Harnessing Large Language Models in Banking: Banking Innovation with Operational and Security Risks
Senior Data Scientist, SAB Bank - Innovation Department.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 369–377.
Article DOI: 10.30574/wjaets.2024.13.1.0426
Publication history:
Received on 09 August 2024; revised on 17 September 2024; accepted on 19 September 2024
Abstract:
The banking industry is becoming more inclined towards LLM for innovation in the services, better and more efficient operations, and customized services. I possess fantastic skills in utilizing technology in multiple areas related to customer support, fraud identification, and financial actions. Nevertheless, deploying these artificial intelligence systems has implications for operations and security, for instance, model interpretability, model bias, data privacy, and susceptibility to cyber-attacks. In addition, there are aspects of compliance with data protection laws and the fairness of AI decisions. This article describes the advances from LLMs in banking, discusses their inherent risks, and outlines sound application strategies. Thus, by following and sustaining proper data management, constant supervising, and employing the human-AI tandem working methodology, the banks can avail themselves of the opportunities LLMs offer without fearing the possible risks and avoiding ethical and legal norm violations.
Keywords:
Large language models (LLMs); Banking innovation; artificial intelligence (AI); Operational risks; Security risks; Data privacy
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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