A Strategic Framework for Leveraging AI in the Protection of Financial Transactions against Cyber Threats

Abstract

The increase in the number of cases involving cyber threats against financial operations necessitates new ideas for protection. This research aims at using AI to defend financial systems against complex cyber threats and identifies a comparison of machine learning algorithms and NLP with conventional method approaches. The research compares the current performance indicators with ideal values for false negative/positive ratios as well as the number of false positives and identifies AI-based systems or transformer models, such as BERT with a detection accuracy of 97.8 percent and a low number of all sorts of false positives and negatives. Furthermore, the behavior of Autoencoders, unsupervised models, in anomaly detection is studied while the efficacy of the NLP techniques for the detection of phishing messages is also determined to be 92.8%. Still, the study also pinpoints limitations that relate to computational expense and optimization, ethics, and applicability of the developed systems to institutions of lower complexity. Thus, this paper will establish a strategic approach to deploying AI in financial Cyber security that is flexible, respects the regulatory framework and is ethical. The presented results present insights for financial institutions on how to strengthen Cyber security to face ever-changing threats, as well as contemplate practical and ethical issues related to AI implementation.