Just one look at your smartphone is all it takes to remind you that digitization and automation in financial services is nothing new. But the recent heightened interest in artificial intelligence (AI) and banking is. (Go ahead. Ask Alexa.)
The Financial Brand’s Jim Marous chalks it up to technological advances and real business needs:
“The explosive growth of structured and unstructured data, availability of new technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations”—all of these factors, he says—“have created a ‘perfect storm’ for the expanded use of artificial intelligence in financial services.”
For many financial industry leaders, understanding how AI can be incorporated into their business operations can be a storm in and of itself.
With this in mind, we reached out to industry thought leaders in advance of October's BAI Beacon financial services conference, where AI is going to be a huge topic of conversation in a slate of Innovation & FinTech sessions as well as during a pre-conference Learning Lab, and asked: "What can banks and financial institutions (FIs) do right now to get started with AI in their business?" Here’s what they had to say.
Clean up your data
Noted FinTech author Chris Skinner suggests that AI and machine learning means banks must analyze and leverage customer data into actionable knowledge—but that’s virtually impossible if your data is in disarray.
Banks may have data systems through merger and acquisition that is fragmented; data tends to sit in multiple applications across many different machines.
“The first thing you have to do is cleanse your data to be ready for AI,” Skinner says. “For some, this can take five years or more—so you’d better start now before your competitors start stealing your customers by analyzing their data better than you.”
Quick wins with Natural Language Generation
“One of the most straightforward and underutilized areas of banking AI is Natural Language Generation (NLG), says Celent’s Dan Latimore. He defines this as “the ability to take large amounts of data and generate prose that describes the salient aspects.”
Latimore cites two examples that illustrate NLG’s power:
- A quarterly sales report prepared for the CFO is typically compiled manually, taking several “analyst weeks” and might still have human errors. After training, NLG can ingest structured sales data and create a first draft report, saving the analyst many days and improving accuracy.
- A bank ingests the inflows and outflows from a client’s accounts and prepares a prose summary of the highlights, perhaps with a graph summarizing the results. Today, this is impractical and expensive, but NLG can reduce those costs and improve the customer experience for a segment of the bank’s clients.
‘Augmented intelligence’ solutions
AI is increasingly becoming the way for leading financial services to provide everything from customer service to investment advice, says PwC’s Mike Quindazzi. Yet, few banking industry CEOs are considering the impact of AI on future skills, despite the impact that AI is already having on trading desks and reshaping customer interactions.
“Protecting the base and avoiding risks is clear and present in the minds of banking leaders,” says Quindazzi. “Many challenges persist due to bias, privacy, trust, lack of trained staff, and regulatory concerns. In the near term, ‘augmented intelligence’ solutions, in which machines assist humans, are quickly making their way into operation.”
Put customers at the center of your AI strategy
'AI or die!' seems to be the rallying cry at every banking conference these days, according to Bradley Leimer, of Explorer Advisory and Capital.
“But before going down the path of building and implementing solutions leveraging AI and similar tools, financial institutions must ask themselves where they’re falling short in regard to providing their customers true lifetime value around their finances,” Leimer adds.
By more deeply understanding where your financial services fail to meet client expectations, the easier it is to plot out how partnering, building, or investing in AI technologies will succeed.
Knowing your customer better than ever
Burnmark’s Devie Mohan observes that many banks already use chatbots and branch-bots for customer service, as well as machine learning and cognitive computing, for credit scoring, lending, personal financial management (PFM) and fraud prevention.
“However, the real use cases come when AI is used to understand the emotional and psychological behavior of the customer,” Mohan says.
Citing recent projects, such as Lloyds Bank partnering with US-based AI startup Pindrop to detect fraudulent phone (or Skype) calls, Mohan adds: “This is just the beginning of the true potential of AI and I believe we will see several use cases where every customer is mapped on behavior, body language, and emotions. We may see KYC then changing in ways we cannot fathom.”
Personalizing products and service
“While some AI applications may be used in the back office, financial institutions that don’t start using AI to deliver value to the customer will face the risk of [replacement] by FinTech players,” says Eran Livneh of Personetics.
Livneh identifies three use cases that can have an immediate impact:
- Helping customers get better service from their FIs;
- Improving day-to-day financial management and removing friction; and
- Helping customers achieve their financial goals.
“Most financial institutions have enough data to start in these three areas by using AI and predictive analytics,” Livneh concludes.
Chatbots have clear ROI
According to Duena Blomstrom, of Temenos MarketPlace, AI has immediate applications—for instance, chatbots—that enable clear ROI for the bank, even if in the form of cost cutting via call center implementation.
Blomstrom adds: “The real, long-term value of AI is going to be around new customer propositions that may challenge existing business models for some banks still in the relationship game. But for now, what I find encouraging as a platform curator, is that there seems to be enough willingness to experiment and grow with the technology.”
Opportunities on commercial side of business
Ron Shevlin of Cornerstone Advisors suggests banks dive in and learn about AI: what it is and isn't, what types of use cases it's good for and how those use cases map to the problems and opportunities facing the bank.
“Integrating AI-based solutions into the complexities of the existing IT architecture will be time-consuming and expensive, so the focus of AI-related experiments should be on areas of the business where it can have a measurable economic impact,” he says, adding that for many banks, Shevlin says, the AI opportunity may be on the commercial, not retail, side of the business.
Start the learning process ... yesterday
Sam Maule, Managing Partner, North America, at 11FS, says the same is true of AI and the impact to society and banking at large.
"My advice is to start immediately," Maule says. "Where to start? Try Google. There are literally tens of lists of AI thought leaders to follow on different social channels, plenty of videos to watch on YouTube, and a plethora of great books to read. The key is to start by educating yourself and your team, before you jump into the deep end.”
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Jay Palter is the chief engagement officer at Jay Palter Social Advisory, based in Calgary.