Episode 1 – Artificial Intelligence (AI)

Rokture Ramblings – Episode 1 – Artificial Intelligence (AI) in Financial Services

Hello everyone, and welcome to Rokture Ramblings, where we discuss topics around digital marketing, marketing technology, and also marketing operations for the financial services industry. My name is Fernando Pena, I’m the founder of Rokture, where I provide fractional executive services for those looking to master the digital channel. And so I’d like to go ahead and touch upon a topic today that is being discussed just about everywhere, not just within financial services, but I think all of society is really talking about this new innovation and technology and how it should be utilized. And that is the big surprise here is artificial intelligence or AI. And so what I’d like to do is just go over four different sections of my presentation here in terms of what types of AI are there. How can financial services use AI? What are the dependencies on using it? And what does the future hold? So this would just be a short presentation on what you can expect AI to do for you and and how you may be able to make use of it within this.

So these classifications or categories that I created are not necessarily those that are used throughout the industry, but I thought it’s just a simplified way of breaking down exactly what types of AI there are and how they’re relevant to financial services. So the first one is generative AI. And this is the one that you’re mostly going to hear about within the sort of media. And that’s the chat GPTs of the world.

And so that something could be content in terms of an article or maybe a product description. It could be even code. So you can see here, for example, on the illustration where this robot is creating formulas. And in some cases, it could just be imagery. But in any case here, what you’re looking at is that there’s an input, it goes into the AI engine, and from there it creates something. The next one is conversational AI. And so conversational AI are the chatbots of the world. And so what that is, and there’s actually some voice prompted AIs too that are out there where they can be used for servicing. But essentially that is what conversational AI is being used for most often, is to provide servicing or maybe even sales prospecting.

And if the AI can’t manage or handle an answer, then it can actually turn over this conversation to a human so that they can take over for that next level of support. So far on both of these here on generative, there’s some concern in financial services around the lack of controls and parameters. And that’s one reason why, and I should have mentioned this earlier before getting into conversational, but one of the reasons why it’s probably not something that you want to just make use of without a lot of oversight and editing. On the conversational part, that’s also a concern as well. However, on the conversational side, there’s often a lot of sort of training and restrictions that you can place within those discussions that the AI generates to make sure that you’re not providing an incorrect answer, or that if it knows it’s in over its head, it can go ahead and

The third one is where I think that there’s the most potential, and that’s the decisioning AI. And what that is, is that it’s taking a look at your data and also the inputs and and the interactions that your client may have with your institution. And it’s actually creating a process by which it comes to a decision. And so that decision could be around providing an offer. It could be around maybe modifying a digital experience. It could be maybe having a certain conversation with that individual at a retail level. So what that would do is that a monitor would be in front of the teller, for example, or the banker, and they would be able to then ask or prompt the client based on what the decision AI is prompting to them. And so this is really helpful because what it does is it gets you to that next level of personalization that people are really clamoring for. And I think that’s where the future is going as far as interactions are concerned. So instead of running campaigns at a level where it’s based on a segment or a client or customer type, instead it’s running at a very specific at an individual level based on this decisioning AI. So this is really critical when it comes to differentiating yourself within the industry and making sure that your clients understand that you really know them and that they’re able to, they sort of feel as if they’re having a discussion with an individual when in reality it’s with technology.

And of course, it showed again.

So how to use AI in financial services.

And so this is really where the industry is going. And unfortunately, I hate to break it to you, but with financial services, a lot of times customers see it as a commodity, and they don’t really have an understanding of all the differentiation between the different institutions, aside from, let’s say, maybe they like a certain website more, or maybe

Personalization, on the other hand, though, is going to demonstrate to them that you really do care about them as an individual. And with that, it’s able to provide an experience that is unique to that individual. And that’s where this use of AI and financial services is really a game changer if it’s done properly.

The next one is around servicing. And so again, I did touch upon that briefly with the chatbots. But really, what you’re looking at here is that AI can provide servicing that is dependent upon what it’s observing and taking place. So it can help to to resolve certain issues or problems.

And that’s where from a servicing perspective, it cuts down on a tremendous amount of human resources and capital that is utilized to support clients and hopefully done in a way where it’s automated and requires little oversight until that next level of support is needed, at which point a human

The third use case is one that is not really discussed very often, but I think is also very important as well. And that’s proactive nurturing. And what I mean by that is that the AI is able to observe different transactions and interactions that clients have with your organization.

And so one of the issues with trying to establish a positive lifetime value on a customer is to reduce churn or attrition. And so with this proactive nurturing, there’s often a set of sort of interactions or processes that take place when a client is about to attrite. The AI can observe that and can then put out an alert to a banker or an account executive and have them proactively reach out to this individual and see what might be taking place and see if there’s a way to mitigate that attrition and address whatever concerns that individual

So you can come back with then a sort of counter offer to see if you may be able to keep them within your institution. So I think there’s a lot of power in that because it’s able to sort of plug the leaks that you may have if you’re bringing in X number of clients on an annual basis, but you’re losing two times that X, then, of course, your organization isn’t growing. In this way, you’re able to keep those that you worked so hard to earn and hopefully keep them satisfied.

So what are the dependencies and what do you really need to make sure that you can make AI work within your organization? So the first thing, and I can say it more than just three times here, but it’s data, data, data. And that is because in order for this intelligence engine to make informed decisions, it needs to have as much information in front of it as possible. So if you’re just letting it make decisions based on a limited number of interactions that it’s observing, then you’re going to get results that probably aren’t quite as favorable as they should be. So the more data that you can have, and the better that that’s standardized and cleaned, and accessible to the AI engine, the the better those decisions are going to be.

The next thing is customer centricity. So most institutions are trying to go this way, where instead of being laid out by a line of business or a customer type, instead, it’s looking at being very customer-centric. And what that means is that you’re looking at that individual as a whole, in terms of all the different relationships that they may have with your institution, and also all the different products and services that they utilize. When you have all that available and at the disposal of one single database, it really makes a huge difference, again, with trying to create that sort of understanding from an AI perspective, that this individual has many different needs and sort of services that are required.

The last thing really aligns with the first two, and that’s removing any silos. So I know that certain lines of businesses and products are very protective on their products or the data that they have. And that’s completely understandable.

And so again, you could put parameters on what the AI does and how and control how often and it may, for example, reach out to someone if that is something that you allow. But you do have to remove silos within your organization and allow the free flowing of information in order for this to properly be effective and work properly.

And finally, what does the future hold? So really what we’re looking at here is that AI right now is somewhat of a module in terms of how it’s rolled out today. So it’s something that’s added on top of a marketing automation system or on top of a website. But really what you’re looking at here is that there’s going to be tighter integration across the board. So as this technology proliferates across many different types of platforms, you’re going to find that it’s going to be more tightly integrated on all these customer touch points. And that’s a good thing, because again, it’s helping with the sharing of that data and hopefully it simplifies the process by which you can compile all that information that’s needed in order to make this work properly. You’re also gonna see continual refinement and sophistication of these models. So right now, sometimes they’re a little bit too verbose when it comes to conversational aspect or content that’s being generated. You’ll see things become a lot more natural and it might be more difficult in the future to tell whether it’s a human that you’re interacting with or an AI bot.

They get better, more refined, there’s more data coming to them, and also the computing power behind them becomes more powerful as well. There’s also gonna be the inclusion of brand voice, additional parameters and control. This is critical and important in our industry, as if you say the wrong thing, there’s serious repercussions. There could be regulatory concerns, there could be damaged brand reputation, or just dissatisfied customers. Now, some of the systems already have some of this available, but it’s just that you’re going to see it expanded upon and really become more robust compared to how it is today. And finally, I do believe that the innovators and those that accept this technology are going to start pulling away from their competitors. So again, as I mentioned before, there’s not a lot of differentiation from a client’s perspective from one financial institution to another. If you adopt AI properly, though, there will be that aspect of personalization and also efficiency when it comes to operating with your institution, and all of that is going to then provide a differentiating factor when it comes to making a decision who they’ll do business with. So with all of that, I’d like to go ahead and invite you to reach out to me if you do have any more questions on AI and how it might be able to relate to your particular use case and if it’s something that you might be interested in deploying. If you have questions on AI or any other marketing, digital marketing discussions or questions that you’d like to go through, feel free to reach out to me and until the next time where we’ll touch upon the next topic. Thank you so much and we’ll talk again soon.

Bye bye.