Multi-Domain Fraud Detection System (MDFDS) Using Machine Learning and Deep Learning Techniques

Abstract

The rapid expansion of digital financial services, e-commerce platforms, and online communication systems has 
significantly increased the frequency and complexity of fraudulent activities. As transactions, user interactions, and 
data exchanges continue to shift toward digital environments, fraudsters exploit vulnerabilities across multiple 
domains using advanced techniques. Traditional fraud detection systems, which rely mainly on static rules or single
domain analysis, often fail to detect evolving fraud patterns in real time. Therefore, there is a growing need for 
intelligent, adaptive, and scalable solutions capable of identifying fraud across diverse application areas. 
This paper proposes a Multi-Domain Fraud Detection System (MDFDS) using Machine Learning (ML) and Deep 
Learning (DL) techniques to detect fraudulent activities efficiently. The system integrates Long Short-Term Memory 
(LSTM) networks for analysing sequential and behavioral data, Autoencoder models for anomaly detection, and 
ensemble classification algorithms such as Cat Boost and Random Forest for accurate fraud prediction. These models 
are capable of capturing temporal dependencies, hidden anomalies, and complex non-linear patterns commonly found 
in real-world fraud scenarios. 
The proposed MDFDS is designed with a modular and scalable architecture that supports real-time dashboards, 
automated alerts, and efficient high-volume data processing. Experimental evaluation indicates improved accuracy, 
precision, recall, and robustness when compared with traditional fraud detection approaches. The system offers a 
practical and extensible solution for banks, fintech companies, e-commerce platforms, and cybersecurity teams 
seeking next-generation fraud prevention technologies.