Ahikam Kaufman Podcast Transcript
Ahikam Kaufman joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, home of the Digital Executive podcast.
Welcome to the Digital Executive. Today’s guest is Ahikam Kaufman. Today we have Ahikam Kaufman co-founder and CEO of SafeBooks ai, who is revolutionizing financial data governance with AI driven solutions with over 20 years in corporate finance, Ahikam has led major firms like Intuit and has successfully guided startups to acquisitions.
He’ll share insights on FinTech trends, fraud prevention, and the future of payments. Let’s dive into how technology is transforming finance with one of its leading innovators.
Well, good afternoon, Ahikam. Welcome to the show.
Ahikam Kaufman: Thank you, Brian. Thank you so much for having me.
Brian Thomas: I. Absolutely my friend. I appreciate you making the time. You’re hailing out of the great state of California there in San Francisco. I’m in Kansas City, so we’re a couple hours difference, but nonetheless, we are going to enjoy our little conversation here today.
So, Ahikam, jumping into your first question. You’ve had a remarkable journey from founding Check Inc. To leading massive computer payments at Intuit. What inspired you to co-found Safe Book’s AI and what key gap in financial data governance are you aiming to solve?
Ahikam Kaufman: Thank for the question. I think a lot of us are inspired by problem that we, it’s in our business life or even personal life volunteering.
After spending many years in the office of the CFO where I kind of grew up before becoming an entrepreneur, I realized there was, like, today there was like a serious gap between the ability of the office of the CFO to deal with the, the amounts of data they have to deal with between or across disparate systems.
The reason you need to cope with all the data and be able to monitor it and, uh, check it and document it. Is because that’s the nature of finance, right? They need to keep the record straight. They need to close the books, they need to use the data for money purposes, whether it’s to get paid or paid, and that makes the whole, uh, sensitivity of finance to data being such that, uh, they have to get their arms around the data.
The tools today are just not good enough, especially when you have to run transactions across many systems. In order to be able to make sure that there’s no discrepancies or mistakes. So that kind of challenge inspired me. For many years we’ve sold some of it to internal developments in companies like Intuit, and I thought maybe I can take that experience and share it with more companies as a product.
And that’s kind of was the inspiration.
Brian Thomas: Thank you. I appreciate that. The thing I took away from what you said there was, you know, people are inspired by solving problems in the world. You’re a hundred percent right from my vantage point anyway. I fully embrace that we are fulfilled and, and we find our purpose when we do things in that nature.
But I like what you’re doing to solve these financial gaps, right? Ensuring there’s no discrepancies or mistakes across. Massive managing in this financial industry. These financial transactions and checks. So, so important, especially dealing with finances. Ah, chem Safe Book’s. AI focuses on AI powered solutions for financial data governance.
Can you walk us through how AI is transforming the way companies handle financial reporting and compliance today?
Ahikam Kaufman: I’d like to think that actually I would to say that in the office of the CFO penetration of AI has been limited. I’d like to think, if you look at other parts of our business organizations, like in marketing, in content, in sales.
You can see much more AI being involved because the weaknesses of AI today and the vulnerabilities of AI impose a lesser risk to that specific function. So if you use AI to kind of like empower your sales and go to market organization and the AI may call kind of mistakes, you may obviously, unfortunately lose prospects or lose opportunities.
It doesn’t create a significant damage to the units in the office of the CFO because the data is so sensitive. The penetration of AI has been limited, so we’ll think very little penetration of AI today. Even to some extent. I’ve even heard customers talking about, please, I want for this solution, I don’t want any ai.
But I do think that AI can help a lot across the board. We can talk more about that, and I think eventually. By allowing us to automate repetitive tasks and make like collective decisions, but in order together the use cases has to be really contained. I’d like to think that what we’re doing at Facebooks is an example of that, and I can explain why.
So I think AI can definitely change the game in terms of how the office of the CFO can cope with data, complex data and the tasks that are associated with it. Which are consuming a lot of time today from the organization.
Brian Thomas: Thank you. Appreciate that. And I have noticed that as well, being in technology, but working in the C-suite across the different verticals, we definitely see that the finance or the office of the CFO has been slow to adopt ai.
I get it. There’s sensitivity of data. We want to make sure that we don’t have any additional breaches or that sort of thing. And part of it is just people are just skeptical. They don’t understand AI yet. But I know we’re getting there and that’s kind of my role is I help people get there and help adopt and, and be less skeptical.
But I took away is the automation of repetitive tasks, decision making, fast reporting, all that’s great. And that’s exactly what you’re trying to solve there. So I appreciate that. And, uh, Ahikam with your deep experience in mergers and acquisitions, how do you see AI changing the due diligence process, particularly in areas like financial transparency and operational insights?
Ahikam Kaufman: I think, and this is just from my experience, I think, uh, having been involved in many m and as, I think one of the challenges is how do you go through massive amount of data in order to validate and substantiate the business case. Or identify the risks associated with whatever target business you are looking to buy.
So I’d like to think about the contribution AI from the bio side, not from the seller side. From the bio side, I think being able to navigate through mountains of data, whether it’s digital data or documented data, contract, things like that. And identify topics of discussions or risks could make the diligence process a lot easier.
I’d like to think that every diligence process includes data collection, analysis, and then discussion and decision making. I’d like to think that the data collection process, including processing the data. Is maybe 60, 70% of the time where the analysis of the data and the discussions and decisions are maybe 30% most of the time.
And engine money and the resources of the effort is concentrated around pulling all the data and processing it. And we think that’s like a major area where AI can help and then people can make decisions and the required analysis based on how AI help them to collect all that data.
Brian Thomas: Thank you. That’s certainly always been a challenge, as you know, and you explained it, that when you’re doing your due diligence, you’re taking a big risk, right?
Because there are just mountains of data, as you’ve mentioned. There’s so much data to validate and substantiate. But you know, as a buyer, I. If you can leverage AI for that data collection, analysis and discussion decision making, as you said, that’s going to take a lot of the risk out of the whole process, so I appreciate that.
Uh, Ahikam last question of the day. As you look ahead, how do you envision the role of AI evolving in corporate finance over the next five to 10 years? And what should finance leaders be doing now to prepare?
Ahikam Kaufman: I truly think, you know, if I may use an analogy, Then one of the analogies I’m kind of excited about or kind of like appreciate is like, uh, look at the car industry or autonomous driving, right?
So first you collect a lot of data, you develop processes to collect a lot of data. Let’s use Tesla as an example, right? And they’re using cameras for the most part, and then they process that data, right? And then they start with limited driving the system like lane keeping and all of that, right? The ultimate goal would be like autonomous driving, right?
So the ability to use data. So the way it works is that you create the infrastructure to collect data and then you collect the data and then you use ai, I think to actually process the data. So you use AI in order to prepare for ai. Use AI in the, in the whole processing of being able to process massive amount of data and kind of understand the patterns and all of that, and eventually prepare for when you can use all of that data.
Able to power models that can actually based on the real time data that the car gets, plus the model can help the car navigate itself. I personally think that autonomous driving will be even better than human beings. You know, we all think the way more cars in the city in San Francisco. I personally think that that driving experience is better for many, many reasons, including like time decision.
So back to the office of the CFO, I think obviously we have data, but our ability to collect the data today and what we do is, uh, prerequisite to our ai, which is like being able to clean the data and arrange the data to prepare it for AI and then run AI on top of it. Ultimate goal, which is being able to execute based on the data.
So if I try to kind of like do a fast forwarding, I think a lot of the repetitive tasks, which are heavily consuming human resources. Book the entries and checking the entries, which is what we are doing. We’re actually helping you validate your data and automate your controls and, and a lot of the activities and the actions that people are naming as like closing the books or reconciling the books and all of that, being able to automate that and other decisions, like, you know, checking your payroll would allow the finance team to focus on what they were really trained for.
Which is making accounting decisions, making business decisions, supporting the business as opposed to Right. I think we all much enjoy getting from point A to point B. I know many of us like to drive, but I think many of us don’t like to service the car and some people don’t like to drive. But when you own a car and you want to get from point A to point B, you have to take care of the car.
Then you also have to drive. I think being able to focus on like how do you get from point A to point B and then spend the time you need to spend at point B. That’s like the same in the finals, right? Being able to bridge all the repetitive, heavily manual. To some extent, Boeing and annoying tasks are around data and being able to focus on supporting the business, making accounting decisions.
At the data and making business decisions. That’s what really we want finance people to focus on, and the rest is today’s necessary evil, but tomorrow it’s not.
Brian Thomas: Absolutely. And I love how you’re embracing this in the financial vertical, really. You know, you talked about you really compared this whole process to autonomous driving cars, right.
In order to get there and do this. Right. Uh, you mentioned using AI to prepare for ai. There’s many processes to gather. I. These mountains of data, being able to clean, arrange and prepare it with the end goal of automating all the repetitive tasks, leaving that strategic and decision making process to the humans or the financial people.
So I really appreciate you breaking that down and sharing your insights into the future. Ahikam, it was certainly a pleasure having you on today, and I look forward to speaking with you real soon.
Ahikam Kaufman: Thank you so much, Brian. Thank you so much for having me. I truly enjoyed our conversation.
Brian Thomas: Bye for now.
Ahikam Kaufman Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
