1. AJEENKYA D Y PATIL SCHOOL OF ENGINEERING
Department of Computer Engineering
BE Project
Semester-II A.Y. 2022-23
Project Group ID : 29
Team Members PRN number
1. SANKET SUNIL KADAM 72034780H
2. AJAY RAJNDRA THENGALE 72034817L
3. AJAY VINOD MALI 72034715H
4. PRANALI POPAT CHAUDHARI 72034647K
Project Title : CREDIT CARD FRAUD DETRCTION USING MACHINE
LEARNING
Project Guide : Prof. Ishwar Bharambe
Area of Project : Machine Learning
2. Contents
Problem Statement
Motivation
Objectives
Introduction to Domain
Literature Review
Project Feasibility
Scope of Project
Mathematical Model
Software Hardware Requirements
System Architecture
Algorithm
Structural Diagrams
Behavioral Diagrams
Advantages, Limitations, Applications
Conclusion
Future Work
References in Standard Format (At least 15, starting with Base Paper and in Year wise Decreasing Order)
3. The problem we aim to address is credit card fraud detection. This is a significant
concern in the financial industry, where unauthorized transactions can lead to
substantial financial losses for both cardholders and financial institutions. The
objective is to develop an accurate and efficient system that can identify fraudulent
credit card transactions in real-time, preventing financial losses and ensuring the
security of cardholders' accounts.
2.1 Why is it Used?
• To safeguard cardholders
Credit card fraud detection systems help protect cardholders from unauthorized
transactions and potential financial losses.
• To minimize financial impact
Financial institutions can utilize these systems to reduce the financial impact of
fraudulent activities on their business operations.
• To ensure transaction security
By accurately identifying fraudulent transactions, the system ensures the
security and integrity of credit card transactions.
1.INTRODUCTION
2. Motivation
4. 2.2 Who Benefits it?
• Financial Institutions: Banks, credit card companies, and other financial institutions
utilize credit card fraud detection systems to protect their customers and their own
financial assets.
• Cardholders: Credit cardholders benefit from these systems as they provide an additional
layer of security to prevent unauthorized transactions.
2.3 How is it Used?
• Real-time monitoring: The system continuously analyzes credit card transactions in real-
time, identifying suspicious patterns or anomalies that may indicate fraudulent activity.
• Machine Learning Algorithms: Advanced machine learning algorithms are employed to
analyze transaction data, detect patterns, and make accurate predictions about the
likelihood of fraud.
• Integration with existing systems: The credit card fraud detection system is seamlessly
integrated into the existing infrastructure of financial institutions, allowing for efficient and
effective monitoring and prevention of fraudulent transactions.
6. • The credit card fraud detection project operates in the domain
of financial security and fraud prevention within the credit
card industry.
• This domain focuses on developing robust systems and
algorithms to detect and prevent fraudulent transactions,
protecting both financial institutions and cardholders from
financial losses and unauthorized activities.
• By leveraging advanced machine learning techniques and real-
time data analysis, the project aims to enhance the security
measures in place and provide a reliable and efficient solution
to combat credit card fraud.
4.
Introduction
To
Domain
7. 5. Literature Review
• Paper 1: A Survey Paper on Credit Card Fraud Detection Techniques.
* Authors:-
Aisha Mohammad Fayyomi, Derar Eleyan, Amina Eleyan
This paper addresses the growing concern of credit card fraud due to
the widespread use of electronic transactions.
• Paper 2: Literature Review of Different Machine Learning Algorithms for
Credit Card Fraud Detection.
* Authors:-
Nayan Uchhana, Ravi Ranjan, Shashank Sharma, Deepak
Agrawal, Anurag Punde.
It emphasizes the need for, credit card companies to implement
effective technologies and systems to detect and prevent fraud.
comparative analysis of their effectiveness.
8. • Paper 3: A Review on Credit Card Fraud Detection Techniques.
* Author:
Reetu Elza Joseph.
The paper reviews machine learning and deep learning algorithms used
for credit card fraud detection, addressing the increasing occurrence of
credit card fraud in the context of online transactions and ecommerce
platforms.
• Paper 4: Review of Machine Learning Approach on Credit Card Fraud Detection
* Authors:
Rejwan Bin Sulaiman Vitaly, Schetinin Paul Sant.
Research article focuses on the increasing credit card fraud due to the
widespread usage of credit cards and the growth of online businesses.
9. 1. Technical Feasibility:
Assess if the required technologies, programming languages, and computational
resources:
1. Python Programming Language
2. Python Libraries(like NumPy, Pandas, Sklearn ,etc)
3. Machine Learning Algorithms (Decision Tree, Logistic Regression,
Random Forest.
2. Data Feasibility:
Assess the availability of Data:
1. Transaction Data.
2. Available in PCA form.
3. Schedule Feasibility:
Project timeline:
1. Data Pre-processing
2. Model Training
3. Model Evaluation
4. User Interface
6. Project Feasibility
10. 7. Scope of Project
Our Scope of project includes,
1. Develop a machine learning-based system to detect fraudulent credit card
transactions in real-time.
2. Gather and pre-process historical and real-time transaction data to train and
test the fraud detection models.
3. Implement and evaluate multiple machine learning algorithms such as
decision tree, logistic regression, and random forest.
The mathematical model for credit card fraud detection involves
using machine learning algorithms is:
y = f(x)
Where:
- y represents the predicted class label (fraudulent or legitimate)
- x represents the input features (transaction attributes such as
transaction method, time, amount, etc.)
- f represents the decision function learned by the machine learning algorithm
8. MATHAMATICAL MODEL
11. Software Requirements:
- Operating System: Windows, Linux, or macOS
- Python version 3.7 or above
- Python libraries: scikit-learn, pandas, NumPy, Flask, HTML/CSS
Hardware Requirements:
- Processor: Intel Core i5 or equivalent
- RAM: 8 GB or higher
- Storage: 500 GB hard disk or SSD
- Internet connection for data access and model training
- Web browser for accessing the user interface
9. SOFTWARE AND HARDWARE REQUIRENMENT
12. 1. Decision Tree:
- Each internal node in the decision tree represents a decision
rule based on a specific feature and its threshold value.
- For example: if Transaction Amount <= $100, go to the left
child node; otherwise, go to the right child node.
2. Logistic Regression:
- Logistic Function (Sigmoid): The logistic regression model
uses a sigmoid function to map the input features to a
probability value between 0 and 1.
3. Random Forest:
- Ensemble of Decision Trees: Random Forest is an ensemble
method that combines multiple decision trees.
10. ALGORITHMS
15. Advantages:
1. Improved Accuracy:
Machine learning algorithms can analyse large volumes of transaction data
and identify patterns that may not be easily detectable by traditional rule-
based systems.
2. Real-Time Detection:
Machine learning models can provide real-time detection of fraud by
quickly analysing transaction data.
Limitations :
1. False Positives:
Machine learning models may occasionally classify legitimate transactions
as fraudulent.
2. Imbalanced Data:
Credit card fraud datasets are often imbalanced, with a majority of legitimate
transactions and a small number of fraudulent one.
14. ADVANTAGES, LIMITATIONS
17. - Credit card fraud detection using machine learning is an effective approach
for identifying fraudulent transactions.
- Machine learning algorithms such as decision trees, logistic regression, and
random forest can analyse transaction data and classify transactions as
fraudulent or legitimate.
- The project successfully implemented a web application using Flask and
HTML, allowing users to input transaction details and obtain fraud
predictions in real-time.
- The evaluation of the models showed promising precision, recall, and F1-
score values, indicating their ability to accurately identify fraud while
minimizing false positives.
16. CONCLUSION
18. 1. Ensemble Methods:
Investigate the effectiveness of ensemble methods, such as stacking or boosting, to
further improve the performance of the fraud detection system.
2. Deep Learning Approaches:
Explore the application of deep learning techniques, such as neural networks or
recurrent neural networks, to capture complex patterns and relationships.
3. Unsupervised Learning Techniques:
Investigate unsupervised learning methods, such as clustering algorithms or
anomaly detection, to detect fraudulent transactions without relying solely on
labelled data.
4. Explainable AI:
Enhance the interpretability of the machine learning models used for fraud
detection, enabling better understanding of the decision-making process and
providing actionable insights for fraud investigation and prevention.
17. FUTURE WORK
19. 18. REFERENCES
[1] “Fraud Detection in Credit Cards using Logistic Regression” ,
Hala Z Alenzi1, Nojood O Aljehane,
(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 11, No. 12, 2020
[2] “Credit Card Fraud Detection using Machine Learning and Data Science” ,
Aditya Saini, Swarna Deep Sarkar Shadab Ahmed ,
International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org
ISSN: 2278-0181 IJERTV8IS090031 (This work is licensed under a Creative Commons
Attribution 4.0 International License.) Issue 09 September-2019
[3] “Credit Card Fraud Detection using Machine Learning Algorithms’’,
Vaishnavi Nath Dornadulaa , Geetha Sa
- INTERNATIONAL CONFERENCE ON RECENT TRENDS IN
ADVANCED COMPUTING 2019, ICRTAC 2019
[4] “Credit Card Fraud Detection System”
Kartik Madkaikar, Manthan Nagvekar, Preity Parab, Riya Raikar, Supriya Patil,
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878
(Online), Volume-10 Issue-2
