Agentic AI in Financial Services: Former UBS and SAP Group CIO

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[00:03:14]Banks run on trust, but AI agents are not there yet. Oliver Busman is the former group CIO of UBS and advises banks across Europe. Oliver, do AI agents actually work in banking today?

[00:03:32]Absolutely. I think uh to the latest KPMG survey roughly 50% of financial institution already using um agents in their in the environment is it I think most of them are proof of concept um um I would say uh most of the banks are exploring uh agent in the software development and uh so I think we are seeing a big change from I would say co-pilot work with uh using um aentic um uh AI I agentic AI to uh autopilot solution with uh using agent in in different function from from back office to IT to marketing etc. So it's a it's a big change as especially I would say the last the last couple of months. So would you say that banks are actually embracing agents or is it still in practice or is it still kind of theoretical? You know, it seems like a good idea.

[00:04:35]>> Yeah, I would say the the whole um the it it's taking time because you know I'm also working in the non-regulated business with software companies as a as board chairs etc. And the adoption of agent AI is much faster because the financial service industry is a regulated industry. And then every time if you implement a new technology like you know couple of years uh software as a service cloud or blockchain u you have to uh adjust your u uh risk method your control environment your processes. That means the regulator is expecting um you that you um make sure that these new technologies are used in uh you know following the laws, regulation and policies and so that so that means we will see a lot of testing proof of concept uh but uh huge production cases always takes time to make sure everything is bulletproof.

[00:05:38]Are the obstacles technology or compliance and regulatory issues or how >> Yeah. Yeah. The the the the technology is there. I can I can you know working with software companies that are going through the transformation the change the last um I would say 5 months after you know the stock market um uh are you know lowering down the the valuation of software businesses. there is a really huge change on these businesses to embrace um uh agentic AI meaning VIP coding or the next generation of software development and we see a huge change in that non-regulated business from co-piloting in doing software development with 20 40% productivity gain to factor two 4x more output so I I would say regulated institutions ions see this, you know, huge advantages. You know, they they're they're seeing this from a from a software development perspective. But that requires that, you know, procedures, controls, risk frameworks, policy has to be adjusted and um especially if you work out of Europe, the European regulation and laws are much more restricted than the the US one. So that puts even more effort um for the financial institutions. So I would say everybody is working on that.

[00:07:08]Um and for me the best example is that using agent is is also from a cyber cyber security perspective. Now with mythos um being available you know you use uh those kind of agent to scan your entire software libraries and and landscapes in in uh a couple of days and and try to understand any potential issues. So, so I would say very selective um I think in certain non-c customerf facing areas even more uh but the adoption has to follow first that these kind of control and risk frameworks are in place to make sure it is safe and in compliance with regulation.

[00:07:52]So in other words, the technology is the technology works, the technology is in place, people have it available, but there needs to be an alignment with the existing compliance and >> regulatory environment and people are trying to figure out how to make the technology work within that environment.

[00:08:18]>> Absolutely. Absolutely. Number I think you have to distinguish two things. One is making sure that you know in Europe there's the European AI that you have an inventory of all your AI cases including agent cases that depending on the risk classification of these use cases you have to put more human controls and testing in place and so that has to be bulletproof. The second topic is also what I see from a non-regulated software environment. You have to still put some guard rails making sure that these agents are acting in a you know uh in the same consistent traceable auditable way. No that um if you work with the agent that the the output is is in the same uh design principles um coding principles etc. So there is a lot of effort also making sure that you know um these kind of agent produce uh the expected outcome and um so that requires also a lot of governance controls oversight >> folks you can ask your questions if you are watching on LinkedIn just pop your questions into the chat if you're watching on Twitter X use the hashtag CXOT talk and because Twitter sometimes is flaky. You better at me also mcriggsman on Twitter. And take advantage of this opportunity to ask Oliver Busman pretty much whatever you want. And we have a a simple and interesting question on LinkedIn from uh Viab Mandra Wadker who says, "How are the guard rails being configured?"

[00:10:04]That's that's that's a really good question. If I look from a typical software company perspective, I think the the engineering team and head of engineering is is is is you know the job description is changing from you know actually doing the software coding etc into defining how these agent are being defined and set up and um also that is sufficient testing and oversight in place. So the the role of the engineering team is changing rapidly and their major go role is making sure there's high quality uh according to these expectation from a compliance security point of view and um making sure that this almost like the production factory is producing uh good quality outcome as expected.

[00:10:52]and he follows up with a question, another really great one. He says, "What are the core core KPIs that have helped?" He asks about UBS, but just banks in general. What are the core KPIs that help banks after implementing aic AI? I think it's you know if I look deeper into um uh the software development because that's something that I would say from my agentic AI perspective is the most advanced one is is is and that was a topic also during my time at UBS um how to measure um developer uh productivity uh efficiency code quality um I have to say there are now tools over the last 10 13 years in place that measure this kind of um productivity, quality and security of the school and that's now been adjusted also for the agentic AI environment. So whatever you do um and it's pretty standard already in financial service baselining the productivity is is absolute necessary um and then you you then you can bulletproof is the way of automation that you have in place um is that is it really factor two or three or four that everybody is is talking about is it real because that's a proof point that I would say is whatever you can do measure and that truths now in place do it and it gives you much more confidence that this this is really a game changer.

[00:12:24]>> So have your baseline set up in advance >> so that when you implement agentic AI you can actually have a a point of comparison.

[00:12:35]>> Yeah absolutely you need that because it's Yeah.

[00:12:40]>> Can you be uh give us some examples of some of these baseline measurements?

[00:12:46]No, the the baseline is how many um lines of code you you develop. Um there are quality KPIs um how the the code is being structured, how it's been um in line with the coding standards etc. So so these kind of KPIs um they're very sophisticated and uh these these tools that are measuring that they're they're working on that kind of KPIs over the last 10 years. So it's I would say it's pretty pretty standard already and uh that's the same now you have to apply this not only for a human development you have to measure also how an agent a machine is now producing this kind of um quality of code >> okay let's go to Twitter I love questions you guys you guys in the audience you guys ask the best questions you are so smart uh and this is from Arcelon Khan who's a longtime listener and Oliver sounds like you remember him from your last appearances on CXO >> 10 years already you know it's it's fantastic journey I would say know thank you so much for having me by the way >> uh well thank you for thank you for coming back and Arcelon Khan says should we deregulate banking or the AI area AI era so maybe you can talk about this issue of regulation and the restrictions and what can be relaxed or not >> you know if you if you're global if you're global like UBS uh you know with a global um operating model that means you try to to utilize as much as possible the same application um the same infrastructure etc um regulation drives fragmentation so you you have to demonstrate that in each jurisdiction that you follow the policies regulation laws um best practices etc. that that makes um production and work uh complex and uh what we see right now if I look from an AI perspective the European regulation is very restrictive um very riskbased classification um uh controls by the way it's not only for financial institution also normal corporation has to follow uh significant fines and then we see the opposite trend in the US for example that uh uh assume that you um companies will take care about the safety audit etc. and um so that's less restricted and uh the administrative overhead to do that is significant lower. So that's something if you're running a global institution um that that is is is something that you have to take into account.

[00:15:38]All right, let's jump to another question and this is on LinkedIn from Rafal Simonowitz and he says, "Oliver, when giving AI agents right access to core ledgers like SAP S4HANA, >> how should boards combat automation bias to keep humans truly in command?"

[00:16:04]It's a fantastic question and u because um um if you if you write into ledgers I think that that has to be um uh tested has to be traceability audit logs etc to make sure if there's if there's an incident um that uh that you can roll back and figure out what happened uh and I think there will be also um audit agents running so the the audit function going forward is not only that you know you bring in topic by topic and audit team and go deeper and see if there's everything uh computed in the right way I think the future is also that your oversight in you know banking called second um line of defense or third line of defense you know will be part of the production no so that's a that's a and audit firms like BWC already and deoid they're bringing their own agent now into auditing the end reporting also so you see there's a change. Not only that, you know, you bring uh agent in to produce also then the controls will be also more automated and so that you can act immediately if there's something uh is if there's a major deviation point of view. But to what extent are banks able to be handsoff on their agents and trust that a agentic audit report as opposed to require a manual or or human review because we all know the the the problem with generative AI and agents is they can make stuff up. Yep. So that's that's the point that I mentioned before is the engineering team has to put guard rails into these agents. Uh making sure that the design standards are being followed uh certain coding standards uh certain testing uh each time if you develop a new function that certain test cases are built and going through. So I would say the engineering function is changing to really guardrails quality assurance etc. Then you have again also the other function risk and audit constantly monitoring the environment. No and and and there will be always a supervisor human supervisor in the loop. So the discussion going forward will be how much you you know how many agent you you you manage as a department head in the future um in in respect to how many people you're managing. So, so the the the discussion is already changing uh from from that perspective that still as a manager I need supervision not only about my own people also for the agent in my working environment.

[00:18:57]>> I know Rivian uses uh AI agents as part of their financial processes.

[00:19:05]>> Yeah, >> they have very specific points of review. So the human in the loop is very explicitly and very carefully >> Yeah.

[00:19:16]>> built into the process.

[00:19:18]>> Yeah. And then exactly you have to define exception or topics um workflows that you know run into a problem and cannot be fixed through AI that that usually triggers know from a accounts payable perspective. Um you can you know uh uh scan invoices and and map them. um you know that's usually 90 95% and then EI tried to bring it to 100% but there will still a percentage or two out there that you need human oversight and controls perspective and I think everybody's now tried to calibrate that um and um and so that's something that I would say in a non-regulated industry is easier in in a regulated industry that the regulator usually says you know there's a black and white no So you there are rules and and with the agenic AI you know there will be a situation that certain rules um because the environment has changed will apply differently and that's something the the the at least the European regulators are a little bit afraid of that u to move away from a very rulesbased workflows to um bring other parameters into the workflow that drives a different outcome.

[00:20:41]>> Yeah. Well, the idea of handing essentially regulation over to the firms themselves.

[00:20:50]>> Yeah.

[00:20:51]>> Uh is potentially problematic for obvious reasons.

[00:20:55]>> Absolutely. Absolutely.

[00:20:57]>> Okay. We have uh another interesting question and I encourage you guys ask your questions. If you're watching on LinkedIn, pop your questions into the chat. If you're watching on Twitter or X, use the hashtag CXOT talk and and M criggsman me directly as well. And this is from Chris Peterson and he says in the f in the financial realm is agentic AI built on LLMs and GPTs or embracing other forms of AI to get the transparency and explanability that auditors demand.

[00:21:39]I would say it's not only limited to to um large language models, agentic um or to I would say machine learning is still a big portion of a lot of um functionality in uh in AML antimoney laundering or some analytic capabilities. I would say I would not exclude that. So um even I would say the combination with blockchain everybody knows that I was pretty I'm still pretty active in the blockchain DT community is a pretty good way because then um you have a bulletproof lock of your decisions not that you can't manipulate and so I would not exclude any other technology being part of a much higher level of automation and uh driving better outcomes. So definitely I would not exclude that.

[00:22:34]>> Okay. Uh and we have another uh interesting and short question. Uh great question from by Bob Bibb Mandro Wadker and he says is there any specific scenario or use case where agentic AI is not suited to a regulated industry?

[00:22:59]Hm.

[00:23:00]I would say I would say the regulator if you ask me you know in front sitting in front of the regulator um everything that could be biased into a credit decision or security oversight that means monitoring u people on the street that's something the regulators let's say from a European perspective I classify this as a highrisk topic um is something that that um I would be careful because then you you have to put in a lot of effort to make sure that there is not misuse of this kind of information or decisions etc. So, so I would say I I like the classification the way how European governments are looking at these cases and if they're high risk cases and that these are the the most important one credit decision on on consumer etc that you know goes beyond uh information that is usually available and drives decision that's something I would be careful yeah >> that's quite interesting because it sounds from what you're saying that the regulators are very concerned about the uh judgment decisions.

[00:24:17]>> Yeah.

[00:24:18]>> Made by a AI as opposed to the AI making mechanical mistakes.

[00:24:26]>> Yeah. Yeah. Absolutely. Because at the end they're also very keen, you know, from a European perspective kind of consumer protection, client protection, etc. are making sure that these kind of technologies are not driving a disadvantage for um consumer from that perspective. So that's something I would also that angle is maybe not so important in other jurisdiction.

[00:24:50]Yeah, it's uh I I'm I'm halting on this particular point because most technical technology discussions around agentic AI >> focus on the mechanical aspects hallucinations and getting things making things up.

[00:25:10]>> Whereas in this instance, you're looking at the higher level >> Yeah.

[00:25:16]>> set of judgment issues.

[00:25:18]>> Yeah. Yeah.

[00:25:18]>> The real intelligence I think I think the the I think the independent how risky um um uh how risky um AI use case is um the biggest biggest value that you know a bank has to take care is trust.

[00:25:40]So today if you act as a financial institution whatever you provide produce has to be you know according to the highest level of trust because I as a as a bank customer have to rely on on the bank that they're processing all my my business in the right way and it's it's it's accurate it's it's traceable is um auditable etc and And if you know I'm waiting for an incident in the industry that that holination is or some other issues that that drives to an incident um then then your whole trust and reputation can be gone in a few minutes. No, you saw you saw that KPMG a couple of weeks ago was under pressure. that put a research report out there and about usage of AI in financial services and there was a lot of other solution in there uh wrong data and you know financial intuition had to push back on that so that has an impact on trust on uh can I can I in confidence can I use this kind of services now and so that's something I would say you know if you look at big crisis in the banking industry bank run is based on people customers are losing trust and so so that's the worst case scenario that you as a as a bank has to be super careful that whatever you do embracing emerging techniques like AI making sure is there still that the trusted relationship between you and your customers are still intact and will be not not jeopardize that just to maximize certain certain uh profits for example >> very interesting And folks, if you're interested in this topic in September, we have coming up a show with the executive vice president and head of ethical AI and also head of product from Salesforce. So, subscribe to the CXO Talk newsletter. or go to cxot talk.com, subscribe to our newsletter so we can send you updates and you can join us for that conversation which I think uh relates very very directly to this issue of trust that Oliver was just describing.

[00:28:10]So we have another question now from Arcelon Khan who's on Twitter who says are banks creating their own AI infrastructure buying chips and so forth or are they just using Google and others for AI services >> to my knowledge I think I think um I think there's only one bank JP Morgan announced that they are also building their own infrastructure with uh chip provider and not the typical Nvidia etc. Um but that's pretty I would say it's more an ex exemption I think because you need significant investments um people going into this hardware topic no and uh only large bank with you know JP Morgan the US banks are usually are you know among above 10 billion uh dollars of budget IT budget per year so there there is scale etc but that's not the normal spend level for um uh uh normal national banks. So, so I would say that's more an exception and uh um more tech players uh with the volume and scale can can do that.

[00:29:23]>> Yeah, this makes perfect sense. All right, we have another question. Guys, keep your questions coming in. It's like it's so awesome to get your questions and I am I keep saying this. I'm constantly amazed at how smart you guys are. This is from Alexander Yin on LinkedIn who says uh what is your confidence level that agents in a in the same LLM say Claude for example or ChatGpt would be independent enough to quality control their own work by another agent in the same model or do you need to use agents from different companies word does it make no difference?

[00:30:07]>> Yeah. If I if I apply, you know, my experiences uh in my day-to-day work life um using different large language models, crossmodel uh validation for super important uh task is mandatory because it gives you gives you a much higher confidence that whatever you produce is is correct and validated. Now the for again important outputs is always human checks and reviews is um like like the important research uh report that we talked about KPV is mandatory in production. Yes ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab ab absolutely if you automate more um you you use different models or um teams that operates agent in a totally different way not like like the auditors or audit firms are doing.

[00:31:00]All right, let's go. I'm just going from question to question. We have lots and lots of questions stacking up and I encourage you guys keep your questions coming in. And this is from Mari John N who says if you need to make a prediction, how will entrylevel roles change as a AI advances? So the impact of aentic AI on entry-level job, >> it's a it's a fantastic question and it's a it's a question that I would say is um is top of my mind um because I'm I'm member tech advisory board member of a nonprofit organization that helping over 100,000 students to um to get the right jobs etc. And we see in the last couple of years that demand for junior software engineers are dropping you know the job advertisements are down by 40%.

[00:32:00]What we are seeing still is that tech companies um hiring uh juniors uh graduates in uh like I think Salesforce announced that they still hiring even more than last year. The difference that what I see is they're looking uh for uh graders with AI background and using this for their new AI project to automate certain business function. I see uh in high-tech firms and also now in banking this kind of centralized FDE uh forward deployed engineer model that goes into business department understand the the breakdowns the process improvements and use these kind of um agentic AI models to to automate this as much as possible and so they need the juniors to um to work on that and then build expertise within the first three to five years to become a supervisor of this kind of topic. So, so you can't stop hiring juniors because at the end uh someday we need them as a as a as a very senior person. On the other side, the the work project work of financial institution and high-tech firms and transform business requires um uh employees with really deep understanding about AI models and the way how AI is working. So that's something I would say is an opportunity. On the other side, I have to say there is fear out there. Uh if you talk to the jung to my family members etc. they're saying you know I'm studying and what will be my future uh job be my message is don't be afraid be a force mover. Spend time with these models apply this to the maximum and your expertise will be in high demand.

[00:33:54]No, the same also for the older generation. So, um people, you know, um at the end of their career or uh they're saying, "Oh, I don't have to learn that." It's almost like I'm I'm comparing this like 10 years ago saying, "I'm not using the iPhone. This is something that maybe is not so so important." No. So that's something I would say is learning is a lifelong um experience and must do and uh um so don't be afraid that that gives open up more opportunity but definitely job description are changing and changing mean now we have different kinds of jobs. If I look at the software companies that I'm I'm supporting, we have a discussion now what's the role of the CTO, head of engineering, head of product management and sales because work is being shifted from the engine room into more the pre-sales organization. So, so it's natural that uh through technology, you know, and we saw this, you know, Michael, we in the business for so many years, it's changing and you know, it requires an open mindset and the willingness to learn.

[00:35:08]But Oliver, if you're a midlevel programmer, software developer, yeah, >> inside a large organization, >> isn't it undeniably true that your job is going away because AI tools can do software programming so development fabulously well?

[00:35:34]I think first of all there is a need for really good engineers to making sure that these agent are be defined in a professional way and maintained and expanded that there that is definitely a way second topic is is what I see is we are moving into you know you know we've been through this from a very standardized SAS model into more I would say AI customized a custom SAS model so Your engineer uh capability will be required in more on-site customer projects to understand the problem and define pro problem prompts and and input for then your agent product factory. So demand for uh software engineer then onsite within your business environment uh will will be higher. So, so the message is I think um if I look at studies saying the amount of certain work that's very traditional will come down will be compressed but there's more demand for digitalization and customization no that we couldn't do because engineering was always the bottleneck the coding bottleneck that now is going more to customerf facing understanding the problem and and the developing uh new functionality instead of months, we're talking about days now. That's the game changer that everybody's now try to figure out how to do that. And and this kind of change will lead to more business and engineers will then be more project driven than working on core software functionality.

[00:37:29]Well, there's no doubt that uh the key here is having an understanding of the technology so that you can manage that technology.

[00:37:41]>> Yeah. And most importantly, having a a deep understanding of the business and the workflows and what your customers actually care about so that you can create technologies and guide agents to actually solve the most important problems.

[00:38:01]>> And that's that's the the right message.

[00:38:03]At the end, we will go much deeper in the vertical topic. know that you need not not only the software also domain expertise to to understand problems and fix them and and again this will open up more opportunities. So I'm I'm personally I'm not afraid of this kind of change because it it leads to more um chances to more businesses and um and like now who has moved my cheese now the cheese is moving right now you have to figure out where it is now. So that's the that's the best uh way to describe that.

[00:38:36]>> I don't want to belabor this, but I'm a little bit I'm not quite as optimistic as you because of everything you're saying of course is accurate, but I also understand as as you do very well the difficulty that we humans have changing our roles and learning new skills.

[00:38:58]>> Yeah. Yeah. and and that's ba based on my my my my journey my learning curve over the last 30 30 I'm in the business over 35 years to to be not afraid to take something new on so my risk profile is maybe different because I lived in different countries different companies uh been through a lot of new innovation cycles and my learning is if you're the front runner uh in in testing this and utilize that it's eye opening, it's fun, it's motivating and that opens new doors. No.

[00:39:33]So if I if I share with you the way I I'm also using the technology in my own business, it's eye opening. So the productivity gains and work and the quality of work that I'm now uh working with my uh companies with my firms etc. If I compare this with a year ago has changed so much that uh you know if I look at office productivity we are we are used to build powerpoint and reports etc you know it's sometimes you question the value of that that's now possible in a couple of minutes or hours then days and if I look at the the amount of quality work that I'm now able to produce so so that's the that's an experience that I would say helps me to um increase my confidence that if if you do this in the right way, we have the right guard rates in there and the way how you now use know how what what technology is best for your your different use cases is it's is it's really it's it's fun and uh so that's something that I I want to encourage everybody to to to uh don't miss that opportunity.

[00:40:47]>> I have to second that. I mean, my work has changed so much because of agents.

[00:40:52]It's it's amazing. Okay, let's uh let's take some more questions and Oliver, we're going to run out of time and I want to get to as many questions >> and then also monitoring the overall outcome. Is it in line with defined KPIs etc. So that's again the the split is between human and agent and the numbers of agent per human. My experience is depending on the criticality of business processes, regulatory requirements, etc. But that's something that you know will change our day-to-day work.

[00:41:26]>> Okay, fair enough. It's um something of a moving target or let's put it this way requires a level of judgment based on the use case.

[00:41:37]>> Yeah, absolutely. Okay, this is from Yannik Ishimui and he says, "What is the real threshold that separates an agent that a bank will trust to advise uh from one it will trust to execute and who should own that decision?"

[00:41:58]>> Great question. That's a that's a that's a very good question because at the end is is um it it it really depends on how these use cases been classified as a risk risk that uh could lead to wrong outcome impacting customers impacting businesses and that drives this classification provides the level of cross model validation human verification ation cross checks etc. So, so that's the that's the logic that I see in my in my in my board discussion is you know is the governance in place from a inventory from a risk classification and is up the appropriate depending on the risk classification controls cross models human oversight measurement in place to make sure that there is no no deviation.

[00:42:56]>> Okay, great. Uh, and I'm going to people who have not asked questions yet. And once we get through all of those, we'll circle back people who have second questions, of which there's a whole bunch. This is from Reza Satari who says, "What would it take for a regulated bank to accept transformation deliverables, target architectures fit to standard decisions produced by AI agents rather than an army of consultants? Is that a model risk question, an audit trail question, or a culture question?" And I'll just add the commentary that Reza has just brought the entire has just brought the entire consulting industry under a microscope with this.

[00:43:48]>> Yeah, absolutely. And that's the that's the the discussion that it's an ongoing discussion is you know as financial institution are going through that learning curve how these agentic AI tools are being used and then the productivity boost and the quality um improvement um if you have the right again if you have the right graduates in bay um so I think the the the line between external and internal support is shifting that that's the reason why you know software companies and consulting firms like an Accenture etc. they have to change their business model. So business model means they have to ramp up also the way how the AI tools been utilized um uh uh being implemented also the training of those uh consultants etc because the expectation level now if you've been through that learner curve on the customer side the expectation level is much higher and you do um provide services much faster for a reduced cost. You see this already in the legal industry. Legal industry is now using uh large language models totally customized for their jurisdiction. So the amount of work to draft new contracts, review them is coming down. And so the customers are not willing to pay any more $5,000 per per day. So and that changed the whole pricing, valuation or outcome discussion. So I would say absolutely right. It really depends on how quickly you can ramp up your expertise and tools utilize enough tools inside internally and then you see if somebody else outside is still up to that expectation level which is much higher than currently.

[00:45:38]And of course uh the major consulting companies are and have invested billions of dollars in AI training of and tool development because they know their business is >> shifting rapidly.

[00:45:53]>> You see consulting firms also their valuation is down this year 40 and more percent. So I think there there is a crossindustry change going on and and uh and that means there will be winners, there will be losers and and the the companies that embracing this um have a good chance to survive. And folks, if you're interested in consulting, we a few weeks ago had the head of AI for McKenzie as a guest on CXO Talk and he addressed a lot of these issues at at a pretty high level, but it was kind of fundamental to the discussion. So go to cxot talk.com and you can >> listen to that. All right, we have uh another really interesting question from Dr. Alexander Bachelman who is the group chief technology officer of Helvetia Balwis's group and has been a guest on CXO talk.

[00:46:48]>> Yeah. And we work together so at Alian.

[00:46:51]So yeah, great to hear.

[00:46:53]>> Oh, I didn't realize that. Oh, okay. So >> yeah. Yeah.

[00:46:55]>> Oh, so it's a a small world. And Alexander says, Oliver, switching to the customer side of the topic, do you think or by when do you think will customers use their digital twin or agentic AI assistant to execute their banking tasks?

[00:47:19]And by when will the business model include such things as uh machineto-achine banking? Yeah, I think that the super question Alexander, thank you so much. And now if I look at the agent to Asian use cases, I think the the agentic payment topic that Visa and Mastercard working is is the maybe the first one. And here we have exactly the same topic to making sure that the agent is really excell and not somebody else and and um and making sure that this transaction that been initiated are legitimate and not being fake. Um and that's something uh everybody's working on that. Um do I think we are right now ready for prime time? I'm not sure because at the end it requires also that there is a digital identity for Alexander that everybody can verify and and I know that the European uh commission is working on certain governments on on those kind of digital identities and but that has to be in place that a digital twin is verified that's you and not somebody else and uh everybody's working on that I hope hopefully this will accelerate because uh uh without that verification it will be hard to to do mass volume of transaction uh for agent to agent transactions.

[00:48:45]>> Very interesting view into the future of banking. All right, let's go to the next question. Uh keep your questions coming in folks. We have some some time left.

[00:48:54]This is from George Mertens who is a CFO and he says Oliver from your perspective which AIdriven business models are emerging in financial crime compliance for example shared KYCL utilities or compliance as a service.

[00:49:16]Could these models turn compliance from a cost center into a revenue generating capability?

[00:49:25]Yeah, it's a it's a really good question because you know the the the compliance models, utility models now to do KYC known your clients as a utility um it's a it's a known issue in the industry for the last 10 15 years and uh most of these um utility setups failed because there was not enough critical mass um sponsoring this kind of development. If we then move on into more decentralized work that you know an agent could you know reach out to another bank with the right identity to validate this maybe that's that's the chance to do that because these kind of centralized util utility models is hard to establish. So, so overall I would say if you look at the uh value chain of financial crime I think agentic will help us u because uh these financial crime categories are in silos. Now you have a client on boarding client refresh um transaction monitoring sanction etc. These are usually specialized application and data silos and agent you know with AI you can connect them much better uh to a 360 risk counterpart view and that's exactly I think the industry is going is if there's innovation one trigger that maybe will uh drive a refresh of a client data point of view. So I I would say the technology is really um a a gamecher for the financial crime fraud topic and uh we see already companies working on this kind of embedding this kind of technologies >> and on the subject of technology Alexander Bachelman comes back and he says the counterargument is coding is not the bottleneck it is the organizational change required for agentic process redesign and adoption.

[00:51:19]absolutely is spot on because again um uh if you have set up your right agent coding is not the problem is then you move more to the business analysts and business people in the business department that uh you know has to you know you have to train and and and and bring up to speed how to utilize uh um to analyze processes and breakdowns and able to um formulate prompts that you know leads to software development at the end. So I would say as I said from a software development perspective we are shifting the focus into understanding the business business functionality business analyst and then also product manager bringing this kind of um uh uh requirements into the product. So that shift um uh means we will have much more focus on on people front office business analyst product manager dealing with the issue instead of you know that the engineering team is becoming the bottleneck uh for these kind of changes and that that again it's we are coming back to job definition uh allocation of headcons to this kind of value chain change.

[00:52:37]All right, let's uh move on quickly to the next question and this is from Logiroot runtime AI governance with verifiable and I can't read the rest because verifiable audit evidence.

[00:52:52]>> Yeah, >> that's his name. Okay. And lodger runtime says when a regulator or a court asks a bank to prove this agent did exactly what you say it did >> and prove nobody touched the record.

[00:53:11]>> Yeah.

[00:53:12]>> Does today's tooling actually clear that bar or are we one incident away from learning that having logs and having proof are not the same thing?

[00:53:23]>> Yeah. the I think you know it's independent of the technology you know certain governments say you know whatever technology you're using make sure there's traceability audibility there and that you can verify the transaction and then you can see if there's any misstep uh from that perspective you can go to the next uh level of maturity saying you know I'm using blockchain that nobody can even correct u um transaction or information past that uh production time so so I would say um I that's not a specific agent topic. I think we always have to prove that you know these kind of businesses are falling according to the standards and and rules of of uh the local jurisdiction and you you need that evidence in place.

[00:54:11]>> Okay, let's go on to the next one. And this is again from George Mertens. And I'm taking this because he I like questions from CFOs. And his question is something I was going to be asking you anyways.

[00:54:26]>> And he says, Oliver, from your perspective as a supervisory board member, how has AI changed the way you exercise oversight?

[00:54:38]>> I think the the it has changed. Number one, I think the um in if I look at financial financial um institution that I'm uh in general, I would say the the board agenda focus has shifted the last couple of years into much more operation risk. operational risk is most of them is is using of technology and operations um for the day-to-day banking and and that means you know with the new technology uh board members you know first of all have to be up to speed you know what does it mean using AI and what are the risk the limitation and the way how this been oversight etc and their role is making sure you know if if if I talk to the to the ECB as my regulator later they're telling me make sure that you know for this kind of topic there existing regulation like the European AI act the DORA um jurisdiction how to classified vendors and issues um is been followed for this kind of issue so you know as a supervisory board member what you do is you ask questions you know how's the setup how's the governance how show me evidence that this is in place that um we have the right uh safeguards in place to utilize this kind of new emerging technology.

[00:56:02]>> All right, let's go to the next one. Uh plowing through the questions as we drive towards the finish line because we don't have much time left. And this question uh is another really interesting one. Uh you know what? Let me also mention if you're asking questions, connect with me on LinkedIn because I'd love to be connected to you and I'm sure Oliver would as well.

[00:56:25]>> Absolutely. The same too.

[00:56:26]>> So find us on find us on LinkedIn and connect and this one is from Ricardo Werhan and he says Oliver in the past new technology has enabled the creation of digital native banks like Revolute.

[00:56:44]Mhm.

[00:56:45]>> Do you expect AI native banks to emerge and how would they look like?

[00:56:52]>> I think I think um that's a that's a that's a that's a good question. I think first of all these um digital native banks are also embracing like Revolute and other banks this kind of technology from automating processes also or changing the engagement between customers and and the bank. Um do I see um new software companies calling AI agent software out there and saying we have 200 agent? I see this too. Um do I think over time we will be an agentbased um uh bank out there? Yes. But what I learned over the last 35 years is um clients love to talk to a person to a human person. So I don't think that we will have a fully automated uh agent-based bank out there without any humanto human interface >> and just the trust issue of are you really going to trust >> AI agents? I mean eventually maybe but we're not there yet. We're not even we're nowhere close.

[00:57:58]>> And Michael that's the experience of the last 15 years with these robot advisor.

[00:58:01]You thought about you know everybody is you know going online try to analyze their their financial situation and you get a recommendation then you execute to reshuffle your your $100,000 $150,000 portfolio that not really people like to talk to a banker at the end saying you know is it a good decision and then usually you see this kind of execution >> you know it wasn't too long ago where if you had what appeared to be an error on a bill for example from utility company or whatever People would say, "Oh, the computer made a mistake."

[00:58:35]>> We don't we don't say that too much now, but the reality is we don't we don't trust computers. Uh not not when it comes to issues that really matter that seem to be going against what we want.

[00:58:51]>> Exactly. And that's something I think we always have to think about. um the human interface now and there will be wealth management, private bankers, u advisor still in the loop with u with the client but the way this advisor financial advisor will be supported uh will be much more flexible and customized that um that today is more standardized and limited.

[00:59:19]Okay, very quickly, Oliver, uh important question from Arcelon Khan. Really fast, please. Uh with so much use of AI, what can consumers do to protect themselves from hallucinations?

[00:59:33]>> From a consumer perspective, my my advice is always crossmodel uh checks. So whatever I do and depending on the criticality I use different models to verify that and that's is is always reducing the risk of um any misinformation etc. And then my personal last check too. So uh don't forget to read uh important information outcomes that you want to share making sure that everything is correct.

[01:00:07]And the last question, a year from now, are banks running agents in production at scale or are we still having this conversation about all the obstacles and why we can't do it and why it's a good idea and we know it's a good idea, but it's we can't really do it. I'm I'm very optimistic that we'll see um a lot of agents across you know all function in in production with the bank because the the upside the the the the productivity gains and the the quality improvements are significant as I said the co-pilot area is 10 10 20 30% improvement here we're talking about uh 1 2 3x in certain use cases So if you have a front runner of looking at your competition and if that that institution is achieving that has huge impact on performance cost uh uh cost income ratios etc. that's something that the peer pressure will will even drive more adoption.

[01:01:15]>> Okay. Oliver Busman is the former group CIO of UBS. He was the former CIO of SAP many years ago, which is when I first met Oliver, and he now advises banks across Europe. Oliver, thank you so much for taking your time to be with us today. I'm very grateful to you.

[01:01:39]Everybody, thank you for watching, especially you folks who ask such great questions. Before you go, number one, connect with both Oliver and me on LinkedIn. We we love that. And number two, subscribe to the CXO Talk newsletter. We have just I I always say this and it's really true. We have extraordinary shows with amazing people coming up and we want you to join us and participate. Thank you so much everybody and I hope you have a great day and we'll see you next time.