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 501–547 of 547 papers

TitleStatusHype
Credit Card Fraud Detection with Subspace Learning-based One-Class Classification—0
Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies—0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection—0
Dataset shift quantification for credit card fraud detection—0
Data+Shift: Supporting visual investigation of data distribution shifts by data scientists—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
Deep Fraud Detection on Non-attributed Graph—0
Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions—0
Deep Learning Methods for Credit Card Fraud Detection—0
Deep-Net: Deep Neural Network for Cyber Security Use Cases—0
Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
DeepTrax: Embedding Graphs of Financial Transactions—0
Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention—0
Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns—0
Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models—0
Detection of fraudulent users in P2P financial market—0
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection—0
DFraud3- Multi-Component Fraud Detection freeof Cold-start—0
Differentiable Inductive Logic Programming for Fraud Detection—0
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications—0
Differential Privacy Under Class Imbalance: Methods and Empirical Insights—0
Diffusion Boosted Trees—0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs—0
Distributed data analytics—0
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models—0
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks—0
Efficient Vertical Federated Learning with Secure Aggregation—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data—0
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding—0
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning—0
Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach—0
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow—0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation—0
Enhancing supply chain security with automated machine learning—0
Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection—0
Ensemble of Example-Dependent Cost-Sensitive Decision Trees—0
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks—0
Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges—0
Evaluating hypotheses in geolocation on a very large sample of Twitter—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