Data Mining Applications in Finance

This assignment delves into the applications of data mining within the financial sector. It examines how techniques like customer retention analysis, market research, and fraud detection are leveraged. The document highlights the challenges associated with using data mining in finance, such as incomplete and complex datasets. It concludes by emphasizing the competitive advantage financial institutions gain by adopting data mining practices.

Running head: DATA MINING IN THE FINANCE INDUSTRY
Data Mining in the Finance Industry
Name of the Student
Name of the University
Author’s note

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DATA MINING IN THE FINANCE INDUSTRY
Table of Contents
1. Introduction.................................................................................................................................2
2. Discussion....................................................................................................................................2
2.1 Background of Data Mining..................................................................................................2
2.2Different Methods and Techniques covered by Data Mining................................................4
2.3 Data Mining Applications in the Financial Sector................................................................6
2.4 Different Data Mining Applications and Methods Used -Case Studies................................6
2.5 Challenges of Data Mining in Financial Industry and Future Development.........................7
3. Conclusion...................................................................................................................................8
4. References..................................................................................................................................10

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1. Introduction
Data mining is carried out for the purpose of discovering valuable patterns and
information from data warehouses or databases (Larose 2014). Financial data plays a significant
role in the financial sector for analyzing consumer data in order to find legitimate customers such
as the debtors. Data mining has become an integral part of the financial sector as it helps in
predicting the future behaviour and trends in the financial markets.
This report gives an overview of the concept of data mining. It discusses the various
types of methods or operations of data mining in the financial industry. It also examines and
analyzes some of the challenges of data mining in the financial sector. This report gives a brief
overview of the future developments that can be done in the banking sectors, investment sectors
and other financial institutes by applying data mining techniques and methods.
2. Discussion
2.1 Background of Data Mining
Data mining is known to be a process that extracts hidden knowledge from large
databases that contain raw data (Witten et al. 2016). It can also be defined as the science of
extracting valuable information or discovering knowledge from data warehouses (Larose 2014).
Knowledge is discovered in the data mining process. There are various steps in data mining
process that follows an iterative sequence. The steps of data mining process are:
Name of the step Analysis
1. Learning This step focuses on learning and getting prior knowledge
about the application domain. The goals and objectives of the

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application need to be learnt before carrying out the process
of knowledge or information discovery.
2. Creating of target dataset This step focuses on creating and identifying a target dataset.
Selection of the dataset must be done correctly in order to
apply data mining processes and methods for discovering
knowledge.
3. Data cleaning This is a pre-processing method that carries out basic
operations like removal of noise. This stage focuses on
removing inconsistent data and errors (Preethi and
Vijayalakshmi 2017).
4. Data projection Data projection is carried out in this stage. This step focuses
on discovering valuable features for the purpose of data
representation.
5. Data mining function This step focuses on taking a decision regarding the objective
of the data mining model derived. Data mining function is
selected in this step.
6. Data mining algorithm This step focuses on selecting a method for searching for
data patterns.
7. Data patterns This step includes classification trees or rules, clustering,
regression, line analysis and sequence modeling for finding

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out data patterns.
8. Pattern interpretation The discovered patterns of data are interpreted to find a
meaning and the redundant data or patterns are removed in
this step.
9. Discovered knowledge In this step, knowledge is incorporated in the performance
system and actions are taken based on the knowledge that is
discovered.
Table 1: Steps in Data Mining Process
(Source: Larose 2014, p. 56)
Data mining is gaining importance in almost every sector of the financial industry for the
purpose of analyzing data and summarizing the discovered data into valuable information
(Charliepaul and Gnanadurai 2014). The main target of the banking sector is to retain customers
(Chitra and Subashini 2013). This process is used for analyzing customer details for identifying
their tastes and preferences in order to develop new strategies for customer retention. Data
mining tools can facilitate automated discovery of unknown patterns and automatic future trend
prediction. Data mining is needed in the financial sector for forecasting bank bankruptcies, stock
market, and exchange rate of currency, credit rating, money laundering analysis and loan
management. Royal Bank of Scotland Group uses data mining in the loan management process
to determine the loan risk.
2.2Different Methods and Techniques covered by Data Mining
According to Raval (2012), the four main techniques of data mining are:

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Association: This technique focuses on discovering patterns based on the relationship of a
specific item with other items that are present in same transaction. Association method can be
used in market analysis for identifying the products and services that consumers tend to purchase
together. Boolean and quantitative association rules are based on the types of data that are
handled by the process. Single-level and multilevel association rules are applied when
abstraction levels are involved.
Clustering: This technique is used for identifying objects belonging to similar classes. It
can be used as a pre-processing approach for combining similar objects into a single group
(Charliepaul and Gnanadurai 2014). An algorithm called k-means algorithm can be used for
partitioning data. Financial sectors can use this method to segment the demographic data into
groups and clusters. Vaishali (2014) showed how clustering approach can be used for fraud
detection in the credit cards of the consumers. Clustering methods can also be used for fraud
detection purposes. Naïve Bayes algorithm is also used in the clustering techniques.
Prediction: Regression analysis is used for forecasting and predicting. This technique is
useful for discovering patterns and knowledge. Decision trees and neural networks are also used
for predicting future values and data. Financial sectors can use this technique for customer
retention.
Classification: This method is used for classifying large volume of data. Decision tree
algorithm plays a significant role in classifying data. Classification methods can be used for
credit approval systems. Decision tree algorithm applies if-then-else rules to predict the future
values in the method of classification (Kadam and Raval 2014).

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2.3 Data Mining Applications in the Financial Sector
Data mining facilitates a financial institute to analyze customer data and behaviour for
the purpose of predicting future trends and activities (Charliepaul and Gnanadurai 2014). Data
mining process helps to compare and analyze the change between customer behaviours between
two consecutive months. Data mining techniques can be applied to segment the market based on
similar characteristics of the customers. It helps to analyze the market trends for predicting the
behaviour pattern of the customers. Data mining plays a broad role in cross selling and direct
marketing (Pavlovic, Reljic and Jacimovic 2014). Banking industry utilizes data mining
techniques for minimizing risk (Miyan 2017). It helps in distinguishing reliable borrowers from
non reliable borrowers.
2.4 Different Data Mining Applications and Methods Used -Case Studies
Data mining has several applications in the financial sector such as:
1) Customer retention: Financial Institutes collect and analyze customer information like
income status and expenditure. Analysis of the customer status helps the financial institutes to
offer additional services for customer retention (Baumann, Elliott and Burton 2012). Financial
institutes will be able to target new customers by using data mining techniques. Classification
methods are used for the purpose of retaining customers and getting new customers.
Decision tree is a graphical representation that shows the relationships among different
variables. It solves classification as well as prediction problems. The customers are classified
into two groups: risky and safe group. Value prediction models and methods can be used for
predicting the default amount for the application of loan. Regression method plays a significant
role in this case. Clustering techniques help in carrying out customer profiling. k-Means

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technique can be used segment the customer profession into various clusters and identify the
customer need so that banks and financial institutes can fulfil their requirements.
2) Fraud detection: Credit card frauds as well as insurance frauds are the most common
types of fraud activities that take place in the financial sector. It is important to distinguish
between legitimate and fraudulent activities (Sharma and Panigrahi 2013). SPV or support vector
machines techniques are used for getting accurate result. Clustering methods along with
probability estimation methods can be used for the purpose of detecting fraud activities.
Classification methods are also applicable for classifying the fraud activity.
3) Market forecasting: Data mining techniques can be used for predicting any crisis such
as fall in the stock price that will occur in the future by using Bayesian network. Financial
institutes can analyze past data and trends and determine present demand. These techniques can
also be used for predicting customer behaviour in the future by analyzing the historical
purchasing details of the customers.
2.5 Challenges of Data Mining in Financial Industry and Future Development
Data mining is considered to be powerful but it also has several challenges and loopholes.
These challenges can be related to data methods and performance. Some of the challenges are
discussed below.
1) Incompleteness and heterogeneity: The data stored in the databases have different
patterns and rules like email, images and pdf documents. Transformation of the heterogeneous
data into a structured form is a challenge in data mining (Airccj.org 2017). Incomplete or
missing data gives inaccurate results during the analysis process. Carrying out appropriate
analysis is a major challenge in the process of data mining (Verma and Nashine 2012).

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2) Complexity: It is a challenging issue to manage large volumes of data. Data analysis,
retrieval, modelling and data organization are a big challenge because of the complex nature of
the data.
3) Timeliness and privacy: Data analysis consumes more time. In some cases immediate
analysis results are required. For example, detection of fraudulent transaction must be suspected
immediately. But scanning of entire data set requires more time. Privacy is a major issue in data
mining. Customer details are analyzed by the banks and financial institutes without their consent
which leads to privacy issues. For example, there can be unethical hacking of sensitive customer
data.
Relational data mining can solve financial problems and bring great advancements in the
future for the financial applications (Paidi, 2012). Future development of data mining will be to
develop decision support tools for making the operations for financial tasks easier. APIs such as
Braintree API allows customers to accept payments via the payment gateways of Braintree.
3. Conclusion
This report concludes that data mining plays a significant role in the financial industry
such as banking and investment sectors. Data mining techniques like classification and clustering
methods have been discussed in this report. Applications of data mining in the finance sector
have also been discussed in this report. This report examines the data mining methods used in
these applications. According to this report, the main data mining application in the financial
sector are customer retention, market analysis and fraud detection. The main challenges in data
mining are incomplete and complex data. It can be concluded from this report that the financial

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sector will become more competent and gain competitive advantage by adopting data mining
techniques.

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4. References
Airccj.org.,2017. Issues, Challenges, and Solutions: Big Data Mining. [online] Available at:
http://airccj.org/CSCP/vol4/csit43111.pdf [Accessed 21 Nov. 2017].
Baumann, C., Elliott, G. and Burton, S., 2012. Modeling customer satisfaction and loyalty:
survey data versus data mining. Journal of services marketing, 26(3), pp.148-157.
Charliepaul, C. and Gnanadurai, G. , 2014. A Detailed Review on Data Mining in Finance
Sector. International Journal On Engineering Technology and Sciences – IJETS™, 1(3), pp.124-
131.
Chitra, K. and Subashini, B., 2013. Data mining techniques and its applications in banking
sector. International Journal of Emerging Technology and Advanced Engineering, 3(8), pp.219-
226.
Kadam, S. and Raval, M., 2014. Data Mining in Finance. International Journal of Engineering
Trends.
Larose, D.T., 2014. Discovering knowledge in data: an introduction to data mining. John Wiley
& Sons.
Miyan, M., 2017. Applications of Data Mining in Banking Sector. International Journal of
Advanced Research in Computer Science, 8(1), pp.108-114.
Paidi, A., 2012. Data Mining: Future Trends and Applications. International Journal of Modern
Engineering Research (IJMER), 2(6), pp.4657-4663.