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    Jane511/README.md

    Jane (Xia) Wu

    Credit Risk Analyst — PD / LGD / EAD modelling · IFRS 9 / AASB 9 · stress testing · model validation

    📍 Sydney · Email: janewu511@gmail.com · Open to credit-risk and quantitative-modelling roles

    CPA-qualified credit risk analyst with ~4 years across the full credit lifecycle at a private credit lender. I build institutional-grade credit-risk analytics — from PD/LGD/EAD models and IFRS 9 expected credit loss through stress testing, model validation, and portfolio monitoring — on real loan-level and Australian regulatory data.

    English / Mandarin (including reading)

    What I work on

    • Credit risk models: PD, LGD, EAD; IFRS 9 / AASB 9 expected credit loss and staging; credit scorecards (logistic regression + WOE/IV); risk-based pricing
    • Stress testing: downturn scenarios, downturn LGD, macro overlays
    • Model validation & monitoring: discrimination (AUC, Gini, KS), calibration, population stability (PSI), out-of-time / out-of-regime testing; transition matrices, stage movements, early warning
    • Australian regulatory landscape: APRA (APS 112/113/220), Basel PD/LGD/EAD concepts, Pillar 3, and ABS / RBA / APRA public data
    • Tools: Python (pandas, scikit-learn), SQL, Power BI, Git

    Featured projects

    ProjectWhat it demonstrates
    mortgage-credit-risk-pd-lgd-eadPD (logistic, AUC 0.81), real LGD from actual loss data (reconciled to the vendor's own loss field at 0.99), EAD, expected loss, stress testing (~10× downturn), a scorecard master scale, and out-of-time / out-of-regime validation
    consumer-credit-pd-ead-scorecardPD scorecard (logistic regression + WOE/IV) with full validation, monitoring, and governance; EAD analysed and reframed as a documented data-quality finding
    external-benchmarkA reproducible engine turning Australian bank & regulator disclosures (Pillar 3, APRA, RBA) into traceable PD / LGD / ECL / stress model inputs — with governance, an audit trail, and a 595-test suite
    industry-analysisTurns public ABS / RBA / PTRS data into industry risk scores, downturn / stress overlays, and macro-regime flags for commercial credit
    mortgage-portfolio-monitoringLoan-level mortgage monitoring on Freddie Mac data: delinquency transition / migration matrices, roll rates, IFRS 9 stage movements, an early-warning watchlist, and vintage tracking
    commercial-portfolio-monitoringCommercial-loan monitoring on real SBA 7(a) data: industry & state concentration (HHI, top-N), charge-off rates, vintage cohort curves, loan-age transitions, and early-warning flags
    Together these form one stack: macro & industry overlays + external benchmarks → PD / LGD / EAD modelling → portfolio monitoring → validation.

    Background

    • CPA-qualified · Master of Accounting and Applied Finance, University of Sydney
    • ~4 years as a commercial credit analyst at a Sydney private credit lender (~A$50M SME and property-backed book)
    • Earlier: built and deployed logistic-regression and machine-learning models in production as a data analyst — demand forecasting, customer targeting, and reporting automation
    • Google Data Analytics Professional Certificate · AML & KYC Fundamentals (AUSTRAC)
    • Bilingual: English and Mandarin (including reading)

    Pinned Loading

    1. mortgage-credit-risk-pd-lgd-eadmortgage-credit-risk-pd-lgd-eadPublic

      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 cred…

      Jupyter Notebook 1

    2. consumer-credit-pd-ead-scorecardconsumer-credit-pd-ead-scorecardPublic

      Consumer credit risk, PD scorecard (logistic regression + WOE/IV) on Home Credit data, with discrimination, calibration and population-stability validation, ongoing monitoring, and model governance…

      Jupyter Notebook

    3. mortgage-portfolio-monitoringmortgage-portfolio-monitoringPublic

      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.

      Jupyter Notebook

    4. commercial-portfolio-monitoringcommercial-portfolio-monitoringPublic

      Portfolio monitoring on real SBA 7(a) commercial-loan data — industry and state concentration (HHI, top-N), charge-off rates, vintage cohort curves, loan-age transitions, and early-warning watchlists.

      Jupyter Notebook

    5. external-benchmarkexternal-benchmarkPublic

      Reproducible engine that turns Australian bank and regulator disclosures (Pillar 3, APRA, RBA, S&P) into governed, auditable PD / LGD / EL model inputs — base and stressed — with a full audit trail…

      Python

    6. industry-analysisindustry-analysisPublic

      Turns public ABS / RBA / PTRS data into industry risk scores, downturn / stress overlays, and macro-regime flags for commercial credit — sector-risk and concentration support tables for portfolio r…

      Python