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