Financial Fraud Detection

Python Scikit-learn Unsupervised Learning

The "What": Technical Methodology

Using Python and Scikit-learn, I implemented an anomaly detection approach to identify suspicious financial activity without the need for labeled historical fraud data. Specifically, I utilized the **Isolation Forest** algorithm, which isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. This method is highly effective for "needle in a haystack" problems where fraudulent data points are vastly outnumbered by legitimate transactions.

The "Why": Data Science Impact

This project demonstrates the critical role of Data Science in organizational security and financial health. In the "Zero-Discrepancy" mindset, detecting anomalies before they propagate through a system saves significant resources and protects user trust. By automating this detection, I enable real-time responses to threats, proving that predictive analytics is not just for forecasting, but for active defense and risk mitigation.