Loading Portfolio...
Certified Fraud Examiner | Aspiring Data Scientist
Certified Fraud Examiner. Data Scientist. Problem Solver.
I specialize in turning messy data into crystal-clear fraud detection strategies. With machine learning models hitting 99.96% accuracy and systems that slash false positives by 20%, I don't just find fraud—I prevent it before it happens.
Currently pursuing my M.S. in Business Analytics & AI at American University while working as a Fraud Data Analyst, I bridge the gap between cutting-edge technology and real-world fraud prevention.
Leading fraud detection initiatives using advanced analytics and machine learning to identify and prevent fraudulent activities in educational consulting operations.
Contributed to financial crime detection and compliance initiatives by developing fraud detection workflows and enhancing monitoring systems.
Investigated healthcare fraud cases and conducted detailed data analysis to identify billing anomalies and fraudulent claims.
Developed a Random Forest machine learning model achieving 99.96% accuracy in detecting fraudulent credit card transactions across 10,000+ transactions. Implemented advanced feature engineering and anomaly detection techniques.
Built an integrated dashboard combining Power BI with Python and SQL pipelines to automatically detect and visualize high-risk applications. Real-time monitoring and alerting system for fraud prevention teams.
Developed a predictive model using logistic regression and decision trees to assess transaction risk scores. Reduced false positive rates by 20% while maintaining high detection accuracy for genuine fraud cases.
Washington, DC
New York, NY
Association of Certified Fraud Examiners
Expected August 2025
Association of Certified Fraud Examiners
2024
Microsoft
2024
I'm always interested in discussing fraud analytics opportunities, data science collaborations, or connecting with professionals in the fraud prevention field. Feel free to reach out!