Prototype risk modeling simulation for Portfolio using Arbiter.

Reassessment of P2P Credit Risk Modeling with Macroeconomic Factors

Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)

Statistical modeling and simulation project analyzing optimal betting strategies across multi-game sports series using R.

🚗 A dynamic pricing and insurance risk modeling system using Python, XGBoost, SHAP, and DVC. Predicts claim severity and probability, enabling risk-adjusted premium strategies with full reproducibility and CI/CD.

End-to-end Python notebook that engineers a proxy “bad-client” label, explores drivers of credit risk, and builds a leakage-free XGBoost scorecard with threshold tuning and cross-validation.

Ai-powered hurricane risk assessment model combining loads of data regarding social, climate, infrastructure, and meteorology data.

Reusable Python functions and case studies for building internal tools in process development and manufacturing ops

End-to-End Python econometric pipeline for modeling geopolitical risk in international trade using advanced Bayesian filtering, high-dimensional fixed effects, and split-panel jackknife inference. Replicates Hardwick's (2025) methodology with full robustness testing and automated reporting.

Monte Carlo risk modeling tool for utility Transmission & Distribution projects

End-to-end Python computational engine for qualitative financial modeling implementing Bočková et al. (2025) methodology. Employs Constraint Satisfaction Problems (CSP) and graph theory to model the impact of rumours on financial systems. Professional-grade codebase with extensive validation and customization capabilities.

Model and UI components that went into a final GEE engine app testing and comparing different ML models (pulled from literature attempting to show landslide susceptibility and varying scales) against reported landslides instances and their associated damage

Credit risk modeling using machine learning techniques (LightGBM, Optuna) on the Home Credit dataset.

A machine learning project to predict credit risk (GOOD or BAD) for loan applicants using historical loan data from 2007–2014. This solution helps multifinance companies minimize default risk and streamline loan approvals through accurate risk classification and a modern graphical user interface (GUI).

📈 Analyze how rumors affect investment decisions using a qualitative reasoning engine for better outcomes and informed choices.

Quantify 2025 Energy Risk & Insurance Case Modeling high-loss CAT events and developing parametric risk triggers to support renewable energy expansion. Includes ML code, writing report, and Presentation.

Quantitative analysis of macroeconomic indicators and market volatility for investment strategy development