Outlier Briefing: The emerging use cases for AI in financial services with Emerj’s Dan Faggella

Welcome to Tearsheet’s Outlier Podcast. This subscriber-only podcast is exclusive for our Outlier members. We go deeper with subject matter experts, to take actionable steps that can impact your business and market. Today’s guest is Dan Faggella, a global expert on artificial intelligence and its use cases for financial services. Dan’s the founder and head of research at Emerj, an AI research and advisory company. Dan takes a practical approach when it comes to discussing AI’s potential in financial services. The adoption challenges for bank take up of AI are real — Dan polled them and has built a framework around ROI on investments in artificial intelligence and how to overcome implementation hurdles. We talk about concrete use cases for AI in the front office and where we’re headed when it comes to cross-selling or customer acquisition. You can read Dan’s take on the critical capabilities — the prerequisites — needed for AI deployment.

Subscribe: Apple Podcasts I SoundCloud I Spotify I Google Podcasts The following excerpts were edited for clarity.

Takeaways

• The financial industry’s dance with AI: “Artificial intelligence has a somewhat storied history in financial services. Before Google and Facebook and everyone moved to Silicon Valley when they got an awesome AI degree from MIT or Carnegie Mellon, they all moved to New York. That was the original place where AI talent would cluster after they graduated.”
• ROI on AI: “[When we work with a client], we look at two main things. The first is the evidence of ROI. We do hard work, primary research to figure out the hard return on the investment in these technologies. The second is which of these technologies are inevitable? That’s to say the AI researchers, the startups, the buyers of tech in banking are all in agreement that this is a no-turning-back, going-to-change-the-way-we-do-business trend. There are plenty of those in finance but we’re in very early days.”
• Clearing the AI hurdles: “There is a lot to get AI off the ground. Among those ingredients is some semblance of in-house, data science talent. The thought that we can be ignorant of the potential and challenges of AI and a vendor could come in and make AI work — plug AI in — it’s an exceedingly naive notion.”
• AI versus automation: “One of our research advisors is a former head of AI at HSBC. He looks at where IT and automation budgets are headed. Should AI be included under automation? Probably not, but it often gets classed under automation.”
• Bank culture: “After 2008, banks are risk-focused. With the fear of regulatory and compliance, there’s an emphasis on playing defense and playing it safe. So, where attention goes, energy flows. Compliance and fraud will get budget before nifty chatbots.”
• AI skills: “There are two skillsets. One is the skillsets is getting artificial intelligence to produce a result reliably. You can learn this in school. You go to Stanford or Carnegie Mellon, you get a degree. You get enough data and context with business people, you can train an algorithm to give you an output that’s reasonably effective. Enterprise search, for example. If this utterly fails 20 percent of the time, it’s fine for applications we use inside the bank.”
• Customer service apps that use AI: “For apps that directly interface with customers, we now need something much less than an 80 percent working rate. What this implies is that we need really snuggly bounded reality as to what kinds of tickets and inquiries we can handle with this algorithm and which ones we can’t.”
• AI talent in banking: “We need really fast methods of routing these messages to a human being as soon as we get to the end of the rope of what our AI program is capable doing. This flywheel of skills is much more rare than having a PhD from an Ivy League school.”

Listen /read the whole briefing

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