October 17, 2019
By Roman Regelman
As organizations rush to embrace artificial intelligence (AI), are they approaching it the right way?
Banks traditionally see AI as this thing we put over here to fix some operational issue, address a specific use case or save money.
On the flip side, AI zealots think it will take over the world and require no humans.
They’re both wrong.
The future is not AI alone. It’s about unlocking the best of both robots and humans and using them in combination. We call that AI + HI: artificial intelligence plus human intelligence.
There are things computers can do better than humans – and vice versa. It’s not man against machine but both working together. When they do so, an organization achieves the most lasting, impactful performance improvement and creates disruptive innovation.
Within financial services, there are two main applications of artificial intelligence:
At BNY Mellon, AI is not a sideshow; it’s embedded in our approach to digital. We are accelerating our uses of emerging technologies such as robotic processing automation, machine learning and artificial intelligence.
And we are not just experimenting with AI. We were one of the early adopters of robotic processing automation.
We have hundreds of robots in production that are performing repetitive tasks and removing the risks associated with manual entry. That allows for faster processing, gives us data that we can harness and frees people to focus on higher-value work.
Let me share a few examples:
We saw an opportunity to apply natural language processing to identify the intent of these emails so they can be addressed promptly. The machine learning solution determines what a particular inquiry is about, whether trade settlement, or cash, or a corporate action, or taxes, etc. It does that with 90% accuracy for the most common inquiries that represent more than half of all inquiries we receive all year.
To go even further, we are exploring cases where the machine can resolve a routine inquiry itself.
All that sounds like better, faster, cheaper. But there’s more to it than that. It’s allowing our people to spend more of their time doing what they’re hired to do – the work that is interesting, meaningful and moves the needle for our clients.
The application of machine learning in this case also gives us data that we can use to address root causes and reduce the need for those inquiries in the first place.
Let me take it one step further – we can also use AI to perform sentiment analysis. Humans have a couple limitations: they don’t have the ability to process thousands of interactions nearly as quickly as machines and their own biases act as filters. The sentiment data from AI can identify the areas that are frustrating to our clients, and our people can use that insight to take informed action. Together AI + HI can improve the client experience.
As use of AI and data analytics in financial services has becomes more prevalent, it has set off a debate about how these technologies are used. As an organization, BNY Mellon is committed to ensuring that we always do the right thing for our clients, our regulators as well as keep our responsibilities to the wider financial system by virtue of the unique role we play.
We are working with the regulators on this front. Specifically, late last year, the Monetary Authority of Singapore published the Fairness, Ethics, Accountability and Transparency (F.E.A.T) principles to promote high standards within the use of Artificial Intelligence and Data Analytics in Financial Services.
We are joining a group of like-minded companies to validate the F.E.A.T principles systematically, as well as developing tools that institutions can use to validate their models against these principles in a standardized manner. We are specifically focused on anti-money laundering and countering financing of terrorism as well as regulatory reporting.
Second, we’ve thoughtfully constructed a diverse digital team – diversity is one of the lenses we apply in hiring. When you have a highly diverse team, you can expect that their awareness of bias is higher than that of a non-diverse team, which helps avoid unintentionally building bias into algorithms – in addition to all the other advantages of diversity in driving innovation.
We’re just scratching the surface of the potential uses of AI.
By virtue of where BNY Mellon is in the industry, we have tons of data. AI allows us to sit on top of that and harness insights to not only service clients better but also turn those insights into decision-making tools for our clients and the global industry.
The possibilities – for us and our clients – are amazing.