Mr. Richard Henderson, EMEA Team Lead Solution Architect, at TigerGraph said that his team “built a fraud detection application using machine learning and a graph database to demonstrate that a ...

Fraud detection and identity authentication are the two main use cases that Aerospike sees customers using the graph database to build. Fraud detection, where connections to known fraudulent entities ...

Graph databases are uniquely powerful in fraud detection for their ability to model and analyze complex, dynamic relationships between data points, such as transactions, accounts, and user ...

This is where a graph database solution came in ... find the connections and instances of fraud, corruption and tax evasion. After Cabra’s team shared the tool on the ICIJ’s virtual ...

The Fraud detection and Prevention segment to hold the largest market size during the forecast period To protect customer data, mitigate exposure to risk, and deliver the most value to shareholders, ...

Forrester Research Inc. recently estimated that just over half of data- and analytics-intensive organizations are using graph databases ... well-suited for fraud detection, a feature it has ...

Improving the accuracy of fraud detection We’ve all witnessed ... These are just a few of the most common uses for graph databases. Customers are also using graph databases to optimize business ...

the 360 angle and the fraud detection.” Here’s what people on the front lines of this trend had to say. “Today, enterprises are beginning to understand what a graph database is. By 2022 ...

such as Graph databases, NoSQL databases, and PostgreSQL, organizations can tailor their technology infrastructure to meet the specific demands of fraud detection in the digital age. Choosing the ...