Streamlit app that computes per-loan Expected Loss, Lifetime ECL, and Risk Rating from EAD/PD/LGD/WAL Excel portfolios
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Updated
Apr 27, 2026 - Python
Streamlit app that computes per-loan Expected Loss, Lifetime ECL, and Risk Rating from EAD/PD/LGD/WAL Excel portfolios
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)
End-to-end credit risk engine computing Expected Loss (PD × LGD × EAD) on the LendingClub portfolio.
End-to-end credit risk pipeline for PD, LGD, EAD, expected loss, IFRS 9-style staging, and stress testing on LendingClub loan data.
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.
Credit risk modeling project estimating portfolio Expected Loss (PD × LGD × EAD) using the LendingClub dataset with logistic regression and two-stage LGD modeling.
Implements the Basel III credit risk framework (PD, LGD, EAD) using Logistic & Linear Regression on Lending Club loan data (2007–2014)
A collection of applied Debt Finance and Credit Risk modelling projects
Credit risk pipeline on 2M+ loan records — ROC-AUC 0.97, FICO-style scorecard, risk segmentation, and live Streamlit app.
End-to-end ML-based credit risk system for predicting default probability (PD), estimating expected loss (EL), and optimizing FICO score segmentation for risk-based lending.
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