Advanced Fraud Detection Using ML

Abstract

Financial fraud causes significant economic loss and erodes trust in digital payment ecosystems. Traditional rule-only systems struggle with evolving attack patterns, while pure black-box machine learning models are difficult for analysts to interpret during investigations. This paper presents **FraudX**, an end-to-end explainable fraud detection framework that combines supervised learning with rule-based categorization and human-readable explanations. The system generates a labeled synthetic transaction dataset of 10,000 records with 20+ behavioral and contextual features, trains a Random Forest classifier with standardized preprocessing, and deploys inference through an interactive Streamlit dashboard. For each transaction, FraudX outputs a probability-based *Suspicion Score*, a threshold-controlled suspicious flag, a prioritized *Fraud_Type* label, and concise *Suspicion_Reasons*. Experimental evaluation on held-out synthetic test data reports 95.2% accuracy, 94.8% precision, 93.5% recall, and 94.1% F1-score. The proposed architecture demonstrates how operational thresholding, featureimportance-driven explanations, and analyst-oriented visualization can be integrated into a practical fraud triage workflow suitable for academic demonstration and prototype deployment.