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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 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
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly DetectionCode2
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods—0
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment—0
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced DatasetsCode0
Addressing Noise and Stochasticity in Fraud Detection for Service Networks—0
Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection—0
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud DetectionCode3
Dual-channel Heterophilic Message Passing for Graph Fraud DetectionCode0
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID GraphsCode0
Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention—0
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow—0
Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns—0
FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio PlatformCode1
TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud DetectionCode2
A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection—0
Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection—0
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning—0
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning—0
Financial Fraud Detection with Entropy ComputingCode0
Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions—0
Zero-to-One IDV: A Conceptual Model for AI-Powered Identity Verification—0
Perseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes—0
Corporate Fraud Detection in Rich-yet-Noisy Financial GraphCode0
Structural Alignment Improves Graph Test-Time Adaptation—0
Using Machine Learning to Detect Fraudulent SMSs in Chichewa—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—0
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable ExplanationsCode0
Financial fraud detection system based on improved random forest and gradient boosting machine (GBM)—0
Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing InducementsCode1
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud DetectionCode1
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection—0
Explainability in Practice: A Survey of Explainable NLP Across Various Domains—0
Using Causality for Enhanced Prediction of Web Traffic Time Series—0
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review—0
Free Agent in Agent-Based Mixture-of-Experts Generative AI Framework—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
A Survey on Diffusion Models for Anomaly DetectionCode2
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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
1LightGBMRecall @ 5% FPR54.3—Unverified
2CatBoostRecall @ 5% FPR52.4—Unverified
3LightGBMRecall @ 5% FPR51.76—Unverified
41D-CSNNRecall @ 5% FPR50.35—Unverified
5MLP–NNRecall @ 5% FPR49.6—Unverified
61D-CSNNRecall @ 5% FPR42.79—Unverified
7LightGBMRecall @ 1% FPR25.2—Unverified
8FIGSRecall @ 1% FPR21—Unverified
9CART+RIFFRecall @ 1% FPR18.4—Unverified
10CARTRecall @ 1% FPR16—Unverified
#ModelMetricClaimedVerifiedStatus
1LEX-GNNAUC-ROC96.4—Unverified
2JA-GNNAUC-ROC95.11—Unverified
3GTANAUC-ROC94.98—Unverified
4BOLT-GRAPHAUC-ROC93.18—Unverified
5SplitGNNAUC-ROC92.03—Unverified
6GAT+JKAUC-ROC90.04—Unverified
7RLC-GNNAUC-ROC85.44—Unverified
8RioGNNAUC-ROC83.54—Unverified
9PC-GNNAUC-ROC79.87—Unverified
10CARE-GNNAUC-ROC75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1LEX-GNNAUC-ROC97.91—Unverified
2GTANAUC-ROC97.5—Unverified
3RLC-GNNAUC-ROC97.48—Unverified
4RioGNNAUC-ROC96.19—Unverified
5PC-GNNAUC-ROC95.86—Unverified
6CARE-GNNAUC-ROC89.73—Unverified
#ModelMetricClaimedVerifiedStatus
1GCNAUC0.83—Unverified
2GraphSAGEAUC0.83—Unverified
3GATAUC0.81—Unverified
4GINAUC0.81—Unverified
5Node2vecAUC0.53—Unverified
6DeepwalkAUC0.45—Unverified
#ModelMetricClaimedVerifiedStatus
1BiRankAUC0.79—Unverified
2GraphSAGEAUC0.67—Unverified
3metapath2vecAUC0.51—Unverified
#ModelMetricClaimedVerifiedStatus
1XBNETAccuracy71.33—Unverified
2DevNetAUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR40.71—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR47.08—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR41.83—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR35.54—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR34.96—Unverified
#ModelMetricClaimedVerifiedStatus
1SplitGNNAUC-ROC68.98—Unverified