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Healthcare Graph Database: How Graph Powers Cost Control, Fraud Detection, and Referral Intelligence
Healthcare

Healthcare Graph Database: How Graph Powers Cost Control, Fraud Detection, and Referral Intelligence

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Graph Database for Risk and Fraud Analytics: Why Unified Detection Beats Siloed Systems
Risk and Fraud Analytics

Graph Database for Risk and Fraud Analytics: Why Unified Fraud Detection Beats Siloed Systems

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Graph Database vs NoSQL_ Where Graph Fits in the Modern Data Stack
Graph Databases

Graph Database vs NoSQL: Where Graph Fits in the Modern Data Stack

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KYC Risk Assessment: How Graph Analytics Helps
KYC risk assessment

KYC Risk Assessment: How Graph Analytics Strengthens Know-Your-Customer

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Graph-Powered Recommendation System Algorithms: Beyond Collaborative Filtering
Graph Algorithms

Graph-Powered Recommendation System Algorithms: Beyond Collaborative Filtering

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A diagram from TigerGraph shows entities linked by dotted lines between past, time, and future, with warning icons and conflicting resolution, illustrating temporal conflicts in entity resolution. Text reads: Why Temporal Conflicts in Entity Resolution Cause Chaos.
Entity Resolution
Tue 3 Feb 2026
Why Temporal Conflicts in Entity Resolution Cause Chaos
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A graphic with icons representing a bank, email, and user profiles, connected by arrows and X marks. The title reads: Failed Update Patterns in KYC. How Identity Graphs Catch Trying to Become Someone Else. TigerGraph logo is in the corner.
Entity Resolution, KYC risk assessment
Wed 28 Jan 2026
Failed Update Patterns in KYC. How Identity Graphs Catch “Trying to Become Someone Else”
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A graphic shows interconnected icons representing data nodes and users, with one highlighted in orange, symbolizing anomaly detection. Text reads: Anomaly vs. Outlier Detection: How Hybrid Graph+Vector Search Discovers What Others Miss. TigerGraph logo appears.
Fraud / Anti-Money Laundering, Graph + AI, Graph Databases
Tue 27 Jan 2026
Anomaly vs. Outlier Detection: How Hybrid Graph+Vector Search Discovers What Others Miss
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A network diagram illustrating AML graph analytics for structuring and layering detection, with icons of banks, money, and stores connected by dotted lines. The TigerGraph logo and the title are shown at the bottom.
Fraud / Anti-Money Laundering
Thu 22 Jan 2026
Money Laundering Detection with AML Graph Analytics’ Structuring and Layering 
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A diagram shows money transfer from Account A to Account B through a central bank icon, with two other banks below. Text: Capturing Cross-Border Routing Signals That Hide in Plain Sight. TigerGraph logo in the top left.
Finance, Fraud / Anti-Money Laundering
Wed 21 Jan 2026
Capturing Cross-Border Routing Signals That Hide in Plain Sight
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A network diagram with icons for people, banks, houses, and locations, illustrating connections. Text reads: How Graph Analysis Finds Repeating Laundering Patterns. TigerGraph logo appears in the top left corner.
Fraud / Anti-Money Laundering
Tue 20 Jan 2026
How Graph Analysis Finds Repeating Laundering Patterns
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A TigerGraph graphic titled Understanding the Fraud Fighting Market shows interconnected icons of computers and stores, with question mark and exclamation point circles indicating uncertainty in networked relationships.
Fraud / Anti-Money Laundering
Thu 15 Jan 2026
A Buyer’s Guide to the Fraud Technology Market
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A graphic compares isolated transaction amounts on the left with a connected network of icons (person, devices, location) on the right, illustrating how graph context reveals structuring and evasion patterns. TigerGraph logo is present.
Fraud / Anti-Money Laundering, Graph Databases
Wed 14 Jan 2026
Structuring and Evasion Patterns Become Clearer With Graph Context
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A central orange user icon is connected to smaller nodes, surrounded by six gray icons representing various business concepts. Text reads: High-Impact Graph Database Project Ideas for Modern Data Teams. TigerGraph logo is in the top left.
Graph Databases, TigerGraph
Tue 13 Jan 2026
High-Impact Graph Database Project Ideas for Modern Data Teams
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Orange and black TigerGraph banner showing folders with icons for databases and networks, connected by lines to a central user icon, illustrating connections. Text reads: Practical Graph Database Schema Design: Modeling Connections for Speed and Clarity.
Graph Databases, TigerGraph
Thu 8 Jan 2026
Practical Graph Database Schema Design: Modeling Connections for Speed and Clarity
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A diagram compares RDF dots on the left with a property graph on the right, showing connected nodes and icons. Text reads: RDF vs. Property Graph – Choosing the Right Foundation for Knowledge Graphs. TigerGraph logo is in the top left.
Graph + AI, Graph Databases
Wed 7 Jan 2026
RDF vs. Property Graph: Choosing the Right Foundation for Knowledge Graphs
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An infographic shows three documents with question marks, connected by arrows and warning icons, illustrating repeat investigations. The TigerGraph logo is in the top left, and a caption below reads: “Repeat Investigations Signal Entity Resolution Failures That Inflate Casework.”.
Entity Resolution, Fraud / Anti-Money Laundering
Tue 6 Jan 2026
Repeat Investigations Signal Entity Resolution Failures That Inflate Casework
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An infographic from TigerGraph shows a comparison: on the left, nodes linked in a network labeled Before point to a central LLM circle; on the right, nodes with icons branch from LLM. Text reads, Should Graphs Power AI Before or After the LLM?.
Graph + AI
Wed 17 Dec 2025
Should Graphs Power AI Before or After the LLM?
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Flowchart showing LangChain connecting to a database and feeding information to a human head with AI symbols, then to a workflow diagram. TigerGraph logo is at the top left. Text reads: What is LangChain? A Practical, Look at How It Actually Works.
Graph + AI
Wed 17 Dec 2025
What is LangChain: A Practical, Grounded Look at How it Actually Works
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Blue graphic with the TigerGraph logo, diagrams showing interconnected icons (people, computers, globes), and a central AI circle. Text reads: Knowledge Graphs as the Missing Context Layer for AI.
Graph + AI
Wed 17 Dec 2025
Knowledge Graphs as the Missing Context Layer for AI
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