APPLICATION OF DATA MINING IN BANKING AND FINANCE (original) (raw)
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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.
Effectiveness of Data mining in Banking Industry: An empirical study
International Journal of Advanced Research in Computer Science, 2017
Data mining is becoming important area for many corporate firms including banking industry. It is a process of analyzing the data from numerous perspective and finally summarize it into meaningful information, so data mining assist the bankers to take concrete decision. This paper is an attempt to analyse the data mining technique and its useful application in banking industry like marketing and retail management, CRM, risk management and fraud detection.
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.
In today"s globalization and cut throat competition the banks are struggling to gain a competitive edge over each other. Apart from execution of business processes, the creation of knowledge base and its utilization for the benefit of the bank is becoming a strategy tool to compete. In recent years the ability to generate, capture and store data has increased enormously. The information contained in this data can be very important. The wide availability of huge amounts of data and the need for transforming such data into knowledge encourage IT industry to use data mining. The banking industry around the world has undergone a tremendous change in the way business is conducted. The banking industry has started realizing the need of the techniques like data mining which can help them to compete in the market. Leading banks are using Data Mining (DM) tools for customer segmentation and profitability, credit scoring and approval, predicting payment default, marketing, detecting fraudulent transactions, etc. This paper provides an overview of the concept of DM and highlights the applications of data mining to enhance the performance of some of the core business processes in banking industry.
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.
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 in Banking and Its Applications-A Review
Journal of Computer Science
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.
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...
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.
Realtime Fraud Detection in the Banking Sector Using Data Mining Techniques/Algorithm
2016 International Conference on Computational Science and Computational Intelligence (CSCI), 2016
The banking sector is a very important sector in our present day generation where almost every human has to deal with the bank either physically or online. In dealing with the banks, the customers and the banks face the chances of been trapped by fraudsters. Examples of fraud include insurance fraud, credit card fraud, accounting fraud, etc. Detection of fraudulent activity is thus critical to control these costs. This paper hereby addresses bank fraud detection via the use of data-mining techniques; association, clustering, forecasting, and classification to analyze the customer data in order to identify the patterns that can lead to frauds. Upon identification of the patterns, adding a higher level of verification/authentication to banking processes can be added