Book Image

Learning Neo4j 3.x - Second Edition

By : Jerome Baton
Book Image

Learning Neo4j 3.x - Second Edition

By: Jerome Baton

Overview of this book

Neo4j is a graph database that allows traversing huge amounts of data with ease. This book aims at quickly getting you started with the popular graph database Neo4j. Starting with a brief introduction to graph theory, this book will show you the advantages of using graph databases along with data modeling techniques for graph databases. You'll gain practical hands-on experience with commonly used and lesser known features for updating graph store with Neo4j's Cypher query language. Furthermore, you'll also learn to create awesome procedures using APOC and extend Neo4j's functionality, enabling integration, algorithmic analysis, and other advanced spatial operation capabilities on data. Through the course of the book you will come across implementation examples on the latest updates in Neo4j, such as in-graph indexes, scaling, performance improvements, visualization, data refactoring techniques, security enhancements, and much more. By the end of the book, you'll have gained the skills to design and implement modern spatial applications, from graphing data to unraveling business capabilities with the help of real-world use cases.
Table of Contents (24 chapters)
Title Page
Credits
About the Authors
Acknowledgement
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Fraud detection systems


We have seen a number of customers that are using graph database management systems such as Neo4j for fraud detection systems. The principle is quite simple: in many cases, the fraud of a particular nature is not defined by one transaction only, but by a chain of transactions that have their specific characteristics and that need to be compared to one another to see if they really do constitute a case of fraud.

In the following example, we are just looking at a suspect case of credit card fraud:

A suspect case of credit card fraud

A particular user always uses his credit card for transactions at a particular store. Another user uses his credit card for similar transactions at a different store. All of a sudden, there is this new transaction in the middle, which uses the credit card (let's say for a similar kind of transaction) in the other store. This kind of pattern may become flagged as a suspect pattern in some fraud detection systems.

The system would not necessarily...