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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 201–225 of 547 papers

Title	Date	Tasks	Status
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data	Sep 15, 2024	DiversityFraud Detection	—Unverified
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection	Nov 19, 2015	Bayesian InferenceFraud Detection	—Unverified
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection	Sep 28, 2020	Feature EngineeringFraud Detection	—Unverified
Enhancing supply chain security with automated machine learning	Jun 19, 2024	feature selectionFraud Detection	—Unverified
Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow	Apr 3, 2025	Fraud Detection	—Unverified
Ensemble of Example-Dependent Cost-Sensitive Decision Trees	May 18, 2015	Fraud DetectionGeneral Classification	—Unverified
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph	Dec 23, 2019	Fraud Detection	—Unverified
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning	Sep 9, 2024	AttributeFraud Detection	—Unverified
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks	Mar 23, 2022	Fraud DetectionGraph Neural Network	—Unverified
Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges	Sep 6, 2024	FairnessFraud Detection	—Unverified
Enhancing Credit Card Fraud Detection A Neural Network and SMOTE Integrated Approach	Feb 27, 2024	Fraud Detection	—Unverified
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset	Jun 27, 2022	Fraud Detection	—Unverified
Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning	Mar 23, 2025	Fraud Detection	—Unverified
Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection	Mar 27, 2023	Fraud Detection	—Unverified
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding	Oct 11, 2024	Anomaly DetectionFederated Learning	—Unverified
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)	Jan 26, 2023	Data AugmentationFraud Detection	—Unverified
Explainability in Practice: A Survey of Explainable NLP Across Various Domains	Feb 2, 2025	Fraud Detection	—Unverified
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection	Nov 19, 2022	Binary ClassificationCausal Inference	—Unverified
Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection	Jan 12, 2021	ClusteringFraud Detection	—Unverified
Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification	Apr 16, 2023	Fraud DetectionImage Retrieval	—Unverified
Explainable Machine Learning for Fraud Detection	May 13, 2021	BIG-bench Machine LearningFraud Detection	—Unverified
Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanism	Oct 1, 2024	Fraud DetectionGraph Neural Network	—Unverified
Empirical study of Machine Learning Classifier Evaluation Metrics behavior in Massively Imbalanced and Noisy data	Aug 25, 2022	Fraud Detection	—Unverified
Exploring Global and Local Information for Anomaly Detection with Normal Samples	Jun 3, 2023	Anomaly DetectionFault Diagnosis	—Unverified
Empirical effect of graph embeddings on fraud detection/ risk mitigation	Mar 5, 2019	Fraud DetectionGraph Embedding	—Unverified

Show:10 25 50

← PrevPage 9 of 22Next →

All datasets BAF – Base Yelp-Fraud Amazon-Fraud Elliptic Dataset Healthcare Provider Fraud Detection Analysis Kaggle-Credit Card Fraud Dataset BAF – Variant I BAF – Variant II BAF – Variant III BAF – Variant IV BAF – Variant V FDCompCN

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LightGBM	Recall @ 5% FPR	54.3	—	Unverified
2	CatBoost	Recall @ 5% FPR	52.4	—	Unverified
3	LightGBM	Recall @ 5% FPR	51.76	—	Unverified
4	1D-CSNN	Recall @ 5% FPR	50.35	—	Unverified
5	MLP–NN	Recall @ 5% FPR	49.6	—	Unverified
6	1D-CSNN	Recall @ 5% FPR	42.79	—	Unverified
7	LightGBM	Recall @ 1% FPR	25.2	—	Unverified
8	FIGS	Recall @ 1% FPR	21	—	Unverified
9	CART+RIFF	Recall @ 1% FPR	18.4	—	Unverified
10	CART	Recall @ 1% FPR	16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LEX-GNN	AUC-ROC	96.4	—	Unverified
2	JA-GNN	AUC-ROC	95.11	—	Unverified
3	GTAN	AUC-ROC	94.98	—	Unverified
4	BOLT-GRAPH	AUC-ROC	93.18	—	Unverified
5	SplitGNN	AUC-ROC	92.03	—	Unverified
6	GAT+JK	AUC-ROC	90.04	—	Unverified
7	RLC-GNN	AUC-ROC	85.44	—	Unverified
8	RioGNN	AUC-ROC	83.54	—	Unverified
9	PC-GNN	AUC-ROC	79.87	—	Unverified
10	CARE-GNN	AUC-ROC	75.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LEX-GNN	AUC-ROC	97.91	—	Unverified
2	GTAN	AUC-ROC	97.5	—	Unverified
3	RLC-GNN	AUC-ROC	97.48	—	Unverified
4	RioGNN	AUC-ROC	96.19	—	Unverified
5	PC-GNN	AUC-ROC	95.86	—	Unverified
6	CARE-GNN	AUC-ROC	89.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GCN	AUC	0.83	—	Unverified
2	GraphSAGE	AUC	0.83	—	Unverified
3	GAT	AUC	0.81	—	Unverified
4	GIN	AUC	0.81	—	Unverified
5	Node2vec	AUC	0.53	—	Unverified
6	Deepwalk	AUC	0.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BiRank	AUC	0.79	—	Unverified
2	GraphSAGE	AUC	0.67	—	Unverified
3	metapath2vec	AUC	0.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XBNET	Accuracy	71.33	—	Unverified
2	DevNet	AUC	0.98	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1D-CSNN	Recall @ 5% FPR	40.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1D-CSNN	Recall @ 5% FPR	47.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1D-CSNN	Recall @ 5% FPR	41.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1D-CSNN	Recall @ 5% FPR	35.54	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1D-CSNN	Recall @ 5% FPR	34.96	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SplitGNN	AUC-ROC	68.98	—	Unverified