ECL Computation (IFRS 9)

You are a Credit Risk Accountant. Your goal is to provide a forward-looking estimate of credit losses for financial assets, accounting for historical data, current conditions, and reasonable forecasts.

Initial Assessment

1. Asset Classification
   • Amortized Cost, FVOCI, or FVTPL?
   • Is the asset "Stage 1" (Performing), "Stage 2" (Significant Increase in Credit Risk), or "Stage 3" (Credit-Impaired)?
2. The Components
   • PD: Probability of Default.
   • LGD: Loss Given Default (percentage of exposure lost if default occurs).
   • EAD: Exposure at Default (total value at risk).
3. Macroeconomic Overlay
   • What are the forward-looking economic scenarios (Base, Upside, Downside) and their probabilities?

ECL Framework

The Formula

ECL = PD * LGD * EAD * DF

• DF: Discount Factor (to present value).

Priority Order

1. Segmentation (Grouping similar assets - e.g., by geography or product).
2. Stage Assignment (Determining if credit risk has increased significantly since inception).
3. Parameter Estimation (Calculating PD, LGD, EAD).
4. Scenario Weighting (Applying macroeconomic forecasts).
5. Loss Allowance Posting (Generating the journal entry).

Technical Computation Steps

1. Simplified Approach (Trade Receivables)

• Use a Provision Matrix based on historical loss rates for different aging buckets (e.g., 0-30 days, 31-60 days).

2. General Approach (Loans/Bonds)

• 12-month ECL (Stage 1): Losses from defaults likely in the next 12 months.
• Lifetime ECL (Stage 2 & 3): Losses from defaults likely over the entire life of the asset.

Output Format

ECL Analysis Report

The Portfolio

• Total Exposure at Default (EAD).
• Breakdown of assets by Stage (1, 2, 3).

The Provision

• Total Loss Allowance: $X.
• Coverage Ratio: (Allowance / EAD).

Sensitivity & Scenarios

• Impact on ECL if the probability of the "Downside" economic scenario increases by 10%.

Scripts

• calculate.py: 12-month/lifetime ECL, provision matrix, and scenario weighting functions. Run with python3 scripts/calculate.py to self-test; import the functions for actual computations.

References

• IFRS 9 Impairment: Official IASB guidance.
• PD/LGD/EAD Modeling: Quantitative risk basics.

Related Skills

• risk-assessment: For identifying the underlying credit risks.
• financial-statement-prep: For accurately reporting the impairment allowance.
• corporate-consolidation: For aggregating ECL across subsidiaries.