ifrs9

IFRS accounting knowledge skill for AI coding agents — Claude Code, Codex, Cursor. Every IFRS and IAS standard, paragraph-cited: IFRS 15 revenue recognition, IFRS 16 lease accounting, IFRS 9 ECL, IFRS 17, and IFRS 18, IFRS 19, IFRS S1/S2. Disclosure checklists, worked journal entries, GAAP-to-IFRS transition guide.

R-codebase for a scientific research article, titled "Approaches for modelling the term-structure of default risk under IFRS 9: A tutorial using discrete-time survival analysis"

Bank-style Credit Risk Scorecard using Logistic Regression, IFRS-9 Expected Credit Loss, and an Interactive Streamlit Risk Dashboard for loan default prediction.

Practitioner-grade IFRS 9 expected-credit-loss engine: PD term structures, three-stage SICR waterfall, EBA macro overlays and a Vasicek/ASRF point-in-time PD. Parallel Python + Excel with a methodology deck.

End-to-end credit risk modeling in Python: WoE/IV feature engineering, logistic regression PD scorecard with points scaling, XGBoost challenger, model validation suite (KS, Gini, AUROC, PSI, calibration), SHAP explainability, and IFRS 9 three-stage expected credit loss (ECL) — on real Lending Club data, with a Streamlit dashboard.

End-to-End IFRS 9 LGD Model development by Advance Workout Model.

Interactive WOE (Weight of Evidence) and IV (Information Value) binning web tool for credit risk scoring, segmentation, and transparent scorecard development.

This model estimates the 12-month Probability of Default (PD) for prime residential mortgage customers in the United Kingdom, aligned with the IFRS 9 impairment framework and calibrated to an adverse macroeconomic scenario. Version 1 (v1) is developed using gradient-boosted decision trees (GBDT)

IFRS 9 / AASB 9 mortgage credit-risk suite on Freddie Mac loan-level data — PD (logistic, AUC 0.81), real LGD from actual loss data (reconciled to the vendor loss field at 0.99), EAD, expected credit loss, stress testing (~10× downturn), a scorecard master scale, and out-of-time / out-of-regime validation.

Streamlit app that computes per-loan Expected Loss, Lifetime ECL, and Risk Rating from EAD/PD/LGD/WAL Excel portfolios

Credit risk, IFRS 9 ECL, and model validation projects for risk analytics roles.

End-to-End IFRS 9 PD Model development by Empirical Migration Matrix and Credit Cycle Index Models.

End-to-End IFRS 9 PD Model development by Cohort Model.

Monthly loan-level portfolio monitoring on real Freddie Mac data: transition/migration matrices, roll rates, IFRS 9 stage movements, early-warning watchlist, and vintage tracking.

IFRS 9 expected credit loss engine on 1.35M real loans: PD, LGD, EAD, three-stage staging, and probability-weighted macroeconomic scenarios

End-to-end PD/LGD/EAD credit risk platform reflecting industry-standard model development workflows across Canadian financial institutions and credit unions. Built to OSFI E-23 / IFRS 9 standards.

IFRS 9 SICR model Mortgages

IFRS 9 expected credit loss engine: point-in-time PD term structure, LGD from realised losses, SICR staging, and a Federal Reserve stress-scenario overlay producing IFRS 7 disclosure tables.

Automated IFRS 9 ECL calculation system with Python, BigQuery, and SQL analytics