modelling ecl under ifrs 9

​Modeling Expected Credit Loss (ECL) under IFRS 9 involves a process where financial institutions estimate the likely losses on their financial instruments due to credit risk. This process includes several key steps:

1. Identification of Financial Instruments: First, identify the financial instruments that are subject to ECL accounting, such as loans, debt securities, and trade receivables.

2. Credit Risk Assessment: Assess the credit risk associated with each financial instrument. This involves analyzing the borrower’s creditworthiness, past payment history, and any relevant economic factors that might impact their ability to repay.

3. Stage Allocation: Financial instruments are categorized into one of three stages based on their credit risk:

• Stage 1: Instruments that have not had a significant increase in credit risk since initial recognition. ECL is measured over the next 12 months.

• Stage 2: Instruments that have had a significant increase in credit risk but are not yet credit-impaired. Lifetime ECL is measured.

• Stage 3: Instruments that are credit-impaired. Lifetime ECL is measured.

4. Calculation of ECL: Calculate the Expected Credit Loss for each stage. This involves estimating the present value of all cash shortfalls over the expected life of the financial instrument. The ECL is a probability-weighted estimate, taking into account the likelihood of default and the loss given default.

5. Forward-looking Information: Incorporate forward-looking economic information into ECL estimates. This could include forecasts of economic conditions like GDP growth, unemployment rates, and interest rates.

6. Measurement Frequency and Updates: Regularly measure and update the ECL calculations to reflect changes in credit risk and economic conditions.

7. Disclosure and Reporting: Financial institutions need to disclose their ECL calculations, methodologies, and the impact on their financial statements in accordance with IFRS 9 requirements.

8. Data Management and Model Governance: Ensure robust data management practices and model governance to support the ECL calculation process. This includes data quality checks, model validation, and regular reviews of the assumptions and parameters used in the models.

This process requires a combination of statistical modeling, credit analysis, and understanding of economic factors. Institutions typically use a range of data sources and modeling techniques to estimate ECL accurately.