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 226–250 of 547 papers

Title	Date	Tasks	Status
Extracting the Native Language Signal for Second Language Acquisition	Jun 1, 2013	Fraud DetectionLanguage Acquisition	—Unverified
ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes	Aug 1, 2014	Fraud DetectionText Summarization	—Unverified
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations	Jun 13, 2025	Fraud DetectionLanguage Modeling	—Unverified
Fair Anomaly Detection For Imbalanced Groups	Sep 17, 2024	Anomaly DetectionContrastive Learning	—Unverified
FairGen: Fair Synthetic Data Generation	Oct 24, 2022	Fraud DetectionSynthetic Data Generation	—Unverified
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding	Oct 11, 2024	Anomaly DetectionFederated Learning	—Unverified
Fairness-aware Outlier Ensemble	Mar 17, 2021	FairnessFraud Detection	—Unverified
Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification	Apr 16, 2023	Fraud DetectionImage Retrieval	—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
Empirical effect of graph embeddings on fraud detection/ risk mitigation	Mar 5, 2019	Fraud DetectionGraph Embedding	—Unverified
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.	May 1, 2018	Action DetectionAnomaly Detection	—Unverified
Federated learning in food research	Jun 10, 2024	Federated LearningFraud Detection	—Unverified
Efficient Vertical Federated Learning with Secure Aggregation	May 18, 2023	Federated LearningFraud Detection	—Unverified
Behavioral graph fraud detection in E-commerce	Oct 13, 2022	Fraud DetectionGPU	—Unverified
An Information-Theoretic Approach to Personalized Explainable Machine Learning	Mar 1, 2020	BIG-bench Machine LearningDecision Making	—Unverified
Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models	Aug 9, 2023	Fraud DetectionQuantum Machine Learning	—Unverified
A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection	Mar 27, 2025	Ensemble LearningFraud Detection	—Unverified
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods	May 15, 2025	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	—Unverified
A comparative evaluation of novelty detection algorithms for discrete sequences	Feb 28, 2019	Fraud DetectionIntrusion Detection	—Unverified
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection	Oct 12, 2023	Federated LearningFraud Detection	—Unverified
Bayesian and Dempster-Shafer models for combining multiple sources of evidence in a fraud detection system	Apr 15, 2021	Fraud Detection	—Unverified
BadVFL: Backdoor Attacks in Vertical Federated Learning	Apr 18, 2023	Backdoor AttackFederated Learning	—Unverified
A new perspective on classification: optimally allocating limited resources to uncertain tasks	Feb 9, 2022	Fraud DetectionLearning-To-Rank	—Unverified
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks	Sep 15, 2024	Fraud Detection	—Unverified

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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