Survey on CreditGuard: Machine Learning for Credit Card Fraud Detection
Volume: 13 - Issue: 01 - Date: 01-01-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU173 | Page No.: 7-12
Author: Neha Ahirwar
Co- Author: Dr Divakar Singh,Dr Kamini Maheshwar,Dr Amit Kumar Jha
Abstract:-Credit card fraud has become
a significant concern for financial institutions and cardholders worldwide,
leading to substantial financial losses and compromised personal information.
Traditional rule-based methods for detecting fraud often struggle to keep up
with the evolving tactics of fraudsters. In recent years, artificial
intelligence (AI) techniques have emerged as promising solutions for credit
card fraud detection due to their ability to learn from large datasets and
adapt to new patterns.This research presents an
innovative approach to credit card fraud detection using AI. The proposed
system leverages machine learning algorithms, specifically deep learning
models, to analyze transactional data and identify fraudulent activities in
real-time. The system utilizes a comprehensive feature set derived from
transaction attributes, including transaction amount, merchant information,
time of the transaction, and cardholder details.
To develop an accurate and
robust fraud detection system, the study employs a supervised learning
framework, training the deep learning models with a labeled dataset that
includes both fraudulent and legitimate transactions. The models are optimized
using techniques such as cross-validation and hyperparameter tuning to improve
their performance. The research also explores various deep learning
architectures, such as convolutional neural networks (CNNs) and recurrent
neural networks (RNNs), to capture complex patterns and dependencies within the
transactional data.
Key Words:-Content based image retrieval, Joint equal contribution, low level features, High level features, Color histogram
Area:-Engineering
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