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
FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance—0
Knowledge Sharing via Domain Adaptation in Customs Fraud Detection—0
Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
Application of Deep Reinforcement Learning to Payment Fraud—0
Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection—0
Network In Graph Neural Network—0
Social Fraud Detection Review: Methods, Challenges and Analysis—0
Graph Neural Network Training with Data Tiering—0
Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection—0
Credit Card Fraud Detection Using Machine Learning Approach—0
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture—0
Tradeoffs in Streaming Binary Classification under Limited Inspection Resources—0
Deep Fraud Detection on Non-attributed Graph—0
Sample Complexity of Deep Active Learning—0
Synthetic Data Generation for Fraud Detection using GANs—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Credit Card Fraud Detection using Machine Learning: A Study—0
Incremental Feature Learning For Infinite Data—0
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud DetectionCode0
Relational Graph Neural Networks for Fraud Detection in a Super-App environment—0
Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning—0
Applications of Machine Learning in Fintech Credit Card Fraud Detection—0
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production—0
Active learning for imbalanced data under cold start—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
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
How effective are Graph Neural Networks in Fraud Detection for Network Data?Code0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
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
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
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
Markov model with machine learning integration for fraud detection in health insurance—0
LIME: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information NetworksCode0
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
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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