The potential for artificial intelligence to address challenges faced by custodian banks

Hsien-Hui Tong and Martin Lim
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Hsien-Hui Tong: SGInnovate, Singapore
Martin Lim: Investment Analyst Intern, SGInnovate, Singapore

Journal of Securities Operations & Custody, 2024, vol. 16, issue 4, 328-343

Abstract: The pace of technological advancement over the last three decades has led to a slew of new companies adopting the latest technologies and marrying them to innovative new business models to threaten more traditional businesses. Start-ups such as Google, Meta and Amazon, to name a few, have revolutionised the way consumers engage with service providers, consume information and purchase goods. Fintech start-ups have also threatened to change the way financial services are provided, albeit with varying degrees of success due to barriers such as consumer trust in new brands, regulatory compliance and the financial strength of banks to build those same services internally. There is no denying, however, that custodian banks today face many challenges that are slowly eroding margins. Regulators are demanding shorter settlement times, clients are demanding greater control over their accounts, staff costs are rising and cyber security threats are increasing. This paper seeks to highlight some of the threats the industry is facing while exploring the role that artificial intelligence (AI) may be able to play in addressing some of these challenges. It offers a broad overview of not just areas of application but also weaknesses of the technology that the bank needs to be aware of and also possible issues with implementation. It also seeks to highlight the fact that AI is not a single technology, unlike distributed ledger systems. There are many nuances to AI, such as convolution neural networks, natural language processing and generative AI, and the judicious application of the right nuance of AI to the problem will be key to a successful implementation.

Keywords: artificial intelligence; AI; large language models; LLM; natural language processing; NLP; generative AI; GenAI (search for similar items in EconPapers)
JEL-codes: E5 G2 K22 (search for similar items in EconPapers)
Date: 2024
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