SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–450 of 547 papers

TitleStatusHype
Weight-of-evidence 2.0 with shrinkage and spline-binningCode0
BAAAN: Backdoor Attacks Against Auto-encoder and GAN-Based Machine Learning Models—0
Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks—0
Cost-sensitive Semi-supervised Classification for Fraud Applications—0
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative FrameworkCode0
Deep Learning Methods for Credit Card Fraud Detection—0
Teaching the Machine to Explain Itself using Domain Knowledge—0
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation—0
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph EmbeddingCode0
Precision-Recall Curve (PRC) Classification Trees—0
Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach—0
On the intrinsic robustness to noise of some leading classifiers and symmetric loss function -- an empirical evaluation—0
Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking—0
Teaching the Machine to Explain Itself using Domain Knowledge—0
Credit card fraud detection using machine learning: A survey—0
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications—0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs—0
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
Gamma distribution-based sampling for imbalanced data—0
Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model—0
Social network analytics for supervised fraud detection in insuranceCode0
Fraud Detection using Data-Driven approach—0
Profiling US Restaurants from Billions of Payment Card Transactions—0
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance—0
Against Membership Inference Attack: Pruning is All You Need—0
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Multi-stream RNN for Merchant Transaction Prediction—0
SeismoFlow -- Data augmentation for the class imbalance problem—0
Unsupervised anomaly detection for discrete sequence healthcare data—0
Towards Credit-Fraud Detection via Sparsely Varying Gaussian Approximations—0
Multi-future Merchant Transaction Prediction—0
Weak error analysis for stochastic gradient descent optimization algorithms—0
I call BS: Fraud Detection in Crowdfunding Campaigns—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
DFraud3- Multi-Component Fraud Detection freeof Cold-start—0
Applying support vector data description for fraud detection—0
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?—0
Bayesian Stress Testing of Models in a Classification HierarchyCode0
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced DataCode0
robROSE: A robust approach for dealing with imbalanced data in fraud detectionCode0
Industrial Scale Privacy Preserving Deep Neural Network—0
Link Prediction using Graph Neural Networks for Master Data Management—0
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance—0
An Information-Theoretic Approach to Personalized Explainable Machine Learning—0
A Semi-supervised Graph Attentive Network for Financial Fraud DetectionCode0
Uncovering Insurance Fraud Conspiracy with Network Learning—0
Adapted tree boosting for Transfer Learning—0
Higher-Order Label Homogeneity and Spreading in GraphsCode0
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All datasetsBAF – BaseYelp-FraudAmazon-FraudElliptic DatasetHealthcare Provider Fraud Detection AnalysisKaggle-Credit Card Fraud DatasetBAF – Variant IBAF – Variant IIBAF – Variant IIIBAF – Variant IVBAF – Variant VFDCompCN

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LightGBMRecall @ 5% FPR54.3—Unverified
2CatBoostRecall @ 5% FPR52.4—Unverified
3LightGBMRecall @ 5% FPR51.76—Unverified
41D-CSNNRecall @ 5% FPR50.35—Unverified
5MLP–NNRecall @ 5% FPR49.6—Unverified
61D-CSNNRecall @ 5% FPR42.79—Unverified
7LightGBMRecall @ 1% FPR25.2—Unverified
8FIGSRecall @ 1% FPR21—Unverified
9CART+RIFFRecall @ 1% FPR18.4—Unverified
10CARTRecall @ 1% FPR16—Unverified
#ModelMetricClaimedVerifiedStatus
1LEX-GNNAUC-ROC96.4—Unverified
2JA-GNNAUC-ROC95.11—Unverified
3GTANAUC-ROC94.98—Unverified
4BOLT-GRAPHAUC-ROC93.18—Unverified
5SplitGNNAUC-ROC92.03—Unverified
6GAT+JKAUC-ROC90.04—Unverified
7RLC-GNNAUC-ROC85.44—Unverified
8RioGNNAUC-ROC83.54—Unverified
9PC-GNNAUC-ROC79.87—Unverified
10CARE-GNNAUC-ROC75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1LEX-GNNAUC-ROC97.91—Unverified
2GTANAUC-ROC97.5—Unverified
3RLC-GNNAUC-ROC97.48—Unverified
4RioGNNAUC-ROC96.19—Unverified
5PC-GNNAUC-ROC95.86—Unverified
6CARE-GNNAUC-ROC89.73—Unverified
#ModelMetricClaimedVerifiedStatus
1GCNAUC0.83—Unverified
2GraphSAGEAUC0.83—Unverified
3GATAUC0.81—Unverified
4GINAUC0.81—Unverified
5Node2vecAUC0.53—Unverified
6DeepwalkAUC0.45—Unverified
#ModelMetricClaimedVerifiedStatus
1BiRankAUC0.79—Unverified
2GraphSAGEAUC0.67—Unverified
3metapath2vecAUC0.51—Unverified
#ModelMetricClaimedVerifiedStatus
1XBNETAccuracy71.33—Unverified
2DevNetAUC0.98—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR40.71—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR47.08—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR41.83—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR35.54—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNRecall @ 5% FPR34.96—Unverified
#ModelMetricClaimedVerifiedStatus
1SplitGNNAUC-ROC68.98—Unverified