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
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks—0
Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications—0
Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms—0
Perseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes—0
Textual Data Mining for Financial Fraud Detection: A Deep Learning Approach—0
The AI Revolution: Opportunities and Challenges for the Finance Sector—0
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection—0
The Importance of Future Information in Credit Card Fraud Detection—0
The Many Faces of Link Fraud—0
TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysis—0
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial—0
Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection—0
Towards Credit-Fraud Detection via Sparsely Varying Gaussian Approximations—0
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources—0
Transaction Fraud Detection Using GRU-centered Sandwich-structured Model—0
Transaction Fraud Detection via an Adaptive Graph Neural Network—0
Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer—0
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production—0
Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection—0
Trustable and Automated Machine Learning Running with Blockchain and Its Applications—0
Trustworthy Anomaly Detection: A Survey—0
Ultra-imbalanced classification guided by statistical information—0
UMGAD: Unsupervised Multiplex Graph Anomaly Detection—0
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning—0
Uncheatable Machine Learning Inference—0
Uncovering Insurance Fraud Conspiracy with Network Learning—0
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions—0
Unsupervised anomaly detection for discrete sequence healthcare data—0
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning—0
Unsupervised Frequent Pattern Mining for CEP—0
Unsupervised Machine Learning for Explainable Health Care Fraud Detection—0
Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection—0
Using Causality for Enhanced Prediction of Web Traffic Time Series—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Using Machine Learning to Detect Fraudulent SMSs in Chichewa—0
Using Person Embedding to Enrich Features and Data Augmentation for Classification—0
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection—0
VertexSerum: Poisoning Graph Neural Networks for Link Inference—0
Weak error analysis for stochastic gradient descent optimization algorithms—0
Weakly Supervised Multi-task Learning for Concept-based Explainability—0
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning—0
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review—0
0/1 Deep Neural Networks via Block Coordinate Descent—0
Gamma distribution-based sampling for imbalanced data—0
Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks—0
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?—0
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection—0
GenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets—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