Graph Technology for Enterprise Master Data Management (MDM)

Editor’s Note: This presentation was given by Aaron Wallace at GraphConnect San Francisco in October 2016. Presentation Summary Modern enterprises need to have a full, 360-view of their customers drive their bottom line. This requires the integration of data from… Learn More →

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Financial Services & Neo4j: Anti-Money Laundering

Reducing the risk of money laundering presents a similar challenge to that of fraud detection when it comes to today’s financial services landscape. Firms need to know where funds come from and where they are headed, but criminals use indirection… Learn More →

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Financial Services & Neo4j: Fraud Detection

Identifying and stopping fraudulent activity is harder than ever for financial services organizations. Standard anti-fraud technologies — such as a deviation from normal purchasing patterns — use discrete data. This is useful for catching individual criminals acting alone, but discrete… Learn More →

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Fraud Prevention with Neo4j: A 5-Minute Overview

Fraud is becoming increasingly difficult to discover and prevent as fraudsters are increasingly employing complex techniques and advanced technologies to perpetrate fraud. Who Are Today’s Fraudsters? Today, fraudsters are organized in groups, possess synthetic or manufactured identities – which in… Learn More →

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The 5-Minute Interview: Chuck Kane, Director of Global Solutions at Pitney Bowes

“Moving from relational to graph, it takes time to learn to step back from your data and ask, ‘What more can I do?’” said Chuck Kane, Director of Global Solutions at Pitney Bowes. The company partnered with Neo4j to provide… Learn More →

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The 5-Minute Interview: Andrés Natanael Soria, Senior Software Architect at Cablevisión

“Due to native graph storage, the Neo4j queries run really quickly, which is amazing,” said Andrés Natanael Soria, Senior Software Engineer at Cablevisión Fibertel. The company uses a broadband network to provide cable television and internet services to customers throughout… Learn More →

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Couchbase & JDBC Integrations for Neo4j 3.x

Editor’s Note: This presentation was given by Lorenzo Speranzoni at GraphConnect Europe in April 2016. Here’s a quick review of what he covered: Converting JSON documents into nodes Couchbase integration architecture How the JDBC driver makes use of the Bolt… Learn More →

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Detect & Investigate Financial Crime Patterns with Linkurious

Linkurious is a Bronze sponsor of GraphConnect San Francisco. Meet their team on October 13-14th at the Hyatt Regency SF. Using Graphs to Fight Financial Crime Financial crime can take many forms: money laundering, terrorism funding, corruption, tax evasion, insurance… Learn More →

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The 5-Minute Interview: Mar Cabra, Editor at the ICIJ

For this week’s 5-Minute Interview, I sat down with Mar Cabra, the Data & Research Unit Editor at the International Consortium of Investigative Journalists (ICIJ). I caught up with Mar at GraphConnect Europe after her standing-room-only keynote: “How the ICIJ… Learn More →

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The 5-Minute Interview: Jean Villedieu, Co-Founder of Linkurious

For this week’s 5-Minute Interview, I chatted with Jean Villedieu, a co-founder of Linkurious. I sat down with Jean at GraphConnect Europe last April. Here’s what we discussed: Q: For people who might not be familiar with it, what is… Learn More →

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Stop Fraud Rings in Their Tracks with Graph Databases [Infographic]

Fraud rings are big business. First-party bank fraud costs banks (and their customers) over $16 billion each year in the United States, and insurance fraud costs nearly $80 billion annually. And fraud rings organized around ecommerce fraud rack up nearly… Learn More →

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(Un)common Use Cases for Graph Databases

At GraphAware, we live and breathe Neo4j. For three years, we have been helping customers around the world embrace this amazing technology as a solution to many interesting problems. Mainstream applications of graphs, such as real-time recommendations, fraud detection, impact… Learn More →

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Graphs Are Everywhere and Their Possibilities Are Endless

Graphs are everywhere, and with the ability of a graph database to solve multiple business problems, pre-empt potential situations and – most importantly – save companies huge amounts of money in the meantime, why shouldn’t you use Neo4j? Graphs in… Learn More →

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From the Neo4j Community: February 2016

In the Neo4j community last month, love was in the air. That love expressed itself as more nodes than ever in our community content. From articles and podcasts to GraphGists and other projects, our global graph of community members keeps… Learn More →

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Why Enterprises Are Abandoning RDBMS (and Adopting Graphs)

Relational databases (RDBMS) were conceived to digitize paper forms and automate well-structured business processes, and they are still extremely relevant today. But, many companies are choosing to explore other database options to solve some of their biggest challenges. Why? The… Learn More →

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Neo4j + Cassandra: Transferring Data from a Column Store to a Property Graph

We’ve previously talked about the concept of polyglot persistence and why it can make sense to use multiple database technologies together. Today, we will examine a possible use case for using Neo4j (a graph database) and Cassandra (a column store)… Learn More →

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How Graph Databases Stop E-commerce Fraud in Real Time

As our lives become increasingly digital, a growing number of financial transactions are conducted online. Fraudsters have been quick to adapt to this trend, and to devise clever ways to defraud online payment systems. While this type of activity can… Learn More →

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Catching Insurance Fraud Using Graph Database Technology

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… Learn More →

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Improving First-Party Bank Fraud Detection with Graph Databases

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… Learn More →

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Why Modern Fraud Detection Needs Graph Database Technology

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… Learn More →

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How Gamesys Harnessed Neo4j for Competitive Advantage

Gamesys is the largest cash and social gaming operator in UK and Europe, created the world’s first cash gaming application on Facebook. With games like Jackpotjoy, Instant Slots and Here Be Monsters, the company features online bingo, slots and casino… Learn More →

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The Sustainable Competitive Advantage of Connected Data

As data sizes and customer expectations accelerate at lightning speed, how can your business achieve sustainable competitive advantage? By unlocking the value of data relationships – in both new and reimagined applications – your enterprise can easily improve performance, simplify… Learn More →

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The 5-Minute Interview: Hamish Boland-Rudder, Online Editor of the ICIJ

For this week’s 5-minute interview, I sat down with Hamish Boland-Rudder, the Online Editor for the International Consortium of Investigative Journalists (ICIJ). Hamish and I got to chat at GraphConnect San Francisco. Q: Can you tell me about how you… Learn More →

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The Competitive Advantage of Graph Databases in the Enterprise

“Big data” grows bigger every year, but today’s enterprise leaders don’t only need to manage larger volumes of data – they critically need to generate insight from their existing data. So how should CIOs and CTOs generate those insights? To… Learn More →

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Graph Databases in the Enterprise: Fraud Detection

Banks and insurance companies lose billions of dollars every year to fraud. Traditional methods of fraud detection fail to minimize these losses since they perform discrete analyses that are susceptible to false positives (and false negatives). Knowing this, increasingly sophisticated… Learn More →

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