The term Artificial Intelligence was coined 70 years ago as the stuff of fantasy fiction and about 50 years post that nothing much moved. Then, in 1997 like a bolt from the blue, IBM’s Deep Blue defeated world chess champion Garry Kasparov 4-2 in a six game series. Since then, machines have beaten humans at far more complex games – Go, Poker, Dota 2.
What changed? Two things.
Computing power grew over a trillion times in the last 50 years. Can you name any industry/trend that has evolved by this order of magnitude? The computer that helped navigate Apollo 11’s moon landing had the power of two Nintendo consoles. You have a lot more power in your smartphone today.
Data availability exploded. Any AI we build is only as good as the data we give it. In the last two years, humanity has created 90% of the data that has ever existed. One of the reasons an Open AI can be built today is this availability of data and the tools to crunch the same. We are now on our way to full circle – 40% of internet data this year will be machine generated.
So, what does this all mean? Let’s zoom into financial services.
Digital transformation has been a buzzword for banks for decades now. This is on-going and inevitable. We have been “transforming” for the last 100 years, and this remains true today. While in the past it was moving from paper to calculators to computers, today it will be moving to machine learning and AI.
AI/ML essentially looks to extract meaningful, actionable insights from raw data. Further, they are continuous learners and completely unemotional decision makers – a very powerful combination. Financial services is a data-rich sector and is therefore ripe for disruption.
Understanding consumer behaviour
This is one of the low hanging fruits of new age tech as there is enough structured data through a customer lifecycle. At the front end, tech is changing how products are distributed, as more customers start buying and paying for financial products online (just like they buy a t-shirt online now) – this is true for payments, loans, credit cards, insurance, mutual funds and stocks. AI and machine learning are making the engines that learn your online financial behaviour smarter.
Back end functions
At the back end these can include credit decisions, risk decisions, portfolio management, compliance, fraud prevention, security, process automation, insurance premia, etc. Each of these are non-trivial problems that multiple startups are tackling individually.
The Times They Are A-Changin’
I think we need to understand that AI is a tool, just like electricity. And like electricity, we must design the problems and use it to come up with the solutions. For example, with investing, we can use it to cover human blind spots of bias and emotion. It can overhaul our cost structures, investing processes and generally deliver a better, more efficient product for customers.
Therefore, companies that have been making and selling us financial products are all being disrupted by neo banks, new age lenders, online-first brokers, tech-based investment products. As more companies become data-driven, and more users interact digitally with financial institutions, it becomes a virtuous cycle which feeds itself.
Products built in deep tech are simply more efficient, accurate, safe, and respond to a customer better than the old way of doing things. What’s more, reducing people-dependency, lowers costs, human error and improves productivity.
As with adoption of any technology, there are many issues to tackle – robustness of the models, data quality, privacy issues, availability of talent and HR mindset change.
But at the end of the day, it’s important to remember that this is not a push problem, but a pull one – we are all moving into an AI world, whether we like it or not. Either we adapt, or we perish.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)