Fraud Detection: Discovering Connections with Graph Databases

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Download the latest Whitepaper "Fraud Detection: Discovering Connections with Graph Databases" on Whitepapers.onlineWith cybercrime on the rise and hackers becoming more sophisticated in their operating methods, traditional means of fraud detection are growing increasingly outdated. Keeping these new-age fraudsters at bay is posing a significant challenge for the large number of businesses shifting to online operating models. In light of these developments, companies are looking for new fraud detection solutions and graph database fraud detection is proving to be one of the best ways to safeguard against this growing epidemic.

Preventing Fraud by Recognizing Patterns

The key to fraud detection is analyzing customer transactions and recognizing trends. If the right patterns are determined early-on, then potential fraud can be detected and prevented in time, without any dire consequences. Unfortunately, organizations feed past customer data into their fraud detection systems, due to which they can only trace suspicious activities that have already taken place. However, thanks to tools like the real-time analytics module developed by Neo4j fraud detection system, companies can cut their losses and limit the impact of fraud.

Real-Time Fraud Detection

Neo4j has devised preventive measures for organizations to improve their fraud detection abilities. By delving past individual data points and connecting the dots that link them, frauds ranging from bank and credit card fraud to money laundering and insurance scams can be effectively countered.

Key Takeaways from Fraud Detection: Discovering Connections with Graph Databases

  • The most detrimental kinds of frauds
  • How graph databases prevent frauds
  • How common frauds go undetected
  • Detecting frauds in real-time

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