With news articles, industry talks and business conversations full of exchange on Artificial Intelligence over the last few months, it has been hard to imagine the next major innovation being in any other area.
But as these discussions turn to worry about its direction, or the requirements of regulation on the topic, let us consider the realities of how we can best exploit the Artificial Intelligence tools available to us today.
Since its launch at the end of 2022, ChatGPT has been at the front of people’s minds. There is good reason for this: it has dramatically changed the way we perceive Artificial Intelligence and how we interact with it. With the chat-focused interface, we anthropomorphise the tool, seeing in it human-like qualities. This allows us to see it as a colleague, an adviser, and a threat to jobs at the same time. How we live alongside it, how we rely on it for critical processes, and how we restrict it are all important areas to consider, but these are academic and policy topics, not immediate business development and optimisation topics. Far better for us in the Financial Services industry to turn our attention to the myriad other tools which sit under the Artificial Intelligence and Machine Learning umbrella, and which have continued their development in the same leaps and bounds as OpenAI’s zeitgeist. With our products and businesses built fundamentally on top of data – models, analytics and sharing – we should be pivoting the focus from rushing to use large language model-driven chatbots and virtual colleagues to artificial data analysis and outcome predictions.
With Machine Learning tools built into the most popular cloud platforms used across the industry, we have cutting-edge technology sitting directly alongside our core business data sets. There are also some mature, proven technologies developed by well-known names which provide pre-configured machine learning approaches, allowing accelerated but less-bespoke learning such as fraud or anomaly detection systems, or loss quantification for large-scale events.
No matter your scale, budget and current data setup, a toolset exists today which can deliver direct business value. Perhaps you’re a large-scale group of companies with a dedicated data science team, using Azure’s Machine Learning service paired with Open Datasets and your own Azure-based data platform to analyse and predict consumer investment trends. Maybe you’re a small-scale building society using IBM Watson Studio – with the support of a data science consultancy – to identify the factors of your diverse mortgage product range which give most value to your members. Or you could be a scale-up enterprise with a product in the Financial Services market, building in Google Cloud Platform using Tensor Flow Enterprise to manage predictive scaling for your infrastructure as you build out a multi-tenanted SaaS offering for your clients.
It is often said when investing that the best time to invest was yesterday, the next best time to invest is today. Perhaps it is time we stop waiting to see what we could do with ChatGPT in the future, and instead invest in the tools which will make a real difference today.
