basel

CreditRisk Intelligence — AI-Powered Credit Risk Infrastructure for Fintechs

VaR/CVaR investment portfolio risk modeling with backtesting

Pipeline de credit risk end-to-end: PD logística, Monte Carlo vectorizado, métricas Basel III (EL, VaR, Expected Shortfall) y stress testing. Python · NumPy · pandas · statsmodels

Motor de riesgo de mercado (VaR, Expected Shortfall, GARCH, EVT, stress testing, backtesting de Basilea) + econofísica + ML, con dashboard FastAPI

An object-oriented, Walk-Forward quantitative risk engine estimating VaR and Expected Shortfall using a Student-t Copula and GJR-GARCH margins

Eight VaR/ES models for a $10m multi-asset book, validated over 4,611 trading days with Kupiec, Christoffersen, Basel traffic-light and Acerbi-Szekely tests.

Local tools for querying and visualizing changes in the Basel-Stadt nature inventory.

Monitors SEC/CFTC/FCA/Basel/Federal Reserve publications and generates structured regulatory impact assessments

IFRS 9 ECL engine on 2.26M Lending Club loans: PD scorecard, LGD/EAD models, Basel IRB capital, 3-stage ECL with macro scenarios and full model validation.

Bank-grade credit-risk platform — calibrated PD scorecard + ML challenger, LGD/EAD -> Expected Loss, OOT validation, SHAP reason codes, fairness & drift. Basel/IRB on Freddie Mac data.

Basel Vasicek credit risk stress testing model with TTC PD estimation, stressed PD mapping, and facility-level Unexpected Loss (UL) analysis.

Vérifier si un modèle estime correctement les prêts qui ne seront pas remboursés, puis construire un dossier de crédit sur Enbridge.