Data Mining Report: Analyzing Data Mining in Barclays Bank - IMAT5238

This report, focusing on data mining, explores its application within Barclays Bank. The report begins by introducing data mining and its significance for organizations, emphasizing its role in gaining a competitive advantage and enhancing business efficiency. It then delves into the specific context of Barclays Bank, detailing the types of customer data stored, such as personal information, and how the bank leverages online transactions to generate vast amounts of data. The report outlines how Barclays Bank uses data mining to improve customer loyalty, predict customer behavior, detect fraudulent activities, and analyze online transactions. Furthermore, it touches upon the use of data mining in detecting credit fraud and retaining credit card customers through the analysis of customer purchase history and special offers. The report also poses key questions that data mining can answer for the banking sector, such as identifying customer preferences, detecting fraudulent patterns in credit transactions, and predicting customer attrition. The report concludes by providing references to the sources used. This assignment offers valuable insights into the practical applications of data mining within the banking industry and its impact on customer relations, fraud prevention, and overall business strategy.

DATA MINING 1
IMAT5238: DATA MINING
By: (STUDENT NAME)
(COURSE TITLE)
(PROFESSOR NAME)
(UNIVERSITY NAME)
(DATE)

DATA MINING 2
Data Mining
Introduction
Data mining is the process which analyzes a very large amount of data to find hidden and
new information which can improve organizational efficiency. Most of the organizations have
adopted data mining so as to help their business grow and also to gain a competitive advantage.
Barclays bank as one of the organizations that stores my personal data
One of the organizations which I have been dealing with for the last three years now is
Barclays bank. Some of the information which the bank stores about their customers are their
names, date of birth, customer Identity card numbers, personal Information numbers, email
address, account numbers, home address, and phone numbers. As a Barclays bank customer I am
able to access my personal data online, this means that the bank generates a huge amount of
information from online transactions. These means that if the bank is able to identify the right
information at the right time then it means the difference between losing and gaining millions of
dollars (Eboch, 2018, p. 37).
How customer’s data can be used for Barclays bank advantage
To start with, by applying data mining to the huge amount of online data, Barclays bank
is able to collect and analyze customer behavior thus able to increase customer loyalty. In
addition, the bank is able to predict customer behavior. Third, through data mining, Barclays
bank is able to discover and detect any suspicious activities with very high potential risks. Forth,
through data mining to the online transactions, the bank can is able to identify any form of non-
fraudulent and fraudulent actions via the collection of customer historical data and turning it into
useful and valid information.

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Barclays bank can also use the latest data mining techniques to detect credit frauds. They
can also use the data to extract valuable data from customer’s purchase history. In addition to
this customer’s data, Barclays bank can help in maintaining or retaining credit card customers.
This can be done by analyzing past data. Also, with data mining it can help Barclays bank to
predict those customers who are likely to change their credit card affiliation; this can help the
bank in planning and launching special offers so as to retain the customers.
Some of the key questions that Barclays bank would like answers from data mining
In banking sector, some of the key questions which data mining can possibly answer are;
1. What benefits and services would the current bank customers likely desire? This
questions helps in customer retention and increasing customer loyalty.
2. What are the patterns in credit transactions that can lead to fraud? This question helps in
detecting and deterring fraud
3. What are some of the transactions that a customer does before the shift to another a bank?
This question helps in preventing attrition
4. What are some of the profiles for high-risk borrower? This question helps in preventing a
bad loans, and defaults
5. What are some of the profiles of ATM customers and the types of products that the
customer buys? This helps in cross-selling (Han & Kamber, 2017, p. 69).

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References
Eboch, M. M., 2018. Data mining. 1st ed. New York: Greenhaven Publishing.
Han, J. & Kamber, M., 2017. Data mining : concepts and techniques by Jiawei Han. 1st ed.
Haryana: Elsevier Press.