One of the paradoxes of banking’s enthusiasm for generative artificial intelligence (gen AI) is that the technology has excelled more in words than numbers.
The ChatGPT model that triggered all the excitement around gen AI two years ago was notoriously bad at maths. OpenAI has sought to improve this in its latest iterations. But the ability of large language models such as ChatGPT to reliably do complex calculations remains in question.
This has not stopped bankers from embracing gen AI for tasks such as summarising documents or sifting through internal policies and procedures – mainly internally, but with some attempts now to make these systems customer-facing. Banks are also getting gen AI models to write marketing content and to help with coding, among other relatively low-risk use cases.
Yet, most banking data is numerical: securities trading, payments, risk metrics and so on. At Royal Bank of Canada (RBC), for example, 80% of the data on its servers is not in language form. The ability of language-based models alone to get to the financial heart of banking, and add value to it, is consequently limited.
