5 Steps for Better Fraud Detection Using Graph Technology

It is anticipated that financial fraud will cost more than $5 trillion in 2019, which is equivalent to more than 6% of the global gross domestic product. This issue is becoming more widespread. To put a stop to the avalanche of monetary losses, continual attention is required since those who commit fraud are always refining their strategies, which enables them to avoid being discovered.

To win the war against money laundering, we need a system that can more effectively collect data from transactions – and other sources – and detect suspicious activity in real-time and at scale. Companies perform billions of transactions every day involving tens of millions of participants, making this endeavor difficult.

The procedures outlined below illustrate a common graph-based approach to fraud detection.

  1.  Create a graph of informational linkages between persons. Connect all accessible data, including account IDs, user names, account numbers, names, IP addresses, social media accounts, email addresses, identification numbers, postal addresses, and birth dates.
  1.  Define what suspicious behavior to search for. For example, consider:

Common qualities (same email addresses, tax identification numbers, or phone numbers, for instance). Multiple parties share the same account. Brief distances between transactions (a rapid return of purchase with no support call or reason given, for example)

  1.  Utilize graph queries or graph algorithms to analyze these features. By identifying isolated islands of activity or groups that interact more with one other than the rest of the graph or network, algorithms expose fraud rings.
  1.  Investigate and verify fraudulent behavior using a visualization tool to explore result sets Graph visualization halves the time required for manual analyst evaluation, allowing them to detect fraudulent transactions faster and reducing wait times for legitimate consumers.
  1.  Automate the preceding stages by translating graph algorithm scores into features to add to your machine-learning model so that you can discover fraud more quickly, reduce false positives, and shut it down more quickly.

Graph technology is the ideal enabler for fraud detection solutions that are both effective and controllable. Graph database technology identifies several significant fraud tendencies in real-time, ranging from fraud rings and collusive organizations to trained criminals working on their own.