Catching Insurance Fraud Using Graph Database Technology
January 18, 2016 4 mins read
The impact of fraud on the insurance industry is estimated to be $80 billion annually in the US, a number that has been growing in recent years. From 2010 to 2012, questionable claims in the U.S. jumped 27 percent, to… Read more →
Explore: fraud detection • fraud ring • graph database • Insurance Fraud • Network Analysis • relational database • social network analysis • soft tissue injuries • whiplash
Join Us for a Full Day of Graphs at FOSDEM 2016
January 15, 2016 2 mins read
The FOSDEM open source conference is the largest (5000+) gathering of users and developers of open source projects in Europe. Happening annually in Brussels, Belgium it has been home of the Graph Processing devroom for the last five years. Every… Read more →
Explore: apache flink • data mining • FOSDEM • Graph Analytics • graph database • graph processing • graph visualization • MySQL • open source • opencypher
3 Major Graph Database Technology Trends to Watch in 2016 [Community Post]
January 14, 2016 4 mins read
[As community content, this post reflects the views and opinions of the particular author and does not necessarily reflect the official stance of Neo4j. This post originally appeared on the GrapheneDB blog and is used with permission.] In a recent… Read more →
Explore: apache kafka • data model • Data Relationships • database technology • graph database • graph technology • MongoDB • neo4j • polyglot persistence • Redis
Containerized Neo4j: Automating Deployments with Docker on Azure
January 13, 2016 10 mins read
Editor’s Note: Last October at GraphConnect San Francisco, David Makogon – Senior Azure Architect at Microsoft – and Patrick Chanezon – Technical Staffer at Docker – delivered this presentation on how to automate cloud deployments of Neo4j using Docker and… Read more →
Explore: azure • azure resource manager • Cloud • container • Docker • docker compose • graphconnect • microsoft azure • neo4j • virtual machine
Improving First-Party Bank Fraud Detection with Graph Databases
January 11, 2016 6 mins read
First-party bank fraud involves fraudsters who apply for credit cards, loans, overdrafts and unsecured banking credit lines, with no intention of paying them back. It is a serious problem for banking institutions. U.S. banks lose tens of billions of dollars… Read more →
Explore: bank fraud • cypher • first-party fraud • fraud detection • fraud prevention • fraud ring • graph database • graph traversal • link analysis • relational database
The 5-Minute Interview: Karl Urich, DataFoxtrot
January 8, 2016 2 mins read
This week’s 5-minute interview is with Karl Urich, the President of DataFoxtrot based in Albany, New York. Karl is also a community contributor to the Neo4j blog. I chatted with Karl at GraphConnect San Francisco. Q: Can you tell us… Read more →
Explore: 5-minute interview • data outliers • graph • graphconnect • neo4j • spatial • spatial database • spatial intelligence • tech conference • wikipedia data
Building a Global Issues Graph for World Leaders
January 6, 2016 14 mins read
Editor’s Note: Last October at GraphConnect San Francisco, Scott David – the Director of Information Interaction at the World Economic Forum – gave this presentation on how the Forum uses Neo4j to inform world leaders on tomorrow’s emerging global issues.… Read more →
Explore: data complexity • davos conference • disruptive change • global issues • graph database • graphconnect • issue mapping • klaus schwab • transformation map • world economic forum
Why Modern Fraud Detection Needs Graph Database Technology
January 4, 2016 3 mins read
Banks and insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection play an important role in minimizing these losses. However increasingly sophisticated fraudsters have developed a variety of ways to elude discovery – both… Read more →
Explore: bank fraud • Connected Data • data analysis • e-Commerce Fraud • fraud detection • fraud prevention • fraud ring • graph database • Insurance Fraud • real-time fraud detection