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Developed a Financial Fraud Detection system during my internship at Infotact Solutions. The project combined Python-based machine learning with Power BI dashboards to identify and visualize fraudulent activities in financial transactions.
🔹 Approach:
Cleaned and preprocessed 100K+ credit card transactions in Python.
Built a Random Forest Classifier to predict fraudulent cases.
Evaluated the model using Confusion Matrix, Precision-Recall, and ROC Curve.
🔹 Key Insights from Dashboard:
Fraud Rate: 1% of total transactions (1K frauds out of 100K).
Top High-Risk Cities: Chicago, San Diego, Dallas.
Monthly Fraud Trends: Peaks in January and July.
Fraud by Transaction Type: Refunds and Purchases equally impacted.
Fraud vs Legitimate Distribution clearly showing class imbalance.
🔹 Impact:
Enabled risk teams to quickly analyze fraud hotspots, trends, and transaction anomalies, making fraud detection more interpretable for decision-makers.
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