AI should augment the human and automate the mundane

We’ve all seen the massive discussions about artificial intelligence (AI) lately. Can we regulate it? What does it mean? Will we all lose our jobs? How can it be used in way that helps humans and does not destroy them?

It is a debate we had in financial markets for years. Back in the last century, neural networking threatened to replace traders and investment bankers. Today, for many in those desks, it has.

The thing is that it is also very confusing. AI is not one thing. It is many things. It’s a bit like cloud computing. Using AI or cloud is not a generic. It’s a specific. Add to this that financial markets and technologists are great at coming up with three-letter acronyms, or TLAs for short, then we confuse things even more.

TLAs are a great way to complicate and confuse markets which means that, unless you are working in this area full-time, things emerge that are confusing or difficult. CDOs, ETFs, FTP, HTML and even AI can confuse. So, let’s add another one to the queue: LLM. LLM stands for Large Language Models (LLM) which, according to Tech Target,  stands for a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarise, generate and predict new content. It is the basis of things like ChatGPT, Bard and more.

So, how are financial firms using LLMs? Well, there are probably many of examples but I’m going to pick out just two: AmEx and JPMorgan.

AmEx is rolling out pilot projects with LLM-based AI to, according to Laura Grant, VP of product development for emerging platforms and AI at AmEx Digital Labs, understand how it can help with its “3 Ps,” making a product more personalized to an individual customer, more proactive and more predictive.

Then there’s JPMorgan who are using AI for trading. In April, Jamie Dimon revealed that the company has over 300 AI use cases in production for risk, prospecting, marketing, customer experience and fraud prevention. He stated:

“AI and the raw material that feeds it, data, will be critical to our company’s future success — the importance of implementing new technologies simply cannot be overstated.”

What gets me here is that, in both cases, banks and financial institutions are using AI to enhance the customer experience, augment the human and automate the mundane. That all makes absolute sense, but how far can we push this?

If we consider flash trading, the idea is that we can push this to an extreme where machine competes against machine using time. If you want the best deal in financial services, you have to make the deal first. No-one remembers second place.

In trading, dealing and business, this becomes even more critical. If you got the right price at the right time, you’re the winner. If you came second, you’re the loser. If you won the contract, you’re the winner. If you lost, you’re the loser. You get the idea.

But, with generative AI now such a focus and change, what happens if generative AI gets you the deal first or the best price before others? Place this at a personal level. For some years, there have been automated bidding services on eBay, the auction site, that will guarantee to make your bid within a second of the item closing. Amplify this to everything, and you get markets that are no longer fair trading. They are connected by bots that bid and manage on our behalf. They are automated and structured in a way where, for those who don’t have the knowledge, cannot compete.

If you don’t know the acronyms, the technologies and the systems being used, then you shouldn’t be in these markets, because you cannot compete.

On the one hand, AI will benefit customer services, as evidenced by AmEx’s experiment. On the other, it is a competitive weapon that may create markets that are biased towards those who are inside those markets.

All in all, I always return to a common theme around AI, in that it should augment the human and automate the mundane.