expected-loss

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

End-to-end Credit Risk & Expected Loss Analytics Platform | PD Modelling, XGBoost, SHAP, Streamlit | LendingClub 890k loans

Implements the Basel III credit risk framework (PD, LGD, EAD) using Logistic & Linear Regression on Lending Club loan data (2007–2014)

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 and a 595-test suite.

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)

Proyecto de Titulación: Cálculo de Pérdidas Esperadas basado en 3 modelos de Credit Scoring para una institución financiera del Ecuador

Actuarial insurance risk scoring using frequency–severity modelling to estimate expected loss for underwriting and pricing.

End-to-end credit risk analytics project using calibrated gradient boosting, economic threshold optimization, expected loss, explainability, and fairness analysis.

Interactive collections intelligence dashboard using Streamlit, SQL, and Expected Loss analytics for portfolio risk analysis and collections prioritization.

End-to-end credit risk engine computing Expected Loss (PD × LGD × EAD) on the LendingClub portfolio.

End-to-end credit risk pipeline for SMEs: calibrated PD, LGD, Expected Loss, contextual score and early warning

A collection of applied Debt Finance and Credit Risk modelling projects

End-to-end credit risk modeling: PD/LGD/EL on 1.35M Lending Club loans. Test AUC 0.719, 32 bps portfolio error. Includes drift monitoring, MRM documentation, and live dashboard.

End-to-end credit risk pipeline for PD, LGD, EAD, expected loss, IFRS 9-style staging, and stress testing on LendingClub loan data.

Personal-loan credit-risk & unit-economics platform: vintage delinquency curves, expected-loss modeling, and an interactive approval-threshold scenario simulator. Built with dbt, DuckDB, Python, and Streamlit.

Credit and payments risk decision engine translating borrower and transaction data into underwriting decisions, fraud controls, expected-loss estimates, policy-threshold tradeoffs, and model-risk validation evidence.

End-to-end credit risk modelling framework using LightGBM, SHAP, PD calibration, LGD/EAD estimation and portfolio stress testing.

Governed PostgreSQL credit decisioning framework for synthetic applications, pre-production strategy simulation, matched comparison, counteroffer governance, Expected Loss tradeoffs, and Power BI release validation—without PII.

End-to -end credit collections risk system using PD, LGD, and EAD to estimate expected loss, prioritize accounts, evaluate model performance and support capacity-constrained collection decisions.