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How to Use AI in the Finance Department

January 29, 2024
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Artificial intelligence (AI) is poised to have another year in the spotlight, with companies across industries leveraging the technology to create efficiencies. This is reflected in our 2024 Trends Survey, with 72% of finance executives sharing that their department is already using AI technology. Only 4% of respondents said they have no interest in using AI.  

How Are Finance Departments Using AI?

In our survey, conducted in September 2023, we asked 500 finance executives what AI applications their departments are currently using. We learned that customer service, fraud detection, risk management, investment management and automation are the most common use cases.

What are the main AI use cases in your finance department?

Customer service
Fraud detection
Risk management
Investment management

Source: AvidXchange 2024 Trends Survey

According to AvidXchange’s David Tareen, AI can make great contributions towards improving customer service. On a recent episode of our “Net 30” podcast, he said, “I think a customer service [chat bot], thats expectedespecially in the finance field. But an AI algorithm can not only get all the contracts [when a vendor calls in], but all the previous histories, everything else that youve been doing with that supplier, and give you insights on next best actions.” 

Tareen agreed that fraud detection is another logical application of AI in the finance department. “Machine learning and deep learning absolutely thrive when you have a lot of data. And when I say thrive, I mean its decisions get more accurate and the learning gets better,” he said. “Nowhere do you have more data than the millions and billions of transactions that are happening around the world. This technology is really primed to look at all that data and pick out anomalies.” 

Types of AI in the Finance Department

Our 2024 Trends Survey revealed that natural language processing (55%), computer vision (48%) and natural language understanding (48%) are the top three types of AI used in finance departments. Tareen is excited for more finance professionals to discover the power of machine learning and other more advanced types of AI. 

“I feel like the best of what AI has to offer is still in front of us.”

Optimization, in particular, is an AI technology that Tareen is bullish about in the finance department. Optimization builds models using mathematical logic to inform strategy and decision-making. “The exciting thing is when you think about what AP professionals want to optimize on,” he said. “I could have an algorithm where I would just tell it my preference and it could automate when my payments are made and how my payments are made to suppliers – that’s optimization. And that’s fantastic because it could have such a profound impact to your business.” 

Optimization can help finance teams in a number of ways, including better supplier relationships, on-time and early payment discounts and improved cash flow.  

Concerns About AI in the Finance Department

As professionals get more hands-on experience using AI in the finance department, sentiments towards the technology are improving. “I think the perceptions of people are changing towards the better, which I think is a great thing,” said Tareen.  

of finance executives feel "hesitant" or "very uncomfortable" using AI
0 %

Our 2024 Trends Survey found that the vast majority (88%) of finance executives feel positive to neutral about using AI at work. Tareen feels this number may fluctuate as finance departments adopt new AI tools and experience a learning curve following their implementation.  

One of the most common concerns about AI in the finance department is its “black box” nature, lacking the ability to explain the logic behind its decisions. Additionally, some organizations fear the inherent bias in AI, which can be especially problematic if they operate globally.  

Overcoming Concerns and Embracing AI in the Finance Department

To take advantage of the AI revolution, organizations must support employees through education. Upskilling, reskilling and mentorship programs are popular ways to train staff to use AI and identify potential applications where it can make an impact.  

According to our 2024 Trends Survey, 92% of organizations are helping employees learn AI and other tech skills with education and engagement programs. These efforts are vital to cultivating an AI-savvy workforce that’s using the technology to its full advantage.  

In finance departments that are currently affected by staffing shortages and the digital skills gap, internal training and education are the most effective and economic means to empower your workforce with technology.  

“It’s just not possible to hire your way out of the challenge that you have as an AP leader.”

To learn more about implementing AI in the finance department, stream our podcast episode, “AI 201: The State of AI in Finance Departments,” on your favorite platform, including Spotify, Apple, iHeart and Pandora, or click below to listen now.  

To read up on how today’s finance departments are leveraging AI to create efficiencies and optimize processes, download our e-book, “The Ultimate Guide to AI in Finance.”  

Full Transcript

Please note: The “Net 30” podcast is designed for audio consumption. Transcripts are generated using speech recognition software and may contain errors. Please check the corresponding audio before quoting in print.


Hi, my name is Chris Elmore. I am AvidXchange’s Chief Evangelist and I’m the host of the Net 30 podcast. On the Net 30 podcast, we meet with industry leaders to unpack problems and solutions and talk about innovations that are impacting financial professionals. The best part about this, we’re going to do it all within 30 minutes. So let’s get into it.

We’re here at episode number two of our three part series on artificial intelligence. I’m joined by David Tareen. David, you are a senior director of product marketing at AvidXchange. You’re an expert in artificial intelligence. So give folks a little bit about your background. All right.


That’s too generous, man. I appreciate it Chris. Thank you for having me on. Thanks to everyone who’s listening. I’ve done a number of roles in the world’s largest companies like IBM and Lenovo on artificial intelligence. Some really deep companies were deep into it, like SAS and I’ve worked in startups, which is really some cool experiences that push the envelope of what you can do with this technology. I’ll tell you – I think it’s a topic that’s coming up. We’ll talk about multimodal artificial intelligence. Sounds like a big word. But it is so cool. So we’re going to get into that today.


I can’t wait. I can’t wait. So, as David mentioned, and I mentioned it also – this is the second part of a three-part series. And so we were going to go even deeper on part number three, but on the last episode, we talked about basics. By the way, if you haven’t heard the last episode, you know, keep listening to this one, but we recommend that you go back. It’s going to give you a pretty good base.

Now on this episode, like I said, we’re going to go a bit deeper. and we’re going to base it on AvidXchange’s 2024 trends survey that we conducted in September of 2023 where we asked 500 finance executives at middle market companies about their use of AI.

And I thought what we’d do on this episode, David, is I have five topics to read. I’ll give you the highlights and you can do the color commentary and explain to folks what they’re talking about and what they’re using. And so we’re going to talk a little bit about whether the finance pros are embracing artificial intelligence, the most popular artificial intelligence use cases within financial departments. And then lastly, how organizations are supporting their staff in this AI revolution. Is it a revolution?


It certainly feels like a revolution. It does. It does. All right, let’s do stat number one. Statistic number one. So from our research and our survey data, we found that finance pros are embracing artificial intelligence. 72% of respondents said their organization currently uses artificial intelligence within their finance department. So what’s your reaction to that? 72 percent seems pretty high.

Yeah, it seems like a really big number, right? It seems like, “Oh my god, we just said it might be a revolution, but 72% of people are already doing it. Really?”

Here’s how I look at this and we have these conversations with customers all the time. Yeah, they’re doing it. They might’ve scratched the surface on implementing a machine learning algorithm, like let’s say fraud detection or anomaly detection is really what’s underneath it.

There are so many use cases when it comes to artificial intelligence that can help you reduce cost improve your customer’s experience – just get you a better understanding so you can make better decisions. 72% doing it. I love it I would say keep building on it, find new use cases to get to those three benefits and do it faster because I’m betting your competitors are doing it.


It’s interesting perspective and I have a feeling because artificial intelligence is growing and morphing and improving and changing that people’s use of it are going to do exactly the same thing – grow, morph and change.

Let me give you the rest of the statistics. So 72% said yes, 16% said no. But they have plans to do it in 2024. 8% say no, but they are interested in doing it at some point. And then 4% said no way.


I think it’s great. I think the potential is there. To me, a lot of the conversations that we have end up in, “Okay, how are you going to do it?” We know that there is a scarcity of artificial intelligence talent. We know there’s no scarcity when it comes to the use cases. So how do you bridge that gap? I know that’s on AP professionals minds because, you know, let’s face it, the tech industry, a lot of the other industries, they take a lot of artificial intelligence talent from the market. So the moral question is going to be how you do it. I know we’re going to get into it later on in the podcast. So I’m kind of excited about sharing where I’ve seen it done really, really well. Some practical, you know, hopefully advice for some of the folks listening here on how possibly you could approach it.

The last one – 4% said they’re not interested in doing so. I mean, I expect that’s right. I also expect that a lot of those folks don’t know that they’re already using it. I was looking at this statistic the other day and it was really interesting. This one comes from Better Cloud and what they said is that the average middle market companies uses 110 SaaS solutions just to run their businesses. And I am betting that a lot of those applications already have artificial intelligence baked into them. So even when we say, I don’t think I’m using any artificial intelligence, the fact is, the applications that we use all the time probably have machine learning and deep learning and some of these algorithms built in.


All right, let’s go on to statistic number two. So our survey revealed that natural language processing, computer vision and natural language understanding are the most common types of artificial intelligence use while optimization and forecasting are the least. So how does that hit you?


Yeah, it’s exciting because forecasting and optimization, I think what you said is that that’s the least used right now in AP. That means that the best of what AI has to offer is still in front of us. Machine learning, convolutional neural networks, computer vision, natural language, all of those – the market is starting to understand and people are starting to use.

But the exciting thing is when you think about optimization, I mean, you think about what AP professionals want to optimize on. When you’re paying suppliers – are you optimizing on getting a better relationship with your suppliers, which may mean paying on time? Are you optimizing on getting the biggest discount you can get? That may mean paying at a certain amount of time. Are you optimizing cashflow? So if I could have an algorithm where I would just tell it my preference and it could automate when my payments are made and how my payments are made to a suppliers, that’s optimization.

And that’s fantastic because it could have such a profound impact to your business more so than what you do with some of these technologies that are more popular. So yeah, I feel like the best of what AI has to offer is still in front of us with these stats.


Especially in a finance department. If people aren’t utilizing artificial intelligence for forecasting, it’s coming their way and it’s going to be pretty good. I love that.

All right. So you had mentioned this at kind of the top of the podcast is that popular artificial intelligence applications. We’re going to talk applications. So statistic number three. So we asked in our survey what the main artificial intelligence use cases are. And what I thought I’d do is I’m just going to read them all to you and you can give me kind of your overall reaction.

So, 67% Said that they’re using artificial intelligence in customer service. 64% said in fraud detection, 64% said in risk management, 57% said investment management, 52% in automation and 39% compliance. So it’s customer service, fraud, risk, investment, automation.

First of all, out of that whole list, what’s kind of the use case or the application that sticks out to you the most?


I think customer service. That’s expected, especially in the finance field. It is such a great use case – whether it’s a chatbot, whether it is on the back end, you’re still talking to a real person. That makes a lot of sense to me. The way it kind of looks like and just for the folks who are listening, who are thinking, how would I use that?

We deal with a lot of suppliers and in accounts payable. And when we receive a lot of these calls, but one of the ways this customer service looks like is that when we call in, the AI algorithm is able to sort of. Not only get all the contracts, but all the previous histories, but everything else that you’ve been doing with that supplier and give you insights on sort of next best actions, and there’s algorithms who absolutely do that for you.

To me, that’s, you know, customer service, both on the front end, but then on the backend with suppliers as well. So you have no surprise – customer service is probably at the top of the list here for use cases for these AP professionals.


You mentioned in the last podcast about fraud detection was how artificial intelligence could look for patterns. And what’d you mean by that?


Machine learning and deep learning absolutely thrive when you have a lot of data. And when I mean thrive, I mean, those decisions get more accurate. And that learning gets better and nowhere you have more data than millions and billions of transactions that are happening around the world.

So this technology is really primed to be able to look at all that data in the stream and be able to pick out anomalies. I get 100,000 invoices from the supplier. They generally range in this amount. They generally range in this type of look and feel and T’s and C’s. This one looks different. That one looks different. So, I’m not going to stop it, I am going to flag it for someone to review. We talked about that partnership between artificial intelligence and people and that becomes really important as well, but that’s exactly how fraud detection works in these type of algorithms.


So on this list that I kind of talked about customer service, fraud detection, risk, investment management, automation and compliance, is there something that’s not on this list that we might see like in the next couple of years? Or is there an up and comer, a sleeper?


Yeah, I mean, there absolutely is. So we get risk management, we get investment management automation. I’m surprised is that little because I would sort of expect automation to be up there. I think just accelerating closing the books at the end of the quarter, there’s a lot of artificial intelligence applications that are now being used for that.

I think that is going to come up really, really fast.


Something around the reconciliation process. Absolutely. Absolutely. All right. Statistic number four. So there may be some mixed reactions to artificial intelligence, both for finance leaders and AP staffs, but our survey found that only 12% of finance leaders said that they feel “hesitant” or “very uncomfortable” working with artificial intelligence. What do you make of the 12%?


I think there’s real reasons to be cautious. The good news is there’s real techniques that you can employ to get more comfortable. I’ll tell you about just two areas where there’s a lot of hesitancy. The first one is this bias that exists within algorithms.

What that means is that if an algorithm is only trained on data from, let’s say, North America, and when you take that same algorithm and you give it real data and ask it to make decisions from data that’s, you know, decisions that are focused on, let’s say, Australia or Africa, and I know there’s big continents, but just to give you that sense, it’s not going to make the right decisions because there’s a bias in how that algorithm is trained. How that looks like in finance departments as well as companies get more global, that they want to make sure that the biases that they’re looking at their portfolio gets broader as well.

The other one is really interesting and that’s called the black box, which is what artificial intelligence is. I can give an algorithm a lot of data. It’s going to make the best decision. However, I can’t explain why that is the best decision. And that’s just so interesting to me.

So for example, in hiring, right? A recruiter makes a call on 10 candidates. that one of them is the best candidate. And you ask the recruiter why that is and the person is generally going to tell you why. Here’s why I chose this person because they represent the best of the candidates.

If I use an algorithm, which a lot of HR departments are now using again in partnership with real recruiters, but the algorithm is going to pick the best candidate for you. It’s not going to be able to explain to you why that is the best candidate. That’s called the black box nature of artificial intelligence and there’s lots of things you can do to make that more transparent, but just the fact that you cannot explain the decisions, even though it might be the best decision. That’s a cause for concern.


Absolutely fascinating. So, and actually, when you looked at the 12 percent are hesitant or very uncomfortable, the reverse of that is 60 percent are pretty comfortable with it. To me, that seems like a pretty high number because of the notion that robots are going to take over my job. You know, that’s a pretty consistent fear. Did that feel comfortable to you?


I think two thoughts there, uh, first, I think it’s definitely the trend is so fast in the adoption and I think the perceptions of people are changing towards the better, which I think is a great thing.

I will reiterate what we talked about in the first episode is sometimes you only think about what you’ve done with artificial intelligence. So yeah, there’s a lot of comfort because I got the fraud detection algorithm and it’s ready to go. Hey, but now I want to look at a convolutional neural network and I want to see what that can do on my invoices, on my payments. Suddenly my comfort level may shift and I’m back to uncomfortable now. So, I think it’s going to go back and forth as people explore the different areas and the different benefits in their businesses, but I see the 60 percent trend is very positive. I think that’s great.


All right. Here’s the fifth and final statistic and that’s around education. Uh, so finance leaders that we surveyed reported a lack of understanding was the biggest hurdle preventing their organization from adopting artificial intelligence. But 92% of the organizations are helping employees learn new AI and tech skills with education and engagement programs. How important, David, is it for organizations to support employees around just understanding it?


It’s one of the top three things. It’s just not possible to hire your way out of the challenge that you have as an AP leader.


So to me, that feels like the revolution right there. I think it’s closer to a revolution than, take another word, evolution. I think it’s closer to a faster moving thing than anything else. We talked about earlier in this podcast some practical advice on how to go about it. Here’s some of my conversations with our customers who want to get started.

I think education is important. The other very practical way is sending, setting up sort of these centers of excellences within companies and staffing that with the talent that you have, because now with that, what happens is that not every department is left up to sort of themselves to figure out their play with artificial intelligence. If you form sort of an entity within your company or your group or your department, sort of that center of excellence where you house your skills – now, all the groups have a place to go to bring their ideas and that’s where you house your talent. And as this talent grows, that’s where you focus your education and skills.

And they’re just able to do a better job and deliver these projects on time. I’ve seen a lot of customers pursue that approach. It’s almost the most successful approach right now when it comes to how you educate, how you get people trained up and just build skills within your departments.


Yeah, so in the surveys it kind of stacked rank like this. So organizations supporting finance employees through the revolution. It said 49% are going through just kind of upskilling and reskilling opportunities. That’s what it said. We might need to dig into that a little bit more and then 28% we’re talking about mentorship programs, 15% open forums for feedback, 4% not sure. And another 4% – my organization is not doing anything. Oh my gosh. We got the bottom 8%, but if you look at the upskill, the mentor programs and the forums, that does sound a lot like your center of excellence.


Right. I agree with you – the mentorship programs because – and this talks a little bit about what we said earlier, there’s so many different sub technologies within artificial intelligence that when it comes to mentorship, you really want to find the right people who understand, let’s say, you know, the tech that you’re working with, whether it’s natural language processing or generative AI, we talked a little bit about sort of a new job that’s now in the market, which is similar to sort of query engineering. It used to be query engineering. I think it’s called something else. Chris, you probably remember what it’s called.


It’s called prompt engineering. We’re going to talk about it in the next episode, but that’s, that’s the whole notion of it’s a technical and it’s kind of like a psychological position where you have the ability to understand what the AI is spitting out.


Yeah, absolutely. So finding, you know, the right mentors who are exploring and pushing the limit on these new type of skills and jobs, I think that’s pretty important.


I completely agree. We learned a lot in this episode, but I think the most interesting thing is we’re now – for me personally David in kind of in closing, folks in the middle market finance groups are using artificial intelligence a lot more than I thought they were and I believe that we’ve just scratched the surface. What’s what’s kind of your reaction to that?


I think so. It’s taken off faster than I would have expected and it’s just amazing to see the use cases. At AvidXchange, we consider ourselves pretty smart when it comes to AP automation. We didn’t think of all the cool things we could do with this technology. When we sit down with our customers and have that conversation, man, it’s exciting because the problems that they’re trying to solve, the approaches that they have, the diversity of data that they have, the challenges that they have, it’s just exciting to hear those and really gives you the sense that we are absolutely just scratching the surface.


David Tareen, artificial intelligence expert. Thank you for joining us.

And by the way, if you haven’t listened to part one, listen to part one. This is part two, and then part three is coming your way. David, thanks.


Yeah, thank you so much for having me, Chris.


Thanks for listening to the Net 30 podcast presented by AvidXchange.

I desperately need to know what you think about this. Leaving a five-star review would be fantastic. You can subscribe to the channel too. And, oh, by the way, while you’re waiting for the next episode, head over to avidxchange. com to learn more. One more thing. If this conversation has somehow piqued your interest, which I really hope it has, about accounts payable automation from AvidXchange, there is an additional link to get a demonstration of our solution. I always say it shows way better than it tells, so click on that link, fill out the information, and someone will be in contact with you. So thanks again for listening to the Net 30 podcast, and we’ll see you really soon.

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