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Artificial intelligence in financial services: opportunities, key risks (bias, hallucination, data quality, cyber risk, third-party dependence), and implications for prudential regulation, conduct, outsourcing, data protection and financial stability

Published on: 06 June 2024

Published by a LexisNexis Banking & Finance expert

Legal News

Article summary

Opportunity for financial institutions—enhanced processing and generation of data

AI systems built on foundation models—deep learning models trained on extensive data—are able to process and analyse vast, highly unstructured information, covering text, computer code, voice and images, at scale and at speed. For financial institutions, potential applications, explored further below, include more efficient risk assessment alongside capital and liquidity planning, and can also support firms in aligning products more effectively with customers.

Associated risk—data quality issues

Foundation models tend to reflect the biases and mistakes embedded in the data used for training, so the outputs they generate are likewise susceptible to bias and error. A further issue concerns data privacy: whether input prompts that may contain firm‑specific material can remain confidential, and whether there is a possibility of information leakage.

Opportunity for financial institutions—adaptability, flexibility, and scalability

AI's general‑purpose base architecture can be fine‑tuned for specialised tasks, for example by training the model on specific data, tailored to the task at hand. This substantially enhances a model’s capability in a targeted domain while preserving its generative strengths, improving performance where focus is required. Consequently, AI remains adaptable and scalable for a variety of use cases across the organisation...

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