May 21, 2018 | Written by: Ron Lefferts
Categorized: AI | Banking
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Last week, I had the pleasure of hosting a panel at the SIFMA Ops conference in Phoenix. Joined by leaders from UBS, Morgan Stanley, DTCC and Price Waterhouse Cooper, we explored where the industry was with adopting AI technologies.
In what offered inside views of how leading financial institutions are prioritizing and planning AI adoption projects, the panel highlighted three trends that I’ve seen throughout the industry in my own experience:
- AI technology is coming at us quickly
We’re living through a moment of immense technological transformation, where we will see businesses completely change because of AI. In the last few years alone, we’ve seen AI advance leaps and bounds, and the technology is gaining momentum in its application. Thus, the pressure to adopt is becoming very real, especially in financial services.Banks have a track record of applying cutting edge technology before other industries, and AI is no different. While we might not be seeing major banks transform total departments or core processes just yet, we are seeing strategic projects in areas like risk and compliance, legal billing, and customer service. Banks are looking for processes to automate. They are experimenting with processes that perhaps couldn’t be automated in the past because they required manual entry or reading long form descriptions. Things like prospectuses can now be used in analysis because they can be better databased.
- Financial Institutions are taking the long view on AI projects
Despite the technology’s rapid improvement, AI roll-outs are still happening in bite-sized projects. The initial projects financial institutions are targeting are the low risk, do-no-harm processes rather than testing on core processing. Many of those projects fall under automation. While automation isn’t anything new (email auto-filing and macro), learning from complex rules and routing operations at a broader, more complicated scale, is. There are strategic, but low risk, places we know AI can show returns in the short run and still be a significant step for longer-term objectives. We typically see automation projects start with some experimentation in how AI can make one piece more efficient.The next thing we see is the processes themselves being reengineered to use AI. Once processes are infused with AI, they start to get connected to other bigger processes. The long-term view is that AI will manage the process. We’re already seeing the beginnings of AI in automation in several areas. Three areas that come to mind are regulatory compliance, Know-Your-Client (KYC) due diligence, and front-line customer service.
- AI is augmenting the workforce, not replacing it
In all of the examples of AI projects underway that I’ve seen, the objective was around making knowledge workers more effective. That’s the thrilling thing about AI – we have the opportunity to elevate insights to experts who can act on those insights. They can make better decisions, faster. We’re seeing these small AI projects expand quickly as teams become more powerful, showing bigger sales numbers, better risk management, or faster response times. The returns are clear for those banks and their employees, when they have the long-term view of AI applications.
A final thought that came out of the discussion, was the idea that AI is only as good as the data and process it is working with. As AI projects gather momentum, the spotlight is now on of the processes themselves and the cultural shift required to manage new thinking, working, and delivery. The good news: You’re not in it alone.
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