SOTAVerified

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 251–300 of 547 papers

TitleStatusHype
A Survey on Actionable Knowledge—0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
A Systems Theoretic Approach to Online Machine Learning—0
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection—0
ATM Fraud Detection using Streaming Data Analytics—0
Attention is All You Need Until You Need Retention—0
Auditing: Active Learning with Outcome-Dependent Query Costs—0
AutoFraudNet: A Multimodal Network to Detect Fraud in the Auto Insurance Industry—0
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach—0
Automatic Model Monitoring for Data Streams—0
Automatic Procurement Fraud Detection with Machine Learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
RNNSecureNet: Recurrent neural networks for Cyber security use-cases—0
RNNSecureNet: Recurrent neural networks for Cybersecurity use-cases—0
Robust Fraud Detection via Supervised Contrastive Learning—0
Robust Prediction Model for Multidimensional and Unbalanced Datasets—0
Robust Principal Component Analysis using Density Power Divergence—0
Role of Secondary Attributes to Boost the Prediction Accuracy of Students Employability Via Data Mining—0
Safety in Graph Machine Learning: Threats and Safeguards—0
Sample Complexity of Deep Active Learning—0
Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection—0
Scalable Similarity Joins of Tokenized Strings—0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning—0
SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection—0
Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability—0
Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search—0
SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning—0
SeismoFlow -- Data augmentation for the class imbalance problem—0
SelectNet: Learning to Sample from the Wild for Imbalanced Data Training—0
Self-Reinforcement Attention Mechanism For Tabular Learning—0
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks—0
Sequence embeddings help to identify fraudulent cases in healthcare insurance—0
Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection—0
"Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysis—0
Similar Document Template Matching Algorithm—0
Social Fraud Detection Review: Methods, Challenges and Analysis—0
Solve fraud detection problem by using graph based learning methods—0
Some Experimental Issues in Financial Fraud Detection: An Investigation—0
Spectrum-based deep neural networks for fraud detection—0
Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection—0
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization—0
Structural Alignment Improves Graph Test-Time Adaptation—0
Subspace Clustering of Very Sparse High-Dimensional Data—0
Synthetic Data Generation for Fraud Detection using GANs—0
Synthetic ID Card Image Generation for Improving Presentation Attack Detection—0
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method—0
Teaching the Machine to Explain Itself using Domain Knowledge—0
Teaching the Machine to Explain Itself using Domain Knowledge—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—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
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