AI & Business Analytics • Fraud Detection • Financial Risk Analytics

🎓 MBA in Business Analytics (STEM), Midwestern State University, Texas
💼 2+ years of experience in Financial Risk & Compliance Analytics
📊 Research-focused work spanning fraud analytics, risk modeling, and decision intelligence

I work at the intersection of AI, business analytics, and regulation, applying machine learning and data-driven methods to analyze and mitigate financial risk. My work supports data-driven approaches to consumer protection, financial stability, and regulatory decision-making in the United States.

My work focuses on:

• Fraud & cybercrime analytics
• Business failure and financial risk prediction
• AI-supported financial decision-making

I primarily work with real U.S. regulatory and economic datasets, including: FTC Consumer Sentinel Network, FBI IC3, FDIC, BLS, and CFPB.

My research includes peer-reviewed journal publications and IEEE conference papers on:

• AI-enabled risk management systems
• Predictive analytics in banking and finance
• Behavioral and consumer analytics
• Advanced AI/ML for predictive modeling | Cloud-based data pipelines
• Optimizing ML models for large-scale financial datasets | Improving real-time BI dashboards

(Selected applied analytics projects supporting this research are pinned below.)

Programming & Analytics

• Python (pandas, scikit-learn, matplotlib, plotly)
• SQL

Machine Learning

• Logistic Regression
• Random Forest
• XGBoost

Visualization & BI

• Power BI
• Tableau

This GitHub showcases applied analytics projects, including:

• Nationwide fraud & cybercrime analysis
• Predictive models for business failure and financial risk
• NLP-based consumer complaint analysis