management. Thus, to establish a profound credit ecosystem, the concept of Credit Risk Analytics has been brought to effective execution. Moreover, the financial ecosystem has witnessed the inclusion of tech stacks in its atmosphere which has not just boosted the reliability of the data but has ensured ease of use too. So, let’s find out how Credit Risk Analytics is shaping the lender and borrower’s relationship by acting as a vibrant Fintech tool.

Understanding Credit Risk Analytics

Precisely, the term Credit Risk refers to the risk of loss resulting from the borrowers’ inability to repay the principal to the lenders. Though in normal course lenders use interest payments to compensate for the potential losses from the borrower’s end yet it creates cash flow disruptions in the longer run. Thus, Credit Risk Analytics is a type of analysis performed by Credit Analysts for determining and defining the potential repayment risk associated with the borrowers. While conducting the process of Credit Risk Analysis the 5C’s of credits which are considered as the qualitative and quantitative measures are taken into consideration.

  • Borrower’s CAPACITY to repay credit
  • CHARACTER of the borrower
  • CAPITAL framework
  • Legal CONDITIONS
  • COLLATERAL attached

Purpose of Credit Risk Analytics

Backed by the important purpose of quantifying the level of risks, Credit Risk Analytics helps lenders to find out the creditworthiness of the borrowers and their ability to pay off debts. Alongside, analytics play a crucial role in determining the rate of interest, the higher the risk more is the interest rate, and vice-versa. The process involves assigning measurable numbers to estimate the probability of default by the borrowers.

The complete outcome of the Credit Risk Analytics helps to determine the risk rating which is then assigned to the borrowers and their ability to access credit is defined. In case the probability of the risk is higher and the borrower presents an acceptable level of documentation, the analyst can certainly recommend the approval of the credit application. Based on such probability of default the credit application is either approved or rejected by the Credit Analyst representing Financial Institutions.

Credit Risk Analytics Model

It refers to the analysis technique which helps to determine the probability of default for the potential borrower. Based on the information provided by this model level of credit risk is defined for the particular time frame. The detection of such credit risk is important to avoid future exposure to the risk of default and loss of funds. As a result, lenders rely on such validations which are backed by the theory of credit risk analytics model. With the evolution of technology financial institutions have made credit risk modeling systems more refined and have incorporated machine learning fundamentals with the basic model. The analytics–friendly languages such as Python and R are used to produce accurate and scientific data for establishing the precise definitions of credit risk probability.

To analyze Credit Risk these have been categorized into the following main types.

TypeDescription
Credit Default RiskThese types of risks occur when the borrower fails to pay the loan obligation in full or has already passed 90 days from the due date of the loan repayment. Such credit risks affect all credit-sensitive financial transactions and create a negative impact on the company’s ability to mitigate recession. 
Country RiskThese types of risks occur due to freezing foreign currency payment obligations. Such risk brings political instability and an adverse impact on the macroeconomic performance of the country as an institution.
Concentration RiskSuch risk arises as a result of exposure to a single counterparty/ sector and leads to a large number of losses which further threaten the core financial operations of the lenders. Since concentrated portfolios lack diversification, therefore, returns on related underlying assets are correlated and so is the credit risk.

Key Drivers for Quantifying Credit Risk

The analysts use certain key parameters to define the correlative relationship between the borrowers and their creditworthiness. These parameters are defined as the rate of default.

ParameterDescription
Probability of Default (POD)It is defined as the probability of the borrower not being able to make required principal and interest payments in a respective period. In most cases, default probability is defined based on the characteristics of the borrower and related economic environment. For Individual borrowers, the FICO score is used for the purpose as it helps in deciding the credit extension period of the borrowers. On the other hand, Credit Rating is used to understand the default probability of business entities.
Loss given Default (LGD)The term is used to represent the amount of money that the lender has suffered as a loss due to payment default from the borrower’s end. However, there is no defined method for quantifying the loss yet most lenders determine loss given default as follows:
Percentage of Total Exposure / Loss in the entire loan portfolio
Exposure at Default (EAD)As the name suggests this term measures the intensity of the loss in amounts that lenders are usually exposed to at the given point in time. The financial entities use internal risk management models to estimate the expected level of exposure to default. It is calculated for each loan separately to determine the overall default risk for the overall loan portfolio. There exist a negative relationship between the loan repayment and exposure to default as the value of exposure to default decreases with the ongoing principal and interest payments.

Credit Risk Analytics Process

The process of evaluating credit risk begins with borrowers’ loan applications and ends with the determination of their financial health. The credit analytics process helps to develop fair scenarios of the person’s creditworthiness and the credit risk thereupon.

  • The first stage of the credit risk analytics process is collecting information regarding borrowers’ history, financial transactions, source of income, credit score, market reputation, etc.
  • The next step calls for analyzing the information to establish an accurate credit report. A credit analyst evaluates all the related documents and financial statements to produce genuine credit risk analysis.
  • The final stage of the credit risk analysis process is the decision-making one and this is where the analysts either approve or reject the loan application.

Metrics used for Credit Risk Analytics

Credit analysts use various financial analysis techniques, such as ratio analysis and trend analysis for obtaining measurable numbers which further helps to quantify credit loss. The trend and ratio analysis being the profound techniques have certainly accelerated the credit risk analysis process.

Trend Analysis

 It acts as the important component for credit risk analysis for helping understand the client’s financial creditworthiness by incorporating techniques in understanding trends. The key components of credit risk trend analysis are mentioned below:

  • Client‘s business performance
  • Industrial and Market Ecosystem
  • National and Global Economic Trends
  • Client‘s Debt to Income Trends.

Ratio Analysis

The ratios analysis helps analysts and investors to determine the capability of the borrowers for fulfilling financial obligations. Some of the key ratios which act as the metrics for credit risk analytics are as follows:

RatioObjective
Profitability RatioIt measures the ability of the company to generate profit with respect to the revenue, shareholders’ equity, assets, and balance sheet.
Leverage RatioThe ratio compares the level of debt against the balance sheet figures, cash flow statement, income statement and helps gauge the financial ability of the borrower.
Coverage RatioSuch ratios measure the income, cash, and asset coverage to mitigate the potential debt and interest expenses.
Liquidity RatioUsing these ratios, analysts can determine the potential of the assets be readily convertible into cash and cash equivalents.

Roles and Responsibilities of Credit Analysts

Credit analysts are hired by institutions to assess the borrowing potential of the client and understand their financial health, In general, credit risk analysts are employed by credit card issuing companies. Commercial banks, investment banks, and credit rating agencies. These professionals are required to review the financial status of the potential and existing customers for the following crucial purpose.

  • Evaluating Credit Risk
  • Analyzing and reviewing financial data
  • Periodically reviewing the credit limit of the existing clients
  • Determining the credit limit of the new prospects. 

Final Words

Credit risk analytics is dominating the financial ecosystem owing to its immense support mechanism for leveraging fair transparency between lenders and borrowers. Moreover, to adjust well to this fast-changing world, professionals are also expected to stay updated with the latest technological advancements such as the emergence of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. Evidently, in recent times, the concept of Credit Risk Analysis has shifted to Credit Risk Analytics which denotes the perfect blend of Financial and Technical ecosystems into one common space.

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