Prototype risk modeling simulation for Portfolio using Arbiter.

A comprehensive guide to designing economically safe ERC-20 tokens using formal invariants and strict permission boundaries. Covers supply caps, fee ceilings, liquidity guarantees, transfer safety, oracle integrity, upgrade control, and economic threat modeling. Focused on real-world DeFi security and robust token design.

A research-grade lab for stress-testing DeFi protocols using Solidity mini-systems, a Python simulation engine, and a Streamlit dashboard. Simulates price crashes, liquidity shifts, AMM behavior, lending liquidations, and systemic risk dynamics. Designed for DeFi engineers, auditors, and researchers.

A deep exploration of the economic physics governing DeFi crashes, AMM decay, liquidity spirals, and liquidation cascades. This article models decentralized finance as a nonlinear system driven by invariants, thresholds, and feedback loops, revealing why crashes follow predictable laws of motion.

A research-grade tool that analyzes Solidity smart contracts for economic vulnerabilities such as unbounded minting, toxic fee mechanisms, liquidity traps, oracle manipulation, centralized control, and broken financial invariants. Focused on economic correctness, incentive risks, and DeFi system stability.

A research-grade framework for forecasting tokenomic gene evolution across market cycles. Analyzes historical gene frequencies, models behavioral drift, and predicts future gene expression using interpretable trend and moving-average forecasting. Designed for tokenomics research, risk analysis, and evolutionary cryptoeconomics.

Reassessment of P2P Credit Risk Modeling with Macroeconomic Factors

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.

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.

🌍 Model political distance and trade dynamics using a quantitative framework to enhance understanding of international trade relationships.

A collection of quantitative finance and risk modeling projects demonstrating skills in time series forecasting, financial derivatives pricing, and credit risk assessment.

Breach probability simulator for CISOs. Quantifies defense-in-depth effectiveness using Poisson modeling. SOC aesthetic, risk quantification dashboard.

Interpretable credit risk modeling using real-world lending data, with emphasis on probability calibration, decision relevance, and scalable machine learning workflows.

Credit scoring machine learning pipeline with MLflow experiment tracking. Includes data preparation, baseline and advanced models, Optuna tuning, and threshold optimization for business cost. Uses Home Credit data, with CSVs tracked via Git LFS.

Bank-style Credit Risk Scorecard using Logistic Regression, IFRS-9 Expected Credit Loss, and an Interactive Streamlit Risk Dashboard for loan default prediction.

Default-Risk Prediction & Screening at Loan Origination in P2P Consumer Lending, with a Double Machine Learning Extension of the Effects of Longer Terms and High Interest Rates

Mortgage Risk & Retention Analytics Platform: predicts loan fallout and refinance risk, supports portfolio segmentation, and optimizes underwriter capacity with executive-ready reporting.

No description provided.

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

Fraud Risk Modeling for Credit Card Transactions: an end-to-end Machine Learning (ML) project utilizing advanced algorithms to detect fraudulent activities and mitigate Fraud Risk.

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.

Independent rapid-options brief outlining rule-based fraud controls, minimum viable safeguards, KPI structures, and scalable deployment pathways for public finance and payment systems.

End-to-end credit risk pipeline: leakage-safe PD modeling, holdout evaluation, and full-portfolio risk band segmentation (50k loans) with stakeholder-ready visuals + Streamlit scoring app.

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

Deterministic research infrastructure for digital asset factor modeling and systematic strategy validation.

Quantitative model for measuring organizational security risk caused by human dependencies, decision concentration, and bus-factor effects.

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

🔒 Design safe tokens with a focus on economic security and permission boundaries to prevent common pitfalls in token creation.