agentsclimarketplace

Ecl computation

Skill GAJETOso/financeskills/skills/ecl-computation

AI-powered skills for financial professionals. Comprehensive collection of finance, accounting, audit, and compliance skills for AI agents. IFRS/GAAP compliant with industry-specific applications.

Install
npx -y skills add GAJETOso/financeskills --skill ecl-computation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

When the user wants to calculate Expected Credit Losses (ECL) under IFRS 9 or CECL (ASC 326). Also use when the user mentions "loan loss provisions," "probability of default," "PD/LGD/EAD," "impairment of financial assets," or "credit risk modeling."

SKILL.md

3.0 KB, as published. Nobody here has run it

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

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.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.