This project studies the effects of the shape parameter estimator uncertainty at different threshold levels on the value-at-risk confidence interval for quantitative risk management (QRM) using the Generalized Pareto Distribution (GPD) from the Extreme Value Theory (EVT) approach.

The significance test for Granger causality of financial risks in stock indices

🏦 Machine Learning system for credit default prediction using a RandomForestClassifier. Features an end-to-end pipeline including synthetic financial data generation, robust preprocessing (ColumnTransformer), and comprehensive evaluation with ROC-AUC and Confusion Matrices.

A comprehensive implementation of the ID3 Decision Tree algorithm from scratch for financial risk assessment, featuring custom entropy calculations, information gain optimization, and detailed data preprocessing.

Global Systemic Fragility Observatory 2026 · Markov Model + Monte Carlo · Synthetic Bubble · 10 non-conventional systemic risk indicators · MCSH Framework · semanticfieldscience.org

A股个人择时选股系统 — 三级择时漏斗 × 财务排雷 × 量化策略引擎 | FastAPI + Next.js 16 + SQLite

Macro-financial stress testing framework projecting UK bank capital under baseline and adverse macroeconomic scenarios.

AI & Data Governance Operating System for Financial Institutions. Audit-ready ML, LGPD compliant analytics and governed data platforms.

Institutional-grade early warning system for systemic deleveraging events

This is a front-end system for systematic financial risk data visualization. It provides comprehensive monitoring and analysis of financial market pressure indicators across multiple dimensions including bond market, foreign exchange market, stock market, derivatives market, and money market.

Fraud detection case study applying machine learning, decision thresholds and explainable AI to financial transaction data.

POC antifraude baseada em Machine Learning, que demonstra como estruturar modelos como motores de decisão, com avaliação temporal honesta, baseline sólido e comparação entre algoritmos.

A Power BI fraud detection system analyzing 6.4 million transactions. Identified $12B in financial losses, detected "Night Owl" attack patterns (2 AM–6 AM), and uncovered critical security loopholes in mobile money transfers.

Power BI dashboard analyzing loan default risk using borrower financial data and risk indicators.

Predictive modeling system for identifying high-risk financial transactions using time-based validation.

Power BI dashboard analyzing loan default risk using borrower financial data and risk indicators.

End-to-end Canadian Credit Risk & PD modeling project using public Canadian lending data, ML models, SHAP explainability, Streamlit UI, and Power BI dashboard.

A collaborative fraud detection pipeline handling extreme class imbalance (0.17%). Features a RobustScaler data workflow and trade-off analysis between KNN and Random Forest classifiers.