Study of Advanced ML & DL Models for Credit Card Fraud Detection: A Comprehensive Survey on Advanced Techniques of Machine Learning and Deep Learning Approaches

Price: $87.00 – $79.98
(as of Dec 28,2024 14:36:15 UTC – Details)

Publisher ‏ : ‎ LAP LAMBERT Academic Publishing (July 5, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 180 pages
ISBN-10 ‏ : ‎ 6206180662
ISBN-13 ‏ : ‎ 978-6206180661
Item Weight ‏ : ‎ 10.1 ounces
Dimensions ‏ : ‎ 5.91 x 0.41 x 8.66 inches

Fraud detection in the financial sector is crucial for maintaining the integrity of transactions and protecting customers from unauthorized activities. With the rise of online transactions and digital payments, credit card fraud has become a prevalent issue that requires advanced technological solutions.

In recent years, machine learning (ML) and deep learning (DL) techniques have shown promising results in detecting fraudulent activities in real-time. In this comprehensive survey, we delve into the study of advanced ML and DL models for credit card fraud detection, exploring the latest techniques and approaches used by researchers and industry experts.

We will discuss the various ML algorithms such as logistic regression, decision trees, random forests, and support vector machines, as well as DL models like artificial neural networks, convolutional neural networks, and recurrent neural networks. We will also explore ensemble learning methods, anomaly detection techniques, and feature engineering strategies that have been successfully applied to credit card fraud detection.

Furthermore, we will analyze the challenges and limitations of existing models, and propose future research directions to enhance the performance and efficiency of fraud detection systems. By understanding the latest advancements in ML and DL technologies, financial institutions can better protect their customers and mitigate the risks associated with credit card fraud.

Stay tuned for our in-depth analysis of advanced ML and DL models for credit card fraud detection, and learn how these cutting-edge techniques can revolutionize the way we combat financial fraud in the digital age.
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