Comment
Author: Admin | 2025-04-28
Data mining techniques would appear ‘subjective’ and somewhat arbitrary. Some widely used techniques in data mining include artificial neural networks, genetic algorithms, K-nearest neighbour method, decision trees, and data reduc- tion.3 DATA MINING IN THE BANKING INDUSTRYThe banking industry across the world has undergone tre- mendous changes in the way the business is conducted. With the recent implementation, greater acceptance and usage of‘electronic’ banking, the capturing of transactional data hasbecome easier and, simultaneously, the volume of such data has grown considerably. It is beyond human capability to ana- lyse this huge amount of raw data and to effectively transformthe data into useful knowledge for the organisation. The enormous amount of data that banks have been collecting over the years can greatly influence the success of data mining ef- forts. By using data mining to analyse patterns and trends, bank executives can predict, with increased accuracy, how customers will react to adjustments in interest rates, which customers will be likely to accept new product offers, which customers will be at a higher risk for defaulting on a loan, and how to make customer relationships more profitable. The banking industry is widely recognizing the importance of the information it has about its customers. Undoubtedly, it has among the richest and largest pool of customer information, covering customer demographics, transactional data, credit cards usage pattern, and so on. As banking is in the service industry, the task of maintaining a strong and effective CRM is a critical issue. To do this, banks need to invest their resources to better understand their existing and prospective customers. By using suitable data mining tools, banks can subsequently offer ‘tailor-made’ products and services to those customers.There are numerous areas in which data mining can be used in the banking industry, which include customer segmentation and profitability, credit scoring and approval, predicting pay- ment default, marketing, detecting fraudulent transactions, cash management and forecasting operations, optimising stock portfolios, and ranking investments. In addition, banks may use data mining to identify their most profitable credit card customers or high-risk loan applicants. There is, there- fore, a need to build an analytical capability to address the above-stated issues and data mining attempts to provide the answer.Following are some examples of how the banking industry has been effectively utilizing data mining in these areas.Marketing: One of the most widely used areas of data mining for the banking industry is marketing. The bank’s
Add Comment