SSLBM: A New Fraud Detection Method Based on Semi- Supervised Learning

The increment of computer technology usage and rapid development of the Internet and electronic business lead to an increase in financial transactions. With the increase of these banking activities, fraudsters also use different methods to boost their fraudulent activities. One of the ways to cope their damages is fraud detection. Although, in this field, some methods have been proposed, there are essential challenges on the way. For example, it is necessary to propose methods that detect fraud accurately and fast, simultaneously. Lack of non-fraud labeled data and little fraud labeled data for learning is another challenge in this field particularly in banking. Therefore, we propose a new fraud detection method for bank accounts called SSLBM. In this method, after preprocessing phase, a helpful learning method called SSEV is used that is based on semi-supervised learning and evolutionary algorithm. The results imply improvement of detection by using SSLBM with 68% accuracy and acceptable speed.