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 201–250 of 547 papers

TitleStatusHype
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—0
Ensemble of Example-Dependent Cost-Sensitive Decision Trees—0
Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection—0
Enhancing supply chain security with automated machine learning—0
Blockchain Data Analysis in the Era of Large-Language Models—0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—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
Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation—0
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset—0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)—0
Explainability in Practice: A Survey of Explainable NLP Across Various Domains—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
Explainable Machine Learning for Fraud Detection—0
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow—0
Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning—0
Extracting the Native Language Signal for Second Language Acquisition—0
ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes—0
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations—0
Fair Anomaly Detection For Imbalanced Groups—0
FairGen: Fair Synthetic Data Generation—0
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding—0
Fairness-aware Outlier Ensemble—0
Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification—0
Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanism—0
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data—0
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Federated learning in food research—0
Efficient Vertical Federated Learning with Secure Aggregation—0
Behavioral graph fraud detection in E-commerce—0
An Information-Theoretic Approach to Personalized Explainable Machine Learning—0
Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models—0
A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection—0
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods—0
A comparative evaluation of novelty detection algorithms for discrete sequences—0
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection—0
Bayesian and Dempster-Shafer models for combining multiple sources of evidence in a fraud detection system—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
A new perspective on classification: optimally allocating limited resources to uncertain tasks—0
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks—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