Analytical Review of Deep Learning Architectures in Financial Frameworks for Fraud Identification of Credit Cards

Abstract

The finance sector has made cybersecurity a
crucial focus due to the exponential rise in cyberattacks and
digital financial crimes. One of the most significant issues is
credit card fraud, which has grown in frequency alongside the
proliferation of both online and physical shopping. When faced
with increasingly complex fraud strategies, traditional rulebased
and anomaly detection systems might be resourceintensive
and fail to detect them. The purpose of this research is
to examine state-of-the-art deep learning (DL) methods for
efficient and precise detection of credit card fraud. Its main
focus is on deep neural architectures, including LSTM, CNN,
autoencoders, and hybrid ensemble models. These methods
demonstrate superior performance in capturing non-linear
relationships and temporal dependencies within transactional
data. The use of data preprocessing methods like Principal
Component Analysis (PCA) and Synthetic Minority
Oversampling Technique (SMOTE) helps with class imbalance
and dimensionality reduction. Experimental results validate the
effectiveness of deep learning approaches in enhancing detection
accuracy and robustness, ther