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 501–525 of 547 papers

Title	Date	Tasks	Status
Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences	Jan 3, 2024	Fraud DetectionTime Series	CodeCode Available
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs	Sep 3, 2019	Automated Feature EngineeringFeature Engineering	CodeCode Available
Neurochaos Feature Transformation and Classification for Imbalanced Learning	Apr 20, 2022	ClassificationDecision Making	CodeCode Available
Fairness-aware Multi-view Clustering	Feb 11, 2023	ClusteringContrastive Learning	CodeCode Available
Efficient Fraud Detection Using Deep Boosting Decision Trees	Feb 12, 2023	Fraud DetectionRepresentation Learning	CodeCode Available
A Variational Approach for Learning from Positive and Unlabeled Data	Jun 3, 2019	Fraud Detectiontext-classification	CodeCode Available
Dynamic Feature Fusion: Combining Global Graph Structures and Local Semantics for Blockchain Fraud Detection	Jan 3, 2025	Fraud Detectiongraph construction	CodeCode Available
Dual-channel Heterophilic Message Passing for Graph Fraud Detection	Apr 19, 2025	Fraud DetectionInductive Bias	CodeCode Available
Social network analytics for supervised fraud detection in insurance	Sep 15, 2020	Fraud Detection	CodeCode Available
NFAD: Fixing anomaly detection using normalizing flows	Dec 19, 2019	Anomaly DetectionBayesian Inference	CodeCode Available
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data	Apr 16, 2020	Binary ClassificationClassification	CodeCode Available
Improving Fraud Detection with 1D-Convolutional Spiking Neural Networks Through Bayesian Optimization	Nov 16, 2024	Bayesian OptimizationFairness	CodeCode Available
One-Class Adversarial Nets for Fraud Detection	Mar 5, 2018	Fraud DetectionOne-Class Classification	CodeCode Available
Deep Anomaly Detection under Labeling Budget Constraints	Feb 15, 2023	Anomaly DetectionFraud Detection	CodeCode Available
A Survey of Predictive Modelling under Imbalanced Distributions	May 7, 2015	Fraud DetectionSurvey	CodeCode Available
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration	Jul 19, 2023	Fraud DetectionIntrusion Detection	CodeCode Available
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding	Feb 12, 2024	Fraud Detectiontext similarity	CodeCode Available
Credit Card Fraud Detection Using Autoencoder Neural Network	Aug 30, 2019	DenoisingFraud Detection	CodeCode Available
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm	Jun 5, 2023	Fraud Detection	CodeCode Available
robROSE: A robust approach for dealing with imbalanced data in fraud detection	Mar 22, 2020	Anomaly DetectionFraud Detection	CodeCode Available
Data Distribution Valuation	Oct 6, 2024	Data ValuationFraud Detection	CodeCode Available
Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection	Nov 17, 2024	Bayesian OptimizationFairness	CodeCode Available
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints	Mar 11, 2024	Fraud Detection	CodeCode Available
Robust Graph Representation Learning via Neural Sparsification	Jan 1, 2020	Fraud DetectionGraph Representation Learning	CodeCode Available
Spotting Collective Behaviour of Online Frauds in Customer Reviews	May 31, 2019	Fraud DetectionSpam detection	CodeCode Available

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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