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
Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data—0
Minimum Enclosing Ball Synthetic Minority Oversampling Technique from a Geometric Perspective—0
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance—0
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—0
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection—0
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection—0
Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks—0
Multi-future Merchant Transaction Prediction—0
Multimodal and Contrastive Learning for Click Fraud Detection—0
Multiple Attribute Fairness: Application to Fraud Detection—0
Multiple Inputs Neural Networks for Medicare fraud Detection—0
Deep Feature Fusion for Mitosis Counting—0
Multi-stream RNN for Merchant Transaction Prediction—0
Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection—0
Network In Graph Neural Network—0
Neural-based classification rule learning for sequential data—0
New User Event Prediction Through the Lens of Causal Inference—0
Non-Parametric Stochastic Sequential Assignment With Random Arrival Times—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
Online Anomaly Detection via Class-Imbalance Learning—0
On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning—0
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods—0
On the intrinsic robustness to noise of some leading classifiers and symmetric loss function -- an empirical evaluation—0
On the Potential of Network-Based Features for Fraud Detection—0
Open ERP System Data For Occupational Fraud Detection—0
Open-Set: ID Card Presentation Attack Detection using Neural Transfer Style—0
Opinion Fraud Detection via Neural Autoencoder Decision Forest—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
OTLP: Output Thresholding Using Mixed Integer Linear Programming—0
Parametric entropy based Cluster Centriod Initialization for k-means clustering of various Image datasets—0
Partitioning Message Passing for Graph Fraud Detection—0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Personalized Influence Estimation Technique—0
Precision-Recall Curve (PRC) Classification Trees—0
Prediction of motor insurance claims occurrence as an imbalanced machine learning problem—0
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction—0
Privacy-Enhancing Collaborative Information Sharing through Federated Learning -- A Case of the Insurance Industry—0
Privacy-Preserving Collaborative Learning through Feature Extraction—0
Privacy Preserving PCA for Multiparty Modeling—0
Proactive Fraud Defense: Machine Learning's Evolving Role in Protecting Against Online Fraud—0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
Profiling US Restaurants from Billions of Payment Card Transactions—0
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels—0
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection—0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection—0
Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis—0
Radial Autoencoders for Enhanced Anomaly Detection—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)—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