Implementing a Data Mining Solution Approach to Identify the Valuable Customers for Facilitating Electronic Banking (original) (raw)

customer Behavior in Banking Industry Comparision of Data Mining Techniques

Nowadays organizations have perceived the importance of managing customer relationship and its potential benefits. Customer relationship management supports organizations to deliver beneficial relations with customers. Customer satisfaction and retention are the leading objectives of any organization and this cannot be done without knowing the loyalty of the customer. Accordingly, to identify the loyalty of the customers, Kmeans algorithm was applied to the bank customers' data and clustering was conducted. The customer behavior is estimated by neural network and C5.0 models. Results show that C5.0 better fits the customer behavior. In addition, estimation of customer behavior leads organizations to more successful customer management strategies. 

Data Mining Techniques and its Applications in Banking Sector

Data mining is becoming strategically important area for many business organizations including banking sector. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data. Today, customers have so many opinions with regard to where they can choose to do their business. Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. Customer retention is the most important factor to be analyzed in today's competitive business environment. And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management.

Data Mining Applications In Banking Sector For Effective Service Delivery

Data Mining plays important roles in many organizations especially the customer service oriented establishments like banks. It is helpful in scrutinizing the collected data and delivering it into an understandable pattern. In the present scenario, banking is an emerging sector where large volumes of electronic data are being maintained. The important task in banking is handling huge transactional data and making decisions regarding customer retention, fraud detection and prevention, risk and marketing management. But making decisions by manual is time consuming and error prone. To process these data in an effective manner, data mining techniques and methods are pertinent. By using these techniques several interesting patterns and knowledge base can be retrieved. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition or churns. Customer retention is the most important factor to be analyzed in today's competitive business environment. And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management and business performance.

Data Mining. Concepts And Applications In Banking Sector

2018

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 l...

Data mining techniques for analyzing bank customers: A survey

Intelligent Decision Technologies, 2018

In today's business world, identifying the customers and analysis of their behavior is important for banking industry. Customer Relationship Management (CRM) is the process of maintaining profitable customer relationships by delivering customer value and loyalty. Moreover, CRM helps to improve the business relationships with customers. The goal of CRM is to maximize the lifetime value of a customer to an organization. Customer Lifetime Value (CLV) can rank and classify customers based on their lifetime value to identify valuable customers and retain them. There are several models for CLV estimation using the past data of customers. This subject helps organizations in their attempts to retain valuable customers. The banks must use appropriate data mining techniques to extract pattern and information from the existing data to gain competitive advantage. Therefore, data mining techniques have an important role to extract the hidden knowledge and information. The goal of this study is to review data mining techniques used for analyzing bank customers in order to help the banks to better identify their customers and design more efficient marketing strategies. The literature covered in this paper is related to the past seventeen years (2001-2017) and these approaches are compared in terms of data sets, prediction accuracy, and so on. We also provide a list of data sets available for the scientific community to conduct research in this field. Finally, open issues and future works in each of these items are presented.

Effective Use of Data Mining in Banking.

International Journal of Engineering Sciences & Research Technology, 2014

Data mining is one of the tasks in the process of knowledge discovery from the database. In the corporate world every organization is competing the other organization in terms of their value towards the business and the financial growth. Apart from execution of business processes, the creation of knowledge base and its utilization for the benefit of the organization is becoming a strategy tool to compete. In this paper we discuss about the basic details of data mining and the the use of knowledge discovery process and the new techniques from the business point of view.In our approach we make an efficient system so that the organization will get the right information at the right time and right to access the necessary information for their growth.The growth of the organization depends on the quality of service, competing with the other organizations,provide required information to the customers,satisfaction of the employees working in the organization. In the banking sector all the financial work can be done in the computers and their connectivity through World Wide Web the softwares get automatically updated in time,use of internet banking and ATM makes the big change in the banking sector. The banks have realized that their biggest asset is the knowledge and the planning to implement the right knowledge at the right time,the financial resources and the techniques of datamining for customer segmentation and profitability, marketing, risk management and customer relationship management and the fraud detection.

A Case Study of Predicting Banking Customers Behaviour by Using Data Mining

2019

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques-Neural Network and Association Rules-are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model.

Using Data Mining Methods To Solve Classification Problems in Financial-Banking Institutions

ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH

Data Mining refers to the analysis of large amounts of data stored in computers. The Big Data era is already present, with current sources indicating that more data have been created over the last two years than they have been generated throughout the entire human history. Big Data involves data sets so large that traditional data analysis methods are no longer usable due to the huge amount of data. Lacking or ignoring the data structure is an extremely important aspect, even more important than size, in data analysis, transformation, innovation and value for business. It was said that there are important data about analysis rather than data themselves.

A Review on Data Mining in Banking Sector

American Journal of Applied Sciences, 2013

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.

Customer Relationship Management Based On Decision Tree Induction

With the rampant competition in the domestic and international business, the Customer Relationship Management (CRM) has become one of matters of concern to the enterprise. CRM takes the customers as the center; it gives a new life to the enterprise organization system and optimizes the business process. In an effort to help enterprises understand their customers' shopping behavior and the ways to retain valued customers, we propose data mining techniques. As a rising subject, data mining is playing an increasingly important role in the decision support activity of every walk of life. This paper mainly focused on the research of the customer classification and prediction in commercial banks based on Decision Tree classifier that accommodates the uncertainty inherent in predicting customer behavior.