Artificial Intelligence in Banking

January 11, 2017 Maria Abbe

On Jan. 31, PrecisionLender will host a webinar with Justin LaFayette, managing partner at Georgian Partners, “Artificial Intelligence in Banking.” This webinar is part of our BankOnPurpose webinar series, in which we give a sneak peek into some of the topics that will be covered at the BankOnPurpose Conference, May 3-5, in Austin, TX.

justinlafayette

Justin LaFayette, managing partner at Georgian Partners

As we began to put more time and resources into Artificial Intelligence and analytics, we were introduced to Georgian Partners. Their team helped us take stock of the data and knowledge we had and explore new ways
to deliver value to our customers. It quickly became apparent that this was a relationship worth strengthening. So Georgian invested in PrecisionLender, Justin joined our board and an exciting partnership was forged.

In this webinar, Justin will share his deep knowledge of advanced analytics and Artificial Intelligence, and how they will shape the banking industry going forward. He’ll show how Artificial Intelligence can provide actionable feedback to better serve your customers and your bank and how it will allow you to spend more time focused on your customers and less time focused on the numbers.

You can sign up for the webinar here: http://ww1.precisionlender.com/bankonpurpose-webinar/.

Q&A With Justin LaFayette at Georgian Partners and Carl Ryden, CEO at PrecisionLender

We had the pleasure of sitting down with Justin back in October during this podcast to talk about ways banks can use Artificial Intelligence to cut costs, save time, and improve decision-making and efficiency. You can click on that link to listen to the full discussion between Justin and Carl, but here are a few of our favorite excerpts from the conversation.

Q: Can you give us general explanations of what we mean about when we talk about  Artificial Intelligence and Machine Learning in banking?

Carl: The real idea behind Artificial Intelligence is taking the vast amounts of data we have on how loans and opportunities are priced, and how relationships evolve, and delivering back actionable, valuable information to our customers in ways that help them better serve their customers. I really like the term Automated Intelligence or Augmented Intelligence or other things like that. I think that’s a better description of what our customers experience within PrecisionLender right now.

What we’re going to do next is find ways to deliver more actionable intelligence right at the moment where it can have the biggest impact.

Justin: One of the ways we like to think about Artificial Intelligence is that there’s a spectrum of sophistication. Sometimes, people use Artificial Intelligence to describe things where it seems intelligent, like alerts and people being notified of things. But the really interesting things start to happen when all that data can be turned into action that no person was going to take on their own, and then automated into large transaction volumes. That’s where real value can get created.

Q: Banking often has a reputation, sometimes undeserved, of being a little slow to adopt new technologies. Do you have a sense of what the attitude of the banking industry is towards Artificial Intelligence and Machine Learning?

Carl: The idea of machine learning and predictive analytics – being able to predict what’s going to happen based on the data that’s in front of you – isn’t new in the banking industry. A lot of the best banks have been doing this for a long time already. And it’s really a part of the credit process in a lot of them. I just saw a talk from a guy named Dave LeGassa, from Capital One Bank, about the foundational principles of using that stuff. They built their card business around that.

I think you’re starting to see that coming into the bank from the card and consumer business, and from leaders in that area who have already done a lot of work, like Capital One. Now it’s starting to move into different parts of the organization as well. One of the things we’re excited about is taking a lot of these same things and making them operate on a different data set within the banks, particularly around commercial lending, commercial relationship management, and commercial pricing.

Justin: Yeah, this is a fascinating time in banking. I used to run a software company that sold primarily to the banking industry for 10 years, back in the ‘90s. It’s a particularly interesting market to me, and I’ve never seen banks as an industry so simultaneously excited about the potential of a technology, but also threatened by it. I think it’s part of the larger FinTech movement. We’ve seen very interesting behavior within banks in the last couple years. Things like taking investment positions in funds just so they can get more access to what’s happening from a software disruption standpoint. You see banks trying to get connected to the local software and development communities in the cities they’re based in by participating in incubators. In some cases, I’ve seen banks moving parts of their IT staff into technical hotbed kind of centers, so they can be immersed in the culture of startups.

It might be happening for two reasons. On one hand, you’ve got companies like PrecisionLender, who are arming them with software that can accelerate their business and create efficiencies. They’re realizing that some of those companies, because of the nature of SaaS business models, are aggregating data sets around specific domains, like commercial lending, bigger than any one bank has on their own. That’s a new phenomenon.

Historically, there were only a few data businesses that worked very hard to get people to share data. Now, you’ve got lots of businesses that sell data to interested banks, and by the nature of how the SaaS business model works, can see an aggregated set of data across a huge swath of the industry if they get permission and do it with trust. Suddenly, banks are saying, “There are now companies that can help me do things based on data sets, with Artificial Intelligence, machine learning, predictive analytics, etc.,” on data sets bigger than they have themselves. That’s quite amazing.

On the other end, you’ve got startups coming in and saying, based primarily on the capabilities of data and ability to underwrite risk better, that they’re competing directly with banks and more aggressively than ever before. Banks are simultaneously getting all these amazing new tools brought to them, giving them more capabilities on broader data sets, but in turn new competitors are entering their market. That’s certainly causing banks a lot of concern.

So how do banks mitigate that concern and take the leap forward into Artificial Intelligence? Make sure to sign up for our webinar with Justin LaFayette on Jan. 31 at 11 AM EST to learn more. http://ww1.precisionlender.com/bankonpurpose-webinar/

The post Artificial Intelligence in Banking appeared first on PrecisionLender.

 

About the Author

Maria Abbe

Content Manager at PrecisionLender

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