Data Mining in Banking Sector Finance Essay

Published: 2021-06-26 19:40:04
essay essay

Category: Finance

Type of paper: Essay

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Hey! We can write a custom essay for you.

All possible types of assignments. Written by academics

GET MY ESSAY

Data mining defines the nontrivial extraction of hidden, previously unknown, and theoretically useful information from data. It is the facts of extracting valuable information from large databases. Banks have several and vast databases. The important business information can be extracted from these data stores. The main application areas of data mining in banking are Customer Relationship Management, Marketing, Risk Management, Data cleansing, Fraud Detection, Software Support, and Present Industry Status. Data mining tools are used by leading banks for customer segmentation and profitability, forecasting payment default, credit scoring and approval, detecting fraudulent transactions, etc. The study of private and public banks has been done to appraise the feasibility of implementation of techniques of data mining. The fundamental aim of this paper is to check the feasibility of implementation of the techniques in banking sector in India. It highlights the perspective applications of data mining to increase the performance of some of the core business processes in banking sector.

INTRODUCTION:
Data Mining is the process of extracting knowledge hidden from large volumes of raw data. The knowledge must be new, not clear, and one must be able to use it. Data mining has been defined as the nontrivial extraction of implicit, previously unknown, and possibly useful information from data. It is the knowledge of extracting useful information from large databases. Data mining is one of the tasks in the process of knowledge discovery from the database. Data mining applications has two primary components namely Data manager and Data mining tools/algorithms. Data mining techniques can be classified as artificial neural networks, genetic algorithms, nearest neighbour method, decision trees and rule induction.
Data Mining In Banking Sector:
The computerization of financial operations connectivity through World Wide Web and the support of computerized software’s has completely changed the basic concept of business and the way the business operations are being carried out. The banking sector is not exclusion to it. It has also observed a tremendous change in the way the banking operations are carried out. Since 1990’s the entire model of banking has been moved to online transactions, centralized databases, online transactions and ATM’s all over the world, which has accomplish banking system technically robust and more customer oriented. In the present day environment, the large amount of electronic data is being preserved by banks around the globe. The enormous size of these data bases makes it impossible for the organizations to analyse these data bases and to retrieve useful information as per the need of the decision makers. [4]
Banks have to adjust to the changing requirements of the societies, where people not only repute a bank account as a right rather than a privilege, but are also appraised of the fact that their business is valuable to the bank, and if the bank does not aspect after them, they can take their business elsewhere. Technology in banking is not just the computerization of process, but it is much more than this. The amount of data collected by banks has grown rapidly in recent years. Current statistical data analysis techniques find it difficult to manage with the large volumes of data now available. This volatile growth has leads to the need for new data analysis techniques and tools in order to find the information unknown in this data. Banking is an area where massive amounts of data are collected. This data can be produced from bank account transactions, loan repayments, loan applications, credit card repayments, etc. It is expected that valuable information on the financial profile of customers is hidden within these enormous operational databases and this information can be used to improve the performance of the bank. [7]
The banks in India and abroad have started using the techniques of data mining. Chase Manhattan Bank in New York, Fleet Bank Boston, ICICI, IDBI, Citi bank, HDFC and PNB in India are using data mining to analyse customer profiles to use them for their benefits. The banking industry across the world has undergone tremendous changes in the way the business is conducted. With the recent implementation greater acceptance and usage of electronic banking, the capturing of transactional data has become easier and simultaneously, the volume of such data has grown considerably. It is beyond human capability to analyses this huge amount of raw data and to effectively transform the data into useful knowledge for the organization [2].
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 A¢â‚¬Å¾tailor-madeA¢â‚¬Å¸ products and services to those customers [2].
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 payment default, marketing, detecting fraudulent transactions, cash management and forecasting operations, optimizing 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. To help bank to retain credit card customers, data mining is used. By analysing the past data, data mining can help banks to predict customers that likely to change their credit card affiliation so they can plan and launch different special offers to retain those customers. Credit card spending by customer groups can be identified by using data mining. Following are some examples of how the banking industry has been effectively utilizing data mining in these areas.
HISTORY OF DATA MINING IN BANKING:
Keeping the requirement of use of information technology in the banking sector, the Reserve Bank of India constituted a committee on technology up gradation in the banking sector, the committee emphasized the usage of management information systems by the banks and recommended that by the use of data mining techniques data available at several computer systems can be accessed and by a combination of techniques like classification, clustering, segmentation, sequencing, association rules, decision tree various ALM reports such as Statement of Structural Liquidity, Statement of Interest Rate Sensitivity etc. or accounting information like Balance Sheet and Profit & Loss Account can be generated instantaneously for any desired period/date. Trends can be examined and predicted with the availability of historical data and the data warehouse assures that everyone is using the same data at the same level of extraction, which removes conflicting analytical results and arguments over the source and quality of data used for analysis.
In Indian Express Newspapers highlights the Citibank, HDFC Bank and ICICI Bank have taken the lead in using data mining along with leading mobile telephony service providers. The data mining techniques can be of enormous help to the banks and financial institutions in this arena for better targeting and acquiring new customers, fraud detection in real time, accommodate segment based products for better targeting the customers, search of the customers’ obtaining patterns over time for better retention and relationship, detection of developing trends to take proactive approach in a highly competitive market adding a lot more value to existing products and services and launching of new product and service bundles. The leading banks are using data mining tools for credit scoring and approval, customer segmentation and profitability, detecting fraudulent transactions, predicting payment default, marketing, etc. [7]
APPLICATION OF DATA MINING IN BANKING SECTOR:
As banking competition becomes more and more global and powerful, banks have to fight more creatively and proactively to gain or even maintain market shares. Banks which still trust on reactive customer service techniques and conventional mass marketing are doomed to failure or degenerate. The banks of the future will use one asset, information and not financial resources, as their control for survival and excellence. Most of this knowledge are currently in the banking system and generated by daily transactions and operations. This valuable information need not be collected by intrusive customer surveys or expensive market research programs.
Marketing:
One of the most widely used areas of data mining for the banking industry is marketing. The bank’s marketing department can use data mining to analyse customer databases and develop statistically complete profiles of individual customer preferences for products and services. By offering only those products and services that customers really want, banks can save substantial money on promotions and offerings that would otherwise be unprofitable. Bank marketers, therefore, need to focus on their customers by learning more about them. Bank of America, for instance, uses database marketing to improve customer service and increase profits. By consolidating five years of customer history records, the bank was able to market and sell targeted services to customers.
Uses of Data mining in the area of Marketing:
Customer Acquisition – Marketers use data mining methods to discover attributes that can predict customer responses to offers and communications programs. Then the attributes of customers that are found to be most likely to respond are matched to corresponding attributes appended to rented lists of noncustomers. The objective is to select only noncustomer households most likely to respond to a new offer.
Customer Retention – Data mining helps to identify customers who contribute to the company’s bottom line but who may be likely to leave and go to a competitor. The company can than target these customers for special offers and other inducements.
Customer Abandonment – Customers who cost more than they contribute should be encouraged to take their business elsewhere a customer has a negative impact on the company’s bottom line.
Market basket analysis – Retailers and direct marketers can spot product affinities and develop focused promotion strategies by identifying the associations between product purchases in point-of-sale transactions.
Risk Management:
Risk management covers not only risks involving insurance, but also business risks from competitive threat, poor product quality, and customer attrition. Customer attrition, the loss of customers, is used in finance, retail, and telecommunications industries to help predict the possible losses of customers. Losing customers to competitors is a major concern for industries today, with the increasing amount of competition businesses are facing. Therefore, methods must be found to determine the number of customers who are likely to be lost to competitors so that a business can be used is to build a model of customers who are likely to leave and go to a competitive company. An analysis of customers who have recently left can often show non-nutritive patterns, such as after a customer has a change of address or a recent protracted exchange with one of the agents of the company.
Data mining is broadly used for risk management in the banking industry. Bank executives necessity to know whether the customers they are dealing with are reliable or not. Providing new customers credit cards, prolonging existing customers lines of credit, and approving loans can be unsafe decisions for banks if they do not know anything about their customers. Data mining can be used to reduce the risk of banks that issue credit cards by determining those customers who are likely to default on their accounts.
Credit and market risk present the central challenge, one can observe a major change in the area of how to measure and deal with them, based on the advent of advanced database and data mining technology. Today, integrated measurement of different kinds of risk (i.e., market and credit risk) is moving into focus. These all are based on models representing single financial instruments or risk factors, their behaviour, and their interaction with overall market, making this field highly important topic of research.
Financial Market Risk
Credit Risk
Fraud Detection:
Another popular area where data mining can be used in the banking industry is in fraud detection. Being able to detect fraudulent actions is an increasing concern for many businesses; and with the help of data mining more fraudulent actions are being detected and reported. Two different approaches have been developed by financial institutions to detect fraud patterns. In the first approach, a bank taps the data warehouse of a third party and use data mining programs to identify fraud patterns. The bank can then cross-reference those patterns with its own database for signs of internal trouble. In the second approach, fraud pattern identification is based strictly on the bank’s own internal information. Most of the banks are using a "hybridA¢â‚¬Å¸ approach [2].
One system that has been successful in detecting fraud is Falcon’s, "fraud assessment systemA¢â‚¬Å¸. It is used by nine of the top ten credit card issuing banks. The data mining techniques will help the organization to focus on the ways and means of analysing the customer data in order to identify the patterns that can lead to frauds [10]. The bank mines customer demographics and account data along different product lines to determine which customers may be likely to invest in a mutual fund, and this information is used to target those customers. Bank of America’s West Coast customer service call centre has its representatives ready with customer profiles gathered from data mining to pitch new products and services that are the most relevant to each individual caller. [1]
Portfolio Management:
Portfolio management refers to the selection of securities and their continuous shifting in the portfolio to optimize returns to suit the objectives of an investor. Portfolio management package is one of the merchant banking activities recognized by Securities and Exchange Board of India (SEBI). The service can be extracted either by merchant bankers or portfolio managers or discretionary portfolio manager. There are three major activities involved in an efficient portfolio management which are as follows: Identification of assets or securities, allocation of investment and also defining the classes of assets for the purpose of investment, They have to decide the major weights, percentage of different assets in the portfolio by taking in to consideration the related risk factors, To end, they select the security within the asset classes as identify.
Risk measurement approaches on an aggregated portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an induction of the expected return or price of a financial instrument. Both make it possible to manage firm wide portfolio actively in a risk/return efficient manner. The application of modern risk theory is therefore within portfolio theory, an important part of portfolio management. With the data mining and optimization techniques investors are able to allocate capital across trading activities to maximise profit or minimise risk. This feature supports the ability to generate trade recommendations and portfolio structuring from user supplied profit and risk requirement.
With data mining techniques it is possible to provide extensive scenario analysis capabilities concerning expected asset pricesor returns and the risk involved. With this functionality, what if simulations of varying market conditions e.g. interest rate and exchange rate changes) cab be run to assess impact on the value and/or risk associated with portfolio, business unit counterparty, or trading desk. Various scenario results can be regarded by considering actual market conditions. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub portfolio can be benchmarked against common international benchmarks.
Customer Relationship Management:
In the era of cut throat competition the customer is considered as the king and it’s the customer only who is ruling the whole show. The concept of selling a product to the customer is outdated and obsolete, now the objective is to reach to the heart of the customer and hence to develop a sense of belongingness for the organization. The huge data bases of various organizations are storing billions of data items about the customers. Data mining can be useful in all the three phases of a customer relationship cycle: Customer Acquisition, Increasing value of the customer and Customer retention [5]. Data mining technique can be used to create customer profiling to group the like-minded customers in to one group and hence they can be dealt accordingly [8].
The information collected can be used for different purposes like making new marketing initiatives, market segmentation, risk analysis and revising company customer policies according to the need of the customers [9]. The profiling is usually done on the basis of demographic characteristics, life style and previous transactional behaviour of a particular customer. Customer profiling is to characterize features of special customer groups [10].

Warning! This essay is not original. Get 100% unique essay within 45 seconds!

GET UNIQUE ESSAY

We can write your paper just for 11.99$

i want to copy...

This essay has been submitted by a student and contain not unique content

People also read