The research paper published by IJSER journal is about Data Mining: A Tool for Enhancing Business Process in Banking Sector 1
ISSN 2229-5518
Dr.R.Mahammad Shafi, Porandla Srinivas
Data might be one of the most valuable assets of any corporation, but only if it knows how to reveal valu able knowledge hidden in raw data. Data mining allows extracting diamonds of knowledge from the historical data, and predicting outcomes of future situations . It helps optimize business decisions, increase the value of each customer and communication, and improve customer satisfaction. Data mining is the process of extracting previously un- known information, typically in the form of patterns and ass ociations, from large databases. Today, organizations are realizing the numerous advantages that come with data mining. It is a valuable tool, by identifying potentially useful information from the large amounts of data collected. An organization can gain a clear advantage over its competitors.
The banking sector consists of public sector, private sector and foreign banks, apart from smaller regional and cooperative b anks. In the market, various IT-based banking products, services and solutions are available. The most common of them are Phone Banking; ATM facility; Credit, Debit and Smart Cards; Internet Banking & Mobile Banking; SW IFT Network & INFINET Network; connectivity of bank branches to facilitate anywhe re banking.
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Data Mining is the process of extracting knowledge hidden from large volumes of raw data. The knowledge must be new, not obvious, and one must be able to use it. Data mining has been defined as “the nontrivial extraction of implicit, previ- ously unknown, and potentially useful information from data [1]. It is “the science of extracting useful information from large databases” [3]. Data mining is one of the tasks in the process of knowledge discovery from the database [5]. Fig. 1 shows the process of knowledge discovery. The steps involved in Knowledge discovery are [4, 5]:
1. Data Selection The data relevant to the analysis is decided and retrieved from the various data locations.
2. Data Preprocessing: In this stage the process of data clean- ing and data integration is done.
3. Data Cleaning: It is also known as data cleansing; in this phase noise data and irrelevant data are removed from the
collected data.
4. Data Integration: In this stage, multiple data sources, often
heterogeneous, are combined in a common source.
5. Data Transformation: In this phase the selected data is transformed into forms appropriate for the mining proce- dure.
6. Data Mining: It is the crucial step in which clever tech- niques are applied to extract potentially useful patterns. The decision is made about the data mining technique to be used.
7. Interpretation and Evaluation: In this step, interesting pat- terns representing knowledge are identified based on giv- en measures. The discovered knowledge is visually pre- sented to the user. This essential step uses visualization techniques to help users understand.
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Dr.R.Mahammad Shafi is currently working as a professor in Sree Vidyan- iketan Engineering Coleege, Rangampet, India.
Porandla Srinivas is currently pursuing Ph.D. in Computer Science engi-
neering in Universityof Allahabad, India .
E-mail: porandla.srinivas@gmail.com
IJSER © 2012
The research paper published by IJSER journal is about Data Mining: A Tool for Enhancing Business Process in Banking Sector 3
ISSN 2229-5518
Data mining operations are classified in different ways. A few categorizations of data mining operations are: clustering/ segmentation, visualization, predictive modelling, link analy- sis, deviation detection, dependency modelling, and summari- zation. With most analysis tools, users know what questions they want to ask before using the tool. With data mining, users do not necessarily need to know what questions to ask; the software uncovers patterns for the users. Data visualization tools allow users to ‘see’ patterns in the data. These tools allow users to build quite complex, three-dimensional representa- tions of the data.
Many of the methodologies that are commonly described as forming part of a standard data mining tool kit have been de- veloped either by the machine-learning community or by sta- tistical data analysts. In fact, it is really difficult to specify any particular technique as being unique to the data mining com- munity. Therefore, any listing of data analysis methodologies as 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.
The 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 has
become 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 transform
the 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 marketing department can use data mining to analyse customer data- bases and develop statistically sound 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 offer- ings 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 mar- keting 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.
Risk Management: Data mining is widely used for risk man-
IJSER © 2012
The research paper published by IJSER journal is about Data Mining: A Tool for Enhancing Business Process in Banking Sector 4
ISSN 2229-5518
agement in the banking industry. Bank executives need to know whether the customers they are dealing with are reliable or not. Offering new customers credit cards, extending exist-
ing customers lines of credit, and approving loans can be risky decisions for banks if they do not know anything about their customers. Data mining, however, can be used to reduce the risk of banks that issue credit cards by determining those cus- tomers who are likely to default on their accounts. An example was reported in the press of a bank discovering that cardhold- ers who withdrew money at casinos had higher rates of delin- quency and bankruptcy. It is a common practice on the part of banks to analyse customers’ transaction behaviours in their deposit accounts to determine their probability of default in their loan accounts. Credit scoring, in fact, was one of the ear- liest financial risk management tools developed. Credit scor- ing can be valuable to lenders in the banking industry when making lending decisions. Lenders would not have expanded the number of loans they give out without
having an accurate, objective, and controllable risk assessment tool. The examples of both a ‘good’ and ‘bad’ loan applicant’s histories can be used to develop a profile for a good and bad new loan applicant.
Data mining can also derive the credit behaviour of individual borrowers with instalment, mortgage and credit card loans, using characteristics such as credit history, length of employ- ment and length of residency. A score is thus produced that allows a lender to evaluate the customer and decide whether the person is a good candidate for a loan, or if there is a high risk of default. Customers who have been with the bank for longer periods of time, remained in good standing, and have higher salaries/wages, are more likely to receive a loan than a new customer who has no history with the bank, or who earns low salaries/wages. By knowing what the chances of default are for a customer, the bank is in a better position to reduce the risks.
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 dif- ferent approaches have been developed by financial institu- tions to detect fraud patterns. In the first approach, a bank taps the data warehouse of a third party (potentially contain- ing transaction information from many companies) and uses 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 in- formation. Most of the banks are using a ‘hybrid’ approach. One system that has been successful in detecting fraud is Fal-
con’s ‘fraud assessment system’. It is used by nine of the top ten credit card issuing banks, where it examines the transac- tions of 80 per cent of cards held in the US. Mellon Bank also
uses data mining for fraud detection and is able to better pro- tect itself and its customers’ funds from potential credit card fraud.
Customer Acquisition and Retention: Not only can data min- ing help the banking industry to gain new customers, it can also help retain existing customers. Customer acquisition and retention are very important concerns for any industry, espe- cially the banking industry. Today, customers have so many opinions with regard to where they can choose to do their business. Executives in the banking industry, therefore, must be aware that if they are not giving each customer their full attention, the customer can simply find another bank that will. Data mining can also help in targeting ‘new’ customers for products and services and in discovering a customer ’s previ- ous purchasing patterns so that the bank will be able to retain existing customers by offering incentives that are individually tailored to each customer ’s needs. When Chase Manhattan Bank in New York began to lose customers to competitors, it began using data mining to analyse customer accounts and make changes in its account requirements, thereby allowing the bank to retain its profitable customers. Data mining is also being used by Fleet Bank, Boston, to identify the best candi- dates for mutual fund offerings. The bank mines customer demographics and account data along different product lines to determine which customers may be likely to invest in a mu- tual fund, and this information is used to target those custom- ers. 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. Mortgage bankers are also concerned with retaining customers. The program uses leadingedge Internet technologies, predictive models, and customer-direct marketing to enable lenders to identify new customers and retain those that they already have.
Data Mining can help by contributing in solving business problems by finding patterns, associations and correlations which are hidden in the business information stored in the data bases. What Customer Data the industry needs to explore
& Why?
What is the profile, taste and preferences, attitude of the cus- tomer and what is the purchasing behavior of the customer since the time he/she is with the bank? (Used to Cross sell the products).
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The research paper published by IJSER journal is about Data Mining: A Tool for Enhancing Business Process in Banking Sector 5
ISSN 2229-5518
What transactions does a customer do before shifting to a competitor? (To prevent shifting of customers)
Which products are often purchased together by the custom- ers of which particular profile? (For target marketing)
What patterns in credit transactions lead to fraud? (To detect and deter fraud)
What is the profile of a high-risk borrower? (To prevent de- faults, bad loans, and improve screening)
What services and benefits would current customers likely desire? (To increase loyalty and customer retention)
Identifying the customers who are getting all types of services from your company? (Identifying ‘Loyal’ Customers)
The banks who have realized the importance of data mining are in the process of reaping huge profits and considerable competitive advantage. According to the regulations given by Reserve Bank of India, the banks have to Provide Off-site Monitoring Surveillance (OSMOS) reports on regular basis in electronic format only and Regulatory requirement of filing of statutory returns such as the one under Section 42 of the Re- serve Bank of India Act, 1934 for working out Cash Reserve Ratio (CRR) and Statutory Liquidity Ratio (SLR) obligations in electronic format [2]. According to the Committee formed by Reserve Bank of India Headed by Dr. A. Vasudevan to go through the details of this topic, gave his report on 17th Ju- ly,1999, the committee highlighted that by the use of data min- ing techniques, data available at various computer systems can be accessed and by a combination of techniques like classifica- tion, clustering, segmentation, association rules, sequencing, decision tree various ALM reports such as Statement of Struc- tural Liquidity, Statement of Interest Rate Sensitivity etc. or accounting reports like Balance Sheet and Profit & Loss Ac- count can be generated instantaneously for any desired peri- od/ date [2]. Trends can be analyzed 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 ex- traction, which eliminates conflicting analytical results and arguments over the source and quality of data used for analy- sis. In short, data warehouse enables information processing to be done in a credible, efficient manner. The Committee recog- nizes the need for data warehouses and data mining both at the individual bank level and at industry level [1]. The impli- cation of adopting such technology in a bank would be as un- der [2]:
All transactions captured at the branch level would get consol- idated at a central location. Such a central location could be
called the Data Warehouse of the concerned bank. For this to happen, one of the requirements would be to establish connec- tivity between the branches on the one hand and the Data
Warehouse platform on the other.
For banks with large number of branches, it may not be desir- able to consolidate the transaction details at one place only. It can be decentralized.
By way of data mining techniques, data available at various computer systems can be accessed and by a combination of data mining techniques various decisions can be made.
Keeping in mind the usefulness and applicability of data min- ing techniques in various sectors, the software development companies have come up with various applications, which can automate the task of data mining. Some such software are:
STATISTICA Data Miner, A venture of StatSoft worldwide, is a revolutionary product in the data mining applications. It ena- bles financial institutions to Detect patterns of fraud; Identify causes of risk; create sophisticated and automated models of risk, Segment and predict behavior of homogeneous groups of customers, Uncover hidden correlations between different indicators.
11Ants Analytics Ltd is a venture backed company located in Hamilton, New Zealand. 11Ants Analytics is committed to making advanced data mining accessible to non-technical us- ers. They have built incredibly powerful data mining software which is deceptively simple to use.
Data Mining with SAS® Enterprise Miner: SAS data mining software helps customers to: detect fraud; anticipate resource demands, increase acquisitions, curb customer attrition
Data mining is a tool used to extract important information from existing data and enable better decision-making throughout the banking and retail industries. They use data warehousing to combine various data from databases into an acceptable format so that the data can be mined. The data is then analysed and the information that is captured is used throughout the organisation to support decision-making. It is universally accepted that many industries (including banking, retail and telecom) are using data mining effectively. Un- doubtedly, data mining has many uses in industries. Its practi- cal applications in such areas as analysing medical outcomes, detecting credit card fraud, predicting customer purchase be- haviour, predicting the personal interests of Web users, opti- mizing manufacturing processes etc. have been very success- ful. It has also led to a set of fascinating scientific questions
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The research paper published by IJSER journal is about Data Mining: A Tool for Enhancing Business Process in Banking Sector 6
ISSN 2229-5518
about how computers might automatically learn from past experience. The retail industry is also realising that data min- ing could give them a competitive advantage. A majority of
the banks in developing countries (particularly in the public sector) are not usually known to exploit their information ‘as- set’ for deriving business value through data mining and gain competitive advantage. But with progressive liberalisation of rules on entry for private and foreign multinational banks, under the GATS framework of WTO, competitive pressure on domestic banks is increasing. Thus, customer retention and acquisition will be an important determinant of the banks’ bottom lines. Those banks and retailers that have realized the utility of data mining and are in the process of building a data mining environment for their decision-making process will reap immense benefit and derive considerable competitive advantage to withstand competition in future.
[1]. Hillol Kargupta, Anupam Joshi, Krishnamoorthy Siva Kumar, Yelena Yesha, "Data Mining: Next Generation Chal- lenges and Future Directions", Publishers: Prentice-Hall of India, Private Limited, 2005.
[2]. Dr. A.Vasudevan, “Report of the Committee on Technolo- gy Up gradation in the Banking Sector ”, Constituted by Re- serve Bank of India, Chairman of Committee, 1999.
[3]. D. Muraleedharan, “Modern Banking: Theory and Prac-
tice”, PHI Learning private Limited, 2009.
[4]. Bharati M. Ramager, "Data Mining Techniques and Appli- cations", International Journal of Computer Science and Engi- neering.
[5]. S.P. Deshpande, Dr. V.M. Thakare, "Data Mining System and Applications: A Review".
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