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

Showing 51100 of 547 papers

TitleStatusHype
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud DetectionCode1
Promoting Fairness through Hyperparameter OptimizationCode1
AD-LLM: Benchmarking Large Language Models for Anomaly DetectionCode1
SimMLP: Training MLPs on Graphs without SupervisionCode1
New Benchmarks for Learning on Non-Homophilous GraphsCode1
Modelling graph dynamics in fraud detection with "Attention"Code1
Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity AnalysisCode1
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly DetectionCode1
Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud DetectionCode1
KGLM: Integrating Knowledge Graph Structure in Language Models for Link PredictionCode1
Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental EvaluationCode1
NLP-ADBench: NLP Anomaly Detection BenchmarkCode1
Raising the Bar in Graph-level Anomaly DetectionCode1
Stock price prediction using Generative Adversarial NetworksCode1
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection AlgorithmsCode1
Continuous-variable quantum neural networksCode1
Credit Card Fraud Detection Using Convolutional Neural NetworksCode1
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly GenerationCode1
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud DetectionCode1
Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public ProcurementCode1
Customs Fraud Detection in the Presence of Concept DriftCode1
Crypto Pump and Dump via Deep Learning TechniquesCode1
Customs Import Declaration DatasetsCode1
Leveraging the Urysohn Lemma of Topology for an Enhanced Binary ClassifierCode0
LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud DetectionCode0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
How effective are Graph Neural Networks in Fraud Detection for Network Data?Code0
High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation ComparisonCode0
Graph similarity learning for change-point detection in dynamic networksCode0
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph EmbeddingCode0
Higher-Order Label Homogeneity and Spreading in GraphsCode0
Improving Fraud Detection with 1D-Convolutional Spiking Neural Networks Through Bayesian OptimizationCode0
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
GraphFC: Customs Fraud Detection with Label ScarcityCode0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
BigDL: A Distributed Deep Learning Framework for Big DataCode0
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling AlgorithmCode0
FPR Estimation for Fraud Detection in the Presence of Class-Conditional Label NoiseCode0
Bayesian Stress Testing of Models in a Classification HierarchyCode0
Financial Fraud Detection with Entropy ComputingCode0
RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud DetectionCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Local Multi-Label Explanations for Random ForestCode0
Exploring Neural Joint Activity in Spiking Neural Networks for Fraud DetectionCode0
An engine to simulate insurance fraud network dataCode0
A Variational Approach for Learning from Positive and Unlabeled DataCode0
Fairness-aware Multi-view ClusteringCode0
Federated Graph Learning with Structure Proxy AlignmentCode0
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud DetectionCode0
Explainable Fraud Detection with Deep Symbolic ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LightGBMRecall @ 5% FPR54.3Unverified
2CatBoostRecall @ 5% FPR52.4Unverified
3LightGBMRecall @ 5% FPR51.76Unverified
41D-CSNNRecall @ 5% FPR50.35Unverified
5MLP–NNRecall @ 5% FPR49.6Unverified
61D-CSNNRecall @ 5% FPR42.79Unverified
7LightGBMRecall @ 1% FPR25.2Unverified
8FIGSRecall @ 1% FPR21Unverified
9CART+RIFFRecall @ 1% FPR18.4Unverified
10CARTRecall @ 1% FPR16Unverified
#ModelMetricClaimedVerifiedStatus
1LEX-GNNAUC-ROC96.4Unverified
2JA-GNNAUC-ROC95.11Unverified
3GTANAUC-ROC94.98Unverified
4BOLT-GRAPHAUC-ROC93.18Unverified
5SplitGNNAUC-ROC92.03Unverified
6GAT+JKAUC-ROC90.04Unverified
7RLC-GNNAUC-ROC85.44Unverified
8RioGNNAUC-ROC83.54Unverified
9PC-GNNAUC-ROC79.87Unverified
10CARE-GNNAUC-ROC75.7Unverified
#ModelMetricClaimedVerifiedStatus
1LEX-GNNAUC-ROC97.91Unverified
2GTANAUC-ROC97.5Unverified
3RLC-GNNAUC-ROC97.48Unverified
4RioGNNAUC-ROC96.19Unverified
5PC-GNNAUC-ROC95.86Unverified
6CARE-GNNAUC-ROC89.73Unverified
#ModelMetricClaimedVerifiedStatus
1GCNAUC0.83Unverified
2GraphSAGEAUC0.83Unverified
3GATAUC0.81Unverified
4GINAUC0.81Unverified
5Node2vecAUC0.53Unverified
6DeepwalkAUC0.45Unverified
#ModelMetricClaimedVerifiedStatus
1BiRankAUC0.79Unverified
2GraphSAGEAUC0.67Unverified
3metapath2vecAUC0.51Unverified
#ModelMetricClaimedVerifiedStatus
1XBNETAccuracy71.33Unverified
2DevNetAUC0.98Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR40.71Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR47.08Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR41.83Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR35.54Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR34.96Unverified
#ModelMetricClaimedVerifiedStatus
1SplitGNNAUC-ROC68.98Unverified