Analyzing detection algorithms for cybersecurity in financial institutions

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.