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 451–500 of 547 papers

TitleStatusHype
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective—0
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges—0
A Survey on Actionable Knowledge—0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
A Systems Theoretic Approach to Online Machine Learning—0
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection—0
ATM Fraud Detection using Streaming Data Analytics—0
Attention is All You Need Until You Need Retention—0
Auditing: Active Learning with Outcome-Dependent Query Costs—0
AutoFraudNet: A Multimodal Network to Detect Fraud in the Auto Insurance Industry—0
Automated Data Slicing for Model Validation:A Big data - AI Integration Approach—0
Automatic Model Monitoring for Data Streams—0
Automatic Procurement Fraud Detection with Machine Learning—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models—0
Backdoor attacks on DNN and GBDT -- A Case Study from the insurance domain—0
BadVFL: Backdoor Attacks in Vertical Federated Learning—0
Bayesian and Dempster-Shafer models for combining multiple sources of evidence in a fraud detection system—0
Behavioral graph fraud detection in E-commerce—0
Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification—0
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
Blockchain Data Analysis in the Era of Large-Language Models—0
Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector—0
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection—0
Building High-Quality Auction Fraud Dataset—0
CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks—0
Causality from Bottom to Top: A Survey—0
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection—0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection—0
CoDetect: Financial Fraud Detection With Anomaly Feature Detection—0
Coherent Feed Forward Quantum Neural Network—0
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data—0
Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation—0
Cost-sensitive Semi-supervised Classification for Fraud Applications—0
Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data—0
Credit Card Fraud Detection: A Deep Learning Approach—0
Credit card fraud detection - Classifier selection strategy—0
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach—0
Credit Card Fraud Detection Using Advanced Transformer Model—0
Credit Card Fraud Detection Using Asexual Reproduction Optimization—0
Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data—0
Credit card fraud detection using machine learning: A survey—0
Credit Card Fraud Detection using Machine Learning: A Study—0
Credit Card Fraud Detection Using Machine Learning Approach—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