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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
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
"Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysis—0
Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks—0
Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanism—0
Review of Digital Asset Development with Graph Neural Network Unlearning—0
Application of AI-based Models for Online Fraud Detection and Analysis—0
Credit Card Fraud Detection: A Deep Learning Approach—0
Fair Anomaly Detection For Imbalanced Groups—0
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection—0
Ultra-imbalanced classification guided by statistical information—0
Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges—0
AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge—0
Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework—0
RIFF: Inducing Rules for Fraud Detection from Decision Trees—0
Federated Graph Learning with Structure Proxy AlignmentCode0
Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice—0
Minimum Enclosing Ball Synthetic Minority Oversampling Technique from a Geometric Perspective—0
IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection—0
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection—0
Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervisionCode0
Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models—0
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection—0
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Advanced Financial Fraud Detection Using GNN-CL Model—0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning—0
New User Event Prediction Through the Lens of Causal Inference—0
Enhancing supply chain security with automated machine learning—0
SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning—0
Federated learning in food research—0
Higher-order Structure Based Anomaly Detection on Attributed Networks—0
Credit Card Fraud Detection Using Advanced Transformer Model—0
Robust Prediction Model for Multidimensional and Unbalanced Datasets—0
Diffusion Boosted Trees—0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
OTLP: Output Thresholding Using Mixed Integer Linear Programming—0
Safety in Graph Machine Learning: Threats and Safeguards—0
Network analytics for insurance fraud detection: a critical case studyCode0
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation—0
Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)Code0
A Systems Theoretic Approach to Online Machine Learning—0
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection—0
Temporal Graph Networks for Graph Anomaly Detection in Financial Networks—0
Causality from Bottom to Top: A Survey—0
Cost-Sensitive Learning to Defer to Multiple Experts with Workload ConstraintsCode0
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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