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Engineering and Technology OPEN ACCESS

Deep Learning Applications in Financial Crime Detection: AWS Solutions for Enhanced Customer Experience and Security

Vimal Pradeep Venugopal
Independent researcher, USA
DOI Crossref DOI 10.37547/tajet/Volume07Issue07-07
tajet 2025
VOL. 7 / NO. 07 JULY
FULL TEXT PP. 62-71
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VOLUME 7
ISSUE 07
YEAR 2025
PAGES 62-71
RESEARCH OVERVIEW

Abstract

This article explores the transformative role of AWS deep learning technologies in financial crime detection and prevention. It examines how advanced neural networks and cloud infrastructure enable financial institutions to overcome the limitations of traditional rule-based systems, significantly enhancing both security capabilities and customer experience. The article shows various deep learning frameworks, including CNNs, LSTMs, and GNNs, for detecting different types of financial crimes, analyzes implementation architectures on AWS, and presents a comprehensive case study demonstrating substantial improvements in fraud detection rates and operational efficiency. Additionally, the article addresses emerging trends, implementation recommendations, and regulatory considerations that will shape the future of AI-based financial crime prevention.

RESEARCH TOPICS

Keywords

Deep Learning Financial Crime Detection Cloud Infrastructure Customer Experience

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