AI Strategies for Finance Leaders to Streamline Audits and SOPs Using GenAI with Natalia Toronyi
In this episode of Future Finance, hosts Paul Barnhurst and Glenn Hopper welcome Natalia Toronyi, a finance executive with nearly 20 years of experience in financial transformation. The conversation delves into Natalia’s impressive journey from navigating life during the collapse of the Soviet Union in Ukraine to leading financial transformations in major global companies. The episode explores how AI is reshaping finance, particularly in the areas of internal audits, treasury, and talent management. Natalia shares insights on the real-world applications of AI in finance, discussing both its potential and challenges.
Natalia Toronyi is a finance executive with nearly two decades of experience leading financial transformations across various sectors, particularly in Fortune 500 global industrial manufacturing companies. Known for her problem-solving mindset and focus on data-driven decision-making, Natalia has led numerous automation and digital transformation projects. Her passion for people-first leadership, integrity, and innovation has made her a trusted leader in the finance space.
In this episode, you will discover:
How AI is transforming the finance function, from internal audits to treasury management.
The importance of creating well-defined processes and SOPs as AI continues to integrate into business functions.
How automation and AI tools can optimize working capital, cash flow forecasting, and financial risk management.
The evolving role of talent in the finance industry and how leaders should focus on developing strategic and critical thinking skills.
Key considerations when evaluating AI tools for finance teams, including scalability, security, and ROI.
Natalia’s expertise makes this episode a must-listen for anyone interested in AI and finance. Her insights on AI in audits, treasury, and talent management offer valuable takeaways for both finance professionals and tech enthusiasts, inspiring new ways to approach decision-making and business processes.
Follow Natalia:
LinkedIn - https://www.linkedin.com/in/nataliatoronyi/
Join hosts Glenn and Paul as they unravel the complexities of AI in finance:
Follow Glenn:
LinkedIn: https://www.linkedin.com/in/gbhopperiii
Follow Paul:
LinkedIn - https://www.linkedin.com/in/thefpandaguy
Follow QFlow.AI:
Website - https://bit.ly/4fYK9vY
Future Finance is sponsored by QFlow.ai, the strategic finance platform solving the toughest part of planning and analysis: B2B revenue. Align sales, marketing, and finance, speed up decision-making, and lock in accountability with QFlow.ai.
Stay tuned for a deeper understanding of how AI is shaping the future of finance and what it means for businesses and individuals alike.
In Today’s Episode:
[03:27] - Natalia’s Background
[10:09] - Treasury & AI
[13:08] - AI in Audits
[17:07] - Evaluating AI Tools
[24:39] - Talent Development & AI
[28:10] - Future of Finance
[32:41] - Advice to Younger Self
[33:23] - Leadership Across Cultures
[35:02] - Episode Wrap-Up
Full Show Transcript:
[00:01:47] Host 2: Glenn Hopper: Welcome to Future Finance. I'm one of your hosts. Glenn Hopper and our other host, Paul Barnhurst, are with me here today. And our guest today is Natalia Toronyi. She's an executive with almost 20 years of experience leading financial transformation across fortune 500 global industrial manufacturing companies. Natalia is known for her sharp problem solving skills, curiosity and transformation mindset, especially when it comes to driving impact through AI, automation and data. She's led major restructurings, M&A, and operational turnarounds, all while staying grounded in integrity and leading by example. She's also a passionate mentor and a strong advocate for people first leadership always focused on building trust, empowering teams, and creating cultures where innovation and inclusion thrive. Natalia, welcome to the show. Thank you.
[00:02:35] Guest: Natalia Toronyi: Inviting a nice introduction.
[00:02:38] Host 2: Glenn Hopper: Yeah, you sound like you are what Paul and I aspire to be.
[00:02:43] Guest: Natalia Toronyi: I don't think so. I have a long way to go. So we're all learning.
[00:02:48] Host 1: Paul Barnhurst: I just expired, actually.
[00:02:50] Host 2: Paul Barnhurst: Be considered an adult, Glenn. I can get there.
[00:02:53] Host 2: Glenn Hopper: That's why you have the beard.
[00:02:54] Host 2: Paul Barnhurst: You'll both laugh at this. Then we'll jump into the questions. So someone on LinkedIn the other day shared that he was invited to speak at something and they listed him as Microsoft Excel Adult, which meant he was the winner of the adult category, not the college. But the way you read it is that he was a Microsoft. He's like, I've never been considered a Microsoft Excel adult before, and we all kind of tease him like, welcome to adulthood. It's amazing what not having a dash or just those simple little things in grammar will do. Yep. All right. So let's start here. We'd love for you to share a little bit of your journey. I know you live in Ukraine, Budapest. Yeah. Now in the US you've worked for several different companies. Just tell us a little bit about your journey and background and how you ended up where you're at today.
[00:03:40] Guest: Natalia Toronyi: You know, I was born in Ukraine in a small town called Uzhgorod. It's in western Ukraine, and I grew up during the very interesting time when the Soviet Union collapsed. It was a very chaotic time of chaos. I lived through hyperinflation with a daily currency revaluation. You know, people worked for six, nine months without getting paid until a majority of the companies just completely collapsed and shut down, and everybody had to figure out how to survive and how to live. So when I'm looking back, I still don't know how my parents managed to put the food on our table every day. For us, it was just normal. We were kids. We didn't pay too much attention to it. But now when I think back, it always comes to my mind. You know, there was the great lesson to learn no matter what's happening around you. Giving up is not an option because you have to survive. So in my early 20s, I moved to Hungary. You know, I graduated from National University with a major in accounting and audit. And when I moved to Hungary, I realized I actually have to start from the very beginning, from the scratch, because not only my degree is from Ukraine and I need to now reevaluate it. I need to start from the very beginning, which is Hungarian, is one of the most complex languages in the world. So I started to learn the language from the cartoons, which at that time my daughter was watching. And the second thing I learned in very small details was the law about accounting and audit.
[00:05:25] Guest: Natalia Toronyi: Because I was ambitious and I wanted to continue my career or restart my career. And I had to deep dive into it. How can I do that? So I reevaluated, I had my diploma reevaluated, and I kicked off my finance career in Hungary in a small CPA office where I was the best employee because I could not participate in any other discussion than Then processing the numbers. But, you know, that gave me an opportunity to learn the language and improve it every day. So shortly after I joined my first corporate company, Alcoa, where, you know, with help from excellent teachers and mentors, I learned I first was exposed to the U.S. GAAP. I was given a better visibility of what it meant to operate within global processes, what Sox compliance means, and how to contribute to performance. In the global corporate company. Uh, I think that company Alcoa has had the best, uh, and cleanest processes I've seen during my 20 years, with a huge focus on sod segregation of duties and Sox compliance. So that was my base, which gave me a very strong step into finance. After this, I joined multiple companies changing, uh, you know, growing in my career through BP and signaled I let global finance teams, uh, delivered numerous transformation projects, you know, implementing different automations. Now, I, I help to build finance operations from scratch, uh, centralizing multiple departments into one finance departments through cyber incidents and fraud recoveries. And always in my work, I always champion innovation, automation, data analytics and better processes. So that's my story.
[00:07:29] Host 2: Glenn Hopper: That's amazing. And I think that the adaptability that you had to have in moving to a new country, learning a new language, learning, accounting in that new language had to be just so much to work through. But it seems like what the takeaway from that is, is that you learn to be resilient, you learn to be adaptive. And that's I mean, things that you would apply for the rest of your career. So, you know, putting in the hard work early and going through that. It seems like it served you well through the rest of your career. It did.
[00:08:00] Guest: Natalia Toronyi: Now, I can say it that way. Back then I probably didn't.
[00:08:04] Host 2: Glenn Hopper: Yeah. At the time. Yeah.
[00:08:05] Host 2: Paul Barnhurst: Yeah. Often the case right at the time you look at something and go, why am I dealing with this?
[00:08:10] Host 2: Glenn Hopper: Now, though, I kind of want to watch Hungarian cartoons.
[00:08:14] Guest: Natalia Toronyi: Well go ahead.
[00:08:16] Host 2: Paul Barnhurst: Let's do that tomorrow, Glenn. We can carve out.
[00:08:18] Host 2: Glenn Hopper: Yeah. All right. Yeah. Special episode. Well, going through all that, I mean, you know, future of finance. And Paul knows every time I get up, the first question I ask is going to be about AI. So as soon as you mentioned AI. I kind of locked on to that. And I'm, I'm wondering with your journey through accounting and finance and all the transformations you've done, how are you seeing the latest generative AI and technology reshape the way that you work today? And will you work in the way that the profession is, is changing and will change?
[00:08:51] Guest: Natalia Toronyi: I think it's completely reshaping industries and, you know, industry and all the departments, not only finance, uh, a lot of times people are asking questions, you know, which department will be impacted, which department will not be impacted. I can see even looking into finance organizations, every department will be impacted. Uh, and we already saw a lot of automation in, um, complete order to cash process, procure to pay process, GL cost accounting. I can see a lot of departments transitioning and merging. Where now? , they will be more complex and more connected. So AI is bringing opportunities, you know, to different analytics. It gives us visibility. Uh, it changes how we forecast. It's providing now different business insights, which before we didn't have. I think it's shifting the departments and changing the entire departments and how we do the how we even process transactions.
[00:09:56] Host 2: Glenn Hopper: Yeah, absolutely.
[00:09:58] Host 2: Paul Barnhurst: I agree. It's you mentioned, you know, consolidation, uh, really maybe some complexity. There's just so much going on. And I'm curious. I know you've worked in the Treasury. What's your thoughts in the Treasury space as far as I. Are you seeing or do you think we'll see a lot of benefit around cash flow forecasting? You know, working capital optimization, FX risk? We'd love to kind of get your thoughts in that space of maybe, you know, what you've seen and what you think we'll see there.
[00:10:26] Guest: Natalia Toronyi: Uh, you know, I mentioned a couple of minutes ago that departments will start to transform and change the way we are pulling the information and analyzing that information. Uh, you mentioned cash flow forecasting, which historically was a Treasury function and working capital, which is r AP. Inventory management. I think those two processes will now be more blended and more merged, because now we will not use only information from the bank statement to forecast cash, but we will dip into our ERP systems, gaining visibility of customer behaviors, vendors, terms and how we schedule inventory demand. All those activities will be transformed and changed on a more precise data point. We can now put incorporate macroeconomic indicators or we can build our models more precisely using all that information.
[00:11:34] Host 2: Glenn Hopper: Yeah. And you know, when you talk about bringing in macroeconomic data and sort of pulling in data other than whatever drivers you have internal to the company. That makes me think back to, I guess we'll call it classical AI. Machine learning what's been around for years, and a lot of people just today are just talking about generative AI. And of course, that's taken the world by storm, and it's made classical AI more approachable and accessible because you don't have to get through it, through Python and coding and all the barriers to entry we used to have. And as I travel around and and talk to different groups, um, an area that is I was going to say near and dear, I don't know if that audit is near and dear to anyone, but it's certainly.
[00:12:15] Host 2: Paul Barnhurst: I was going to say, when is the audit near and dear? I usually run from the auditors. I say that out loud.
[00:12:21] Host 2: Glenn Hopper: So near and dear was the wrong choice of words. Audit is something that anyone in the public company space certainly is very familiar with, and it's an area that is very time intensive and very, you know, a lot of the work that we're doing in an audit is or for on the auditor side as well, is, um, Very, uh, it could be repetitive and it's it's complex. And I get asked all the time, is AI going to help with this? And the answer is AI is going to help with everything. But I don't know. You know exactly when. But I'm wondering from your perspective. How do you see AI helping? I don't manage the audit process. Do you have tools that you're using today and that could be generative AI or even classical machine learning?
[00:13:13] Guest: Natalia Toronyi: I think probably.
[00:13:15] Guest: Natalia Toronyi: One of the first starting points, which I would implement if I would start utilizing AI in multiple finance processes, would be, uh, internal audits. And I would start with creating well defined written processes and controls. Probably the first option which comes to my mind would be genii through OpenAI, creating the GPT, uploading the clean defined processes SOPs into that, uh, AI agent or uh, assistant and starting to, uh, create the instructions for all the, you know, departments. And based on that, also utilizing the same document created and and cleaned for, for internal audits or for pulling information for the audits. I can't even say that it would be an annual audit now because, uh, before we would create some, you know, processes where we would, uh, use, uh, random samples selected for auditing. Now, uh, this quick, fast tools, they give us an opportunity to, uh, to test not only samples or not only 30 or 50 samples a month, but we can test, you know, 30% of the samples or 50% of the information to look for deviations, to look for exceptions, and only pull those which don't align with those clean, defined processes and SOPs. So I didn't see and I can't mention any AI tool which is helping with the audits right now, but that would probably be my main focus because from there you can build all the processes correctly without, you know, that's your base.
[00:15:10] Host 2: Glenn Hopper: Yeah, it goes hand in hand with controls. And, um, it's funny, as I talk to people, I don't know what it is. And Paul's probably heard me say this before, but I would go and I would talk to a room full of CFOs, controllers, auditors, you know, just all across finance and accounting. And it was always an auditor who came up to me and would say, hey, I could never replace my job. And I always think, is there anything more rule based than audit. You are perfectly set up to be replaced by the bots. And if Paul, if you can let me get up on my soapbox here. What I love that you said.
[00:15:42] Host 2: Paul Barnhurst: Go ahead, get on your soapbox, Glenn. It's been a while. Yeah.
[00:15:47] Host 2: Glenn Hopper: Natalia, I loved what you said. Um, about documenting processes. So I got my career started in the military. And in the military. You had to have standard operating procedure for everything you did. And I have every role I've had. Um, I'm documenting what I do. And then when I had direct reports and all the way down through my departments, we're going to document everything. And now, you know, seeing where we are with AI, I feel like, well, that was a really prescient approach. If I do pat myself on the back here because the same, uh, the same SOPs that we would use to train new employees. Well, guess what? Those are going to be used to train agents that are going to be doing this job in the future. So if you haven't put your SOPs together, this is my soapbox, Paul. If you haven't put SOPs for sops for everything you do. Do that now, because in the very near future, we're going to be using those SOPs not to train new employees, but to train the bots who are going to take our jobs, I guess. Yep.
[00:16:43] Guest: Natalia Toronyi: So if your SOPs are not correct, the AI will generate full cows everywhere.
[00:16:50] Host 2: Paul Barnhurst: So I shouldn't admit my SOPs are a mess. How good are your recipes for your business?
[00:16:57] Host 2: Glenn Hopper: I didn't know you had a procedure for anything, Paul. I thought you were just winging it from show to show.
[00:17:02] Host 2: Paul Barnhurst: That's why I don't have any SOPs.
[00:17:04] Host 2: Glenn Hopper: Yeah.
[00:17:05] Host 2: Paul Barnhurst: They kind of go together. So, you know, we've talked quite a bit about AI, a little bit in Treasury audit, you know, different areas in finance, you know, but I think something a lot of leaders struggle with and I've had them ask me is how do you distinguish, you know, between what's real and what's hype. I think I saw the other day in the last two, two and a half years, whatever it's been now two and a half since, you know, version 3.5 of ChatGPT came out. 75,000 AI native companies have started. Now, not all of those are in finance, but just think how much that is. Right. I think people are just overwhelmed. So how do you, you know, kind of look at that and evaluate technology.
[00:17:45] Host 1: Paul Barnhurst: What advice or thoughts would you give for somebody who just, you know, kind of looks at it all and goes, I don't know what's real. I don't know where to start. They're just overwhelmed. Ever feel like your go-to market teams and finance speak different languages? This misalignment is a breeding ground for failure in pairing the predictive power of forecasts and delaying decisions that drive efficient growth. It's not for lack of trying, but getting all the data in one place doesn't mean you've gotten everyone on the same page. Meet QFlow AI, the strategic finance platform purpose built to solve the toughest part of planning and analysis. B2b Be revenue. Chefo quickly integrates key data from your go to market stack and accounting platform, then handles all the data prep and normalization. Under the hood, it automatically assembles your go to market stats, make segmented scenario planning a breeze, and closes the planning loop, create airtight alignment, improve decision latency, and ensure accountability across the teams.
[00:19:02] Guest: Natalia Toronyi: It is. And you know, it's a great question. It's very easy to get lost with all the solutions. Um, you know, offered right now, uh, pretty much every second company is offering some AI solutions, which in a lot of cases, it's not even AI. It's machine learning, which was there before. Uh, so we need to make sure that we distinguish well, between, you know, marketing, um, tool and enter real AI and machine learning involved in the processes. But I always come back to three things. Um, does it solve the problem? The real problem I currently face. Is it scalable and can it be measured? I look for solutions that either save time, bring some efficiencies, reduce some risks, or help in decision making in different processes that add complexity to already existing processes and require additional multiple steps. And we don't have, you know, clear return on investment. It's a no go. Another test would be probably, uh, how that particular solution plays with our existing systems and how also very important how it comply with our security requirements and, um, Sox compliance requirements in the past. Uh, are those solutions often require the replacing or upgrading legacy systems, building very complex integration, cleaning data, migrating data which would lead to high infrastructure costs. I'm not an expert. I'm not a technology person. But based on what my research is, it seems like with AI it's changing because now I especially if we use different, uh, you know, uh, JNI, uh, RPA, it reduces the need of full system implementation. So it brings, uh, potential solutions to the companies which don't want to invest into ERP reimplementation or bringing new ERPs, and it still can bring efficiencies into the processes. You need to know, again, going back to clean processes and SOPs and defined, you know, rules. We need to know what we expect from that tool to solve. Because if we just think, oh, I'll bring AI and it will solve all my problem, it probably will create more cows than it will solve problems.
[00:21:33] Host 2: Glenn Hopper: I guess before I go in I think you're spot on with all of that. And before I go into my next question, an update. So I got interviewed by CFO brew the other day, and they asked me, I was thinking about tools that have hype. And, you know, everybody's talking about AI agents right now, and everybody wants to pretend they have agents. And I just use all the ones that I've used there, if it's if you're dealing with a low stakes part of the company, if you're if you're doing something that's not mission critical, it's interesting to use these agents and see what they can do. But in finance and accounting, there's no gray area. We have to be spot on and right. And we can't adjust for hallucination and so equal credits.
[00:22:16] Host 2: Paul Barnhurst: Glenn.
[00:22:19] Host 2: Glenn Hopper: So CFO brew, Jessi klein the reporter from Kpho that interviewed me was asking me what I thought about agents, and I was just bluntly clear. I said, they're not ready for prime time. Um, everybody wants to say they have an agent. I wouldn't put an agent anywhere near my accounting. I wouldn't let an agent reconcile my accounts. At this point, just. They're not ready for prime time. Well, this morning, CFO brew came out with the article, and it's me and some executive from Salesforce. So I thought, great, now I'm at war with Salesforce because, you know, they've got their big agent force and all that. But it's you know and I don't know they may be doing agentic workflows. And obviously they're working through hallucinations. And certainly with the capital and resources they have behind it, I think Salesforce is going to get there. But they're just two people quoted in the article, you know, some bigwig from Salesforce and then me over there just saying it's all hype. But, um, it is I mean, it's, you know, we're I think all three of us are evangelists around AI and what it can do, but we also have to be practical, about it.
[00:23:21] Guest: Natalia Toronyi: And I can add to that. You know, I had the similar opinion as what you just described maybe two months ago until I started to work, uh, helping some startup company implementing or setting up AI agents. The capabilities of those agents are just super, super good. I do think we can implement AI agents in finance, but what we need to keep in mind, we need to set up them based on segregation of duties. So there should be one agent for the process following only that particular scenario. And they should not be overruling each other because there is a problem with that. Uh, when you give the full exposure to your AI agent to all different tools, like, for example, you know, um, compensation data should not be accessible to AI agents because otherwise it can expose that information to the people who should not have access to that information. It's possible, and it's evolving with every day, not even every week. But we need as finance as you think. Everybody should be cautious and measure where it's reasonable and where it's too, too soon to implement them.
[00:24:38] Host 2: Glenn Hopper: Yeah. And, you know, there's a difference between a truly agentic tool versus agentic workflows. Now, the workflows I build all the time and that's that's what I'm saying. Yes, we can use those every day. I told a client this morning, give me any digital process in your company and I promise you I can automate it. And that's but that's not just flipping on a bot and saying, go do this. Even with that SOP, it's still you have to define the steps and keep it in those guardrails. And that's and I'm sure that's what Salesforce is, is doing as well. I haven't had an opportunity to use any of their tools yet. And I guess with all that, that kind of leads into my next question of we are seeing as these agents get better, it is going to change the workforce. And Natalia, I know you said before the show that, you know, companies need to rethink their talent retention and development. And, um, and just obviously automation has done a lot over the years, but we're at a whole new level now. So tell me, tell me your thoughts around that.
[00:25:37] Guest: Natalia Toronyi: Yeah. We as leaders, you know, we are responsible for how we shape our talent and what to look for. Now, knowing what is coming our way, that you know, the talent is changing, the required skills are changing. There are no way back. So, uh, you know, if before attention to the details, Excel knowledge and, you know, being precise were very valued in in finance background today, we need to pay attention to the critical thinking, uh, how they can, uh, you know, do a strategic judgment, ability to translate the insights provided by AI, making sure that the information is correct, making, you know, like, how do they decide if what they receive from AI is correct or not? They need to know where to check that information. We need to introduce our teams and give them an opportunity to work in transformation projects and digital projects. So they are not scared to work with, with AI tools, with machine learning, but it definitely changes who we hire and how we develop the talent within, you know, finance organizations. How do we ask questions correctly? What exactly do we ask? You know.
[00:27:02] Host 2: Paul Barnhurst: Yeah, it's definitely going to change the rules a lot. Like you said. You know, the questions we ask, how you validate things, how you think about projects. You know, there will be many cases where, you know, you might be able to ask an AI to do the Excel part for you, but you still have to be able to validate it. You still have to be able to think through that, and it means you have to understand it, but you also have to think and do things in a different way. So it's going to be really interesting to watch how it changes roles. And like you said, retaining and developing talent. And hopefully I think we've all, you know, see this coming. It will really help people be more of thought leaders and strategic and less of the drudgery that we've all dealt with. How many of us have spent a day cleaning data? Right? And it's not to say AI is going to eliminate the need to ever clean data. I don't think that's in the near future. Maybe I'm wrong. You guys could disagree with me, but it's definitely going to help for sure. And so as we think about all this and bring it together, what's your perspective, say five years from now, where do you think we'll kind of be with? I realize none of us have a crystal ball. So it's just, you know, kind of your thoughts. But where do you see us heading? And maybe what are some of the things you see changing is, you know, the role of AI continues to increase in the office of the CFO.
[00:28:18] Guest: Natalia Toronyi: Well, as you said, we definitely need to, you know, invest in a digital education for our team, providing them learning tools, um, so they can utilize those tools and test them. Uh, but in five years, I, I can envision that, uh, you know, I will be a personal assistant not only for executives, but for each employee in their work. It will be not just one person, it will be a person. And that particular, you know, AI agent or whatever we call it, it won't probably replace, uh, leadership judgment and business understanding that will still be required skills from, you know, leadership from executives. But it will definitely dramatically improve decision, uh, accuracy. We will have more real time data for all the forecasts. And, you know, now if we have information on a month end close or quarter end close, we probably will have every day close information because it will be updating on a daily basis versus just closing the amounts.
[00:29:30] Host 2: Paul Barnhurst: I love the one of the continuous close that you said there, right? Something we all want to see. It's been a big dream of every finance person for a long time, or at least a day. One close. Yeah, probably even a day two for most of us. Glenn, how long did it usually take you? I know you worked for a lot of small companies to close those.
[00:29:48] Host 2: Glenn Hopper: Yeah, small. I mean, so that was one of mine. When I'd come in as a CFO, I would usually when I came in, companies were like on a 30 day close cycle. And I'd say, how about I get us down to ten? And I think we ended up at seven at one company, but, uh, yeah, it's yeah. Small companies don't have the budget for blackline, you know.
[00:30:06] Guest: Natalia Toronyi: Yeah.
[00:30:08] Guest: Natalia Toronyi: But my best month is probably two and a half to three days. But that starts with a lot of pre-work, which of course can be implemented because those are repetitive task repetitive journal entries. Those can be all automated and posted by AI in future. So even those three days can probably be changed, but I can envision every day information being available. We would just need to rethink all the processes. So entries which are posted, you know, once a month like I don't know, payroll or some other information. We can divide them and post them every day to have that visibility on a daily basis.
[00:30:53] Host 2: Paul Barnhurst: I think we're heading that way. And you reminded me of a favorite quote I have of this conversation. We've talked about processes and technology. Let's quote out there that says new technology plus old processes equals expensive, broken old processes.
[00:31:12] Host 2: Glenn Hopper: Yep. Yep.
[00:31:13] Host 2: Paul Barnhurst: You know. And it's going to require rethinking how we do everything. If you're not going to do that then don't waste a ton of money to bring in new technology.
[00:31:22] Guest: Natalia Toronyi: I agree.
[00:31:23] Guest: Natalia Toronyi: And that's why I'm saying if I would start implementing robust AI solutions in finance, I would start with SOPs and clean defined processes following Sox compliance, following whatever rules the company decides to follow. If it's not a public company. Um, but that's where it starts. Because if your input is wrong, your output will be wrong too.
[00:31:47] Host 2: Paul Barnhurst: Yep. Garbage in, garbage out. Always has been. Always will be. Alright, so we have the section where it's kind of our fun little section. We use AI here, so we fed AI your questions we had for you. Your LinkedIn profile and uh, asked it to come up with 25 personal questions to ask you. And so we, Glenn and I both take a different approach on how we ask the questions. So I'm going to give you two options. There's 25 questions. You can pick a number between 1 and 25. And I'll ask that question. Or I can go to the random number generator and let it pick the number.
[00:32:27] Guest: Natalia Toronyi: You can go to the random number.
[00:32:29] Host 2: Paul Barnhurst: You're like, ah, it's all the same. All right. Let's see what we get. It says 15. Not even sure what 15 is. Let's see. Oh this is a good one. What advice would you give your younger self starting out in finance?
[00:32:47] Guest: Natalia Toronyi: I would probably.
[00:32:48] Guest: Natalia Toronyi: Give advice, be open to criticism and other people's opinion and listen to what people have to say, not take it personally. I had to learn that.
[00:33:01] Host 2: Paul Barnhurst: You know you're not alone. I had to learn that one too. That's great advice. All right, Glenn, your turn.
[00:33:07] Host 2: Glenn Hopper: All right, so I take a different approach. I just completely turn everything over to the bots. And since we AI generate the questions, I just ask it to pick one of them and give me one second.
[00:33:19] Guest: Natalia Toronyi: I'm already scared.
[00:33:20] Host 2: Glenn Hopper: Okay? Yeah. Oh, no. This one. Actually, this would have been a follow up question that if we had a longer if we had like Lex Fridman length podcast, I probably would have asked this anyway because it kind of goes with what you were saying with your origin story in the beginning. How has living and working across multiple countries shaped your approach to leadership?
[00:33:40] Guest: Natalia Toronyi: It taught me that I need to accept different people, different cultures, the way they are. And I cannot measure people just using who I am. I need to accept them and learn who they are and allow them to be their true selves, which is difficult at the beginning when you start to be a manager because you, you just measure everybody by what you know, what your experience is. So, uh, for me, it's probably, uh, just just being open to different cultures, different opinions, different diverse environments, and accepting people as they are. I like to say, you know, uh, my son plays soccer, so I usually use soccer. Uh, comparison. You cannot build a team, uh, of, 11 goalies or 11 strikers. You need to have a team of different people and different talents. And it's my responsibility as a leader to place those people and those talents where they belong and develop them where they are the strongest. So that's what I had to learn during this whole transition. And changing. The country is moving through different countries and areas.
[00:34:59] Host 2: Glenn Hopper: Perfect, perfect. I love that answer. All right, well, this flew by fast. So we've covered everything we, uh, set out to. And I think that, uh, some great, great insights. And, Natalia, really, really appreciate you coming on the show.
[00:35:15] Guest: Natalia Toronyi: Well, thank.
[00:35:15] Guest: Natalia Toronyi: You for inviting. It was very interesting to share my perspective.
[00:35:20] Host 2: Paul Barnhurst: Appreciate it. Thank you again for joining us. It was a lot of fun, and we look forward to having our audience get the opportunity to listen to this. I'm sure they'll love it as much as we did. So thank you Natalia. Really appreciate it.
[00:35:31] Guest: Natalia Toronyi: Thank you.
[00:35:33] Host 2: Paul Barnhurst: Thanks for listening to the Future Finance Show. And thanks to our sponsor, QFlow.ai. If you enjoyed this episode, please leave a rating and review on your podcast platform of choice, and may your robot overlords be with you.