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 151–200 of 547 papers

TitleStatusHype
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)Code0
Deep Anomaly Detection under Labeling Budget ConstraintsCode0
Deep Anomaly Detection with Deviation NetworksCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervisionCode0
Exploring Neural Joint Activity in Spiking Neural Networks for Fraud DetectionCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Local Multi-Label Explanations for Random ForestCode0
Cost-sensitive Semi-supervised Classification for Fraud Applications—0
Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Applying support vector data description for fraud detection—0
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data—0
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data—0
Applications of Machine Learning in Fintech Credit Card Fraud Detection—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection—0
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
Coherent Feed Forward Quantum Neural Network—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
Application of AI-based Models for Online Fraud Detection and Analysis—0
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection—0
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Advanced Financial Fraud Detection Using GNN-CL Model—0
A Comparison of Decision Forest Inference Platforms from A Database Perspective—0
AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge—0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection—0
A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection—0
Causality from Bottom to Top: A Survey—0
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Building High-Quality Auction Fraud Dataset—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection—0
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector—0
Anomaly Detection in Power Generation Plants with Generative Adversarial Networks—0
Addressing Noise and Stochasticity in Fraud Detection for Service Networks—0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis—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
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—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