So why is it that there is such a huge demand or a huge need for a bank like FNP to be bringing in this young talent into the organization, especially like in regards to data science, why are they important nowadays?
Okay, I’ll talk about a bit about the main use cases for AI and generative AI. Obviously, those are not the only things we’re doing. There’s a lot of more basic things that we’re doing, including, you know, developing whatever decisioning models, etc. That would be based on more traditional algorithms and including AI, but particularly on generative AI, let’s say I can point out four main applications that we’re seeing, and that’s not just an F&B. That is really, I guess, what is happening with banks and financial institutions across the world and other institutions as well.
Interesting. I always tell people, if you think that if you use Chat, G, GTP or any of these tools, you would have figured out that these things are pretty good at Shakespeare and it can write the sonnet, etc. For you.
But it is even better at coding than it is at human language. So if you are looking for, you know, to program in Java and Python, Cobo, whatever languages you’re interested in, and they really are good at that. And it is not just that writing code better, it is at helping people write business requirement specifications, translating them to functional requirement specifications. It is about documenting code that may already exist. It may be in refactoring code. Yes, it is about generating new code as well. But it’s also about tasting code, generating taste coverage that actually gives you, so that you can do better testing of all the different scenarios that can develop when code is executed, etc. So it is actually going to quite transform the way that it development happens.
And that’s just one area, but it is a big area in the bank. Two other big areas. The first one would be really customer facing. So for example, where we have got on the FNB app, we’ve got search and chat capability. Now that same capability translates into, for example, when you’re quoting our contact center and then we have the speech to text technology there, and we can integrate some of our, the replies that you can get from a, from a, basically a robotic voice. But to be able to get to customers quickly, to route it to the right human agent is still, you know, what happens in a lot of cases. But as it becomes more powerful and we should be able to give you answers, and you can imagine people don’t like waiting on a line for somebody to answer. If I have a simple question, we should be able to answer it. You know, whether you are typing that on secure Chat on the apple, whether you are calling our contact center, we should able to do it very quickly.
Another big application is really personalization in terms of marketing. Nowadays, a lot of customers will research us on the internet before they, even if there are existing customers, before they buy a product, they will look around, etcetera. So the ability to integrate information about what’s happening with our customers on the internet into our own channel infrastructure, and to be able to give them the right contextual offering, etc, being able to integrate advanced components into that, how we are positioning the marketing, etc. Is an area that we’re really developing. It’s not that mature yet. So if you’re seeing ads from us that are not highly personalized yet, this is still something that we’re really working on. But you can imagine it is very powerful if you can do marketing to a segment of one customer. So if you admire or going in, we can really use your information if we have the you’re consent to do that to give you highly tailored and precise offerings that suit your needs as opposed to more generic content. And then the fourth really application is where we build internal chat capabilities for knowledge management. So now today, if I want to know anything about human capital policies, risk policies, compliance policies, I can go onto my FNB chat and I can ask questions and get answers and not just get an answer, but get a link to the actual documents that contain the answer. So those are some of the ways in which we, which AI can democratize access to information in the bank and that really, we see this as really stole only the tip of the iceberg.
The technology has advanced so much in the last year and a half. Absence said was it was first launched that there is still so much more runway on it. And we think that, yes, the direct impact is on people that are employed in data analytics because they’re helping to create these solutions. And that’s why I say there’s, you know, the specific skill sets to be able to do that. But more generically, actually, all our stuff that, you know, if you think about it, we work in a knowledge economy. So in the bank, you know, we don’t really have physical factories, etc. We have some cash and itms. So there are some physical components and branches. But really the, what most of our staff do is they are processing information, you know, relating to a customer or to a process and, you know, coming up with recommendations and answers and taking decisions. And a lot of that can be assisted using AI. And generative AI has really enabled it to be able to use unstructured data like speech or take story images, which is really now made it much more mainstream because our staff can ask an agent questions themselves instead of asking another expert. I don’t need any, I don’t even know, need to know how AI works to be able to use it because it is relatively easy.