International Financial Reporting Standard 9 (IFRS 9)

International Financial Reporting Standard 9 (IFRS 9)
11 Tem 2023

IFRS 9 (International Financial Reporting Standard 9) is an international financial reporting standard that specifies reporting requirements for financial instrument accounting, valuation and risk of default. It aims to enable financial institutions to value financial instruments more effectively, manage risks and report their financial performance more accurately.

IFRS 9 fulfils the previous accounting standard IAS 39 and was introduced to address issues arising from the financial crisis. IAS 39 has been a standard that has been criticized for not having an adequate forecast in its default risk valuation and for failing to adequately reflect the true risks that surround many financial institutions during the financial crisis. IFRS 9 is intended to provide a further approach by addressing these issues. When calculating default risk and expected loss, it adopts an approach based on macroeconomic data. Macroeconomic data includes general economic indicators such as economic growth, unemployment rates, interest rates and inflation.

Expected Credit Loss (ECL) is one of the core concepts of IFRS 9. ECL refers to the estimated amount of possible losses for financial instruments exposed to the risk of default. The ECL calculation is made by taking into account the Probability of Default (PD) of all expected future cash flows of financial instruments at risk of default and the Loss Given Default (LGD) rate that will occur after this default. 

The PD model quantifies the probability of default of a particular debtor or addressee. Financial indicators include a variety of factors such as past performance, industry trends, and macroeconomic variables. PD models analyze large data sets using statistical techniques such as logistic regression, process analysis, or machine learning algorithms and attempt to accurately predict the likelihood of the default. PD is usually expressed as a percentage.

Financial institutions use historical data, credit ratings, credit scores, and other relevant information to calibrate PD models. By assigning probabilities to different default scenarios, institutions can set appropriate risk-based provisions for expected credit losses. Verification processes should be implemented regularly to ensure the accuracy and robustness of these models. 

LGD refers to the percentage of loss (loss of repayments) incurred by a bank or lender in the event that a borrower defaults (fails to repay) the loan.  LGD models analyze past recovery rates and estimate the amount of possible loss by applying statistical techniques. Factors such as collateral quality, asset type, order of priority, and economic conditions have an impact on the forecast. In addition, stress tests are often performed to assess how LGD will be affected under adverse scenarios.

Macroeconomic data can influence PD (Probability of Default) and LGD (Loss After Default) estimates. For example, during periods of economic recession, the likelihood of default in general may increase and loss rates may rise. Therefore, financial institutions often analyze macroeconomic data to meet IFRS 9 requirements and develop models and scenarios to estimate default risk and expected loss based on this data.

However, the macroeconomic data and approaches to be used by each financial institution may be different. This may vary depending on the industry in which the organization operates, geographical location and other factors. Therefore, while it is correct to say that IFRS 9 is based on macroeconomic data, how organizations use this data in line with their strategies and requirements may vary.

In this article, we have discussed the definition of IFRS9 and explained one of its basic concepts, the calculation of expected loss.

As GTech, we are able to perform ECL calculation quickly and effectively within the scope of IFRS9 through Risk Confidence, a product of Moody’s Analytics. You can contact our GTech Risk Management experts for detailed information.

Author: GTech Risk Management Consultant İrem Tüfekçi