Applications of Data Mining in Banking Sector

Abstract

The data mining (DM) is a great task in the process of knowledge discovery from the various databases. In the corporate sectors, every system has the tough competition with the other system with respect to their value for the business and the financial improvement. Data mining, a dynamic and fast-expanding field, which applies the advanced data analysis techniques, from machine learning, statistics, artificial intelligence or database systems to find out relevant trends, patterns and relations present within the data, information impossible to observe manually. This paper presents the applications of data mining in the banking sectors. It contains a general overview of data mining, providing a definition of the concept, primary data mining techniques and mentioning the main fields for which the data mining can be applied. It also presents the banking business sector which can benefit from the use of DM tools, along with their use cases i.e., retail and insurance banking sector. Also the commercially available DM tools and their key features are discussed within the paper. With the analysis of DM and the business areas that can successfully apply it, it also presents the main features of the DM solution by using the DM software and data languages i.e., based on survey of users throughout the world that can help to improve the customer experiences and decision making that can be applied for the banking system and the architecture, with its main components for the solution, customer segmentation, banking profitability factor, marketing, risk management and customer relations management and the fraud detection. Keywords: Architecture; Banking system; Business; Data mining; Data warehouse.