Empirical effect of graph embeddings on fraud detection/ risk mitigation
Graph embedding technics are studied with interest on public datasets, such as BlogCatalog, with the common practice of maximizing scoring on graph reconstruction, link prediction metrics etc. However, in the financial sector the important metrics are often more business related, for example fraud detection rates. With our privileged position of having large amount of real-world non-public P2P-lending social data, we aim to study empirically whether recent advances in graph embedding technics provide a useful signal for metrics more closely related to business interests, such as fraud detection rate.
Code (0)
등록된 구현이 없습니다.
Tasks
Fraud DetectionGraph EmbeddingGraph ReconstructionLink PredictionPositionSimilar Papers 제목 키워드 기반
Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection
Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, existing graph fraud detection methods typ…
Fraud DetectionAlleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
The graph-based model can help to detect suspicious fraud online. Owing to the development of Graph Neural Networks~(GNNs), prior research work has proposed many GNN-based fraud detection frameworks based on either homog…
Fraud DetectionGraph Neural NetworkRelationGraph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces. In order to leverage graph networks to decrease fraud rate efficiently and guarantee the information flow passed t…
Fraud Detectiongraph constructionGraph-Based Fraud Detection with Dual-Path Graph Filtering
Fraud detection on graph data can be viewed as a demanding task that requires distinguishing between different types of nodes. Because graph neural networks (GNNs) are naturally suited for processing information encoded …
Representation LearningFraud DetectionCorporate Fraud Detection in Rich-yet-Noisy Financial Graph
Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively…
Fraud DetectionKnowledge Graph Embeddings