Supervisor
Kashif Qureshi
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Financial fraud poses a growing global challenge, driven by the rapid expansion of digital banking, e-commerce, and mobile payments. Traditional rule-based and early machine learning systems struggle to detect novel and sophisticated fraud patterns in real time. This research investigates the integration of deep learning, explainable artificial intelligence (XAI), and big data technologies to enhance financial fraud detection. A scalable data pipeline is proposed to process large volumes of transactional data, improve detection accuracy, and provide interpretable insights for stakeholders. The study highlights the potential of combining advanced AI techniques with explainability to strengthen the transparency, effectiveness, and trustworthiness of fraud detection systems.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Yang, A.
(2025) Enhancing Financial Fraud Detection Using Explainable Deep Learning Models on Simulated Big Data Architectures: A Comparative Analysis with Traditional Methods. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.95