Data Mining in Banking

Running head: DATA MINING IN BANKING
DATA MINING IN BANKING
Name of the Student
Name of the University
Author Note

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.