This document outlines a technical seminar presentation on using machine learning and deep learning algorithms for credit card fraud detection. It discusses challenges with traditional rule-based systems and proposes an approach using logistic regression, decision trees, neural networks and deep learning models. A literature review covers these techniques while a CNN model and algorithms are presented. Results show the proposed approach outperforms other methods in accuracy, precision and recall.

1. VISVESVARAYA TECHNOLOGICAL UNIVERSITY
Jnana Sangama, Machhe, Belagavi-590018
Technical Seminar Presentation On
"Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep
Learning Algorithms"
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
HKBK COLLEGE OF ENGINEERING
(Approved by AICTE & Affiliated to VTU, Belgaum)
22/1, Opp. Manyata Tech Park, Nagawara, Bangalore-560045
Email: info@hkbk.edu.in URL: www.hkbk.edu.in

2. TECHNICAL SEMINAR ON
CREDIT CARD FRAUD DETECTION USING
STATE-OF-THE-ART MACHINE LEARNING
AND DEEP LEARNING ALGORITHMS
• GUIDE
• Asst. Prof. SIMRAN PAL R
• SHIVARAJ
• 1HK19CS147
• 8 C CSE

3. ABSTRACT
• This paper reviews the latest machine learning and deep
learning algorithms for detecting credit card fraud. It discusses
the challenges and presents various approaches, such as
logistic regression, decision trees, support vector machines,
neural networks, and deep learning. The study compares the
accuracy and limitations of each algorithm and provides
recommendations for choosing the most suitable one.

4. INTRODUCTION
• This paper proposes a solution for credit card fraud detection
using advanced machine learning and deep learning techniques.
Traditional rule-based systems have limitations in detecting
evolving fraud patterns, so there is a need for more accurate
systems. The proposed solution uses logistic regression, decision
trees, random forests, SVMs, neural networks, and DNNs. It
outperforms other systems in accuracy, precision, and recall. This
offers promising insights for addressing this issue in the financial
indust

5. LITERATURE SURVEY
• The authors begin by discussing the importance of fraud detection in financial
transactions, especially with the increasing use of credit cards for online
transactions. They then present a comprehensive review of related works in this
field, highlighting the strengths and weaknesses of each approach.
• The literature survey covers various machine learning and deep learning
techniques, including logistic regression, decision trees, support vector machines,
neural networks, and deep learning models such as convolutional neural networks
and recurrent neural networks.
• The authors provide a detailed analysis of each approach, including the type of data
used, the pre-processing techniques, the feature extraction methods, and the
evaluation metrics used to measure the performance of the models. They also
discuss the challenges and limitations of each approach and propose possible
solutions.

6. METHEDOLOGY
Transaction Authorisation Process

7. CNN MODEL
POOLING LAYER

8. ALGORITHMS
CNN ALGO
BASE LINE ALGO

9. OUT COMES
1.The paper highlights the importance of credit card fraud detection and the
need for advanced algorithms to handle the growing complexity of fraud
patterns.
2.The authors review the state-of-the-art techniques for credit card fraud
detection, including traditional machine learning methods such as logistic
regression, decision trees, and random forests, as well as deep learning
algorithms such as convolutional neural networks (CNNs) and recurrent
neural networks (RNNs).
3.The paper proposes a novel approach for credit card fraud detection that
combines traditional machine learning algorithms with deep learning
techniques. The proposed approach uses feature engineering and feature
selection techniques to extract meaningful features from the transaction data,
which are then used as inputs to the machine learning and deep learning
models.

10. OUTCOMES
4. The authors evaluate the performance of the proposed approach on a real-
world dataset of credit card transactions. The results show that the proposed
approach outperforms traditional machine learning algorithms and achieves
state-of-the-art performance on the dataset.
5. Finally, the paper concludes with a discussion of the implications of the
proposed approach for credit card fraud detection and suggests avenues for
future research in the field.

11. THE COMPARATIVE ANALYSIS OF THE MACHINE
LEARNING AND DEEP LEARNING ALGORITHMS

12. CONCLUSION
• The paper "Credit Card Fraud Detection Using State-of-the-Art
Machine Learning and Deep Learning Algorithms" discusses how
machine learning and deep learning algorithms can be used to
detect credit card fraud. The paper presents various techniques
and experiments that demonstrate the effectiveness of these
algorithms in accurately detecting fraudulent transactions. The
authors highlight the importance of feature selection and
engineering in improving the performance of these algorithms.
Overall, the paper concludes that the use of these techniques can
significantly improve the detection of credit card fraud and should
be further developed to address the increasing prevalence of
such fraudulent activities.