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.

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

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

A forward-looking risk prediction system that analyzes 31 multi-dimensional signals across news, finance, operations, climate, and geopolitics to predict supplier disruption probability 3–6 months in advance.

🔐4- Cybersecurity -Social Engineering - Modular end-to-end AI-powered risk intelligence infrastructure for banking and fintech incidents analytics, integrating semantic analysis, interactive dashboards, APIs and LLM-powered insights to support governance, compliance and regulatory decision-making.

Climate physical-risk analysis, hands-on. Jupyter notebooks for the Alpha-Klima platform API - hazard screening, asset-level impact, real-estate portfolios, and bank Pillar 3 / ECB pipelines.

🏦 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.

Cost-sensitive loan default prediction using Python and machine learning, with threshold optimization, business cost simulation, model interpretation, and responsible AI considerations.

Data-driven optimization of Teradyne’s excess inventory approval process using Python, lead-time adjusted demand modeling, and financial risk analysis to improve capital efficiency and reduce excess spend.

Completed as part of the 365 Data Science Credit Risk Modeling in Python Udemy course. Developed an end-to-end credit risk modeling pipeline for consumer lending, covering data preprocessing, feature engineering, Probability of Default , Loss Given Default , Exposure at Default , scorecard development, model validation, population stability

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

Batch financial risk scoring and Isolation Forest anomaly detection project using Python, SQL, and BI-ready outputs.

Autonomous financial risk intelligence platform, SEC EDGAR ingestion, Isolation Forest anomaly detection, Benford's Law forensic analysis, corporate network graph, SHAP explainability, and Gemini AI analyst. Built with DuckDB, dbt, and Streamlit.

Credit risk & campaign analytics dashboard — Power BI, DAX (55+ measures), Python EDA, SQL. Interactive HTML dashboard live on GitHub Pages.

An end-to-end Machine Learning project for predicting lead conversion using explainable AI and business-focused analytics.

End-to-end MLOps pipeline predicting corporate bankruptcy from 95 financial ratios. Features nested CV, SHAP explainability, MLflow tracking, and Dockerized FastAPI deployment.

Finance Risk Intelligence is an end-to-end machine learning project for financial risk analysis that automates data cleaning, feature engineering, model training, and interactive risk visualization through a Streamlit dashboard.

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

AI-powered financial risk intelligence platform — XGBoost + RAG + LangChain + Streamlit