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 351–400 of 547 papers

TitleStatusHype
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection—0
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs—0
GNNBleed: Inference Attacks to Unveil Private Edges in Graphs with Realistic Access to GNN Models—0
GPT-Guided Monte Carlo Tree Search for Symbolic Regression in Financial Fraud Detection—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning—0
Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Graph Learning—0
Graph Neural Networks for Financial Fraud Detection: A Review—0
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture—0
Graph Neural Network Training with Data Tiering—0
Helen: Maliciously Secure Coopetitive Learning for Linear Models—0
Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection—0
Higher-order Structure Based Anomaly Detection on Attributed Networks—0
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks—0
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
Hybrid Heuristic Algorithms for Adiabatic Quantum Machine Learning Models—0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
I call BS: Fraud Detection in Crowdfunding Campaigns—0
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation—0
Impact of Sampling Techniques and Data Leakage on XGBoost Performance in Credit Card Fraud Detection—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Improved Aggregating and Accelerating Training Methods for Spatial Graph Neural Networks on Fraud Detection—0
Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective—0
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets—0
Improving Fraud Detection via Hierarchical Attention-based Graph Neural Network—0
Incremental Feature Learning For Infinite Data—0
Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care—0
Industrial Scale Privacy Preserving Deep Neural Network—0
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance—0
Inferring about fraudulent collusion risk on Brazilian public works contracts in official texts using a Bi-LSTM approach—0
Instance-Level Explanations for Fraud Detection: A Case Study—0
Integrating Fuzzy Logic into Deep Symbolic Regression—0
Interleaved Sequence RNNs for Fraud Detection—0
Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance—0
JEL: Applying End-to-End Neural Entity Linking in JPMorgan Chase—0
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment—0
Kernel density estimation based sampling for imbalanced class distribution—0
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data—0
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer—0
Knowledge Sharing via Domain Adaptation in Customs Fraud Detection—0
Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice—0
Learning-Based Data Storage [Vision] (Technical Report)—0
Learning Representations for Outlier Detection on a Budget—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Leveraging Machine Learning for Multichain DeFi Fraud Detection—0
Link Prediction using Graph Neural Networks for Master Data Management—0
LogitMat : Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models—0
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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