E255 Transforming Banking with AI: Building a Data-Led Future with Bank of Ireland's Carl Kane

0:00 I've always found that by by storytelling you can actually bring people with you in order to make the work easier >> and more collaborative role was to check hundreds of reams of paper probably even thousands to make sure that where there was once a a pound symbol there is now a euro symbol I probably wasn't just a data person they found that I could probably obviously carry a story.

0:31 and I had a way of articulating things to the business >> that not many data people would have had at the time. Welcome to the AI Island podcast. I've got Carl Kane. Carl is the data culture lead at Bank of Ireland. Carl, thank you very much for coming into the podcast today. >> You're very welcome. Delighted to be here, Mark. Can't wait to get cracking. Tell me a little about your journey into working in the bank because it's not necessarily straightforward one.

1:01 >> Yeah, it was certainly different. they say sometimes you you you fall into into your career. I guess I I certainly fell into mine. I I originally started in the bank in in 2001 as part of the the Euro project. I was a UAT tester. Basically my my role was to check hundreds of reams of paper, probably even thousands, to make sure that where there was once a a pound symbol, there is now a euro symbol. So that was my first my first job in the bank. I also came in to teach people a little bit about Excel. So I was I was a dab hand at the Lotus 123. the bank needed Excel experts. Excel was the new version basically of that. and they thought this fell has Excel all over his CV and he did well in college. Let's maybe get him into teachers about this Excel thing. because it's really in fashion now while he's checking all that paper.

2:02 >> And you're big into boxing before that. >> Yeah, I used to box. I I trained a lot when I was younger. I I thought that if I could make a name for myself in the boxing, maybe I could use the bank to pay the bills. You know, my dad said, you know, you could you could have been a you could have been a good fighter, you know, it's just you don't like getting hit in the face. And of course, I didn't. I mean, who wants to get hit in the face?

2:25 >> So, he never quite understood that, you know, which is probably the reason why he looks the way he does. That's a joke. He's he's he's not that bad looking. but yeah, I come in and I thought that maybe maybe maybe this way of life in a bank could probably supplement what what I really wanted to be when I grow up, which was professional boxer. ultimately it didn't didn't pan out that way. >> But you got into all these different different types of projects but putting your hand up.

2:53 >> the different types of projects in in the bank. Yes. Yes. So I found the interesting thing was through all of my checking the the reams and reams of paper and and through through teaching people bits and pieces of Excel, I grew into what what was probably a data analyst role you'd call it today. I I worked in a team that that basically visualized you know massive or massive at the time data sets or databases and tried to make sense of okay what's what's what are these numbers telling me so I guess it probably would have been a data analyst role at a time when basically the role was business analysts >> you know so I guess I started kind of developing myself in that paid analyst role. I eventually then moved to to a different area in the bank. I knew group marketing were looking for people to work with data. They weren't yet called data analysts.

3:55 >> but database management that kind of stuff. I got a role in like a basically a team that measures customer satisfaction. Yeah. So in the branches we have a lot of satisfaction reports and stuff like that that we run all the time and we try and understand okay so so how long are people waiting in the cues and and are we able to like when people get to the front of that queue are we able to you know handle them and and ask answer their queries and stuff like that. So a lot of that stuff happens throughout the branch network and I started working on those reports, customer satisfaction reports. again this kind of got me in got me very close to understanding the data sets in the bank and customer data and all this kind of stuff. It morphed into a team that that basically worked on contact management, you know. So I I got a new role that that that team got bigger and bigger as obviously the evolution of the bank was Oh. you know we need lots of people doing data stuff. so within that team basically they needed someone to ensure that our customers were getting the right correspondence.

5:09 So postmail the bank I think at the time was one of one of the the bigger customers of of Post. and obviously we didn't want to waste money. We need to make sure that the right customers are getting the right letter at the right time for the right product etc etc. So, so that brought me into a data mining. You know, this is where really my data skills took off. And it was a little bit of code involved in that at the time. And understanding the the the customer base, you know, the different types of customers, the different personas, understanding inclusion and exclusion criteria, etc., etc. So, so that's when I really started learning all about data and bank of world when I got that role. and it all kicked off from there. You've always been a storyteller as well. Why do you think it's important to tell people stories to kind of go on that journey rather than just the regular way?

6:06 I have a huge value on I guess bringing people on the journey with me and I've always had that. So, I find that telling stories in an organization like the bank where there's lots of different types of people of all different shapes and sizes and backgrounds. I've always found that by by storytelling, you can actually bring people with you in order to make the work easier.

6:41 >> Yeah. >> and more collaborative. So kind of by design if you like bringing all these people with you means that you know the end product will be optimized because you know you're telling stories you're bringing people you're collaborating and someone might go oh I don't think you know when you're looking after the students did you think of this person because there might be a flag over here that's saying they're student but but we don't actually use that for students of maybe PLC's and there's another piece so I find that approach kind of removing myself from the actual there's a flag in database 001_6.7 removing that kind of conversation and going you know this is the type of student we're looking for here you know and they may not be flagged as a student but what do you guys have any ideas I I find that more beneficial to actually getting the job done plus a lot more fun you know >> yeah and I think we do have to inspire people because we're up against their phones now 5 seconds concentration. So you need to inspire them to get their attention to kind of want to go on the journey and a story can usually be good to do that.

7:52 >> Absolutely. Absolutely. and it probably makes the story a bit more important because you need to grab people's attention >> and be thoughtful about that story too. >> Absolutely. Yeah. And and buy in and you know get connected. You know you really need to connect with people. Data governance, why is it so important and what do you think people underestimate with it? >> So data governance, why is it so important? for me, I I'm I'm the president of Damma Ireland in my spare time. We run events around town for fun.

8:27 and we kind of subscribe to to the DIM book, the Dhamma Body of Knowledge, which is a book, very old book, brilliant book, very old. It's currently being rewritten. it sees data governance in the middle of what's called the Dhamma Wheel. Now, on the Dhamma wheel is the entire universe of of data. Yeah. >> So, you name it, it's there. There's data security, metadata, there is data architecture, all of these different things. Data quality is there and and basically the idea is that if you sit data governance in the middle in the center of all this entire data versse, you can drive value from your asset. So if I'm a day of the data governance lead and I know that this new solution is being plugged in, I know straight away that okay, I need to connect with my guys in data quality cuz all these people are all probably disperate across the organization. Very rarely you're going to get them all in the same team. It'd be great if they were in the same team, but it's rare. Maybe I can get my data protect the DPO, my data protection office guys in here as well. my metadata guys, the data lineage guys, they're definitely going to have to be in.

9:42 >> So that data governance person becomes an orchestrator for innovation in the organization. >> Yeah. >> Because they know, okay, we're definitely going to need modelers in for this piece, you know, and there's lots of different types of of data geeks. It's not just data scientists and data analysts. We've data modelers, you know, there's there's data risk people. >> the key is getting those guys around the table >> for that initial engagement. >> Yeah. you know, >> yeah, you want to go fast, go alone, but if you want to go far, go together with everyone. So, bringing all that different times together.

10:14 >> That's exactly it. And that's where your story starts. >> You know, get these guys together, then you can come up with the governed approach to actually doing this, right? >> It blows my mind that only 5% of our listeners actually subscribe to the podcast. We've got one favor to ask. Please, can you subscribe to the podcast if you like our content? you subscribing will make a huge difference to the guests that we can get on the show and also be a voice for AI applied across Ireland but doing it the right way. Now back to the show. So customers expectations are are now changing right they expect more the bars kind of risen but it does keep coming back to having that clean accurate relevant data for people to access to give those different services.

11:00 >> Absolutely. you know, it's no surprise that the the bank sees itself as a you know, it want it wants to be the digital bank here in Ireland, >> you know, and that's our ambition. That's our goal. And our CEO has gone on record and saying this. and the bank also knows that if we want to do that, then we need to be data organization. >> So that's why all this data management is so important, you know. we have a number of different programs in the bank to start an I guess bringing everyone's data game up a notch.

11:40 >> Yeah. >> You know, that's what we want. If we want to be if we want to be the digital relationship bank and we want to be data and AI ready and all that stuff, >> we need to get everyone's data game up a notch, you know. So we have a number of programs that run in the bank to improve everyone's data game. So if you want to be a data analyst, we'll show you how to be a data analyst. If you just need to know, oh, I just need to be understand how data flows through our and across our organization. And the data has some crazy things in all banks.

12:16 You know, if you want to understand how data flows through and across our organization, we have data fluency pathway and we actually bring in our data management expert, our metadata expert, our lineage guys to actually give you an idea of the day in the life of of these areas in the bank that are so important >> which is so powerful because you learn so much from that. >> Absolutely. Yeah. >> And then you're building the data so you're data ready. You got the human readiness as well. You're bringing true.

12:44 You're teaching people the path. You're saying there here's the path. We'll take you on a journey. We'll also let people walk in in their own shoes and share what they're actually kind of doing together as well. How else is it important to make people a aware of regulation, doing it the right way, responsible AI, rather than just sign up for a license, off you go, and then suddenly you're at the races. There's more steps involved. We have as part of our data fluency program, we actually have courses kind of on everything you need to to up your data game, you know, and there's obviously the regulation and the the the I guess more risk side of that. We're a very risk averse organization. and we have a number of web- based training courses that everyone has to do, you know. and for example, you know, AI is landing in a very big way in our bank. we are we are now starting to to encourage our people to engage with these tools >> but in an ethical and safe way. no one gets these licenses in our organization without completing without without us being sure that they have completed their usual data compliance, data classification, data risk web- based training and they attest to the fact. Yeah. you know, and but also there's very there's no webinars that really take place without certainly me talking about our data strategy, you know, and our ethics and compliance based approach to all of this stuff because there's no quick kind of jump away from that foundation, right? It's actually about having good solid literacy, data literacy, good governance, understanding, doing it the right way, and then that's a built foundation for trust because if you're going to go on the kind of AI journey as well and then what's lovely about it is because you're building that literacy along the way, you're bringing people with you, people then can start to move into these roles of the future, they can start to upskill and then you're actually doing it at a way that the organization is building trust.

14:52 >> Absolutely. Absolutely. And you have to evidence all that as well, Mark. You know, the interesting thing that has been happening, we have people that are now that have gone through our literacy and fluency programs that have literally got so good at understanding and knowing this stuff, they've actually transitioned into data enabled roles in the bank. Like there's people in our bank that have worked in a branch and they may have like you know qualification in mathematics or something like that and they would have connected with me to go you know I love the way you talk about our data analysts and and your you know the data versse and our data Jedis and I mean I have a maths degree how could I transition to that >> and of course you can and people have done like we have people that worked in branches that are now in >> advanced analytics area, you know, and we've designed on doing the same thing with with with with people that have actually shown the appetite to maybe get to a data science level. And that's the next big thing with AI data scientists is is there's going to be an appetite for them >> personally towards usage. Any hacks you use yourself that you find helpful, maybe taking a picture of your fridge and going, "Give me some recipes, ideas using AI." maybe taking a picture of your garden, go inspire me to plant some plants. any ways you're using AI dayto-day that you got of kind of like hacks to share? Do you know I I I don't someone someone gave me a brilliant bit of logic to we get there's a lot of industry papers that we get in the data management world you know and like white papers and stuff Mark you know and it's hard to try and find the good ones firstly right >> but also some of them are very similar to others >> and you're trying to differentiate it but I I find someone helped me set up an agent that has evolved now to to basically compare and contrast these papers and the thing is they're like 150 pages long each >> and I'm going I'm never going to get through this this weekend.

16:59 >> So it's like summarizing or the kind of key points based on your criteria. >> Absolutely. Yeah. Absolutely. Yeah. And then it's identifying the stuff that I'm looking out for like what's the culture piece here and what different types of archetypes would make really good data modelers and >> so you're kind of interrogating it based on kind of your criteria. >> Absolutely. Yeah. >> Yeah. Really good. How do you feel about the kind of the customer journey and the importance that kind of data is going to give that customer in the future?

17:26 >> Well, the customer journey is really what we're all about in Bank of Ireland. You know, we have we are a customer first business and our our strategy and ambition outlines that we want to be digital first. Theoretically, everything we do for our customer is is is data right now, you know. but not just that, we also want to make sure that customers, you know, get to speak to a human, you know, that's where our branches are still there and customers still come in.

18:01 and some customers actually don't want to speak to humans as well. So, it's up to it's up to us to try and understand >> the kind of self-s serve option as well, isn't it? So, so customer might have a self-s served question at 4 in the morning cuz people aren't working, but also want to talk to a person about certain that thing that's important to them >> at the time, but then also kind of getting to see what's important in their story and make it accessible.

18:25 >> Correct. >> H for for them as as well. any important mentors did you ever have in your journey in Bank of Ireland that maybe kind of shaped you or helped kind of guide your journey? >> Absolutely. I've I've so many mentors. I have a guy called Russell Donovan was a former boss of mine when when I became the I guess the the lead for for the email marketing you know in the bank. He was a huge huge help to me, a huge guide. I spent a lot of time doing not much for a number of years, until kind of Ross got his hands on me and also his boss, Jill Murphy, who who's no longer with the organization, but I suppose if I was to call a couple of people out, it would probably be them.

19:11 >> Yeah, >> I wouldn't be where I am today without them. >> Was that their guidance? You think they spotted some kind of enthusiasm, talent with yourself and kind of pointed in the right direction? Anything that kind of come to mind? >> Yeah, do you know, they they they definitely did. They definitely saw that I probably wasn't just a data person. They found that I could probably obviously carry a story and I had a way of articulating things to the business >> that not many data people would have had at the time.

19:40 >> Yeah. you know, so so I found myself becoming like a data interpreter and where all my key and core group of fan friends were data geeks whom I was sharing lunch tables with and and stories about, you know, oh, do you remember that bit in Star Wars or Superman or any of these movies, but then also being able to connect with people that are building the products, you know, and marketing people as well on an entirely different level. So, so, the guys would have would have seen that in me at the time and would have encouraged it and would have helped me along actually, >> which is fantastic because by them doing that, you've then inspired so many other different types of generations as well and kind of spotting those talents and then bringing different people together and sharing those insights, creating that kind of culture as well. It's funny how those dominoes effects really impact >> Absolutely. effects really >> Yeah, absolutely. Yeah. I'd say if if they were to look at my my profile today and the things that I do, they wouldn't be surprised.

20:39 >> Carl, thanks very much for coming and talk to us on the AI Island podcast today about the importance of being a data organization. >> Thank you very much, Mark. It was a pleasure. >> At AI Island, we're helping over a million people become literate using artificial intelligence. And we do this in a variety of different way. The easiest way is our AR literacy program where we do a 60 to 90 minute program via virtual zoom or Microsoft teams where we teach the rules of the road of AI from basic business concepts to ethical and resourceful decisions on how you actually use AI every day. This is our literacy foundation. The second way we use AI training is within our workshops. These are either virtual or in-person workshops where executive leaders come and use some of the AI tools that we make available for them. So they can actually start to take their own documents and reimagine savings of time and energy but taking away friction from the work that they do each day. Later on we actually empower different departments HR finance sales marketing operations and start to utilize AI across relevant use cases to upskill their different type of departments. If you're interested and ready to go on your AI journey, we'll be able to showcase over 700 applications of applied AI, but how you can actually stop reinventing the wheel and start using AI for your organization today. If you want to start out and find out a little bit about the golden thread of activities that we've got to allow you to start utilize AI straight away, please feel free to reach out to us and we'll give you a good comprehensive overview about the arts of the possible minus the hype purely applied. See you then.