Arnaud Misset – Chief Digital Officer at CACEIS looks at the use of AI in the asset servicing industry covering AI hype and fears to operational efficiency and staff considerations. This exciting new technology might revolutionise the industry, or it could just help increase efficiency – this article seeks to explain the practical realities of leveraging this extraordinary technology.
AI, and especially its close cousin - machine learning, has long been implemented in specific areas of the finance world. It has helped the industry gain operational efficiencies, provide simple chatbots, and sort through vast collections of data to identify trends, report on performance and uncover fraud. However, today, AI is once again right at the centre of the hype machine, with Generative AI and GPT as the new tech buzzwords. Generative artificial intelligence consists of algorithms that can be used to create new content, and a Generative pre-trained transformer (GPT) is a type of model that specialises in generating text. Hype usually leads to over-promising and under-delivering so it is essential to look in depth at the technology’s real-world capabilities and identify practical use-cases. Although the possibilities of generative AI seem endless, our industry, with its reliance on procedural accuracy, speed of operation and huge transaction volumes may well be more suited to traditional or discriminative AI than to generative AI. The key component of both traditional and generative AI is without doubt the datasets it has access to. The now classic “garbage in – garbage out” concept remains entirely valid in the age of AI, where stand-alone off-the-shelf algorithms require quality data in a quantity that allows for accurate training and assessment. Good data governance, boring as that may sound, therefore remains at the heart of the entire headline-grabbing AI revolution.
All companies in our industry will have access to the same standard ‘blank-slate’ AI algorithms promoted by technology companies. This means that the differentiating factor for companies looking to leverage AI tech is less in the algorithm acquired and more related to available datasets, training methods and mastery of prompts or queries. However, there is a huge range of available AI algos, so the selection process is very complex especially as each one needs to be rigorously tested for specific tasks using a large set of quality training data. This could be very time consuming and may [...]
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