Data Mining in Banking and Its Applications-a Review

Abstract

Banking systems collect huge amounts of data on day to day basis, be it customer information, transaction details, risk profiles, credit card details, limit and collateral details, compliance and Anti Money Laundering (AML) related information, trade finance data, SWIFT and telex messages. Thousands of decisions are taken in a bank daily. These decisions include credit decisions, default decisions, relationship start up, investment decisions, AML and Illegal financing related. One needs to depend on various reports and drill down tools provided by the banking systems to arrive at these critical decisions. But this is a manual process and is error prone and time consuming due to large volume of transactional and historical data. Interesting patterns and knowledge can be mined from this huge volume of data that in turn can be used for this decision making process. This article explores and reviews various data mining techniques that can be applied in banking areas. It provides an overview of data mining techniques and procedures. It also provides an insight into how these techniques can be used in banking areas to make the decision making process easier and productive.

Digitalisation and Big Data Mining in Banking

“…Following a comprehensive investigation of existing literature, to the best of our knowledge, only two review papers focused on the DM applications in banking [7,8] and both covered a number of DM implementations before 2013. For such a rapidly developing subject that progresses on a daily basis, it is important to provide researchers and interested parties with the most up to date status of DM and banking collaborations.…”

“…As such, we thoroughly review the DM applications in banking, especially for the recent years, post 2013. It is noteworthy that we will not repeat the contents covered in [7,8], but instead focus on the most recently developed DM applications in the banking sector. This paper aims to serve as the most up to date one stop directory guide for relevant researchers and apprise them of the evolution of big data analytics in banking with an outlook for future research.…”

Analysis of Clustering Technique in Marketing Sector

“…Zhang et al, [9] Marketing is one of the mostly used application area for Data mining by the industry in general. Banking is not an [3] determined how customers will react to a change in interest rates, which customers will be likely to accept new product offers, what collateral would require from a specific customer segment for reducing loan losses.…”

“…Partitioning method is referred as centroid based clustering such as K-means and partitioning around mediods. The clustering technique also plays a significant role in data analysis and data mining applications [3,5,6,8]. Chandan et al, [7] are developed A new Approach Document clustering help in organising documents in groups according to their similarity of contents.…”

An Efficient Data Pre Processing Frame Work for Loan Credibility Prediction System

“…Kotsiantiset presents a well-known algorithm GALA for each step of data preprocessing [2]. Data mining can assist critical decision making processes in a bank [3]. This paper consists of describing a framework for data preprocessing and to retrieve a Boolean value that helps to decide whether or not a loan is sanctioned or not to the customer.…”