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 51–75 of 547 papers

Title	Date	Tasks	Status	Hype
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks	Apr 16, 2021	Fraud DetectionGraph Neural Network	CodeCode Available	1
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum	Dec 11, 2023	Binary ClassificationFraud Detection	CodeCode Available	1
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection	Mar 28, 2023	Fraud DetectionGraph Neural Network	CodeCode Available	1
BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection	Mar 29, 2023	Fraud Detection	CodeCode Available	1
Credit Card Fraud Detection Using Convolutional Neural Networks	Oct 16, 2016	Fraud Detection	CodeCode Available	1
Stock price prediction using Generative Adversarial Networks	Apr 2, 2021	Fraud DetectionGenerative Adversarial Network	CodeCode Available	1
Tabular Transformers for Modeling Multivariate Time Series	Nov 3, 2020	Fraud DetectionSynthetic Data Generation	CodeCode Available	1
Active Learning for Human-in-the-Loop Customs Inspection	Oct 27, 2020	Active LearningFraud Detection	CodeCode Available	1
Text generation for dataset augmentation in security classification tasks	Oct 22, 2023	Data AugmentationFraud Detection	CodeCode Available	1
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations	Nov 30, 2020	Decision MakingFeature Importance	CodeCode Available	1
Training MLPs on Graphs without Supervision	Dec 5, 2024	Fraud DetectionGraph Classification	CodeCode Available	1
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection	Oct 6, 2023	Fraud DetectionGraph Attention	CodeCode Available	1
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection	Mar 29, 2023	Anomaly DetectionFraud Detection	CodeCode Available	1
New Benchmarks for Learning on Non-Homophilous Graphs	Apr 3, 2021	AttributeFraud Detection	CodeCode Available	1
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms	Apr 21, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1
Continuous-variable quantum neural networks	Jun 18, 2018	Fraud Detection	CodeCode Available	1
Crypto Pump and Dump via Deep Learning Techniques	May 10, 2022	Deep LearningFraud Detection	CodeCode Available	1
Customs Fraud Detection in the Presence of Concept Drift	Sep 29, 2021	Fraud Detection	CodeCode Available	1
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection	Aug 23, 2020	Fraud DetectionMulti-target regression	CodeCode Available	1
Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics	May 25, 2023	Anomaly DetectionFraud Detection	CodeCode Available	1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning	Mar 22, 2023	Dynamic Link PredictionDynamic Node Classification	CodeCode Available	1
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks	Aug 11, 2021	Decision MakingFraud Detection	CodeCode Available	1
Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)	Aug 26, 2022	Bayesian OptimizationDisease Prediction	CodeCode Available	1
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods	Jul 29, 2020	BIG-bench Machine LearningFeature Engineering	—Unverified	0
Applying support vector data description for fraud detection	May 31, 2020	Fraud DetectionOne-Class Classification	—Unverified	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