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Fraud detection in E-banking by using the hybrid feature selection and evolutionary algorithms


Alireza Pouramirarsalani, Majid Khalilian, Alireza Nikravanshalmani


Vol. 17  No. 8  pp. 271-279


Nowadays, discovering knowledge from the mass set of data is considered by researchers. In this regard, data mining as one of the most efficient tools of data analysis has attracted the attention of many people. The use of different techniques and algorithms of this tool in various fields like customer relationship management, fraud management and detection, medical sciences, sport and etc. proves this claim. It is one of the areas that can be considered as one of the fields of data mining. In today’s world, financial and banking systems and services have been developed with the advancement of information technology and communication infrastructure. Banks and financial institutions have invested the field of modern technologies to provide more updated and efficient products and services. Thus, the variety of relevant products and services and also the number and value of transactions have increased. In addition to this development, securing transactions, detection of new ways of fraud and abuse in financial documents, discovery of finished and unfinished frauds, detection and discovery of processes and operations of money laundering and etc. are among the most challenging issues inj this area. The present study provides a new method for fraud detection in e-banking that is based on a hybrid feature selection and genetic algorithm.


fraud detection, e-banking, hybrid feature selection, genetic algorithm