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ML-Powered Fraud Detection System
Anomaly detection system analyzing 50,000+ loan applications for DLL Financial Solutions.
Technologies Used
PythonScikit-learnPCAPandasJupyter
Key Results
50,000+
Applications Analyzed
95%
Detection Accuracy
70%
Review Time Reduction
The Challenge
DLL's manual fraud review process was time-consuming and inconsistent. With increasing loan applications, the risk of missing fraudulent patterns was growing.
The Solution
Implemented a machine learning pipeline using PCA for dimensionality reduction and isolation forests for anomaly detection. Created interpretable features that compliance officers could understand and act upon.
Results & Impact
Achieved 95% accuracy in detecting fraudulent applications while reducing manual review time by 70%. The system flagged high-risk applications for priority review, significantly improving the fraud detection rate.
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