Fraud Detections using graph databases

Published on 28 Aug 2020

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 by working together, and by leveraging various other means of constructing false identities. Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real time. 

While no fraud prevention measures can ever be perfect, significant opportunity for improvement can be achieved by looking beyond the individual data points, to the connections that link them. Oftentimes these connections go unnoticed until it is too late — something that is unfortunate, as these connections oftentimes hold the best clues. Understanding the connections between data, and deriving meaning from these links, doesn’t necessarily mean gathering new data. 

Significant insights can be drawn from one’s existing data, simply by reframing the problem and looking at it in a new way: as a graph. Unlike most other ways of looking at data, graphs are designed to express relatedness. Graph databases can uncover patterns that are difficult to detect using traditional representations such as tables. An increasing number of companies are using graph databases to solve a variety of connected data problems, including fraud detection. 

This paper discusses some of the common patterns that appear in three of the most damaging types of fraud: first-party bank fraud, insurance fraud, and e-commerce fraud. While these are three entirely different types of fraud, they all hold one very important thing in common: the deception relies upon layers of indirection that can be uncovered through connected analysis. In each of these examples, graph databases offer a significant opportunity to augment one’s existing methods of fraud detection, making evasion substantially more difficult 

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Example 1:First-Party Bank Fraud


  • First-party 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 every year1 to first-party fraud, which is estimated to account for as much as one-quarter or more of total consumer credit charge-offs in the United States.  It is further estimated that 10%-20% of unsecured bad debt at leading US and European banks is misclassified, and is actually first party fraud. 
  • The surprising magnitude of these losses is likely the result of two factors. The first is that first-party fraud is very difficult to detect. Fraudsters behave very similarly to legitimate customers, until the moment they “bust out”, cleaning out all their accounts and promptly disappearing. 
  • A second factor— which will also be explored later in greater detail—is the exponential nature of the relationship between the number of participants in the fraud ring and the overall dollar value controlled by the operation. This connected explosion is a feature often exploited by organized crime. 
  • However while this characteristic makes these schemes potentially very damaging, it also renders them particularly susceptible to graph-based methods of fraud detection.

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