Late last year, I read an article in the Financial Times that said there were three possible reasons for the turmoil in the worldwide banking industry: a blip induced by excessive regulation, a return to normal after an exceptional pre-crisis boom, or the slow death of banking.
Whether you believe it’s one of these or something else altogether, it’s clear that the industry is not performing as well as it – or its investors – would like.
Sustained low interest rates and the ongoing operational costs – in particular the cost of compliance – are compressing profit margins. In response, most banks are cutting costs and expenses aggressively. But they’re doing it tactically, knowing all the while they cannot save their way to prosperity.
At the same time, commoditization is hitting both commercial and retail banks. Revenue and profits are stagnating while investment in fintechs is surging.
These findings are part of a new IBM Institute for Business Value (IBV) comprehensive study that will be released on September 20, the week before Sibos begins in Geneva. The study, called “The Cognitive Bank,” is based on interviewing and surveying more than 2,000 banking and financial industry C-suite executives across five continents.
The cognitive bank is built on cognitive computing, which IBM popularized in 2011 when its Watson computer defeated the top human contestants on the televised quiz show, Jeopardy. (To learn about Watson, click here.)
A cognitive computing system is based on four key principles:
- It learns and improves with each new piece of information
- It builds speed and scale by using machine learning to handle complex, repetitive tasks
- It uses subject matter experts to collate and curate human intelligence, for rapid reuse or decision support
- It interacts using natural language, context and reason
Cognitive computing – applied to banking – solves a lot of problems.
At the highest level, it allows banks to switch from tactical cost-cutting to begin operating at what I like to call a strategically lower cost.
Perhaps most importantly, cognitive computing finally will help banks exploit their biggest advantage against upstart competitors: data. Banks are awash in data but are only just beginning to use it and recognize its power to drive a competitive edge.
Imagine a system that understands your entire global client base individually, has complete knowledge of both existing and proposed banking regulations across continents, countries, states and provinces, and keeps perfect records for 100 percent compliance. Almost sounds too good to be true, doesn’t it?
Cognitive computing also can help expand a bank’s reach. The difficulty in extending wealth management services for only the ultra-rich out to the mass affluent typically has been one of scale. In India alone there probably are a quarter of a million people who would enjoy wealth management services, but don’t qualify for financial advisors.
To address this, the cognitive bank can use machine learning to review millions of data points, including the client’s threshold for risk, investment horizon and other unique attributes to create a personalized portfolio recommendation and other automated services. Potentially, it could even use the client’s public social media record to learn more about his or her personal preferences and experiences.
This is what I mean by operating at a strategically lower cost. And it’s a much needed influx of innovation.
Ultimately, the cognitive bank is about generating wealth, providing liquidity and protecting assets.
Not surprisingly, the study finds that the highest performing banks already are on their journey towards becoming cognitive banks. Yet only about a quarter of bankers are familiar with cognitive banking and fewer than one in five believe their organization is prepared to embrace it.
My team and I look forward to seeing you at Sibos to talk about the new IBV study, how the cognitive bank is being deployed and how you can prepare for it. To learn about more about IBM activities at Sibos, visit ibm.com/Sibos.
