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Expected Credit Loss or ECL Calculation

Expected Credit Loss (ECL) is used to estimate the risk of loss due to a borrower's failure to repay a loan or meet contractual obligations. It is directly linked to credit risk, which is the possibility of a loss resulting from a borrower's inability to repay a loan or meet credit obligations. ECL calculation involves two key components, Exposure at Default (EAD) and Loss Given Default (LGD). EAD estimates the total amount at risk at the time of default, reflecting the outstanding loan balance or credit exposure. LGD represents the portion of EAD that is not recovered by the lender once a default occurs, after accounting for the recovery of any collateral. By multiplying EAD by LGD and the probability of default (PD), financial institutions can assess the expected loss from credit risk, allowing for better risk management and compliance with regulatory requirements such as those under the International Financial Reporting Standard 9 (IFRS 9). This approach helps in provisioning for potential losses, ensuring that lenders maintain adequate capital reserves to cover credit risks.

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ECL-Calculation

How does Roopya implement ECL Calculation?

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ECL is measured as follows:

Step Description Formula/Specification Notes
1. Exposure at Default (EAD) EAD = Current Balance + Undrawn Commitments EAD measures the total exposure at the point of default. Include all credit lines available to the borrower, not just the current balance.
2. Probability of Default (PD) PD = Historical Default Rate or Scorecard Output PD is the likelihood that the borrower will default over a specific time period (e.g., one year). This can be estimated based on historical data or through statistical models.
3. Loss Given Default (LGD) LGD = (1 - Recovery Rate) * 100% LGD represents the percentage of the EAD that is expected to be lost if a default occurs. Recovery rates can be estimated from past recoveries or industry data.
4. Discount Factor (DF) DF = 1 / (1 + Discount Rate)^t The discount factor is used to present value the future cash flows (losses) back to the reporting date. 't' represents the time in years until the cash flow occurs.
5. Expected Credit Loss (ECL) ECL = PD * LGD * EAD * DF This is the final calculation step, combining the PD, LGD, and EAD, adjusted by the discount factor for the present value of the expected loss.

Notes:

  • PD, LGD, and EAD must be estimated for the lifetime of the exposure for a lifetime ECL calculation, or over a 12-month period for a 12-month ECL calculation.
  • The Discount Rate used in the DF calculation should reflect the current market assessment of the time value of money and the risks specific to the cash flows, often based on the effective interest rate (EIR) of the financial instrument.
  • For IFRS 9 purposes, the ECL calculation may need to consider different scenarios (e.g., base, optimistic, and pessimistic scenarios) to capture economic uncertainties, and the final ECL would be a probability-weighted amount across these scenarios.
  • Accurate estimation of PD, LGD, and EAD is critical. These parameters should be regularly updated based on new information and analysis to reflect the current risk profile of the exposure.

Various Approaches for calculating ECL

The following approaches can be used to calculate ECL based on the loan portfolio:

Approach Description Example Suitability
Standardized Approach Utilizes fixed parameters defined by regulatory authorities for PD, LGD, and EAD. Applying regulator-set PD and LGD for unsecured loans and calculating EAD directly from the loan balance. Suitable for smaller institutions with limited data on loan performance, or for standardized products like personal loans.
Foundation Internal Ratings-Based (F-IRB) Approach Institutions estimate PD internally, while LGD and EAD parameters are provided by regulators. Estimating PD based on internal credit scoring models for a portfolio of auto loans, using regulatory LGD. Suitable for banks with more sophisticated risk management systems, especially for portfolios like auto loans where PD can be closely linked to borrower creditworthiness.
Advanced Internal Ratings-Based (A-IRB) Approach Allows banks to use their own estimates for PD, LGD, and EAD based on historical data and models. Developing comprehensive models to estimate PD, LGD, and EAD for a mortgage loan portfolio, incorporating factors like borrower credit scores, loan-to-value ratios, and real estate market trends. Best for large financial institutions with diverse portfolios (e.g., mortgage loans) and the capability to develop and validate sophisticated risk models.
Historical Average Approach Calculates ECL based on historical loss rates, adjusting for current conditions and forecasts. Using average historical credit loss rates of past years for retail loan portfolios, adjusted for current economic conditions. Applicable to institutions with long historical data sets but limited modelling capabilities. Good for homogeneous loan types like standard retail or small business loans.
Probability of Default/Loss Given Default (PD/LGD) Models Uses statistical models to estimate PD and LGD separately, combining them with EAD for ECL. Implementing a logistic regression model to estimate PD and a regression model for LGD, applied to a portfolio of credit card loans. Suitable for portfolios with rich data on defaults and recoveries, such as credit card loans, where specific borrower and loan characteristics significantly impact PD and LGD.
Exposure at Default (EAD) Models Specifically focuses on estimating the EAD component, often using advanced modelling techniques. Developing a model that predicts future drawdowns on credit lines for corporate loans, to accurately estimate EAD. Particularly relevant for revolving credit products and corporate loans where the drawn amount can vary significantly over time.
Monte Carlo Simulations Uses random sampling and statistical modelling to estimate the distribution of possible ECL outcomes, incorporating uncertainty and correlations between variables. Simulating a range of economic scenarios to estimate ECL for a diversified loan portfolio, including retail, auto, and mortgage loans. Ideal for complex and diversified portfolios where the institution wishes to understand the range of potential outcomes and the impact of different economic scenarios.

We need to ensure the following from IFRS (International Financial Reporting Standards) and Basel framework perspective:

Aspect IFRS Considerations Basel Considerations Common Requirements
Data Quality and Availability IFRS 9 requires high-quality, relevant, and reliable data for ECL calculations, including historical, current, and forward-looking information. Basel guidelines emphasize the importance of accurate and comprehensive data for risk measurement and management, including for ECL calculations. Institutions must invest in data management systems to ensure the availability, accuracy, and comprehensiveness of data used in ECL calculations.
Model Validation and Governance IFRS 9 does not prescribe specific models but requires that the models used for ECL calculations be validated and governed appropriately. Basel guidelines provide detailed requirements for model validation, governance, and use, especially under the IRB approaches. Robust model governance and validation frameworks are needed to ensure that ECL models are appropriate, accurate, and reflective of the underlying risk.
Forward-looking Information IFRS 9 mandates the incorporation of forward-looking information, including macroeconomic data and forecasts, in ECL calculations. While Basel guidelines focus more on historical data, they recognize the importance of forward-looking information in risk management. Institutions must develop capabilities to incorporate and assess the impact of forward-looking information on ECL estimates.
Risk Parameters (PD, LGD, EAD) IFRS 9 requires entities to estimate these risk parameters as part of the ECL calculation process, reflecting an unbiased and probability-weighted amount. Basel guidelines specify detailed requirements for estimating risk parameters, especially under the IRB approaches, for capital calculation purposes. Both frameworks require rigorous estimation and continuous updating of risk parameters to reflect current and future conditions.
Disclosure Requirements IFRS 9 has specific disclosure requirements to provide transparency about the assumptions, models, and information used in ECL calculations. Basel III introduced enhanced disclosure requirements to improve the transparency of banks' risk management practices, including those related to credit risk and ECL. Comprehensive disclosures are required under both frameworks to inform stakeholders about the methodologies, assumptions, and outcomes of ECL calculations.
Stress Testing and Capital Planning While IFRS 9 focuses on accounting for credit losses, it indirectly supports stress testing by requiring the consideration of economic forecasts. Basel guidelines explicitly require stress testing and capital planning, considering adverse economic scenarios that could impact ECL and capital requirements. Institutions must integrate ECL estimates into their stress testing and capital planning processes to ensure resilience under adverse conditions.

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