Graph Technology Overview and Applications – From GraphGPT to Graph Databases

This article presents a comprehensive overview of recent advances in graph technology, covering GraphGPT for large language models, knowledge transfer on complex graphs, financial fraud detection, telecom network optimization, graph foundation models, Baidu's multi‑domain recommendation, high‑availability graph databases, and Kuaishou's efficient recommendation architecture.

Large Language ModelsRecommendation Systemsfinancial fraud detection

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Graph Technology Overview and Applications – From GraphGPT to Graph Databases

DataFunTalk

Jul 16, 2024 · Information Security

Application of Graph Technology in Financial Anti‑Fraud

This article explains how graph‑based techniques are used in financial anti‑fraud, covering background, the role of graphs in perception, judgment, decision and disposal, the evolution of graph solutions, a concrete case study, and future outlooks for AI‑enhanced risk detection.

Artificial Intelligencefinancial fraud detectiongraph technology

0 likes · 15 min read

Application of Graph Technology in Financial Anti‑Fraud

DataFunSummit

Apr 21, 2024 · Big Data

Application of Graph Technology in Financial Anti‑Fraud

This article explains how large‑scale graph technology is applied to financial anti‑fraud, covering background, graph‑driven perception, analysis, decision‑making and enforcement, evolution of graph methods, a comprehensive risk‑control platform, and a Q&A on practical implementation.

AIfinancial fraud detectiongraph learning

0 likes · 13 min read

DataFunSummit

Aug 9, 2023 · Artificial Intelligence

Applying Graph Intelligence to Anti-Money Laundering: Business Background, Process, and Case Study

This article presents Fabarta's application of graph‑based artificial intelligence to anti‑money‑laundering, detailing the business challenges, a five‑step analytical workflow, a synthetic data case study, and a Q&A that explores practical deployment and future directions.

anti-money launderingcase studydata mining

0 likes · 18 min read

Applying Graph Intelligence to Anti-Money Laundering: Business Background, Process, and Case Study

DataFunTalk

May 29, 2023 · Artificial Intelligence

Applying Graph Computing for Risk Control in Wing Pay: Architecture, Algorithms, and Future Directions

The presentation details how Wing Pay leverages graph computing and graph neural networks to detect and mitigate financial fraud across payment, e‑commerce, and credit scenarios, describing business background, system architecture, algorithmic approaches, real‑time subgraph mining, and future research directions.

distributed graph databasefinancial fraud detectiongraph computing

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Applying Graph Computing for Risk Control in Wing Pay: Architecture, Algorithms, and Future Directions

DataFunTalk

Oct 6, 2022 · Information Security

Graph Machine Learning for Security Risk Control: Architecture, Models, and Future Directions

This article presents a comprehensive overview of applying graph machine learning to security risk control, covering background cases, system architecture, dynamic heterogeneous graph modeling with HGT and DDGCL, experimental results, and future research directions for fraud, money‑laundering, and gambling detection.

contrastive learningdynamic heterogeneous graphfinancial fraud detection

0 likes · 10 min read

Graph Machine Learning for Security Risk Control: Architecture, Models, and Future Directions

Ctrip Technology

Jun 24, 2022 · Databases

Practical Experience of Nebula Graph in Ctrip Finance: Architecture, Use Cases, and Optimizations

This article describes how Ctrip Finance built a large‑scale Nebula Graph platform for financial risk control, data lineage, and fraud detection, detailing the system architecture, real‑world applications, performance challenges, and the engineering optimizations applied to achieve sub‑15 ms query latency.

Data LineageGraph DatabaseNebula Graph

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Practical Experience of Nebula Graph in Ctrip Finance: Architecture, Use Cases, and Optimizations