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Leverage power of graphs to detect credit card fraud

Fujitsu Laboratories and LARUS have jointly verified that credit card payment fraud can be detected with high accuracy by integrating Deep Tensor, an explainable graph AI technology, developed by Fujitsu Laboratories into the LARUS platform for graph databases, Galileo.XAI.

Fujitsu and LARUS achieved this by linking Galileo.XAI with Fujitsu’s graph AI technology. Compared with previous, rule-based approaches created manually by data analysts, the fraud detection rate improved from 72% to 89%, while the false detection rate was successfully reduced by 63%.

Additionally, it was confirmed that the creation of rules for fraud detection could be supported by presenting the decision factors of fraud cases detected with graph-AI technology. Going forward, both companies will verify the effectiveness of this technology in other industries with the objective of delivering practical uses for graph databases and graph AI.

Backgroundthe power of graph

In recent years, there have been growing expectations around the use of graph data in AI applications in various fields and industry verticals (SNS activity history, representation of chemical molecules, financial transactions and tracking of virus infections). Instead of utilizing conventional relational databases, storing this kind of real-world relationship into a graph database enables expression of data elements relationships directly, thereby enabling advanced analysis and discovery of new insights into real-world scenarios.  

In the field of finance, for instance, it’s possible to extract important information utilizing graph databases by analyzing the relationships between transactions. Analysis of individual transactions alone remains insufficient, especially for detecting complex, fraudulent transactions.

For example, in the case of self-financing, a type of fraudulent transaction, even if individual transactions appear normal, when the relationship of the different transactions are analyzed together, circular or loop like patterns may become apparent. Graph databases are well suited for detecting fraud from graph patterns like these loops. 

To date, there has been a limit to how much data analysts have been able to create rules for fraudulent trading patterns, and concerns persist around the risk of misidentification and false detection. Consequently, it has become necessary to further streamline graph AI technology.

Overview of the verification trial

In this trial, table data containing details of individual transactions was converted into connected data expressing the relationship between different data elements.

These were subsequently analyzed by combining the platform for graph databases which LARUS provides with Fujitsu Laboratories’ “Deep Tensor” technology. Fujitsu and LARUS used actual credit card data and POS data and verified the degree of improvement with fraud detection rate or false detection rate by comparing with manually created fraud detection rules. In addition, by utilizing the “explanation of detection with visualization” functionality, which is another important feature of “Deep Tensor,” it was possible to show the reasoning behind the different decisions to the satisfaction of data analysts.

The Outcomes of graph in detection of credit card fraud

In the verification of credit card transaction data from a payment services provider, fraud detection rates improved from 72% to 89% and the false detection rate was reduced by an average of 63%, when the new graph-AI based approach was used in comparison with the conventional manual, rule-based approach. 

Furthermore, Fujitsu also confirmed that the explanation of detection with visualization was adequate from the viewpoint of the data analyst, making it possible to support the creation of new rules for improved fraud detection. (Figure 2). 

LARUS and Fujitsu – Future Plans

In the future, both will additionally verify the technology’s effectiveness with data drawn from different industries, paving the way for the practical use of connected data and graph-AI in a broader range of application areas.


Deep Tensor: An AI technology developed by Fujitsu Laboratories that derives new knowledge from data of a graph structure showing connections between people and things.
(October 20, 2016 Press Releases)