DATA MINING. CONCEPTS AND APPLICATIONS IN BANKING SECTOR

Abstract

The concept of banking refers to the multitude of services and products that commercial banks offer to clients and include besides transactional accounts both passive and active products. Due to the increased competitiveness in banking, the relationship between the bank and the client has become an essential factor for the strategy in order to increase customer satisfaction. Currently the banking system is able to store impressive amounts of data that they collect daily, from customer data and transaction details to data on their transactional or risk profile. The process through which large amounts of data are analyzed, extracted, identified and the information obtained using mathematical and statistical models are interpreted is known as data mining. The discovery of knowledge from data involves identifying some models, some patterns with which certain events or possible risks are anticipated. This process helps banks to develop strategies in areas such as customer retention and loyalty, customer satisfaction, fraud detection and prevention, risk management, money laundering prevention. The aim of this paper is to present the concept of data mining and the concept of data discovery (KDD), but also the impact and important use of data mining techniques in the banking sector. This paper explores and reviews various data mining techniques that are applied in the banking sector but also provides insight into how these techniques are used in different areas to make decision-making easier and more efficient.

Keywords

• data mining
• risk management
• fraud prevention
• CRM
• KDD
• marketing
• money laundering.