Fujitsu Laboratories and LARUS Business Automation have collaborated and have confirmed 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.
Fujitsu and LARUS achieved this by linking the LARUS platform for graph databases 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 to deliver practical uses for graph databases and graph AI. Detailed results of this verification trial will be demonstrated at the “AI & Big Data Expo Europe 2020” conference, which will be held online from November 23rd, 2020 (Monday) to 24th (Tuesday) Central European Time.
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. Also, 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.
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.