Every day there are more and more headlines about artificial intelligence (AI) AI and its impact on life, the world and the future. The latest one is a good one, which is AI is creating more jobs than replacing them!
This comes from the World Economic Forum, whose latest report forecasts that, over the next five years, advances in AI will create 170 million jobs while replacing 92 million. The question is: what jobs?
Well, they’re all focused around AI-driven data analysis, networking and cybersecurity and technological literacy. Equally other evolving technologies, like the rise of robots and autonomous systems, highlight the increasing demand for programming expertise and adaptability to automated technologies. Across industries, to remain competitive, employers must seek talent with core technological proficiencies to integrate and collaborate with these evolving systems. Meanwhile, the old jobs of working in a shop or being a lorry driver are the ones disappearing.
More importantly however is that AI processing will transform 86% of businesses, and financial services is one of the top targets. But what transformation?
Well, it was articulated well in this article by Deployyyer, who supplemented my business model of a digital bank based upon apps, APIs and AI with their DeFAI structure (Decentralised Financial Artificial Intelligence).
So, we have a back-office that is based upon distributed ledgers and data analytics linking to our front office internet of things through a middle office leveraging Agentic AI and APIs. It supplements my original contention of front office apps linking to middle office APIs to back office AI with AI being everywhere.
Something like that anyway and yes, that’s pretty transformational.
This is why leading banks are investing heavily in AI, because it offers opportunities to massively increase efficiency and reduce cost.
Two examples: JPMorgan and Bank of America.
Teresa Heitsenrether, JPMorgan’s Chief Data and Analytics Officer, oversees the bank’s AI strategy. She was interviewed recently The Wall Street Journal, and discussed how AI is being developed to be at the centre of how its 300,000 employees work. They have an LLM (Large Language Model)* suite of tools to do this, and the interview with Teresa is strongly recommended reading. For example, here’s just a teaser:
Are you seeing tangible productivity gains across the bank from AI?
It’s very early innings. First we want to put the tool in people’s hands, and let them be able to ask questions and get answers. That already starts to spawn ideas, innovation, some productivity.
The second phase is where you take the models and start to plug them into JPMorgan: our policies, our procedures, what we know about clients. Now you can have the model working on knowledge that’s specific to us.
The third horizon, and we’re not there yet, is for the models to be able to do more reasoning. What happens is they get a chance to think, OK, based on the complexity of the problem that you’re asking me, let me think in the same way a human being would approach it. It lets the model find the resources it needs. Maybe it’s going to go to the internet or some system outside JPMorgan’s databases. You can effectively take the workflow of somebody who’s an investment-banking analyst or client-service person and teach the models the steps they would take to get their jobs done.
We will always have a human in the loop to check the models’ work. I don’t think we would, certainly not at this juncture, let these things be autonomous.
I really enjoyed the interview as she outlines how AI will transform JPMorgan from streamlining the preparation of client briefing materials to analysing legal documents; from enhancing call centre operations to supporting investment banking analysis.
Bear in mind that the bank spends around $17 billion on technology, and you’re looking at around $5 billion of that going into smart intelligence, this is transformational.
Similarly, Bank of America is investing $4 billion this year – a third of its $12 billion technology budget – because they learned a lot from their customer-facing AI agent Erica, an AI chatbot for customer services, which they introduced in 2018. Since then they’ve built upon this, and say that they have seen the following gains across its business units:
- Erica for Employees, an internal AI chatbot built on the customer-facing Erica, is being used by more than 90% of BofA’s 213,000 employees. This has reduced IT support calls by more than 50%.
- Bank of America developers using a generative AI-based coding assistant saw efficiency gains of 20%
- Employees save tens of thousands of hours per year by using AI to prepare materials for business client meetings, which they redirect toward client engagement.
- Customer service representatives use an AI tool to deliver a more personalized interaction with clients, reducing call handling times.
- Sales and trading teams use a generative AI platform developed internally to search and summarize BofA research and market commentary “more quickly and efficiently.
Equally, these lessons applies to central banks, as illustrated by this fascinating interview between JPMorgan and the Bank of England. Eloise Goulder, Head of J.P. Morgan’s Data Assets and Alpha Group and James Benford, Chief Data Officer at the Bank of England.
I particularly enjoyed James’s anecdote about how the weather vane on the Bank’s roof was used to determine monetary supply in the 1800’s. He called that a real-time use of information 200 years ago, but today it is far more sophisticated.
We have been using machine learning at the Bank for at least a decade and it’s grown many different applications actually. There’s a core one in our data collections work to spot errors in the data and make sure that the data is of high quality, critically important. You can also use machine learning to now cast where the economy is, to help predict coming movements in financial market prices. That’s a big use case for us. It’s great for measuring, quantifying risk to machine learning techniques. We’ve also managed to, in some cases, blend the old and the new. So there is one neural net application that estimates the slope of the Philips curve, which is the relationship between unemployment and inflation, which was notoriously hard to estimate.
You can listen to or read the transcript of the whole interview here: https://www.jpmorgan.com/insights/podcast-hub/market-matters/bank-of-england-ai-and-data
Finally, Euromoney published a great report this week on the use cases for AI in banking. Based upon more than 30 in-depth conversations with those in charge of implementing gen and agentic AI at top global banks, and in many tech-leading national banks, the report is well worth a read.
The bottom-line is that if you’re not investing in innovative AI, what are you doing?
* LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. For more background, click here https://en.wikipedia.org/wiki/Large_language_model