Assignment

Kazi Imran Moin*, Dr. Qazi Baseer Ahmed / International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622 www.ijera.com

Vol. 2, Issue 2,Mar-Apr 2012, pp.738-742

738 | P a g e

Use of Data Mining in Banking

Kazi Imran Moin*, Dr. Qazi Baseer Ahmed** *(Department of Computer Science, College of Computer Science & Information Technology, Latur, (M.S), India

** (Department of Commerce & Information Technology, Sir Sayyed College, Aurangabad, (M.S), India

Abstract 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.

Keywords - Banking industry, Data Mining, Fraud Detection, MIS, TBC

1. INTRODUCTION In the financial services industry throughout the world,

the traditional face-to-face customer contacts are being

replaced by electronic points of contact to reduce the

time and cost of processing an application for various

products and ultimately improve the financial

performance. The computerization of financial

operations, use of internet and automated 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 an exception to it. It has also

witnessed a tremendous change in the way the banking

operations are carried out [1].

Since 1990‟s the whole concept of banking has been

shifted to centralized databases, online transactions and

ATM‟s all over the world, which has made banking

system technically strong and more customer oriented

[1]. Data might be one of the most valuable resources of

any bank but only if it knows how to expose valuable

knowledge hidden in raw data. Data mining allows

extracting 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

[2].

The amount of data collected by banks has grown rapidly

in recent years. Existing statistical data analysis

techniques find it difficult to manage with the large

volumes of data now available. This explosive growth

has lead to the need for new data analysis techniques and

tools in order to find the information hidden in this data.

Banking is an area where vast amounts of data are

collected. This data can be generated from bank account

transactions, loan applications, loan repayments, credit

card repayments, etc. It is assumed that valuable

information on the financial profile of customers is

hidden within these massive operational databases and

this information can be used to improve the performance

of the bank [3].

In the beginning Total Branch Computerization (TBC)

software packages being used at various branch for daily

transactions, Designing the new MIS or restructuring the

existing ones would not be possible by just replacing the

existing Total Branch Computerization packages. The

solution to this problem is to implement the concept of

data warehousing and data mining [1].

2. DATA MINING Data mining refers to extracting knowledge from large

amounts of data. The data may be spatial data,

multimedia data, time series data, text data and web data.

Data mining is the process of extraction of interesting,

nontrivial, implicit, previously unknown and potentially

useful patterns or knowledge from huge amounts of data.

It is the set of activities used to find new, hidden or

unexpected patterns in data or unusual patterns in data.

Using information contained within data warehouse, data

mining can often provide answers to questions about an

organization that a decision maker has previously not

thought to ask [4].

Kazi Imran Moin*, Dr. Qazi Baseer Ahmed / International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622 www.ijera.com

Vol. 2, Issue 2,Mar-Apr 2012, pp.738-742

739 | P a g e

 Which products should be promoted to a particular customer? – Targeted Marketing

 What is the probability that a certain customer will leave for a competitor? – Customer Relationship

Management

 What is the appropriate medical diagnosis for this patient? – Bio medical

 What is the likelihood that a certain customer will default or pay back a loan? – Banking

 Which products are bought most often together? – Market Basket Analysis

 How to identify fraudulent users in telecommunication industry? – Fraudulent pattern

analysis

These types of questions can be answered quickly and

easily if the information hidden among the huge amount

of data in the databases can be located and utilized.

Data mining is often referred to as „analytical

intelligence‟. Several recent trends have increased the

interest in data mining because of decreasing cost of data

storage and the increasing ease of collecting data. With

greater data storage capabilities and decreasing costs,

data mining has offered organizations a new way of

doing business. Data mining can help organizations better

understand their business, be able to better serve their

customers, and increase the effectiveness of the

organization in the long run [4].

Today, banks are realizing the various advantages of data

mining. It is a valuable tool by which banks can identify

potentially useful information from the large amounts of

data. This can help banks to gain a clear advantage over

its competitors. Data mining can help banks in better

understanding of the vast volume of data collected by the

CRM systems.

3 DATA MINING ALGORITHM AND TECHNIQUES There are several data mining techniques and algorithms

have been developed and used in data mining like

association, classification, clustering, prediction and

sequential patterns, Regression, Neural Networks etc. [5].

We will briefly examine those data mining techniques.

3.1 Classification

Classification is the most commonly applied data mining

technique, which employs a set of pre-classified

examples to develop a model that can classify the

population of records at large [6]. Basically classification

is used to classify each item in a set of data into one of

predefined set of classes or groups. Classification method

makes use of mathematical techniques such as decision

trees, linear programming, neural network and statistics.

In classification, we make the software that can learn

how to classify the data items into groups [5].

Fraud detection and credit risk applications are

particularly well suited to this type of analysis. This

approach frequently employs decision tree or neural

network-based classification algorithms. The data

classification process involves learning and classification.

In Learning the training data are analyzed by

classification algorithm. In classification test data are

used to estimate the accuracy of the classification rules. If

the accuracy is acceptable the rules can be applied to the

new data tuples. For a fraud detection application, this

would include complete records of both fake and valid

activities determined on a record-by-record basis. The

classifier-training algorithm uses these pre-classified

examples to determine the set of parameters required for

proper judgment. The algorithm then encodes these

parameters into a model called a classifier [6].

Types of classification models:

 Classification by decision tree induction

 Bayesian Classification

 Neural Networks

 Support Vector Machines (SVM)

 Classification Based on Associations

3.2 Association

Association is one of the best known data mining

technique. In association, a pattern is discovered based on

a relationship of a particular item on other items in the

same transaction [5]. Association and correlation is

usually used to find frequent item set findings among

large data sets. This type of finding helps businesses to

make certain decisions, such as catalogue design, cross

marketing and customer shopping behavior analysis [6].

For example, the association technique is used in market

basket analysis to identify what products that customers

frequently purchase together. Based on this data

businesses can have corresponding marketing campaign

to sell more products to make more profit [5]. The

various types of associations include:

 Multilevel association rule.

 Multidimensional association rule

 Quantitative association rule

 Direct association rule.

Kazi Imran Moin*, Dr. Qazi Baseer Ahmed / International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622 www.ijera.com

Vol. 2, Issue 2,Mar-Apr 2012, pp.738-742

740 | P a g e

 Indirect association rule.

3.3 Clustering

Clustering is a data mining technique that makes

meaningful or useful cluster of objects that have similar

characteristic using automatic technique [5]. Clustering

technique also defines the classes and put objects in

them, while in classification objects are assigned into

predefined classes. Classification approach can also be

used for effective means of distinguishing groups or

classes of object but it becomes costly so clustering can

be used as preprocessing approach for attribute subset

selection and classification [7].

For Example: The customer of a given geographic

location and of a particular job profile demand a

particular set of services, like in banking sector the

customers from the service class always demand for the

policy which ensures more security as they are not

intending to take risks, similarly the same set of service

class people in rural areas have a the preferences for

some particular brands which may vary from their

counterparts in urban areas. This information will help

the organization in cross-selling their products, The

bank‟s customer service representatives can be equipped

with customer profiles enriched by data mining that help

them to identify which products and services are most

relevant to callers. This technique will help the

management in finding the solution of 80/20 principle of

marketing, which says: Twenty per cent of your

customers will provide you with 80 per cent of your

profits, then problem is to identify those 20 % and the

techniques of clustering will help in achieving the same

[1].

Types of clustering methods

 Partitioning Methods

 Hierarchical Agglomerative (divisive) methods

 Density based methods

 Grid-based methods

 Model-based methods [6]

3.4 Prediction

The prediction as it name implied is one of a data mining

techniques that discovers relationship between

independent variables and relationship between

dependent and independent variables [5]. Regression

technique can be adapted for predication. Regression

analysis can be used to model the relationship between

one or more independent variables and dependent

variables. In data mining independent variables are

attributes already known and response variables are what

we want to predict. Unfortunately, many real-world

problems are

not simply prediction. For instance, sales volumes, stock

prices, and product failure rates are all very difficult to

predict because they may depend on complex interactions

of multiple predictor variables. Therefore, more complex

techniques (e.g., logistic regression, decision trees, or

neural nets) may be necessary to forecast future values

[6].

Types of regression methods

 Linear Regression

 Multivariate Linear Regression

 Nonlinear Regression

 Multivariate Nonlinear Regression

3.5 Sequential Patterns

Sequential patterns analysis in one of data mining

technique that seeks to discover similar patterns in data

transaction over a business period. The uncover patterns

are used for further business analysis to recognize

relationships among data [5].

4 DATA MINING ALLPLICATIONS IN BANKING 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].

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 [1]. 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 [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

Kazi Imran Moin*, Dr. Qazi Baseer Ahmed / International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622 www.ijera.com

Vol. 2, Issue 2,Mar-Apr 2012, pp.738-742

741 | P a g e

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 [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

analyzing 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.

4.1 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. Data mining carry various analyses on

collected data to determine the consumer behavior with

reference to product, price and distribution channel. The

reaction of the customers for the existing and new

products can also be known based on which banks will

try to promote the product, improve quality of products

and service and gain competitive advantage. Bank

analysts can also analyze the past trends, determine the

present demand and forecast the customer behavior of

various products and services in order to grab more

business opportunities and anticipate behavior patterns.

Data mining technique also helps to identify profitable

customers from non-profitable ones [8]. The data mining

techniques can be used to determine that how customers

will react to adjustments in interest rates, the risk profile

of a customer segment for defaulting on loans [9].

4.2 Risk Management

Data mining is widely used for risk management 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 existing

customers lines of credit, and approving loans can be

risky decisions for banks if they do not know anything

about their customers [2].

Banks provide loan to its customers by verifying the

various details relating to the loan such as amount of

loan, lending rate, repayment period, type of property

mortgaged, demography, income and credit history of the

borrower. Customers with bank for longer periods, with

high income groups are likely to get loans very easily.

Even though, banks are cautious while providing loan,

there are chances for loan defaults by customers. Data

mining technique helps to distinguish borrowers who

repay loans promptly from those who don't [8].

Bank executives by using Data mining technique can also

analyze the behavior and reliability of the customers

while selling credit cards too. It also helps to analyze

whether the customer will make prompt or delay payment

if the credit cards are sold to them [8].

Credit scoring, in fact, was one of the earliest financial

risk management tools developed. Credit scoring can be

valuable to lenders in the banking industry when making

lending decisions. Data mining can also derive the credit

behaviour of individual borrowers with installment,

mortgage and credit card loans, using characteristics such

as credit history, length of employment 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. By knowing what the chances of default are for a

customer, the bank is in a better position to reduce the

risks [2].

4.3 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 „hybrid‟ approach [2].

One system that has been successful in detecting fraud is

Falcon‟s „fraud assessment system‟. 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 analyzing the customer data in order

to identify the patterns that can lead to frauds [10].

Kazi Imran Moin*, Dr. Qazi Baseer Ahmed / International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622 www.ijera.com

Vol. 2, Issue 2,Mar-Apr 2012, pp.738-742

742 | P a g e

4.4 Customer Relationship Management

In the era of cut throat competition the customer is

considered as the king. 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 [11]. Customer acquisition and

retention are very important concerns for any industry,

especially the banking industry [2].

Today customers have wide range of products and

services provided by different banks. Hence, banks have

to cater the needs of the customer by providing such

products and services which they prefer. This will result

in customer loyalty and customer retention.

Data mining techniques helps to analyze the customers

who are loyal from those who shift to other banks for

better services. If the customer is shifting from his bank

to another, reasons for such shifting and the last

transaction performed before shifting can be known

which will help the banks to perform better and retain its

customers [8].

5 CONCLUSION 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 analyzed and

the information that is captured is used throughout the

organization to support decision-making. Data Mining

techniques can be very helpful to the banks for better

targeting and acquiring new customers, fraud detection in

real time, providing segment based products, analysis of

the customers‟ purchase patterns over time for better

retention and relationship. Those banks that have realized

the usefulness of data mining and are in the process of

building a data mining environment for their decision-

making process will obtain huge benefit and derive

considerable competitive advantage in future.

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