DR.

RAM MANOHAR LOHIYA NATIONAL LAW UNIVERSITY, LUCKNOW

2018-2019

FINAL DRAFT

TOPIC: “DATA MINING IN BANKING AND FINANCE”

SUBJECT: INTERNATIONAL BANKING AND FINANCING

SUBMITTED TO: SUBMITTED BY-

Mr. Bhanu Pratap Singh Shailesh Kumar

Dr. RMLNLU, LUCKNOW Enroll no.150101120

VIII SEMESTER

B.A. LL.B. (Hons.)

 CONTENTS
ACKNOWLEDGEMENT ............................................................................................................................ 3
INTRODUCTION................................................................................................................................. 4
Risk Management ................................................................................................................................. 5
Portfolio Management.............................................................................................................7
Customer Profiling and Customer Relationship Management…………………………..10
Conclusion…………………………………………………………………………………15
 ACKNOWLEDGEMENT

I would like to express the deepest appreciation to the Professor (Law) “Mr. Bhanu Pratap
Singh” sir who gave me the opportunity to work on this wonderful research topic. He has also
shown the attitude and the substance of a genius; he continually and persuasively conveyed a
spirit of adventure in regard to research and an excitement in regard to teaching. Without his
supervision and constant help this study would not have been possible.

I would like to express my gratitude towards my parents and my colleagues for their kind co-
operation and encouragement which helped me in completion of this project. I would like to
extend my sincere thanks to all of them.

I am highly indebted to library of Dr. Ram Manohar Lohiya National Law University for
providing necessary information regarding the project.

THANK YOU

SHAILESH KUMAR
DATA MINING IN BANKING AND FINANCE

INTRODUCTION
As knowledge is becoming more and more synonymous to wealth creation and as a strategy
plan for competing in the market place can be no better than the information on which it is
based, the importance of knowledge and information in today’s business can never be seen as
an exogenous factor to the business. Organizations and individuals having access to the right
information at the right moment, have greater chances of being successful in the epoch of
globalization and cut-throat competition.
Currently, huge electronic data repositories are being maintained by banks and other financial
institutions across the globe. Valuable bits of information are embedded in these data
repositories. The huge size of these data sources make it impossible for a human analyst to
come up with interesting information that will help in the decision making process. A number
of commercial enterprises have been quick to recognize the value of this concept, as a
consequence of which the software market itself for data mining is expected to be in excess of
10 billion USD.
Business Intelligence focuses on discovering knowledge from various electronic data
repositories, both internal and external, to support better decision making. Data mining
techniques become important for this knowledge discovery from databases. In recent years,
business intelligence systems have played pivotal roles in helping organizations to fine tune
business goals such as improving customer retention, market penetration, profitability and
efficiency. In most cases, these insights are driven by analyses of historical data. Global
competitions, dynamic markets, and rapidly decreasing cycles of technological innovation
provide important challenges for the banking and finance industry.

Worldwide just-in-time availability of information allows enterprises to improve their

flexibility. In financial institutions considerable developments in information technology have
led to huge demand for continuous analysis of resulting data.

Data mining can contribute to solving business problems in banking and finance by finding
patterns, causalities, and correlations in business information and market prices that are not
immediately apparent to managers because the volume data is too large or is generated too
quickly to screen by experts. The managers of the banks may go a step further to find the
sequences, episodes and periodicity of the transaction behaviour of their customers which may
help them in actually better segmenting, targeting, acquiring, retaining and maintaining a
profitable customer base. Business Intelligence and data mining techniques can also help them
in identifying various classes of customers and come up with a class based product and/or
pricing approach that may garner better revenue management as well.

The broad categories of application of Data Mining and Business Intelligence techniques in
the banking and financial industry vertical may be viewed as follows:

Risk Management
Managing and measurement of risk is at the core of every financial institution. Today’s
major challenge in the banking and insurance world is therefore the implementation of risk
management systems in order to identify, measure, and control business exposure. Here
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.( Other types of risk is also available in the banking and finance
i.e., liquidity risk, operational risk, or concentration risk. ) 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.

o Financial Market Risk

For single financial instruments, that is, stock indices, interest rates, or currencies,
market risk measurement is based on models depending on a set of underlying risk
factor, such as interest rates, stock indices, or economic development. One is interested
in a functional form between instrument price or risk and underlying risk factors as well
as in functional dependency of the risk factors itself.
Today different market risk measurement approaches exist. All of them rely on models
representing single instrument, their behaviour and interaction with overall market.
Many of this can only be built by using various data mining techniques on the
proprietary portfolio data, since data is not publicly available and needs consistent
supervision.

o Credit Risk
Credit risk assessment is key component in the process of commercial lending. Without
it the lender would be unable to make an objective judgement of weather to lend to
theprospective borrower, or if how much charge for the loan. Credit risk management
can be classified into two basic groups:
 Credit scoring/credit rating: Assignment of a customer or a product to risk
level. (i.e., credit approval)
 Behaviour scoring/credit rating migration analysis. Valuation of a
customer‘s or product’s probability of a change in risk level within a given
time. (i.e., default rate volatility)
In commercial lending, risk assessment is usually an attempt to quantify the risk of
loss to the lender when making a particular lending decision. Here credit risk can
quantify by the changes of value of a credit product or of a whole credit customer
portfolio, which is based on change in the instrument’s ranting, the default
probability, and recovery rate of the instrument in case of default. Further
diversification effects influence the result on a portfolio level. Thus a major part of
implementation and care of credit risk management system will be a typical data
mining problem: the modelling of the credit instrument’s value through the default
probabilities, rating migrations, and recovery rates.
Three major approaches exist to model credit risk on the transaction level:
accounting analytic approaches, statistical prediction and option theoretic
approaches. Since large amount of information about client exist in financial
 business, an adequate way to build such models is to use their own database and
data mining techniques, fitting models to the business needs and the business
current credit portfolio.

Portfolio Management
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 prices or 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.

Trading

For the last few years a major topic of research has been the building of quantitative trading
tools using data mining methods based on past data as input to predict short-term
movements of important currencies, interest rates, or equities. The goal of this technique is
to spot times when markets are cheap or expensive by identifying the factor that are
important in determining market returns. The trading system examines the relationship
between relevant information and piece of financial assets, and gives you buy or sell
recommendations when they suspect an under or overvaluation. Thus, even if some traders
find the data mining approach too mechanical or too risky to be used systematically, they
may want to use it selectively as further opinion.

Trading is based on the idea of predicting short term movements in the price/value of a
product (currency/equity/interest rate etc.). With a reasonable guesstimate in place one may
trade the product if he/she thinks it is going to be overvalued or undervalued in the coming
future. Trading traditionally is done based on the instinct of the trader. If he/she thinks the
product is not priced properly he/she may sell/buy it. This instinct is usually based on past
experience and some analysis based on market conditions. However, the number of factors
that even themost expert of traders can account for are limited. Hence, quite often these
predictions fail.

The price of a financial asset is influenced by a variety of factors which can be broadly
classified as economic, political and market factors. Participants in a market observe the
relation between these factors and the price of an asset, account for the current value of
these factors and predict the future values to finally arrive at the future value of the asset
and trade accordingly. Quite often by the time a trained eye detects these favourable factors,
many others may have discovered the opportunity, decreasing the possible revenues
otherwise. Also these factors in turn may be related to several other factors making
prediction difficult.

Data mining techniques are used to discover hidden knowledge, unknown patterns and new
rules from large data sets, which may be useful for a variety of decision making activity.
With the increasing economic globalization and improvements in information technology,
large amounts of financial data are being generated and stored. These can be subjected to
data mining techniques to discover hidden patterns and obtain predictions for trends in the
future and the behaviour of the financial markets. With the immediacy offered by data
mining, latest data can be mined to obtain crucial information at the earliest. This in turn
would result in an improved market place responsiveness and awareness leading to reduced
costs and increased revenue.
Advancements made in technology have enabled to create faster and better prediction
systems. These systems are based on a combination of data mining techniques and artificial
intelligence methods like Case Based Reasoning (CBR) and Neural Networks (NN). A
combination of such a forecasting system together with a good trading strategy offers
tremendous opportunities for massive returns. The value of a financial asset is dependent
on both macroeconomic and microeconomic variables and this data is available in a variety
of disparate formats. NN and CBR techniques can be applied extensively for predicting
these financial variables. NN are characterized by learning capabilities and the ability to
improve performance over time. Also NN can generalize i.e. recognize new objects which
may be similar but not exactly identical to previous objects. NN with their ability to derive
meaning from imprecise data can be used to detect patterns which are otherwise too
 complex to be detected by humans. NN act as experts in the area that they have been trained
to work in. these can be used to provide predictions for new situations and work in real
time. Thus, historic data available about financial markets and the various variables can be
used to train NN to simulate the market.

CBR methodology is based on reasoning from past performances. It uses a large repository
of data stored as cases which would include all the market variables in this case. When a
new case is fed in (in the form of a case containingthe concerned variables), the CBR
algorithm predicts the performance/result of this case based on the cases it has in its
repository. Data mining techniques can be used to detect hidden patterns in these cases
which may then be used for further decision making. CBR methods can be used in real time
which makes analysis really quick and helps in real time decision making resulting in
immediate profits. Thus data mining and business intelligence (CBR and NN) techniques
may be used in conjunction in financial markets to predict market behaviour and obtain
patterned behaviour to influence decision making.

Customer Profiling and Customer Relationship Management

Banks have many and huge databases containing transactional and other details of its
customers. Valuable business information can be extracted from these data stores. But it is
unfeasible to support analysis and decision making using traditional query languages;
because human analysis breaks down with volume and dimensionality.
Traditional statistical methods do not have the capacity and scale to analyse these data, and
hence modern data mining methodologies and tools are increasingly being used for decision
making process not only in banking and financial institutions, but across the industries.
Customer profiling is a data mining process that builds customer profiles of different groups
from the company’s existing customer database. The information obtained from this
process can be used for different purposes, such as understanding business performance,
making new marketing initiatives, market segmentation, risk analysis and revising
company customer policies. The advantage of data mining is that it can handle large
amounts of data and learn inherent structures and patterns in data. It can generate rules and
models that are useful in enabling decisions that can be applied to future cases.
 Customer Behaviour Modeling (CBM) or customer profiling is a tool to predict the future
value of an individual and the risk category to which he belongs to based on his
demographic characteristics, life-style and previous behaviour. This helps to focus on
customer retention. The two important facts that have important implication in selecting
customer profiling methods are:
– Profiling information can consist of many variables (or dozens of them).
– Majority of them are categorical variables (or non-numeric variables or nominal
variables).
Customer profiling is to characterize features of special customer groups. Many data
mining techniques search profiles of special customer groups systematically using
Artificial Intelligence techniques. They generate accurateprofiles based on beam search and
incremental learning techniques.
Customer profiling also uses many predictive modeling methods. Predictive modelling
techniques applicable can be categorized into two broad approaches. They depend on the
type of predicted information or variables, also called target variables. If the type of
predicted values is categorical, classification techniques is preferred to be used.

Classification Methods:

In this approach, risk levels are organized into two categories based on past default
history. For example, customers with past default history can be classified into "risky"
group, whereas the rest are placed as "safe" group. Using this categorization
information as target of prediction, Decision Tree and Rule Induction techniques can
be used to build models that can predict default risk levels of new loan applications.

Value Prediction Methods:

In this method, for example, instead of classifying new loan applications, it attempts to
predict expected default amounts for new loan applications. The predicted values are
numericand thus it requires modelling techniques that can take numerical data as target (or
predicted) variables. Neural Network and regression are used for this purpose. The most
common data mining methods used for customer profiling are:
– Clustering (descriptive)
– Classification (predictive) and regression (predictive)
 – Association rule discovery (descriptive) and sequential pattern discovery
(predictive)
In CRM, data mining is frequently used to assign a score to a particular customer or
prospect indicating the likelihood that the individual will behave in a particular way.
For example, a score could measure the propensity to respond to a particular insurance or
credit card offer or to switch to a competitor’s product.

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. For example, a typical
banking firm let say sends 1 million direct mails for credit card customer acquisition. Past
researches have shown that typically 6% of such target customers respond to these direct
mails. Banks use their credit risk models to classify these respondents in good credit risk
and bad credit risk classes. The proportion of good credit risk respondents is only 16% out
of the total respondents. So, as net result, roughly only 1%of the total targeted customers
are converted into the credit card customers through direct mailing. Seeing the huge cost
and effort involved in such marketing process, data mining techniques can significantly
improve the customer conversion rate by more focused marketing. Using a predictive test
model using decision tree techniques like CHAID (Chi-squared Automatic Interaction
Detection),
CART (Classification And Regression Trees), Quest and C5.0; it can be analyzed which
customers are more probable to respond. And using this with the risk model using
techniques like neural network can help build a test model.

The way data mining can actually be built into the CRM application is determined by the
nature of customer interaction. The customer interaction could be inbound (when the
customer contacts the firm) or outbound (when the firm contacts customers). The
deployment requirements are quite different. Outbound interactions such as direct
“Building Profitable Customer Relations with Data Mining”, Herb Edelstein mail
campaign involve the firm selecting the people whom to be mailed by applying the test
model to the customer database. In other outbound campaigns like advertising, the profile
of good prospects shown by the test model needs to be matched to the profile of the people
the advertisement would reach.
For inbound transactions such as telephone or internet order, the application must respond
in real time. Therefore the datamining model is embedded in the application and actively
recommends an action. In either case, one of the key issues in applying a model to new data
set is the transformations that are made in building the model. The ease with which these
changes are embedded in the model determines the productivity of deploying these tools.

Marketing and customer care

Because high competitions in the finance industry, intelligent business decisions in
marketing are more important than ever for better customer targeting, acquisition, retention
and customer relationship. There is a need for customer care and marketing strategies to be
in place for the success and survival of the business. It is possible with the help of data
mining and predictive analytics to make such strategies.
Financial institutions are finding it more difficult to locate new previously unsolicited
buyers, and as a result they are implementing aggressive marketing program to acquire new
customer from their competitors. The uncertainties of the buyer make planning of new
services and media usage almost impossible. The classical solution is to apply subjective
human expert knowledge as rules of thumb. Until recently, replacing the human expert by
computer technology has been difficult.
An interesting tool available in marketing and financial institution is analysis of client’s
data. This allows analysis and calculation of key indicators that help bank to identify
factorsthat affected customer’s demand in the past and customer’ need in the future.

Information about the customer’s personal data can also give indications that affect future
demand. In case of analysis of retail debtors and small corporations, marketing tasks will
typically include factors about the customer himself, his credit record and rating made by
external rating agencies.

With the advent of data mining and business intelligence tools it has become possible for
banks to strengthen their customer acquisition by direct marketing and establish multi-
channel contacts, to improve customer development by cross selling and up selling of
products, and to increase customer retention by behaviour management. It is possible for
the banks to use the data available to retain its best customers and to identify opportunities
to sell them additional services. The profiling of all the valuable accounts can be done and
the top most say 5-10 % can be assigned to Relationship Managers, whose job will be to
identify new selling opportunities with these customers. It is also possible to bundle various
offers to meet the need of the valued customers.

Data mining can also help the banks in customizing the various promotional offers. For
example the direct mails can be customized as per the segment of the account holders in
the bank. It is also possible for the banks to find out the problem customers who can be
defaulters in the future, fromtheir past payment records and the profile and the data patterns
that are available. This can also help the banks in adjusting the relationship with these
customers so that the loss in future is kept to its minimum.
CONCLUSION
Data mining can improve the response rates in the direct mail campaigns as the time
required to classify the customers will be reduced, this in turn will increase the revenues,
improve the sales force efficiency from the target group. Data mining helps the banks to
optimize their portfolio of services, delivery channels. A record of past transactions can
give useful insight to the bank and different locations /branches of same branch can also
follow some patterns that when noticed can be used as past records to learn from and base
the future actions upon.

Data Mining techniques can be of immense help to the banks and financial institutions in
this arena for better targeting and acquiring new customers, fraud detection in real time,
providing segment based products for better targeting the customers, analysis of the
customers’ purchase patterns over time for better retention and relationship, detection of
emerging trends to take proactive stance in a highly competitive market adding a lot more
value to existing products and services and launching of new product and service bundles.
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