Download Customer Relationshi..

Customer Relationship Management (CRM) on
Banking
ABSTRACT
With the rampant competition in the domestic and international
business, the Customer Relationship Management (CRM) has become
one of matters of concern to the enterprise. CRM takes the customers as
the center; it gives a new life to the enterprise organization system and
optimizes the business process. In an effort to help enterprises
understand their customers’ shopping behavior and the ways to retain
valued customers, we propose data mining techniques. As a rising
subject, data mining is playing an increasingly important role in the
decision support activity of every walk of life. This paper mainly focused
on the research of the customer classification and prediction in
commercial banks based on Naive Bayesian classifier that accommodates
the uncertainty inherent in predicting customer behavior.
EXISTING SYSTEM
Existing data mining had grown in usage and effectiveness; data
mining applications in the commercial world have not been widely.
Disadvantage:
1) Less number of features in previous system.
2) Difficulty to get accurate item set.
PROPOSED SYSTEM
This paper mainly focused on the research of the customer
classification
and
prediction
in
Customer
Relation
Management
concerned with data mining based on Naive Bayesian classification
algorithm, which have a try to the optimization of the business process.
Advantage:
1) As a rising subject, data mining is playing an increasingly
important role in the decision support activity of every walk of life.
2) Get Efficient Item set result based on the customer request.
MODULES
1. User Module.
2. Admin/Manager Module.
3. Association Rule.
4. Apriori Algorithm.
User Module:
In this module, is used to create a new account and apply the user
kit to the user. One user transfers the found to another user and
deposits the amount. User to apply the loan based on the requirement
such as education, car and housing.
Admin/Manager Module:
In this module, is used to view and sanction the user loan request,
user kit request. Manager to sanction the loan request based on the user
details. Admin to view the item set based on the loan details using
association role with Apriori algorithm.
Association Rule:
Association rules are if/then statements that help uncover
relationships between seemingly unrelated data in a relational database
or other information repository. An example of an association rule would
be "If a customer buys a dozen eggs, he is 80% likely to also purchase
milk."
Association rules are created by analyzing data for frequent if/then
patterns and using the criteria support and confidence to identify the
most important relationships. Support is an indication of how frequently
the items appear in the database. Confidence indicates the number of
times the if/then statements have been found to be true.
Apriori Algorithm:
Apriori
is
designed
to
operate
on
databases
containing
transactions. The purpose of the Apriori Algorithm is to find associations
between different sets of data. It is sometimes referred to as "Market
Basket Analysis". Each set of data has a number of items and is called a
transaction. The output of Apriori is sets of rules that tell us how often
items are contained in sets of data.
SYSTEM SPECIFICATION
Hardware Requirements:
•
System
: Pentium IV 2.4 GHz.
•
Hard Disk
: 40 GB.
•
Floppy Drive
: 1.44 Mb.
•
Monitor
: 14’ Colour Monitor.
•
Mouse
: Optical Mouse.
•
Ram
: 512 Mb.
•
Keyboard
: 101 Keyboard.
Software Requirements:
•
Operating system
: Windows XP.
•
Coding Language
: ASP.Net with C#
•
Data Base
: SQL Server 2005.