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Abstract

This project focuses on developing a machine learning-based credit card fraud detection system that aims to accurately identify fraudulent transactions in real-time. It employs various classification algorithms and feature engineering techniques to analyze transaction patterns, demonstrating that ensemble methods can effectively minimize fraud-related losses for financial institutions. The system provides an automated solution to protect both consumers and businesses from financial fraud.

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13 views4 pages

Abstract

This project focuses on developing a machine learning-based credit card fraud detection system that aims to accurately identify fraudulent transactions in real-time. It employs various classification algorithms and feature engineering techniques to analyze transaction patterns, demonstrating that ensemble methods can effectively minimize fraud-related losses for financial institutions. The system provides an automated solution to protect both consumers and businesses from financial fraud.

Uploaded by

madgamingyt094
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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ABSTRACT

Credit card fraud is a major financial concern that causes significant losses to both consumers and
financial institutions worldwide. As fraudsters increasingly use sophisticated methods to exploit
vulnerabilities, it becomes imperative to develop advanced systems for detecting fraudulent
transactions in real-time. This project aims to build a robust machine learning-based credit card
fraud detection system that can identify fraudulent activities with high accuracy and low false
positive rates. Using a dataset of historical transaction data, various feature engineering techniques
are applied to extract meaningful insights. The system utilizes classification algorithms such as
Logistic Regression, Decision Trees, Random Forest, and Neural Networks to analyze transaction
patterns and detect anomalies. Additionally, techniques like data balancing, cross-validation, and
hyperparameter tuning are implemented to improve model performance. The results of the project
demonstrate that machine learning models, particularly ensemble methods, can effectively identify
fraudulent transactions, providing financial institutions with a powerful tool to minimize fraud-
related losses. The system offers an automated, real-time solution to safeguard both customers and
businesses against financial fraud.

Keyword:

Fraud detection, bank server, classification, Machine Leaning

i
LIST OF FIGURES

FIGURE DESCRIPTION PAGE


NO. NO.

1.1 COUNTRIES AND REGIONS WHERE THE CREDIT 2


CARD USAGE
1.2 SDG GOALS 6
1.3 BLOCK DIAGRAM OF PROPOSED APPROACH 21
1.4 UML DIAGRAM OF PROPOSED APPROACH 22
1.5 DATAFLOW DIAGRAM OF PROPOSED APPROACH 23
1.6 PERFORMANCE MEASURE 20% 51
1.7 PERFORMANCE CHART OF PROPOSED APPROACH 52
1.8 F1 AND PRECISION CONFIDENT CURVE 53
1.9 PREDICTIVE IMAGE OF SYSTEM 54

ii
LIST OF ABBREVIATIONS

LR Logistic Regression
RF Random Forest
NBC Naïve Bayes Classifier
DT Decision Tree
SVM Supervised Machine Learning
KNN K-Nearest Neighbors
NNA Neural Network Algorithm
ET Ensemble Technique
RNN Recurrent Neural Network
LST Long Short Term Memory

iii
LIST OF TABLES

TABLE NO. NAME OF THE TABLE PAGE NO.

1 Performance of ML models with test size 20% 51

2 Performance of ML models with the test size 80% 52

3 Comparative Analysis Table 52

iv

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