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Fraud Detection

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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

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Fraud Detection

논문

Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment

Graph Learning

Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network

Secure Energy Transactions Using Blockchain Leveraging AI for Fraud Detection and Energy Market Stability

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations

Advanced fraud detection using machine learning models: enhancing financial transaction security

EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering

Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies

Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective

AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection

Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods

Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets

Addressing Noise and Stochasticity in Fraud Detection for Service Networks

Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection

Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection

Dual-channel Heterophilic Message Passing for Graph Fraud Detection

Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs

Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention

Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow

FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform

Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns

TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection

A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection

Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection

Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning

Enhance GNNs with Reliable Confidence Estimation via Adversarial Calibration Learning

Financial Fraud Detection with Entropy Computing

Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions

Zero-to-One IDV: A Conceptual Model for AI-Powered Identity Verification

\textsc{Perseus}: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes

Corporate Fraud Detection in Rich-yet-Noisy Financial Graph

Structural Alignment Improves Graph Test-Time Adaptation

Using Machine Learning to Detect Fraudulent SMSs in Chichewa

Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations