Data Mining and Big Data: Improving Business in the Banking Sector

This report explores the application of data mining techniques within the banking industry to improve business performance. It highlights the use of data mining for security and fraud detection by monitoring transaction records, risk management through credit scoring, and customer relationship management through segmentation and profiling. The report also touches on ethical considerations and potential disruptions in the banking sector due to emerging technologies. It emphasizes how banks can leverage their vast customer data for personalization and accurate risk assessment, ultimately improving overall business outcomes. Desklib is a platform where you can find similar assignments and study tools.

Running head: DATA MINING IN BANKING
DATA MINING IN BANKING
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DATA MINING IN BANKING 1
Introduction:
The industry of banking is highly competitive. This is so much sensitive to the
economic and political conditions in the domestic countries of them as well as all over the
world. As there are so much risk, a major strategy of several banks are for improving the
performance of them through reducing the revenues that are increasing as well as by reducing
the costs. One of the better ways for realizing both of the objectives is for using the data
mining for extracting the information and data that are so much valuable for the database of
the consumers. The purpose of this paper is to present the strategies that can be taken with
using the concepts of the big data and data mining.
Brainstorming:
After the testing of several methodologies about “How the business performance can
be improved in the banking sector. The collection of more data may lead to important
improvements in the business performance.” This can be concluded generally that the sector
of banking primarily adopts the technique of data mining for the following purposes.
Security and fraud detection:
The big secondary data such as the transaction records have been monitored as well as
analysed for enhancing the security of banking as well as distinguishing the patterns and
behaviour that can be considered as unusual indicating phasing, graud and lastly the money
laundering.
Risk management and investment banking:
The analysing of the data of the in house credit cards are accessible freely for the
banks that enable the credit scoring as well as granting of credit that too from the tools that
are the popular most for the management of risk as well as investment evaluation.

2DATA MINING IN BANKING
CRM:
The data mining techniques have been applied wisely in the industry of banking for
the management of the relationships with the customers and the main purpose is the
marketing. The customer’s relationship is including the customer segmentation, customer
profiting and lastly the up/cross selling. These can help the sector of banking for having the
better understanding of the consumers of them, predicting the behaviours of their customers;
accurately target the consumers who are considered as the potential customers.
Ethics:
The Credit scoring frameworks and misrepresentation assurance strategies are notable
uses of information mining investigation in banking industry. An advanced pattern in this
gathering of data that is utilized as indicators in information models. Internet based
cooperation’s, exchanges could be utilized as extra wellsprings of data in risk management.
Financiers' ethics is the duty of the Board of Directors or Trustees. They are in charge of
conveying the principles that speak to their organizations. To guarantee viable
correspondence, all workers ought to be required to finish ethics preparing and to sign an
announcement recognizing their receipt and comprehension. Arrangements and preparing
ought to be refreshed as required and the board ought to occasionally help their staff to
remember their significance of the code of ethics. The Board of Directors bears duty
regarding guaranteeing the code of ethics is being authorized. To do as such, the bank needs a
sound system for checking, testing and revealing worker directly.
Disruption:
In the mid of 90’s, it was said by Bill Gates the banking is an important thing but the
banks are not. The sentiment has a dependency on the population from the last decade. Apple,
Square and Stripe are a few of the organisations to revolute how people pay for the things.

3DATA MINING IN BANKING
One of the major advantages which the traditional banks are having is that they hold huge
amount of data for their millions of customers. The applications for data and analysis in the
sector of banking is just endless. An advanced pattern in this gathering of data that is utilized
as indicators in information models. Internet based cooperation’s, exchanges could be utilized
as extra wellsprings of data in risk management. Financiers' ethics is the duty of the Board of
Directors or Trustees. These can help the sector of banking for having the better
understanding of the consumers of them, predicting the behaviours of their customers. The
banks are able to use the information and data for grater personalization. One of the better
ways for realizing both of the objectives is for using the data mining for extracting the
information and data that are so much valuable for the database of the consumers. The data is
also means that the banks are having the ability for more accurately gauge the risk is for
providing loan to the customers.

4DATA MINING IN BANKING
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