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 351–400 of 547 papers

TitleStatusHype
Active learning for imbalanced data under cold start—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
Machine Learning for Fraud Detection in E-Commerce: A Research Agenda—0
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection—0
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data—0
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs—0
Non-Parametric Stochastic Sequential Assignment With Random Arrival Times—0
XBNet : An Extremely Boosted Neural NetworkCode1
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-TrainingCode2
OCT-GAN: Neural ODE-based Conditional Tabular GANsCode1
How effective are Graph Neural Networks in Fraud Detection for Network Data?Code0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud DetectionCode1
Explainable Machine Learning for Fraud Detection—0
Multimodal and Contrastive Learning for Click Fraud Detection—0
Weakly Supervised Multi-task Learning for Concept-based Explainability—0
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud DetectionCode1
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural NetworksCode1
Bayesian and Dempster-Shafer models for combining multiple sources of evidence in a fraud detection system—0
Generating Multi-type Temporal Sequences to Mitigate Class-imbalanced ProblemCode0
New Benchmarks for Learning on Non-Homophilous GraphsCode1
Stock price prediction using Generative Adversarial NetworksCode1
Promoting Fairness through Hyperparameter OptimizationCode1
Fairness-aware Outlier Ensemble—0
Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook—0
Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook—0
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
Markov model with machine learning integration for fraud detection in health insurance—0
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations—0
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data—0
Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection—0
Weight-of-evidence 2.0 with shrinkage and spline-binningCode0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks—0
Cost-sensitive Semi-supervised Classification for Fraud Applications—0
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative FrameworkCode0
Deep Learning Methods for Credit Card Fraud Detection—0
TimeSHAP: Explaining Recurrent Models through Sequence PerturbationsCode1
Teaching the Machine to Explain Itself using Domain Knowledge—0
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation—0
xFraud: Explainable Fraud Transaction DetectionCode1
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph EmbeddingCode0
Precision-Recall Curve (PRC) Classification Trees—0
Tabular Transformers for Modeling Multivariate Time SeriesCode1
Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach—0
Active Learning for Human-in-the-Loop Customs InspectionCode1
On the intrinsic robustness to noise of some leading classifiers and symmetric loss function -- an empirical evaluation—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