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ABSTRACT
The use of credit cards is prevalent in modern day society. But it is obvious that the
number of credit card fraud cases is constantly increasing in spite of the chip cards
worldwide integration and existing protection systems. This is why the problem of
fraud detection is very important now. The credit card fraud detection features uses
user behavior and location scanning to check for unusual patterns. These patterns
include user characteristics such as user spending patterns as well as usual user
geographic locations to verify his identity. If any unusual pattern is detected,
the system requires revivification.
In this project, a technique for `Credit Card Fraud Detection’ is developed. As
fraudsters are increasing day by day. And fallacious transactions are done by the
credit card and there are various types of fraud. So to solve this problem combination
of technique is used like Genetic Algorithm, Behavior Based Technique and SET
(Secure Electronic Transaction), Machinelearning, Data Mining. By this transaction is
tested individually and whatever suits the best is further proceeded. And the foremost
goal is to detect fraud by filtering the above techniques to get better result. In this
project the general description of the developed fraud detection system and
comparisons between models based are (pattern reorganization). In the last section
of this paper the results of evaluative testing and corresponding conclusions are
considered. When a Invalid user(fraud) uses a bank transaction, While transaction
first bank authorizercheck weather the user is valid user or a invalid user. If the user
is invalid then the bank authorizer block the transaction.
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TABLE OF CONTENTS
Chapter
No.
TIT
LE
Page
No.
ABSTRACT
v
LIST OF FIGURES
viii
LIST OF ABBREVIATIONS
ix
1
INTRODUCTION
1
1.1. INTRODUCTION
1
1.2 . PROBLEM DEFINITION
1,2
1.3 OVERVIEW
3
1.4 MACHINE LEARNING
4
1.5 MACHINE LEARNING STRATAGIES
5
1.6 SUPERVISED LEARNING
5,6
2
LITERARURE SURVEY
8
3
METHODOLOGY
3.1 EXISTING SYSTEM
13
3.2.DISADVANTAGES OF EXISTING SYSTEM
13
3.3 PROPOSED SYSTEM
14
3.4.DISADVANTAGES OF PROPOSED SYSTEM
13
3.5 SOFTWARE AND HARDWARE
REQUIREMENTS
15
3.5.1 HARDWARE REQUIREMENTS
15
3.5.2 SOFTWARE REQUIREMENTS
15
3.6 MODULES
19
3.6.1 USER GUI
19
3.6.2 CRITICAL VALUE IDENTIFICATION
20
3.6.3 FRAUD DETECTION
20
3.7 DATA FLOW DIAGRAM
21
4
RESULTS AND DISCUSSION
23
vii
5
CONCLUSION
24
REFERENCES
25
APPENDICES
SOURCE CODE
27
SCREENSHOTS
47
PLAGIARISM REPORT
54
