Stop Fraud Rings in Their Tracks with Graph Databases [Infographic]
By Bryce Merkl Sasaki
May 16, 2016|Reading time: 1 minute
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… Read more →
Explore: bank fraud • ecommerce fraud • fraud analytics • fraud detection • Fraud Rings • Graph Databases • infographic • Insurance Fraud • neo4j • organized fraud
Improving First-Party Bank Fraud Detection with Graph Databases
By Bryce Merkl Sasaki
January 11, 2016|Reading time: 6 minutes
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
Why Modern Fraud Detection Needs Graph Database Technology
By Bryce Merkl Sasaki
January 4, 2016|Reading time: 3 minutes
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
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