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 ...

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 ...

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 ...

In applications such as fraud detection or recommendation engines ... For example, with LLMs and RAG systems, “using graph databases to map relationships within vast datasets can uncover deeper ...

Increasingly sophisticated criminal organizations are also using AI to perpetrate financial crimes ... However, AI is improving financial institutions' fraud detection efforts. Graph neural networks ...

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 ...