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Fraud Detection

Fraud Detection is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.

Source: Applying support vector data description for fraud detection

Papers

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Showing 1–10 of 547 papers

TitleStatusHype
Graph Learning—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network—0
Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability—0
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial StatementsCode1
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies—0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
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All datasetsBAF – BaseYelp-FraudAmazon-FraudElliptic DatasetHealthcare Provider Fraud Detection AnalysisKaggle-Credit Card Fraud DatasetBAF – Variant IBAF – Variant IIBAF – Variant IIIBAF – Variant IVBAF – Variant VFDCompCN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR41.83—Unverified