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 201–250 of 547 papers

TitleStatusHype
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection—0
Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
Enhancing supply chain security with automated machine learning—0
Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection—0
Ensemble of Example-Dependent Cost-Sensitive Decision Trees—0
AlertMix: A Big Data platform for multi-source streaming data—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks—0
Evaluating Fairness in Transaction Fraud Models: Fairness Metrics, Bias Audits, and Challenges—0
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation—0
Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset—0
Building High-Quality Auction Fraud Dataset—0
Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection—0
A Survey on Actionable Knowledge—0
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)—0
Explainability in Practice: A Survey of Explainable NLP Across Various Domains—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies—0
Challenges and Complexities in Machine Learning based Credit Card Fraud Detection—0
Explainable Machine Learning for Fraud Detection—0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
Explaining Deep Learning Models for Structured Data using Layer-Wise Relevance Propagation—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection—0
Extracting the Native Language Signal for Second Language Acquisition—0
ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes—0
FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations—0
Fair Anomaly Detection For Imbalanced Groups—0
FairGen: Fair Synthetic Data Generation—0
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action—0
Fairness-aware Outlier Ensemble—0
DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection—0
FraudJudger: Real-World Data Oriented Fraud Detection on Digital Payment Platforms—0
Data+Shift: Supporting visual investigation of data distribution shifts by data scientists—0
Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection—0
Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization—0
Federated learning in food research—0
Confronting Discrimination in Classification: Smote Based on Marginalized Minorities in the Kernel Space for Imbalanced Data—0
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges—0
Dataset shift quantification for credit card fraud detection—0
Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models—0
Financial fraud detection system based on improved random forest and gradient boosting machine (GBM)—0
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods—0
Advanced Real-Time Fraud Detection Using RAG-Based LLMs—0
Financial Fraud Detection using Quantum Graph Neural Networks—0
Data Obfuscation through Latent Space Projection (LSP) for Privacy-Preserving AI Governance: Case Studies in Medical Diagnosis and Finance Fraud Detection—0
A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective—0
FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance—0
Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework—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