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 276–300 of 547 papers

Title	Date	Tasks	Status	Hype
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection	Aug 20, 2022	Fraud DetectionIngenuity	—Unverified	0
Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance	Aug 19, 2022	Causal InferenceFraud Detection	—Unverified	0
Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection	Aug 16, 2022	feature selectionFraud Detection	—Unverified	0
Scalable and Sparsity-Aware Privacy-Preserving K-means Clustering with Application to Fraud Detection	Aug 12, 2022	ClusteringFraud Detection	—Unverified	0
Customs Import Declaration Datasets	Aug 4, 2022	Fraud DetectionManagement	CodeCode Available	1
Unsupervised Frequent Pattern Mining for CEP	Jul 28, 2022	Active LearningDescriptive	—Unverified	0
Multiple Attribute Fairness: Application to Fraud Detection	Jul 28, 2022	AttributeFairness	—Unverified	0
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions	Jul 13, 2022	Decision MakingFairness	—Unverified	0
Local Multi-Label Explanations for Random Forest	Jul 5, 2022	ClassificationDecision Making	CodeCode Available	0
Using Person Embedding to Enrich Features and Data Augmentation for Classification	Jun 30, 2022	BIG-bench Machine LearningClassification	—Unverified	0
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset	Jun 27, 2022	Fraud Detection	—Unverified	0
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction	Jun 27, 2022	FairnessFraud Detection	—Unverified	0
A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection	Jun 24, 2022	BIG-bench Machine LearningFraud Detection	—Unverified	0
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods	Jun 24, 2022	Experimental DesignFraud Detection	—Unverified	0
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets	Jun 20, 2022	BenchmarkingFraud Detection	CodeCode Available	0
0/1 Deep Neural Networks via Block Coordinate Descent	Jun 19, 2022	10-shot image generation	—Unverified	0
Learning-Based Data Storage [Vision] (Technical Report)	Jun 12, 2022	Fraud Detectionimage-classification	—Unverified	0
Open ERP System Data For Occupational Fraud Detection	Jun 9, 2022	ERPFraud Detection	—Unverified	0
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning	Jun 9, 2022	Active LearningDrug Design	—Unverified	0
Learning Backward Compatible Embeddings	Jun 7, 2022	Fraud DetectionProduct Recommendation	CodeCode Available	1
Impact of the composition of feature extraction and class sampling in medicare fraud detection	Jun 3, 2022	Dimensionality ReductionFraud Detection	—Unverified	0
Raising the Bar in Graph-level Anomaly Detection	May 27, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1
A Combination of Deep Neural Networks and K-Nearest Neighbors for Credit Card Fraud Detection	May 27, 2022	Fraud Detection	—Unverified	0
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection	May 25, 2022	Entity EmbeddingsFraud Detection	—Unverified	0
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection	May 25, 2022	Change DetectionFraud Detection	CodeCode Available	0

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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