The document discusses a credit card fraud detection system using logistic regression, highlighting the rise of e-commerce and the challenges posed by fraudulent transactions. It outlines the process of fraud detection, including data analysis techniques and the technological tools employed, such as Python and various machine learning libraries. The proposed system aims to minimize losses for financial institutions by accurately identifying fraudulent transactions, despite current limitations in available datasets for research.

1. Credit Card
Fraud Detection
By Namrata

2. Abstract
Introduction
Problem Statement
Fraud detection and its process
Technology Used
Python & ML
Modules
Dataset for Credit Card Detection
Algorithm Used
Advantages
System Requirements
Source Code/Implementation
Conclusion and future scope
Content

3. Abstract
Due to increase of fraud which results in loss of money across the globe,
several methodologies and techniques developed for detecting frauds.Fraud
detection involves analysising the activities of users in order to understand the
malicious behaviour of users.Malicious behaviour is a broad term including
delinquency,fraud, intrusion, and account defaulting. In the proposed work, we
analyze credit card fraud detection using machine learning algorithm
namely logistic regression

4. Introduction
With the emerging rise of technology today, the dependency on e-commerce and
the online payments has grown exponentially. As the credit card provides
convenience to the users but fraudds caused due to these activities causes
inconvenience. the credit card information is confidential , the bank and the other
financial enterprises doesn’t want to disclose the information about their
customers. Risk management is critical for financial enterrprises to survive in such
competing industry.In the risk management, the chances of false negative could
still be high. However, by leveraging their performance such as credit card
utilization, payment information,risks can further be managed to control
provisional loss.

5. Problem Statement
The challenge is to recognize fraudulent credit card transactions so that the
customers of credit card companies are not charged for items that they did not
purchase.

6. Fraud
Detection and
process
• Fraud detection is a topic which is
applicable to many industries
including banking and financial
sectors, insurances, government
agencies, and low enforcement and
more.Through the use of
sophisticeted use of data mining
tools, millions of transactions can be
searched to spot patterns and
detect fraudulent transactions.
• Its a process of identifying
fraudulent transaction.
• This technique used to recognize
fraudulent creddit card
transactions so that customers
are not charged for items that
they did not purchases.

7. Logistic Regression
Algorithm Used
• Logistic regression is one of the most popular Machine Learning
algorithms, which comes under the Supervised Learning technique. It is
used for predicting the categorical dependent variable using a given set of
independent variables.
• Logistic Regression is a significant machine learning algorithm because it
has the ability to provide probabilities and classify new data using
continuous and discrete datasets.

8. Technology Used
• Python
• Machine
Learning

9. • Frame the problem
• Collect the row data
• Importing Libraries
numpy, sklearn , Pandas etc.
• Process the data for analysis
1. Perform the logistic regression on data
2.Explore the data
• Perform in-depth analysis
• Communicate results of the analysis.
Modules

10. Dataset
for credit
card
fraud
detection
• The Dataset that is usd for
credit card fraud detection is
derivedd from kaggle.
• kaggle is the online
community of data scientist
and machine learners.
• Kaggle allows userrs to find
and publish data sets,explore
and build models in a web-
based data science project
and solve challenges.

11. Advantages
• The results obtained by the Logistic Regression
Algorithm is best compared to any other algorithms.
• Logistic regression is a robust ml algorithm that work
efficiently even at solving a very complex problem
with 95% accuracy.
• Logistic regression outputs well-calibrated
probabilities along with classification results.Theis is
an advantage over models that only give the final
classification as results.

12. • Software Requirements
System
Requirements
• OS: Windows or Linux
• Pythin IDE:Python 3.6
• Jupyter Notebook and related libraries
• Hardware Requirements
• Processor : i3
• Hardware : 5GB
• Memory : 1GB

13. Implementatio
n

16. Conclusion & Future Scope
• Testing Accuracy= 94.26%
• Fraud Detection system have become essential for banks and financial
institution to minimize their losses.
• However ,there is a lack of published literature on credit card fraud detection
techniques,due to the unavailable credit card transaction dataset for
researches.
• We designed a system to detect fraud in credit and transcation. This system is
capable of providing most of the essential features required to detect
fraudulent and legitimate transactions.
• the dataset available on the day to day processing may become outdated, it
is necessary to have updated data for effective fraud behavior identification

17. Thank You!!