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 301–350 of 547 papers

TitleStatusHype
Synthetic ID Card Image Generation for Improving Presentation Attack Detection—0
FairGen: Fair Synthetic Data Generation—0
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection—0
Behavioral graph fraud detection in E-commerce—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Fraud Detection Using Optimized Machine Learning Tools Under Imbalance Classes—0
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data—0
Credit card fraud detection - Classifier selection strategy—0
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection—0
Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance—0
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection—0
Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection—0
Unsupervised Frequent Pattern Mining for CEP—0
Multiple Attribute Fairness: Application to Fraud Detection—0
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions—0
Local Multi-Label Explanations for Random ForestCode0
Using Person Embedding to Enrich Features and Data Augmentation for Classification—0
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset—0
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction—0
A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection—0
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods—0
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasetsCode0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Learning-Based Data Storage [Vision] (Technical Report)—0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning—0
Open ERP System Data For Occupational Fraud Detection—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection—0
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection—0
Maximum Mean Discrepancy on Exponential Windows for Online Change DetectionCode0
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
On some studies of Fraud Detection Pipeline and related issues from the scope of Ensemble Learning and Graph-based Learning—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Data+Shift: Supporting visual investigation of data distribution shifts by data scientists—0
Neurochaos Feature Transformation and Classification for Imbalanced LearningCode0
Prediction of motor insurance claims occurrence as an imbalanced machine learning problem—0
The Importance of Future Information in Credit Card Fraud Detection—0
Radial Autoencoders for Enhanced Anomaly Detection—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
Graph similarity learning for change-point detection in dynamic networksCode0
Distributed data analytics—0
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks—0
Multiple Inputs Neural Networks for Medicare fraud Detection—0
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning—0
Trustworthy Anomaly Detection: A Survey—0
Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection—0
Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—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