Tagged articles

94 articles

Page 1 of 1

Apr 28, 2026 · Industry Insights

A Beginner’s Guide to Payment Risk Control: All You Need in One Article

This article systematically introduces payment risk‑control fundamentals, covering core terminology, risk categories, black‑market attack methods, system architecture, data and feature pipelines, list management, rule engines, modeling techniques, decision flows, key performance metrics, compliance requirements, and operational best practices.

Complianceblack market attacksfeature engineering

0 likes · 34 min read

A Beginner’s Guide to Payment Risk Control: All You Need in One Article

dbaplus Community

Apr 19, 2026 · Databases

Why Vector Databases Exist: Overcoming SQL’s Blind Spot in AI Search

This guide explains how traditional relational databases and SQL struggle with semantic queries needed for AI applications, introduces vector databases and HNSW indexing for efficient similarity search, compares their architectures, and presents a real‑world fraud detection system that combines both technologies.

AIB+TreeHNSW

0 likes · 17 min read

Why Vector Databases Exist: Overcoming SQL’s Blind Spot in AI Search

AsiaInfo Technology: New Tech Exploration

Jan 6, 2026 · Industry Insights

Apache Paimon: Boosting Real-Time Data Lakes for Fraud Detection & Manufacturing

This article examines Apache Paimon’s innovative lakehouse architecture, detailing its LSM‑Tree storage, flexible merge engine, and multi‑engine integration, and showcases two real‑world deployments—an operator’s real‑time fraud‑prevention system and a manufacturing firm’s unified data platform—highlighting performance gains and cost reductions.

Apache PaimonBig DataLakehouse

0 likes · 15 min read

Apache Paimon: Boosting Real-Time Data Lakes for Fraud Detection & Manufacturing

Data Party THU

Aug 20, 2025 · Artificial Intelligence

How Dual‑Granularity Prompting Boosts Graph‑Enhanced LLMs for Fraud Detection

The article analyzes the Dual Granularity Prompting (DGP) framework, which mitigates information overload in graph‑enhanced large language models for fraud detection by applying fine‑grained processing to target nodes and coarse‑grained summarization to neighbors, achieving superior accuracy and token efficiency across multiple public and industrial datasets.

dual granularity promptingfraud detectiongraph foundation model

0 likes · 6 min read

How Dual‑Granularity Prompting Boosts Graph‑Enhanced LLMs for Fraud Detection

Zhuanzhuan Tech

Mar 20, 2025 · Backend Development

Implementing Geolocation‑Based Fraud Detection with Redis GEO Commands

This article outlines a fraud‑detection use case that leverages Redis GEO commands to compare user order addresses with known malicious locations, discusses technology choices among MySQL, Redis, and Elasticsearch, explains Redis’s Sorted‑Set and GeoHash implementation, and provides Java code examples for GEOADD, GEOPOS, GEODIST, and GEORADIUS.

GEOADDGeoHashRedis

0 likes · 9 min read

Implementing Geolocation‑Based Fraud Detection with Redis GEO Commands

DataFunSummit

Mar 18, 2025 · Artificial Intelligence

Application and Implementation of Multimodal Relational Networks in Financial Risk Control

This article presents the background, key technologies, system architecture, data processing pipeline, and practical use cases of multimodal relational networks for enhancing financial risk control, highlighting how integrating image, voice, text, and device data improves fraud detection, modeling, and operational efficiency.

AIfinancial technologyfraud detection

0 likes · 15 min read

Application and Implementation of Multimodal Relational Networks in Financial Risk Control

DataFunSummit

Feb 13, 2025 · Information Security

Building and Optimizing a Comprehensive Security System: Practices, Innovations, and Future Outlook

This article presents a detailed walkthrough of constructing a robust security architecture, covering single‑person security team strategies, risk perception and quantification, rapid incident response, automated detection, precise strike mechanisms, deterrence tactics, and forward‑looking plans for intelligent, data‑driven risk management.

Incident ResponseRisk ManagementSecurity Architecture

0 likes · 21 min read

Building and Optimizing a Comprehensive Security System: Practices, Innovations, and Future Outlook

DataFunSummit

Feb 11, 2025 · Information Security

War‑Like Strategies for URL Anti‑Fraud: Threat Analysis, Detection Techniques, and Operational Intelligence

The article examines the growing threat of black‑market malicious websites, outlines a five‑part war‑themed framework for comprehensive opponent analysis, detection strategies across traffic, channel, content and relationship dimensions, and advanced detection models—including fingerprint, text, image, graph, and multimodal approaches—while highlighting the supporting operational and intelligence systems.

Information SecurityMachine Learningfraud detection

0 likes · 14 min read

War‑Like Strategies for URL Anti‑Fraud: Threat Analysis, Detection Techniques, and Operational Intelligence

AntTech

Oct 26, 2024 · Artificial Intelligence

CCF Technology Achievement Awards Recognize Ant Group’s Advances in AI‑Driven Financial Risk Modeling and Edge Intelligence for Alipay

The 2024 CCF Technology Achievement Awards honored Ant Group’s two projects—complex behavior modeling for digital inclusive finance security and Alipay terminal intelligent technology—highlighting their AI‑driven risk control, edge inference innovations, extensive research output, and large‑scale real‑world deployments.

AlipayAnt GroupArtificial Intelligence

0 likes · 6 min read

CCF Technology Achievement Awards Recognize Ant Group’s Advances in AI‑Driven Financial Risk Modeling and Edge Intelligence for Alipay

DataFunTalk

Jul 27, 2024 · Information Security

Classification of Risk Control and Full-Scenario Anti-Cheat Strategies in the Internet

The article outlines how internet and financial risk control are categorized into anti‑cheat, anti‑fraud, and content security, describes full‑scenario cheating types, and presents a three‑step joint defense framework using perception, identification, and mitigation with feature‑based analysis.

Information Securityanti-cheatfeature engineering

0 likes · 7 min read

Classification of Risk Control and Full-Scenario Anti-Cheat Strategies in the Internet

Model Perspective

Jun 26, 2024 · Artificial Intelligence

Unlocking Fraud Detection: Build a Hidden Markov Model with Python

This article explains the fundamentals and mathematics of Hidden Markov Models, illustrates their core components and basic problems, and walks through a complete Python implementation for credit‑card fraud detection, including data preparation, model training, and evaluation.

Hidden Markov ModelPythonTime-series

0 likes · 10 min read

Unlocking Fraud Detection: Build a Hidden Markov Model with Python

DataFunSummit

Mar 16, 2024 · Information Security

Building a Fraud Advertising Flow Risk‑Control System: Eight Key Elements and Practical Practices

This article shares practical experience from Shumei on constructing a fraud‑advertising flow risk‑control system, detailing eight essential elements, scenario analysis, black‑industry pathways, event design, strategy formulation, implementation methods, value demonstration, and a Q&A session for developers and product teams.

Information Securityadvertising securitybusiness strategy

0 likes · 17 min read

Building a Fraud Advertising Flow Risk‑Control System: Eight Key Elements and Practical Practices

Model Perspective

Dec 30, 2023 · Fundamentals

Why Does Benford’s Law Reveal Hidden Fraud? A Deep Dive into Data

This article explains Benford’s Law—the first‑digit distribution rule—its discovery, mathematical basis, and wide‑range applications, from exposing Enron’s accounting fraud to analyzing 2022 Forbes billionaire wealth, age, and regional data, highlighting both its strengths and limitations.

Benford's LawForbes Billionairesfraud detection

0 likes · 9 min read

Why Does Benford’s Law Reveal Hidden Fraud? A Deep Dive into Data

AntTech

Dec 13, 2023 · Artificial Intelligence

IEEE ICDM 2023 Graph Learning Challenge: Community Detection and Fraud Group Mining

The IEEE ICDM 2023 Graph Learning Challenge, co‑hosted by Ant Group and Zhejiang University, showcased deep graph learning approaches for community detection and fraud‑group mining, highlighting the winning team's Risk‑DCRN method and emphasizing the importance of pretrained models in large‑scale network analysis.

ICDMcommunity-detectionfraud detection

0 likes · 5 min read

IEEE ICDM 2023 Graph Learning Challenge: Community Detection and Fraud Group Mining

Zhuanzhuan Tech

Nov 15, 2023 · Information Security

Association Graph for Fraud Detection: Theory, Architecture, and Applications

This article explains the concept of association graphs, their foundation in graph theory, storage architectures, noise‑reduction techniques, and practical applications such as feature mining, coloring, backend visualization, data analysis, and monitoring for fraud detection in risk control systems.

Graph Databaseassociation graphfraud detection

0 likes · 14 min read

Association Graph for Fraud Detection: Theory, Architecture, and Applications

DataFunSummit

Aug 22, 2023 · Artificial Intelligence

Applying Artificial Intelligence to Cross‑Border Risk Control: Practices and Insights

This article presents how artificial intelligence is applied to cross‑border risk control, covering the company background, intelligent risk‑prevention architecture, transaction and marketing fraud scenarios, model design, data challenges, and practical Q&A insights for overseas fraud mitigation.

AIGraph Neural NetworkMachine Learning

0 likes · 18 min read

Applying Artificial Intelligence to Cross‑Border Risk Control: Practices and Insights

DataFunSummit

Aug 12, 2023 · Information Security

Design and Exploration of Mobile Game Anti‑Fraud Systems

This article examines the mobile game black‑market ecosystem, outlines common fraud patterns such as script cheats, account trading, and illegal recharge, and presents a comprehensive anti‑fraud architecture that combines real‑time risk assessment, offline analysis, and adaptive mitigation strategies for game developers and operators.

Game SecurityMobile GamingRisk Management

0 likes · 21 min read

Design and Exploration of Mobile Game Anti‑Fraud Systems

DataFunSummit

Aug 11, 2023 · Artificial Intelligence

Application of Knowledge Graphs in Risk Control at Wing Payment

This presentation details how Wing Payment leverages a large‑scale, multimodal knowledge graph and AI techniques—including computer vision, unsupervised and supervised learning, federated learning, and graph neural networks—to detect and mitigate fraud across payment, e‑commerce, and credit scenarios, while outlining system architecture, algorithmic approaches, case studies, and future research directions.

Financial Servicesfraud detectiongraph algorithms

0 likes · 17 min read

Application of Knowledge Graphs in Risk Control at Wing Payment

DataFunTalk

Jul 28, 2023 · Artificial Intelligence

Insurance Anti‑Fraud Risk Control System: Architecture, Core Capabilities, and Case Studies

This article presents Taiping Jinke's end‑to‑end insurance anti‑fraud risk control framework, detailing industry pain points, core AI‑driven capabilities, platform blueprint, specific car and health insurance fraud engines, and real‑world case studies that illustrate how big‑data, machine‑learning and knowledge‑graph techniques are integrated into business processes.

Machine Learningfraud detectionknowledge graph

0 likes · 16 min read

Insurance Anti‑Fraud Risk Control System: Architecture, Core Capabilities, and Case Studies

AntTech

Jun 27, 2023 · Artificial Intelligence

Fanglue: An Interactive System for Decision Rule Crafting in Fraud Detection

Fanglue is an interactive, web‑based rule‑development platform that integrates expert domain knowledge with distributed AI algorithms to efficiently generate and evaluate decision rules for anti‑fraud scenarios, leveraging Ray for real‑time processing and achieving VLDB‑2023 acceptance.

AIDistributed computingRay

0 likes · 10 min read

Fanglue: An Interactive System for Decision Rule Crafting in Fraud Detection

Efficient Ops

Jun 26, 2023 · Artificial Intelligence

How Multimodal AI Is Revolutionizing Credit Card Fraud Detection

Amid tightening financial regulations, ICBC's software team proposes a multimodal AI anti‑fraud framework that combines image, video, and structured data to detect deep‑fake, mask, and forged‑document attacks, enriches verification with cross‑modal cues, and outlines future expansion to text and speech modalities.

AIMultimodalcomputer vision

0 likes · 7 min read

How Multimodal AI Is Revolutionizing Credit Card Fraud Detection

Architect

May 31, 2023 · Artificial Intelligence

Applying Graph Neural Networks for Anti‑Cheat in Activity Scenarios

This article presents how graph neural network models such as GCN and SCGCN are employed to detect and recall cheating groups in user‑invitation (master‑apprentice) activity scenarios, addressing the lack of relational features and low sample purity, and demonstrates significant recall improvements through multi‑graph fusion techniques.

GCNGraph Neural NetworkSCGCN

0 likes · 12 min read

Applying Graph Neural Networks for Anti‑Cheat in Activity Scenarios

Ctrip Technology

May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Anomaly DetectionGraph Neural NetworkUnsupervised Learning

0 likes · 12 min read

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

JD Cloud Developers

May 17, 2023 · Artificial Intelligence

Why Graph Computing Is the Hidden Powerhouse Behind AI and Fraud Detection

This article introduces graph computing, explaining its fundamentals, historical origins, key concepts such as nodes, edges, degrees, and graph representations, and explores its algorithms, graph neural networks, and real‑world applications ranging from search engines and social graphs to financial fraud detection and emerging AI technologies.

Artificial Intelligencefraud detectiongraph computing

0 likes · 12 min read

Why Graph Computing Is the Hidden Powerhouse Behind AI and Fraud Detection

Xiaohongshu Tech REDtech

Feb 8, 2023 · Information Security

Exploring and Practicing Community Anti-Cheat Strategies at Xiaohongshu

The presentation outlines Xiaohongshu’s comprehensive community anti‑cheat strategy, defining cheating risks across industries, mapping the black‑gray ecosystem, and detailing a five‑module framework—risk perception, capability building, identification, mitigation, and evaluation—implemented via layered data architecture and multi‑stage detection to protect platform integrity.

Data AnalysisRisk Managementanti-cheat

0 likes · 17 min read

Exploring and Practicing Community Anti-Cheat Strategies at Xiaohongshu

DataFunTalk

Jan 17, 2023 · Information Security

Community Anti‑Cheat Exploration and Practice in Xiaohongshu

This article examines Xiaohongshu's community anti‑cheat efforts, detailing the significance of fraud prevention, the black‑gray industry ecosystem, strategic defense frameworks, system architecture, and practical risk governance and detection methods for data‑inflation attacks.

Communityanti-cheatdata integrity

0 likes · 15 min read

Community Anti‑Cheat Exploration and Practice in Xiaohongshu

DataFunSummit

Jan 11, 2023 · Artificial Intelligence

Intelligent Financial Risk Control Platform Architecture and Expert Insights

This article outlines the architecture of an intelligent financial risk control platform, detailing data sources, big‑data processing, feature engineering, decision engines, model types, and real‑world application scenarios, while highlighting expert‑identified challenges such as compliance, data quality, real‑time performance, and fraud detection.

Financial AIdecision enginefeature engineering

0 likes · 11 min read

Intelligent Financial Risk Control Platform Architecture and Expert Insights

Xiaohongshu Tech REDtech

Nov 30, 2022 · Information Security

Best Practices for Community and E‑commerce Fraud Prevention on Xiaohongshu: Understanding and Combating Fake Traffic

The article outlines Xiaohongshu’s comprehensive anti‑fraud strategy—defining fake traffic, exposing three service‑provider models, detailing identification and governance challenges, and recommending engine‑based risk‑control, a five‑step process, and AI‑driven behavior, clustering, and graph analyses that have already eliminated billions of fraudulent likes.

anti-fraude-commerce securityfraud detection

0 likes · 22 min read

Best Practices for Community and E‑commerce Fraud Prevention on Xiaohongshu: Understanding and Combating Fake Traffic

DataFunTalk

Nov 30, 2022 · Artificial Intelligence

Graph Neural Networks: Theory and Applications in Risk Control

This article introduces a free ebook on graph neural networks, outlines its theoretical foundations, algorithmic techniques for large‑scale computation, expressive power analysis, and multiple fraud‑detection and real‑time risk‑control applications across finance and e‑commerce.

AIfraud detection

0 likes · 6 min read

Graph Neural Networks: Theory and Applications in Risk Control

DataFunSummit

Nov 25, 2022 · Information Security

Black and Gray Market Threats and Countermeasures in the Residential Services Industry

This article presents a comprehensive analysis of black‑gray market activities in the residential services sector, detailing industry service models, typical fraud scenarios, intelligence‑gathering architecture, traceability capabilities, and multi‑stage counter‑measure processes aimed at detection, investigation, and prosecution.

Information SecurityIntelligenceanti-fraud

0 likes · 11 min read

Black and Gray Market Threats and Countermeasures in the Residential Services Industry

AntTech

Nov 21, 2022 · Artificial Intelligence

An Adaptive Framework for Confidence-Constraint Rule Set Learning in Large Datasets

The paper introduces a constraint‑adaptive rule‑set learning framework (CRSL) that combines a constraint‑aware decision‑tree miner (CARM), a rule‑sorting filter, and a Bayesian rule‑combination selector (CBRS), achieving superior performance and interpretability on benchmark and massive industrial fraud‑detection data and being deployed in Alipay’s risk‑analysis platform.

Bayesian methodsDecision Treesconstraint optimization

0 likes · 10 min read

An Adaptive Framework for Confidence-Constraint Rule Set Learning in Large Datasets

AntTech

Nov 6, 2022 · Artificial Intelligence

Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking

This article surveys recent Ant Group research on explainable fraud detection, including constraint‑adaptive rule‑set learning (CRSL), meta‑path guided rule generation (MetaRule), biased sampling for imbalanced ranking, and a semi‑supervised data‑augmentation framework (SDAT) for tabular data, highlighting their motivations, methodologies, deployments, and experimental results.

Semi-supervised Learningconstraint adaptivedata augmentation

0 likes · 18 min read

Advanced Rule Learning, Constraint‑Adaptive Frameworks, and Semi‑Supervised Data Augmentation for Fraud Detection and Imbalanced Ranking

AntTech

Aug 22, 2022 · Information Security

Intelligent Risk Control Enters the 10‑Millisecond Era

In a 2022 IDC China Digital Finance Forum speech, Ant Group's security chief outlines the shift from explicit to implicit online risks, identifies three major challenges for risk control, and presents Ant's AI‑driven IMAGE framework that achieves sub‑10 ms detection across privacy‑preserving, multi‑party, and graph‑based technologies.

AIfraud detectionprivacy

0 likes · 9 min read

Intelligent Risk Control Enters the 10‑Millisecond Era

AntTech

Aug 4, 2022 · Information Security

Ant Group's Full‑Graph Risk Control Architecture and Its Application in Combating Complex Fraud

The article presents Ant Group's full‑graph risk control system, detailing emerging fraud trends, the need for graph‑based anti‑fraud infrastructure, and the multi‑layer architecture that combines data cleaning, graph modeling, multi‑modal computation, and real‑time detection to tackle sophisticated, organized financial crimes.

Information Securityanti‑money launderingfraud detection

0 likes · 11 min read

Ant Group's Full‑Graph Risk Control Architecture and Its Application in Combating Complex Fraud

DataFunTalk

Jul 9, 2022 · Artificial Intelligence

User Behavior Sequence Based Transaction Anti‑Fraud Detection

This presentation explains how leveraging user behavior sequences with supervised and unsupervised deep learning models, including end‑to‑end and two‑stage architectures, improves transaction fraud detection by identifying distinct patterns of account takeover and stolen‑card activities and outlines the engineering deployment pipeline.

Embeddingdeep learningfraud detection

0 likes · 12 min read

User Behavior Sequence Based Transaction Anti‑Fraud Detection

DataFunSummit

Jul 3, 2022 · Artificial Intelligence

Graph Neural Network Approaches for Internet Financial Fraud Detection

The talk examines how the COVID‑19 pandemic accelerated online financial services and fraud, outlines the challenges of traditional and internet‑based fraud detection, and presents graph neural network solutions—including PC‑GNN and AO‑GNN—demonstrating their effectiveness on real‑world and public datasets while discussing future research directions.

AUC optimizationMachine Learningfinancial fraud

0 likes · 12 min read

Graph Neural Network Approaches for Internet Financial Fraud Detection

Bilibili Tech

Jun 15, 2022 · Information Security

Internet Risk Control: Overview, Precise Traffic Perception, and Full-Scenario Joint Defense (Bilibili Case)

The talk, led by Bilibili’s risk‑control head, outlines Internet risk‑control fundamentals, precise traffic perception techniques, and a full‑scenario joint‑defense framework that combines hierarchical identification, cross‑scene signal sharing, statistical anomaly detection, and layered mitigation (soft and hard) to counter black‑market attacks on platforms.

BilibiliInternet SecurityTraffic analysis

0 likes · 13 min read

Internet Risk Control: Overview, Precise Traffic Perception, and Full-Scenario Joint Defense (Bilibili Case)

AntTech

May 26, 2022 · Artificial Intelligence

ITU Approves Ant Group‑Led Interactive Intelligent Risk Control Standard

The International Telecommunication Union (ITU) has officially adopted an Ant Group‑initiated international standard for interactive intelligent risk control, outlining technical capabilities such as interactive functions and machine‑learning abilities to enhance proactive fraud detection across the industry.

Ant GroupArtificial IntelligenceITU

0 likes · 5 min read

ITU Approves Ant Group‑Led Interactive Intelligent Risk Control Standard

DataFunSummit

May 23, 2022 · Artificial Intelligence

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

This article explores how graph machine learning can be leveraged for intelligent anti‑fraud, covering business background, common fraud models and graph algorithm principles, practical deployment of graph algorithms, challenges in fraud modeling, and future research directions.

Graph Machine LearningUnsupervised Learningfraud detection

0 likes · 20 min read

Applying Graph Machine Learning for Intelligent Anti‑Fraud: Models, Algorithms, and Real‑World Applications

DataFunSummit

Apr 26, 2022 · Artificial Intelligence

Macro Trends and Core Challenges of Intelligent Risk Control in the Banking Industry

The article analyzes the downward macroeconomic trend since 2000, its impact on bank asset growth, and outlines the four main pain points—positioning, organization, talent, and digital capability—of intelligent risk control, while also detailing fraud types, regulatory pressures, and future industry directions.

Artificial IntelligenceFinancial Industrybanking

0 likes · 13 min read

Macro Trends and Core Challenges of Intelligent Risk Control in the Banking Industry

DataFunTalk

Apr 19, 2022 · Artificial Intelligence

Intelligent Risk Control Platform: Design Principles, Strategy and Model Lifecycle Management, and Architecture

This article presents a comprehensive overview of an intelligent risk control platform, covering its design background, six core characteristics, the "five‑full double‑core" concept, end‑to‑end strategy and model lifecycle management, business architecture atomization, and real‑world anti‑fraud case studies.

AIModel Managementdata engineering

0 likes · 13 min read

Intelligent Risk Control Platform: Design Principles, Strategy and Model Lifecycle Management, and Architecture

DataFunSummit

Mar 20, 2022 · Information Security

Black and Gray Market Intelligence and Countermeasures in the Residential Service Industry (Beike)

This presentation outlines the landscape of black and gray market activities in China's residential real‑estate platform, describes the various fraud scenarios, details intelligence collection, tracing architecture, and anti‑fraud measures, and shares typical cases such as fake C‑side registrations and crawler attacks.

Information SecurityIntelligenceReal Estate

0 likes · 11 min read

Black and Gray Market Intelligence and Countermeasures in the Residential Service Industry (Beike)

AntTech

Mar 1, 2022 · Big Data

Graph Computing at Ant Group: From Fraud Prevention to Industry‑Wide Impact

The article explains how Ant Group leverages large‑scale graph computing—through its GeaBase and TuGraph platforms and a dedicated research team—to enhance real‑time fraud detection, drive industry standards, and explore future applications across finance, energy, and public services.

Ant GroupBig DataTuGraph

0 likes · 7 min read

Graph Computing at Ant Group: From Fraud Prevention to Industry‑Wide Impact

DataFunTalk

Feb 22, 2022 · Artificial Intelligence

Real‑Time Graph Neural Network for Payment Fraud Detection at eBay

This article describes how eBay applies graph neural networks to real‑time payment fraud detection, covering the anti‑fraud scenario, limitations of traditional GBDT pipelines, challenges of constructing and serving dynamic heterogeneous graphs, the end‑to‑end solution with directed slice graphs and a Lambda‑style architecture, and experimental results comparing GNN with LightGBM.

Machine Learningfraud detectionpayment risk

0 likes · 15 min read

Real‑Time Graph Neural Network for Payment Fraud Detection at eBay

DataFunSummit

Feb 15, 2022 · Artificial Intelligence

Real-time Fraud Detection in E-commerce Payments Using Graph Neural Networks

This article presents an end‑to‑end solution that leverages graph neural networks and dynamic bipartite graph construction to detect payment fraud in eBay's e‑commerce platform in real time, addressing traditional model limitations, graph latency challenges, and demonstrating superior performance over GBDT approaches.

Machine Learninge-commercefraud detection

0 likes · 15 min read

Real-time Fraud Detection in E-commerce Payments Using Graph Neural Networks

DataFunTalk

Feb 8, 2022 · Artificial Intelligence

Large-Scale Graph Platform Dxm Eros for Financial Risk Control

This article introduces the Dxm Eros ultra‑large graph platform, detailing its architecture, storage, analysis, modeling, and visualization modules, and demonstrates how graph machine‑learning techniques are applied to financial risk control, fraud detection, anti‑money‑laundering, and automated credit review.

AIGraph DatabaseMachine Learning

0 likes · 18 min read

Large-Scale Graph Platform Dxm Eros for Financial Risk Control

DataFunTalk

Feb 6, 2022 · Information Security

Black and Gray Market Threats and Countermeasures in the Residential Services Industry (Beike)

This presentation details the current landscape of black and gray market activities in the residential services sector, describes typical fraud scenarios such as fake user registrations and crawler attacks, and outlines Beike's intelligence collection, tracing capabilities, and multi‑stage anti‑fraud operations to detect, investigate, and mitigate these threats.

Information SecurityIntelligenceReal Estate

0 likes · 12 min read

Black and Gray Market Threats and Countermeasures in the Residential Services Industry (Beike)

DataFunTalk

Jan 13, 2022 · Artificial Intelligence

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

This article provides a comprehensive overview of fraud detection using graph neural networks, covering background definitions, fraud categories, GNN application steps, a timeline of key research papers, practical challenges, solutions, and a collection of open‑source resources and datasets.

AIMachine Learningfraud detection

0 likes · 24 min read

Graph Neural Networks for Fraud Detection: Overview, Methods, and Resources

DataFunSummit

Jan 9, 2022 · Artificial Intelligence

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

This article reviews the fundamentals of fraud, surveys the evolution of graph neural network research for fraud detection, outlines practical application steps, discusses key challenges such as disguise, scalability, and label scarcity, and provides representative papers, new research directions, industrial case studies, and open-source resources.

AIGNNMachine Learning

0 likes · 23 min read

Applying Graph Neural Networks to Fraud Detection: Background, Research Progress, Methods, and Resources

Code DAO

Jan 1, 2022 · Artificial Intelligence

Automating Machine Learning Workflows with Scikit‑Learn Pipelines

This article demonstrates how to build a reproducible fraud‑detection workflow using scikit‑learn's Pipeline class, comparing a manual script with a pipeline‑based approach on the IEEE‑CIS Kaggle dataset and showing the benefits of modular, repeatable ML code.

Machine LearningPythonfraud detection

0 likes · 8 min read

Automating Machine Learning Workflows with Scikit‑Learn Pipelines

DataFunTalk

Dec 27, 2021 · Databases

Graph Theory, Graph Databases, and the Graph Intelligent Platform: Concepts, Development, and Tencent Use Cases

This article explores the fundamentals and evolution of graph theory, graph databases, and graph computing, discusses Tencent's self‑built graph stack—including EasyGraph, Angel‑Graph, and visualization tools—and demonstrates real‑world applications such as scheduling, financial payment analysis, and fraud detection, highlighting performance gains and future trends.

This article presents Douyu's intelligent risk‑control system for live streaming, detailing the operational, activity, traffic, account, transaction and content safety challenges, the multi‑layer algorithm architecture, and the evolution of models for spam detection, risk scoring, gang identification, behavior sequencing, device fingerprinting, and interpretability.

Artificial IntelligenceMachine LearningModel architecture

0 likes · 13 min read

Intelligent Risk Control in Live Streaming: Architecture, Challenges, and Model Evolution at Douyu

Baidu Geek Talk

Jun 23, 2021 · Information Security

Black-Gray Industry Attack Detection Based on Community Encoding Using Graph Embedding

The paper introduces a community‑encoding, GraphSAGE‑based detection framework that embeds whole user‑account, IP, device, and phone‑number graphs—both homogeneous and heterogeneous—to identify previously unseen black‑gray industry attacks, achieving about 95% IP‑risk accuracy via an asynchronous near‑real‑time system, though computational and automation challenges persist.

GraphSAGEblack-gray-industrycommunity-detection

0 likes · 12 min read

Black-Gray Industry Attack Detection Based on Community Encoding Using Graph Embedding

DataFunTalk

Jun 7, 2021 · Information Security

Anti‑Fraud Strategies and Practices for the Jimu Social App

This article presents Xu Ming, head of risk control at Jimu, sharing comprehensive insights and practical experiences on combating black‑gray market fraud within the Jimu app, covering the platform’s risk points, common challenges, overall anti‑fraud strategy, detailed operational tactics, and reflective thoughts on future improvements.

App SecurityInformation Securityanti‑fraud

0 likes · 17 min read

Anti‑Fraud Strategies and Practices for the Jimu Social App

58 Tech

Apr 16, 2021 · Artificial Intelligence

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

The article presents a comprehensive anti‑fraud framework that analyzes black‑market fraud characteristics, reviews conventional fraud‑mitigation methods, and proposes a multimodal graph‑neural‑network approach—leveraging device, behavior, and content similarity—to accurately identify fraudulent users on large‑scale internet platforms.

Information Securityanti‑fraudfraud detection

0 likes · 18 min read

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

DataFunTalk

Mar 22, 2021 · Artificial Intelligence

Model Interpretability for Insurance Claim Fraud Detection: Methods, Practice, and Outlook

This article presents a comprehensive overview of model interpretability techniques—global and local methods such as feature importance, LIME, and SHAP—and demonstrates their practical application in insurance claim fraud detection, highlighting challenges, implementation steps, and future research directions.

AIInsuranceLIME

0 likes · 13 min read

Model Interpretability for Insurance Claim Fraud Detection: Methods, Practice, and Outlook

Sohu Tech Products

Feb 17, 2021 · Big Data

Dynamic Broadcast State and Data Partitioning in an Apache Flink Fraud Detection Engine

This article demonstrates how to initialize, broadcast, and dynamically update rule sets in an Apache Flink fraud detection pipeline, using BroadcastProcessFunction and MapState to achieve runtime data partitioning without recompiling, and explains the underlying data exchange patterns such as forward, hash, rebalance, and broadcast.

Apache FlinkBroadcast StateDynamic Key Function

0 likes · 11 min read

Dynamic Broadcast State and Data Partitioning in an Apache Flink Fraud Detection Engine

Sohu Tech Products

Feb 17, 2021 · Big Data

Dynamic Data Partitioning in Apache Flink: A Fraud Detection Demo

This article explains how to implement dynamic data partitioning in Apache Flink using a fraud‑detection demo, covering the system architecture, rule‑driven runtime reconfiguration, custom ProcessFunction code, and the underlying key‑by logic that enables flexible, real‑time stream processing.

Apache FlinkDynamic PartitioningKeyBy

0 likes · 11 min read

Dynamic Data Partitioning in Apache Flink: A Fraud Detection Demo

JD Retail Technology

Dec 31, 2020 · Information Security

Graph Mining Algorithms for Advertising Traffic Fraud Detection and Platform Engineering

This article presents graph‑based fraud detection techniques for advertising traffic, detailing dense subgraph algorithms such as Fraudar and D‑Cube, their engineering optimizations, real‑world case studies, and the design of a scalable graph‑mining platform for large‑scale security applications.

D-CubeFraudaradvertising traffic

0 likes · 18 min read

Graph Mining Algorithms for Advertising Traffic Fraud Detection and Platform Engineering

DataFunTalk

Dec 27, 2020 · Information Security

Evolution of 58.com Risk Control Architecture: From Early Stages to Intelligent Auditing

This talk outlines 58.com’s risk control evolution, detailing the platform’s four development stages, the challenges of fraud, fake traffic, and content abuse, and how architecture, algorithms, and operational strategies have been refined to achieve high‑throughput, intelligent auditing.

AIBig DataInformation Security

0 likes · 12 min read

Evolution of 58.com Risk Control Architecture: From Early Stages to Intelligent Auditing

DataFunSummit

Dec 24, 2020 · Information Security

Evolution and Architecture of Risk Control at 58.com

This article outlines the development stages, architectural evolution, and practical challenges of 58.com’s risk‑control platform, describing how the system progressed from manual review to configurable automation, multi‑scene governance, and intelligent expert‑driven auditing to protect billions of daily transactions.

Information Securityfraud detectionplatform architecture

0 likes · 10 min read

Evolution and Architecture of Risk Control at 58.com

JD Tech Talk

Dec 3, 2020 · Artificial Intelligence

Graph Algorithms and Graph Neural Networks for Fraud Detection

The article explains how building account relationship graphs and applying both traditional graph algorithms and modern graph neural networks—through community detection, anomaly detection, semi‑supervised and unsupervised learning, and dynamic graph techniques—can effectively identify and dismantle fraud groups in online services.

AISemi-supervised Learningdynamic graphs

0 likes · 11 min read

Graph Algorithms and Graph Neural Networks for Fraud Detection

iQIYI Technical Product Team

Oct 30, 2020 · Artificial Intelligence

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

HMGNN introduces hyper-nodes to connect many small heterogeneous mini-graphs, uses attention-weighted heterogeneous convolution and residual feature transmission, achieving superior fraud invitation detection on iQIYI and Cora datasets compared to traditional GNNs and other models.

AIGraph Neural NetworkHypergraph

0 likes · 10 min read

Heterogeneous Mini-Graph Neural Network for Fraud Invitation Detection

DataFunTalk

Sep 9, 2020 · Big Data

NetEase Big Data User Profiling: Architecture, Tagging System, and Real‑World Applications

This presentation details NetEase's massive multi‑domain data ecosystem, the design of its user‑profile center—including basic, behavior, preference, and predictive tags—ID‑mapping techniques, quality assurance processes, and several real‑time and offline use cases such as marketing, recommendation, growth operations, advertising, and fraud detection.

Big DataID-MappingTag Management

0 likes · 13 min read

NetEase Big Data User Profiling: Architecture, Tagging System, and Real‑World Applications

DataFunTalk

Aug 19, 2020 · Artificial Intelligence

Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks

The article presents a comprehensive overview of e‑commerce fraud, especially brush‑order schemes, and introduces the Fraudar algorithm—a graph‑based unsupervised method that leverages bipartite network analysis, global suspiciousness metrics, priority‑tree optimization, and collaborative supervised training to efficiently identify dense fraudulent sub‑graphs.

FraudarUnsupervised Learningbipartite graph

0 likes · 15 min read

Fraudar: Graph-Based Fraud Detection in E‑commerce Transaction Networks

JD Tech Talk

Aug 7, 2020 · Information Security

Fraudar: Graph-Based Fraud Detection in Bipartite Transaction Networks

The article explains how e‑commerce fraud such as fake order brushing can be modeled as a bipartite transaction network and tackled with the Fraudar algorithm, which iteratively removes low‑suspicion nodes using a global suspiciousness metric and priority‑tree structures to uncover dense suspicious sub‑graphs.

Information SecurityUnsupervised Learningbipartite graph

0 likes · 14 min read

Fraudar: Graph-Based Fraud Detection in Bipartite Transaction Networks

Xianyu Technology

Nov 7, 2019 · Big Data

Sequence Pattern Mining for User Behavior Analysis in Xianyu

By applying sequence pattern mining and unsupervised clustering to Xianyu’s massive event logs, the study abstracts high‑level user behaviors, discovers frequent subsequences, uncovers unknown fraudulent account patterns, expands known fraud cohorts with 99 % precision, and enables richer analyses such as PCA‑based cross‑group comparisons.

Big Dataclusteringdata mining

0 likes · 8 min read

Sequence Pattern Mining for User Behavior Analysis in Xianyu

Qunar Tech Salon

Aug 29, 2019 · Information Security

Using Graph Databases for Fraud Detection in Ride‑Hailing Platforms

The article explains how building a Neo4j‑based social graph of users, drivers, devices and other attributes enables detection of individual and group subsidy‑abuse fraud in ride‑hailing services through multi‑hop relationship analysis and targeted rule‑based alerts.

Graph DatabaseNeo4jRide Hailing

0 likes · 6 min read

Using Graph Databases for Fraud Detection in Ride‑Hailing Platforms

DataFunTalk

Apr 28, 2019 · Artificial Intelligence

Graph Algorithms for Fraud Detection and Community Detection: Modularity, Louvain, Infomap, node2vec and comE

This article explains how graph‑based algorithms such as centrality measures, modularity optimization, Louvain, Infomap, node2vec and the comE framework can be applied to financial fraud detection and community discovery, detailing their principles, formulas, implementation steps and evaluation metrics.

Infomapcommunity-detectionfraud detection

0 likes · 14 min read

Graph Algorithms for Fraud Detection and Community Detection: Modularity, Louvain, Infomap, node2vec and comE

DataFunTalk

Feb 11, 2019 · Artificial Intelligence

Machine Learning Applications in Credit Anti‑Fraud

This article explains how machine learning, deep learning, and graph‑based techniques are applied to credit anti‑fraud in finance, covering fraud risk characteristics, the anti‑fraud lifecycle, rule limitations, supervised models, common algorithms, neural networks, time‑series models, and graph analytics for detecting individual and group fraud.

AIMachine Learningcredit risk

0 likes · 11 min read

Machine Learning Applications in Credit Anti‑Fraud

DataFunTalk

Jan 21, 2019 · Artificial Intelligence

Applying Automated Feature Engineering and Auto Modeling to Risk Control Scenarios

This article explains how automated feature engineering and auto‑modeling techniques dramatically reduce development time and improve performance in fraud‑risk detection, detailing the underlying RFM concepts, feature generation workflow, model selection, evaluation, deployment, and continuous monitoring within a risk‑control platform.

Machine Learningauto modelingautomated feature engineering

0 likes · 14 min read

Applying Automated Feature Engineering and Auto Modeling to Risk Control Scenarios

AntTech

Sep 7, 2018 · Artificial Intelligence

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection

This article explains how Alipay combats the surge of mobile payment fraud by upgrading its risk‑identification system with deep‑learning techniques, modeling victim and fraudster behavior sequences using LSTM, and integrating the resulting scores into existing models to achieve a measurable increase in detection coverage.

LSTMbehavior sequencedeep learning

0 likes · 11 min read

How Alipay Leverages LSTM to Strengthen Mobile Payment Fraud Detection

AntTech

Aug 22, 2018 · Artificial Intelligence

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing

The article presents three Ant Financial research papers featured at KDD 2018—one on graph‑learning fraud detection for return‑freight insurance, another introducing the adaptive GeniePath graph neural network, and a third describing a distributed collaborative hashing system for large‑scale recommendation—highlighting their methodologies, experimental results, and practical impact on Ant Financial’s services.

Ant FinancialHashingRecommendation Systems

0 likes · 21 min read

Ant Financial’s KDD 2018 Papers: Graph-Based Fraud Detection, GeniePath GNN, and Distributed Collaborative Hashing

AntTech

Aug 16, 2018 · Artificial Intelligence

Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection

This article reviews deep‑learning‑based text classification techniques—including TextCNN, BiGRU, Capsule Networks, Attention mechanisms, and the novel cw2vec embedding—applied to Alipay complaint fraud data, presents experimental comparisons, and discusses their advantages, challenges, and future directions.

Alipayattentioncapsule network

0 likes · 18 min read

Deep Learning Approaches for Text Classification in Alipay Complaint Fraud Detection

Ctrip Technology

May 9, 2018 · Artificial Intelligence

Ctrip's Real-Time Anti-Fraud System: Architecture, Big Data, and AI Innovations

The article details Ctrip's mature real‑time anti‑fraud platform, describing its big‑data parallel processing, AI‑driven models, device‑fingerprinting, CDNA service, and evolving architecture that together achieve sub‑150 ms decision latency while handling billions of daily transactions.

Artificial IntelligenceCtripRisk Management

0 likes · 10 min read

Ctrip's Real-Time Anti-Fraud System: Architecture, Big Data, and AI Innovations

Alibaba Cloud Developer

May 7, 2018 · Artificial Intelligence

How Active PU Learning Boosts Cash‑Out Fraud Detection by 3×

This article presents an Active PU Learning framework that combines active learning with two‑step PU semi‑supervised learning to improve cash‑out fraud detection, reducing labeling costs, enhancing model performance, and achieving a three‑fold increase in identified fraudulent transactions compared to traditional unsupervised methods.

AIMachine LearningRisk Detection

0 likes · 15 min read

How Active PU Learning Boosts Cash‑Out Fraud Detection by 3×

AntTech

Apr 16, 2018 · Artificial Intelligence

Active PU Learning for Cash‑Out Fraud Detection in Alipay’s AlphaRisk Engine

This article presents an Active PU Learning framework that combines active learning with two‑step positive‑unlabeled learning to improve cash‑out fraud detection in Alipay’s fifth‑generation risk engine, AlphaRisk, achieving three‑fold identification gains over unsupervised methods while reducing labeling costs.

Machine LearningRisk ManagementSemi-supervised Learning

0 likes · 14 min read

Active PU Learning for Cash‑Out Fraud Detection in Alipay’s AlphaRisk Engine

dbaplus Community

Apr 2, 2018 · Databases

Why Titan Outperforms Traditional RDBMS for Complex Graph Queries

The article explains how relational databases struggle with many‑to‑many and deep relationship queries, compares popular graph databases, details Titan's modular architecture, data model, Gremlin query examples, storage layout, and demonstrates its successful deployment at Paipaidai for large‑scale fraud detection, achieving over 25% efficiency gains.

Graph DatabaseGremlinHBase

0 likes · 10 min read

Why Titan Outperforms Traditional RDBMS for Complex Graph Queries

Meituan Technology Team

Jan 13, 2017 · Information Security

Risk Control System Architecture and Practices at Meituan

Meituan’s risk‑control architecture transforms diverse, high‑volume e‑commerce services into a middleware‑based platform that unifies data collection, combines expert rules with machine‑learning models, and employs a three‑stage defense—pre‑risk, real‑time detection, and post‑incident response—to continuously adapt to evolving fraud threats.

Risk ManagementSystem Architecturefraud detection

0 likes · 17 min read

Risk Control System Architecture and Practices at Meituan

Ctrip Technology

Jan 13, 2017 · Information Security

Ctrip Business Security: From Business‑Driven to Technology‑Driven Defense

This article outlines Ctrip's comprehensive business security strategy, detailing four major risk types, three core protection systems—including a unified captcha, a real‑time risk control engine, and a risk data platform—followed by a technology‑driven architecture, new captcha services, and future security directions.

Information SecurityRisk ManagementSystem Architecture

0 likes · 11 min read

Ctrip Business Security: From Business‑Driven to Technology‑Driven Defense

Qunar Tech Salon

Oct 10, 2016 · Information Security

Evolution of Ctrip's Risk Defense Systems: From .NET Era to the Ares Platform

This article reviews the rapid growth of China’s OTA market, the rise of black‑market threats, and how Ctrip’s security team has iteratively redesigned its risk‑defense architecture—from a .NET‑based real‑time system, through an offline risk‑library, to the integrated Ares platform—highlighting each stage’s strengths, shortcomings, and lessons learned.

Ares platformCtripInformation Security

0 likes · 11 min read

Evolution of Ctrip's Risk Defense Systems: From .NET Era to the Ares Platform

Architecture Digest

May 15, 2016 · Information Security

Design and Architecture of a Payment Risk Control System

The article explains the functional and non‑functional requirements, common pitfalls, and detailed architecture—including real‑time, near‑real‑time, and batch engines, rule and penalty centers, and CEP technology—of a payment risk control system aimed at detecting and mitigating fraud while maintaining performance and flexibility.

CEPReal-time ProcessingSystem Architecture

0 likes · 12 min read

Design and Architecture of a Payment Risk Control System

21CTO

Dec 7, 2015 · Information Security

How Tencent Combats Fraudsters with Big Data and AI‑Powered Risk Engines

This article explains how Tencent uses big‑data collection, user profiling, and AI‑driven risk learning engines to detect and block malicious accounts, proxy IPs, and fraudulent activities across e‑commerce and other platforms, detailing the architecture, algorithms, and practical defenses employed.

Big DataInformation Securityanti-fraud

0 likes · 14 min read

How Tencent Combats Fraudsters with Big Data and AI‑Powered Risk Engines

21CTO

Sep 14, 2015 · Artificial Intelligence

How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions

Airbnb’s trust and safety team uses a series of machine‑learning models—starting from defining the prediction target, through data sampling and feature engineering, to evaluating precision and recall—to identify and mitigate fraud risks such as chargebacks across its global peer‑to‑peer rental platform.

AIAirbnbfeature engineering

0 likes · 7 min read

How Airbnb Builds Machine Learning Models to Detect Fraudulent Transactions

Art of Distributed System Architecture Design

Aug 2, 2015 · Artificial Intelligence

Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation

This article explains how Airbnb's Trust & Safety team builds machine‑learning models to detect fraudulent behavior, covering problem definition, role‑based sampling, feature design techniques such as normalization and CP‑coding, and the trade‑offs between precision and recall in model evaluation.

AISamplingfeature engineering

0 likes · 10 min read

Designing Machine Learning Models for Fraud Detection: Sampling, Feature Engineering, and Evaluation