Detecting Dodgy Transactions through High Value Networks in Economic Services
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 6
Abstract
The purpose of this paper is to detect the dodgy or suspicious transactions in financial era with the help of data mining technique and also to detect more suspicious transactions in suspicious transactions. K mean technique is used to detect different clusters. Data Mining is a powerful new technology having great potential to help organizations by focusing on most important data in their corresponding data warehouse. It is the process of extracting information from large volumes of raw data. The need for data mining is because we are having large amount of data with less information in it. Data mining tools are used to analyze this data. WEKA is a data mining tool. This paper also gives use of WEKA in data mining WEKA also provides the facility for classification of data with different algorithms. Different types of bank frauds are also discussed in this paper. With the adoption of new technology as well as changing customer and staff expectations it is challenging for bank to navigate technology strategy alternatives .By using data mining, banks can differentiate their customers that what type of customers can be fraudster in future and what customers can give benefit to banks. Bank can detect highly suspicious networks from suspicious network.
Authors and Affiliations
Sonia, Anil Arora
Keywords
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