Financial Futures: Unlock the full potential of your workforce with robotic process automation

Erin Dangler: Just the mention of automation is enough to spark fears over layoffs or cutbacks in most industries but spend a little time with the experts and you'll find that the bots really aren't here to replace anybody.

Carl Bahneman: There's nobody losing their job over an RPA implementation. They are going to work on other things. They are going to either be working on things that have never gotten done or focused on learning other things. It helps the organization in the long run because I have now got more folks that are cross-trained.

Erin Dangler: In fact, implementing RPA actually helps businesses to do more, and allows employees to achieve more and concentrate on the activities that really matter to them and their clients.

Carl Bahneman: A lot of financial institutions are doing acquisitions. Again, that increases the workload, but it does not increase the number of people that are able to do all that work. By leveraging pre-bots, banks save time. And cutting that 20-minute task out of somebody's job, especially if it is a knowledgeable tenured employee, allows them to focus on other important tasks.

Erin Dangler: This is Financial Futures, the podcast that charts the frontiers of FinTech innovation. In this series, we will be discussing the advances that are modernizing regional banking, bringing cutting-edge tech to institutions and customers alike. I am your host, Erin Dangler.

And in today's season premier, we will examine how robotic process automation is enabling regional banks to scale up operations and utilize their staff to the fullest by assigning the repetitive and time-consuming tasks to the bots.

Today, we speak with Carl Bahneman, business unit manager at FIS to discover how banks can roll out RPA in their operations, what kinds of tasks they can carry out, and whether or not institutions should offer pre-built or custom bots. But first, Carl breaks down what exactly robotic process automation is and how the tech can help financial institutions.

Carl Bahneman: What it is and how our financial services clients are leveraging RPAs and bots is really to take tasks that they do on a regular basis. And they take the process of that and the workflow and build a computer program that basically is doing what the human being would be doing. And typically operationally is where we see a lot of this work being done.

In the back office, a financial institution, for example, when they are doing the same task every day and it takes them an hour every day, that means that person is now losing an hour of their day that they could be focused on something else. And if this is something that can be managed through, do this first, do this second, and follow that workflow all the way to the end, it is pretty much a good candidate for a robotic process automation bot.

It is something that I think is still kind of new. But as we bring it forward to financial institutions and they start thinking about it, their eyes light up and you can kind of see that aha moment when they are like, "Oh my gosh, there's 20 things that we could do to make us more efficient as an organization."

Erin Dangler: I am already like, you're talking about these repetitive tasks, can someone fold my laundry and put my dishes away so that I can spend more time podcasting, right? That may not actually work. But actually, what kinds of tasks can RPA be used for?

Carl Bahneman: Within the client base, we've kind of seen two different focuses. Most of our financial institutions are focused on managing their deposit clients or their lending clients. And so we are seeing tasks, for example, when they put a new loan on the servicing system. What will happen is there are 15 different steps to take place, and that is a human being entering that data, reentering that data, checking that data.

And through an RPA pre-bot, what you are able to do is say, "You know what, the bot knows exactly what to do. It knows the source of where the information's coming from. It can make all of those updates, provide a report back to the user so they can just validate one time at the end." And as we all get busier in our life, we call it the old fat finger syndrome where you hit something, and you are meant to hit a different key.

Well, errors happen. A bot does not make those kind of errors, and it is going to do it repeatedly time and time again. That's one of the areas where banks have also said, "Not only am I going to save time, but I’m also going to reduce my risk of making an error." And so I think that's where it's got the kind of the double benefit to our clients as we see them move forward with RPA and with the use of bots.

Erin Dangler: Oh if I could get a bot to replace my fat fingers. Oh my gosh, I am a terrible texter. Well, that is great. I have also been kind of researching this a little bit. Looks like they could also generate emails, payroll, as you said, check for errors and account balancing, just lots of data entry, data reconciliation, spreadsheets, bots can do all that kind of stuff.

Carl Bahneman: You're only limited really by your imagination. And I think what I have noticed as we've started to get the client base more familiar with this, now we're seeing all of these new creative ideas coming directly from clients saying, "I could use it for that. I could use it for that," things that I would have never thought. As long as, again, you can provide a detailed process of what you are doing and it's pretty much the same every time, it's going to be a prime candidate for a bot.

Erin Dangler: Let us talk about that a little bit. You are talking about if you can provide a detailed process, is that the script? Do you have a script to carry out these tasks?

Carl Bahneman: The script is really the programmatic kind of description of what the bot is going to do. But what it starts out as, really in order to build out that bot, the programmers, they need to understand from a financial institution what are the steps, the manual actual steps that the person is doing. Carl goes in, logs into his computer today. Then he is going to go to this screen because he needs to do some data entry on this screen.

And from there, he will go to the next screen. What is critical as banks are setting up bots is that they really have a good understanding of what the workflow is for any given process. Like I said, a programmer does not have to know anything about financial services. They need to have a detailed workflow that they can then turn into a programmatic script that then can run whenever a bank needs it.

Now, those scripts can be run manually, so I could kick that off and say, "Okay, I'm ready. Let's start it up today at 10:30 in the morning," or I could say, "You know what, every day at 3:00 in the afternoon, I want this script to run and I don't even have to touch it." I think that's really what the script is. The script is what the end result is that then gets run repeatedly.

But if a bank doesn't have a good understanding of what they do, the bot is going to make mistakes if you skip a big step when you're explaining that process to the programmer. It has to start out with that real deep dive into what is the process you're trying to automate. The more we can get those types of written processes from the clients, the better off we are when it comes time to turning that into an automation that it can do all of those different things,

Erin Dangler: The human behind the bot where the creation really needs to have a full understanding of what's happening, and then the bot can carry out that function.

Carl Bahneman: Absolutely. That is the key first step. As I've sat through some of these meetings with clients, some can get pretty painful, because they quickly realize that maybe they don't really know everything or they take some of these steps for granted.

And the good news is, as you're building that out, there's repeated testing working with the client that says, "Okay, here's what you told us to build. Here's what it does. And at the end of it, you tell us if that's what you would've done if you would've manually done that."

It's constantly being tweaked until it's to that point where it's matching up with the exact process that you're doing on a daily basis. A bot is also smart enough to know that there can be places in that process where there may be one option or another. And those there are a little more complex, but absolutely we've seen a lot of the bots built out to do that.

Erin Dangler: So even just taking the initial steps toward adopting RPA can benefit financial institutions. By simply putting in the time to detail how certain processes are carried out, banks can unearth opportunities for efficiencies even before the processes are automated. But once the task has been drawn up in fine detail, the next big question that financial institutions need to ask themselves is whether to implement a pre-made bot or design something completely bespoke to their needs.

Carl Bahneman: Bot is the automation of a process. The bot is going to do the automation of whatever process has been given. And they can get very customized. And we have a lot of our clients that have very specific processes that only applies to their organization. They would want potentially a custom bot. But at the same time, all of these customers that we're talking to are using the same system that FIS offers.

By doing that, there are already standard recommended processes for a lot of repetitive tasks that FIS has provided these clients. Because we already know that, we're able to pre-build a more standardized bot, we call them pre-bots. And basically, we've built out an entire suite of pre-bots based on these functions, based on what we already know we're telling you should do. And the end result is, if you want to get from point A to the end of the job, you've got to do those five steps that we may have pointed out to you.

And if you've got a couple other things that you're doing internally to manage that, the bot can be built to support that. But by doing a pre-built bot, that takes away some of the guesswork from clients, because we already have the base of the workflow already there. What that does is it allows you to implement these much quicker, less room for error and it's much cheaper.

I think by doing pre-bots, we've kind of differentiated ourselves in the market because we know the system that our clients are using, and so we can get a head start kind of on those bots. And that's what we've really seen our customers do. We're not seeing a lot of customized bots being built. We're seeing banks leverage those pre-bots, and they're able to implement them very quickly and hit the ground running.

Erin Dangler: That's great, because it looks like ... I mean, even if you did have a more complicated, specific to you bot you needed to build, it can sometimes take up to a year. And then you need to test it, it costs a lot more money. Do you have the infrastructure to get it started? Is that what you're finding?

Carl Bahneman: Right. And what we're seeing actually is, if you are taking on a pre-bot, we can usually implement that in six weeks, which is huge. And the other thing, by doing that in six weeks, once it's implemented, and that includes all the testing, we actually had a bank that we'd had some challenging implementations with them over the years at FIS.

And so when we told them we could deliver bots within six weeks, they didn't believe it. And so in the contract, they built some penalties and some timeframes in to deliver these bots and implement them. They had six bots that they wanted to deliver. They told us that they'd like them delivered in 20 weeks, all six of them.

And if we didn't do that, there would be some penalties. And the team felt comfortable enough to sign off on that and agree to that. And we delivered those six bots in 12 weeks. And again, none of those were custom bots. They were all kind of the pre-bots as we call them that this client was able to take advantage of, and then immediately start seeing the value around that.

Erin Dangler: I want to get a little bit more of the backstory on that. What prompted them to introduce RPA?

Carl Bahneman: This is the same problem that everybody is seeing in any industry. Nobody's got enough staff to do what needs to be done. Secondly, especially in the financial services industry, you've got a lot of tenured employees who are really smart and know everything. But then, when they leave, you've got folks that may have never even set foot into a financial institution.

Being able to train somebody on a task, and what the business, and what the industry requires is a challenge. And then you throw into the mix that a lot of financial institutions are doing acquisitions. Again, that increases the workload, but it does not increase the number of people that are able to do all that work.

By leveraging pre-bots, banks say, "You know what, I'm going to save time obviously." And cutting that 20-minute task out of somebody's job, especially if it's a knowledgeable tenured employee, allows them to focus on other important tasks. Some of these bots, because again, with acquisitions, I had 500 loans that I was dealing with, now I have 5,000.

Because a bot is scalable, it can manage the increases in volumes very easily and allow banks to do that. There's certain tasks, for example, that basically happen once a year. Everybody has to get their taxes paid to their municipality. Well, when that happens, that is typically all hands on deck for a bank.

And for about a month and a half, nothing else gets done, because that's what has to get done. And so to be able to have bot or multiple bots being able to manage that process, and it becomes just invaluable to a financial institution, to kind of get through that and still be able to do all of the other stuff it needs to get done.

Erin Dangler: Right, it keeps the workflow going. It keeps the big picture intact so that it doesn't come to a screeching halt, because you've got this one pressing need every same time of the year. Can you tell me what some of these bots were that this company employed?

Carl Bahneman: One of the things for example is, and this could be for most of our clients, but loans have an interest rate. And that interest rate is based on some national rate that may be out there. Well, those aren't set by a financial institution, those are set by some national organization. And I, as the bank, every day need to go out to many different websites, look for what those indexes are every day.

Because depending on if they went up or down, that's going to impact the interest rates that your clients are going to be charged on their loans, and so getting them right is critical. You have to have a way to reduce the risk of error. You have to get it in a timely manner, and it takes time. What the bot will do, for example, is you tell the bot, where do you get these indexes every day?

Tell them the website. Tell them the URLs for that website. The bot knows, when the bot gets started, I'm going to go to this website and then I'm going to grab this rate, because it's in the same location every day. And then I'm going to go back and update it into the FIS system that the client is using. And that's normally something that I, as a person, would be doing.

Then I'd have somebody else take that over, because it's such a critical piece, that if you make a mistake on it could have large downstream impacts, and so then it has to be reviewed to make sure nobody made a mistake. The bot is doing all of that, and it's doing it in a very timely manner. And then what it's doing is giving you a report immediately after that's done. Being able to do that in a timely manner and having the bot do that is absolutely huge.

And again, that's one that almost every one of our clients has been taking, because it is something that they can't risk it being wrong and they can't risk it being missed. We have about 30 different bots right now. Most of them are in the lending space and the deposit processing space. A normal task at every bank is transactions come through to be deposited into an account. It could be checks. They could be an automatic deposit.

Well, if when that was being created, there is the wrong account number, what are we going to do with it? Well, through one of the bots we have, it's called the exceptions processing bot for non-posts, it basically goes in, and based on a number of rules that the financial institution has set within the bot, it says, "If you get a payment or a transaction that did not post to an account for any one of a number of reasons, here is how I want you to treat that."

It could be, for example, I had to close my old checking account because of fraud, but I've opened up a new one. Well, guess what? All of those places, if I forgot to go to them and say, "Oh, by the way, I don't have that account anymore," they're going to still try and put the money in that account. Going to come to the bank, the bank's going to say, "I don't have that account anymore." Well, you can set up a rule that basically says, and the bot recognizes this, if you get money that's supposed to go to this account, put it to this account instead.

That one is saving banks on average hours and hours a day. I mean, there are usually multiple people working on that, and it's reduced, most banks tell us, 75% to 80% of the workload of that task, which is just enormous. We really take a consultative approach when we're talking to clients about that. Because one thing I've learned is every bank doesn't see the same value in every bot.

Sometimes it may be because of the size of the bank. It may be because of the complexity of the bank. We see things and say, "Tell me about this process at your bank. Is this a big deal?" I like that approach. It's just a good way of doing it, because then the bank knows that we've kind of worked with them, we've worked with the RPA team to really say, "Hey, here's what we want you to build, and we think that's going to provide most of our clients a big lift in their operational efficiency."

Erin Dangler: Right. And it helps the bank, but it helps the customers.

Carl Bahneman: Absolutely. Because it speeds up a lot of things, again, reducing errors. I mean, nothing is more embarrassing to a bank and nothing is more frustrating to us as consumers to find out that my interest rate is 5% of my loan. And now because you put the wrong index in there, it went to 12%. We've had things like that happen.

And then you have to undo that operationally. That's harder to undo an error like that. Obviously the bank has egg on their face, and they've got to explain to a customer and apologize to the customer. And you lose a lot of credibility and you could lose customers. I think those types of catching those errors and making sure they never happen is even more important, and that's what we're seeing with the bots.

Erin Dangler: I want to go back to this bank you mentioned that said they didn't believe that you could deliver in time, and they had some penalties for you. You implemented these bots. Were there functions that they didn't realize were useful to them?

Carl Bahneman: I think different folks at the organization had a different understanding of really what a bot could do. And I think some of the tasks that they never even thought could be a bot, once they started talking to the team in the implementation process, they're like, "Oh yeah, we can automate that." It's kind of two-pronged.

Within a bot that's already there, there were certain things, where in the past they may have said, "Well, if you can get us 60% there and then these we know are going to kind of fall out and we have to manually look at those, that's fine. We look at that as a win." Well, again, after working with the bot team, they got that up to 75% or 80% because they were able to automate other pieces of that process that the bank didn't even think could be automated.

But I think the other piece, and we're seeing this time and time again, they'll kind of come forward and they'll be a little skeptical. They'll implement a smaller number of bots, and then they're coming back right after. We had this bank that had the six, they immediately came back and had two more. I think obviously they're seeing the efficiency. But I think they're also looking at some of their processes that originally they didn't think would benefit from a bot and saying, "Oh yeah, now I see it."

There is definitely that increase in awareness. And that's why I've been talking about this to clients for close to three years. And some of them, it takes till the third, fourth, fifth, sixth time to kind of say, "Oh, finally I get it," and more importantly to hear it from clients. The bank that I've mentioned, they have really been quite the advocate and really helped us, and being willing to go forward and talk to any of our clients about this.

Erin Dangler: With institutions finding more processes to automate, and RPAs ability to suit almost any challenge, it's no wonder that the banks that have had a taste of robotic process automation are coming back for more. What advice does Carl have for those in regional banks and other financial institutions that are liking the sound of RPA? Of course, word of mouth, live testimonials, right? That's great. Well, let's keep sharing it then. If there are people out there listening that work for financial institutions and they're considering RPA, what advice do you have for them?

Carl Bahneman: Obviously, the better your processes are documented, the better off you're going to be in an implementation of pre-bots. If you don't even know how it's done, it could be a painful process, because the implementation alone is going to be a lot of back and forth as you go through that. That's one piece, really understand what you want to do.

I think that the biggest thing that I've seen working with the RPA team, we've started with the IT folks at the bank. And technical folks are usually a little bit more cutting edge and knowing what's out there from a technology standpoint and what things can be done. When you start talking about pre-bots and RPA to them, they're already halfway there. Because they don't know how it's going to benefit their bank, but they absolutely know about it.

But the problem is, being excited about technology is not enough to get an executive at the bank to sign on the dotted line and say, "Let's go with it." What you need to do is you need to bring together the IT folks, understanding the technology, and then the operations folks, the folks that really understand what could be made more efficient if we had this tool.

And by doing that, you've now got three key groups within the organizations. You've got your deposit operations, your loan operations, you've got your IT folks. That becomes a much easier sell to your management or to your capital committee, whoever's making that final decision to sign on the dotted line.

Erin Dangler: In terms of having a bank prepare for onboarding RPA, you've talked about documenting your processes, getting everybody on board and behind it. What are some other things they need to do to prepare for this? Nobody likes change, right?

Carl Bahneman: Right. Nobody likes change. And if somebody says, "Oh, I'm going to take this away from you," I may complain about it every day that I do it, but the minute you take it away from me, I'm like, "Okay. Well, is this the first step that they're just going to get rid of me?" It's not always about change, it's about fear.

My value to an organization is because I am the only person that does these five processes, and now you're going to automate those. And maybe at the beginning of the RPA journey with our customers there was a little bit more of maybe they'll get rid of headcount. There is nobody losing their job over an RPA implementation. They are going to work on other things.

They're going to either be working on things that have never gotten done or focused on learning other things. It helps the organization in the long run because I've now got more folks that are cross-trained. Just because you've got something that's being done by a bot, you still have to know what's happening in the background.

I think what some banks have done is afterwards, they're now taking those workflows and turning them into process documents within their organization. It's kind of working in reverse in that regard, because they're like, "We never had anything documented. Now we're going to do that." And so that's kind of exciting as well to see it has kind of that extra added benefit.

And so they're looking at that as an opportunity to change some things within their organization as well. When I say that, what we also do, and this, I think, is unique in the industry is, when a bot is implemented, what they actually build into any implementation, there is a set number of RPA development resource that is given to that bank to use every year if they make changes to their process. We're basically saying, "It's okay to change every year. It's okay to do something different. And you're not going to pay again to do that, because it's already built into the solution."

Erin Dangler: Yeah. I mean, well, it sounds like it's an amazing collaborative process where you're really listening to what your client needs and finding the right partnership in moving forward with RPA is really important.

Carl Bahneman: You hit the nail in the head. It is collaborative and kind of all three parts of the triangle that we've got here between the RPA team, the core team, like myself, and then the client that's implementing it. It really does feel that way. And all the kind of testimonials that we've had, there's always two questions.

Did you get a lot of value in it?

And that answer has always been yes so far. But also, how easy was it to get done? And now we can tell them, because we've got every one of them kind of continues down that path of going smooth, following a process. That's, again, just like a bot, it's repeatable. The implementation process is repeatable.

Erin Dangler: Well, Carl, that sounds amazing. And if you can create a fat finger texting for dummies bot for me, I would be forever in your debt. Carl, it's delightful learning about this. I mean, even though I'm familiar with bots in my own world, hearing about how it works in the financial world and hearing about how hard you work for your clients is really inspiring. Any last words of wisdom you want to offer to anyone out there who may be resisting going with RPA?

Carl Bahneman: Well, if you are resisting, I would say talk to someone that's done it, talk to a client that's implemented it, and have an open mind on what the value can be. Especially in financial services, everything we think about is the banks that differentiate themselves are very high touch. It's all about customer focus. And they think by automating something, you're going to lose that.

Well, the reality is it's the exact opposite. You're automating a mundane task that may have customer impacts down the line, but frees you up to do what your customer needs you to do, and that's be there for them and service their needs.

I think really it is the way we're seeing lots of industries going, but I think what I'm seeing in my world that I've spent the last 25 years in is absolutely exciting, and something that all banks should give a look and don't immediately close the door without talking to the right people and listening to what the opportunities are.

Erin Dangler: Carl Bahneman is business unit manager at FIS. That's it for today's show. Thanks for joining us. We'll see you next time when we'll be finding out how regional banks are responding to the future of money and bringing cryptocurrency to the mainstream of financial services.