Analyzing detection algorithms for cybersecurity in financial institutions

Balaji Dhashanamoorthi *

Individual Researcher, Chennai, India.
 
Research Article
International Journal of Science and Research Archive, 2024, 11(02), 558–568.
Article DOI: 10.30574/ijsra.2024.11.2.0478
Publication history: 
Received on 08 February 2024; revised on 16 March 2024; accepted on 19 March 2024
 
Abstract: 
Frauds in financial services are an ever-increasing phenomenon, and cybercrime generates multimillion revenues. Even a small improvement in fraud detection rates would lead to significant savings. Traditional rule-based systems have limitations in blocking potentially fraudulent transactions. This chapter explores how machine learning, specifically supervised and unsupervised learning, can address these limitations more effectively.
We present a novel AI-based fraud detection system that combines supervised and unsupervised models. In the batch layer, transaction data undergoes pre-processing and model training, while the stream layer handles real-time fraud detection based on new input transaction data. The architecture automates fraud detection processes, making it a valuable tool for supporting fraud analysts.
This research aims to enhance cybersecurity in financial institutes by leveraging the power of AI and machine learning. The integration of supervised and unsupervised models provides a robust defense against cyber faults, ensuring the safety of financial transactions.
 
Keywords: 
Artificial intelligence; Fraud detection; Real-time analysis; Machine learning; Automation; Supervised learning; Unsupervised learning
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0