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 151–200 of 547 papers

TitleStatusHype
ExMo: Explainable AI Model using Inverse Frequency Decision Rules—0
Attention is All You Need Until You Need Retention—0
DeepTrax: Embedding Graphs of Financial Transactions—0
ATM Fraud Detection using Streaming Data Analytics—0
Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking—0
Deep-Net: Deep Neural Network for Cyber Security Use Cases—0
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection—0
A Systems Theoretic Approach to Online Machine Learning—0
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset—0
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)—0
Extracting the Native Language Signal for Second Language Acquisition—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
Deep Learning Methods for Credit Card Fraud Detection—0
Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions—0
Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention—0
Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns—0
A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions—0
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data—0
Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models—0
Detection of fraudulent users in P2P financial market—0
DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection—0
DFraud3- Multi-Component Fraud Detection freeof Cold-start—0
Differentiable Inductive Logic Programming for Fraud Detection—0
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications—0
Differential Privacy Under Class Imbalance: Methods and Empirical Insights—0
Diffusion Boosted Trees—0
Automatic Procurement Fraud Detection with Machine Learning—0
Distributed data analytics—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
Deep Fraud Detection on Non-attributed Graph—0
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—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
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks—0
AlertMix: A Big Data platform for multi-source streaming data—0
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation—0
A Survey on Actionable Knowledge—0
Bayesian and Dempster-Shafer models for combining multiple sources of evidence in a fraud detection system—0
Efficient Vertical Federated Learning with Secure Aggregation—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
Empirical effect of graph embeddings on fraud detection/ risk mitigation—0
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data—0
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding—0
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning—0
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies—0
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow—0
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data—0
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action—0
Show:102550
← PrevPage 4 of 11Next →
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