Harshali P. Patil

Advanced Fraud Detection Using ML

FraudX is presented, an end-to-end explainable fraud detection framework that combines supervised learning with rule-based categorization and human-readable explanations and 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.