How to do credit risk analysis and why it makes a difference?
Credit risk analysis is the borrower's ability to assess the
credit or debt securities issuer, such as bonds or loans. The purpose of credit
risk analysis is to measure and manage the potential damage that the lender or
investor may face if the borrower or issuer Fails to pay for your
responsibilities.
Credit risk analysis can help lenders and investors decide
whether to approve or reject the loan application ، or buying or selling debt
security, based on their risk appetite and return expectations.
Improve prices:
Credit risk analysis can help lenders and investors pay the
price of debt or debt security by determining the appropriate interest rate or
production in it The included credit reflects the risk level.
Reduce losses:
Credit risk analysis can help lenders and investors reduce
their losses in the default case ، Different risk mitigation strategies, such
as diversity, collateralization, colorants, hedging, etc.
How to perform credit risk analysis?
Depending on the type and nature of the borrower or issuer,
the type and purpose of debt or debt security. there are different methods and
models for credit risk analysis, and the availability and quality of data.
Some common methods and models are:
This is a method that involves analyzing the financial
statements of the borrower or issuer, such as income statement, balance sheet ،
Cash flow statement etc., Assessing its financial performance, position and
liquidity. Analysis of financial statements can use different proportions and
indicators, such as profit ratio, leverage ratio, liquidity ratio, coverage
ratio, etc., Measuring the ability and consent of the borrower or issuer to pay
their responsibilities.
Default probability models:
These are the models that assess the possibility of a
default (PD) of the borrower or issuer ، Based on various factors and
variables, such as credit history, market conditions, economic indicators, etc.
Default probability models can use different techniques, such as regression
analysis, logistical regression, survival analysis, etc. To predict the possibility of defaulting on
the horizon at a specified time.
Machine Learning Models:
These are models that use artificial intelligence (AI)
Techniques, such as nerve networks, decision trees, auxiliary vector machines,
etc., Learning from large and complex datasets and identifying patterns and
relationships that affect the credit risk of the borrower or issuer. Machine
Learning Model Different features to enhance the accuracy and performance of
credit risk analysis, such as behavioral data, social media data ، can use
alternative data etc.
To conclude
Credit risk analysis is an important skill for lenders and
investors who want to make voice loans or investment decisions. Using various
different methods and models for credit risk analysis ، Lenders and investors
can measure and manage credit risks and the return of their potential and
existing lenders and issuers. Credit risk analysis can help lenders and
investors improve their profits, reduce their losses and gain a competitive
advantage in the market.
