There are 0 repository under risk-modeling topic.
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.
This repository contains cleaned, recoded, and weight-expanded datasets derived from IPUMS NHIS (2010–2023) for use in the LCrisks microsimulation model. It includes input data, preprocessing scripts, and split files optimized for scalable lung cancer screening simulations.
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.
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.
🚗 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 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.
Monte Carlo risk modeling tool for utility Transmission & Distribution projects
Reusable Python functions and case studies for building internal tools in process development and manufacturing ops
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).
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