This paper is published in Volume-3, Issue-1, 2017
Area
Intelligent Systems
Author
Saurabh Dorle, Manish Bendale, Dr. Nitin N. Pise
Org/Univ
Maharashtra Institute of Technology , Pune, Maharashtra, India
Pub. Date
17 February, 2017
Paper ID
V3I1-1332
Publisher
Keywords
Expert System; Suspicious Financial Transactions; Anti-Money Laundering.

Citationsacebook

IEEE
Saurabh Dorle, Manish Bendale, Dr. Nitin N. Pise. An Intelligent System for Detection of User Behavior In Internet Banking, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Saurabh Dorle, Manish Bendale, Dr. Nitin N. Pise (2017). An Intelligent System for Detection of User Behavior In Internet Banking. International Journal of Advance Research, Ideas and Innovations in Technology, 3(1) www.IJARIIT.com.

MLA
Saurabh Dorle, Manish Bendale, Dr. Nitin N. Pise. "An Intelligent System for Detection of User Behavior In Internet Banking." International Journal of Advance Research, Ideas and Innovations in Technology 3.1 (2017). www.IJARIIT.com.

Abstract

Security and making trust is the first step toward the development of both real and virtual societies. Internet-based development is inevitable. Increasing penetration of technology in the internet banking and its effectiveness in contributing to banking profitability and prosperity requires that satisfied customers turn into loyal customers. Currently, a large number of cyber attacks have been focused on online banking systems, and these attacks are considered as a significant security threat. Banks or customers might become the victim of the most complicated financial crime, namely internet fraud. This study has developed an intelligent system that enables detecting the user's abnormal behavior in online banking. Since the user's behavior is associated with uncertainty, the system has been developed based on the fuzzy theory, this enables it to identify user behaviors and categorize suspicious behaviors with various levels of intensity. The performance of the fuzzy expert system has been evaluated using a receiver operating characteristic curve, which provides the accuracy of 94%. This expert system is optimistic to be used for improving e-banking services security and quality.