Your AR/AP data is money left on the table. While your team spends hours analyzing payment data in spreadsheets, you're missing critical insights about customer behavior, cash timing, and strategic opportunities. As CFOs demand greater forecast accuracy and real-time visibility, manual processes can't deliver the precision needed for modern treasury operations.

Watch a recording of “Transform AR/AP Analytics into Strategic Advantage with GSmart Ledger,” hosted by GTreasury's Evan Ryan. This exclusive 30-minute session demonstrates leading finance teams are transforming AR/AP analytics with AI-powered automation to achieve over 30% improvement in forecast accuracy.

In this webinar, you'll learn how to:

• Eliminate hours of manual AR/AP data analysis with automated ERP integration

• Leverage AI to understand customer-specific payment behaviors and timing

• Transform static forecasting assumptions into dynamic, data-driven predictions

• Generate board-ready cash flow forecasts in minutes, not days

Explore how GSmart Ledger delivers intelligent automation for AR/AP analytics while seamlessly integrating with comprehensive treasury intelligence.

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Transcript

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GTreasury Marketing: Hello, everyone, and thank you for joining us today. We will be starting in a few minutes to give all of our attendees time to join the webinar, so sit tight, and we will be starting shortly.

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GTreasury Marketing: Hello, everyone. Thank you for joining us today for our webinar.

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GTreasury Marketing: Transform AR-AP analytics into strategic advantage with GSmart Ledger.

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Evan Ryan: Let's cover a couple of housekeeping items before we get started.

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GTreasury Marketing: Today's event is scheduled to last 30 minutes, including time for questions at the end. All participants are muted, and if you have any questions at any time throughout the presentation, please enter them into the Q&A section on your Zoom control bar.

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GTreasury Marketing: This webinar is being recorded.

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Evan Ryan: And a link to the recording will be sent via email to all participants.

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GTreasury Marketing: Our speaker today is Evan Ryan, therefore I will hand it over to him for a quick introduction.

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Evan Ryan: Thanks very much, Anne. My name is Evan Ryan. I'm a cash forecasting product manager here at,

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Evan Ryan: Chief Treasury, and excited to, jump into the presentation today.

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Evan Ryan: So today, talking through Transform AR and AP Analytics.

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Evan Ryan: And just into strategic advantage, with the GSmart Ledger.

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Evan Ryan: So, to talk through what I'm actually going to be, going through today, brief introduction, then the AI transformation, see how

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Evan Ryan: Ai is, across the G Treasury platform.

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Evan Ryan: Second piece around the hidden cost of manual

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00:07:03.350 --> 00:07:15.029

Evan Ryan: payment analysis, jumping onto the intelligent automation for AR and AP, and then jump into the actual system itself, the Smart Ledger itself, for a quick demo.

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Evan Ryan: So, this slide really illustrates where AI is across the whole GTreasury platform, and, you know.

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Evan Ryan: AI is nothing new to GTreasury. It's actually existed within GTreasury for the last number of years. You can see there are different types of AI models, so, you know, there's the agentic type statistical modeling, fuzzy logic.

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Evan Ryan: And today we're going to be focusing on that second piece, that cash forecasting, GSmart Ledger, and statistical modeling.

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Evan Ryan: So… What does the GSmart Ledger…

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00:07:56.620 --> 00:07:59.709

Evan Ryan: actually tried to do, and what does it achieve?

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00:08:00.090 --> 00:08:03.440

Evan Ryan: So, essentially, it's an AI-driven module.

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Evan Ryan: And it automates the short- to mid-term cash flow forecasting.

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00:08:08.390 --> 00:08:16.370

Evan Ryan: And what it does is it analyzes the real-time accounts receivable, accounts payable.

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00:08:16.600 --> 00:08:24.279

Evan Ryan: And it uses machine learning to learn how your customers have been paying you in the last

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Evan Ryan: year, six months, and also how you've been paying your vendors over the last.

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Evan Ryan: Over the last historical period.

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Evan Ryan: It uses that data, then, to generate a forecast.

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Evan Ryan: And it's continually learning, okay? So every time we take in a new load of data, that data then is added into that database, so the system, the GSmart ledger, actually gets more accurate.

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00:08:55.030 --> 00:08:56.220

Evan Ryan: Over time.

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Evan Ryan: We've actually done some analysis, and we've shown that GSmart Ledger is up to 30% more accurate for accounts receivable, and predicting when customers are going to pay, compared to what our clients see

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00:09:12.040 --> 00:09:17.419

Evan Ryan: within their ERP systems. And I'll touch on that in a little bit more detail

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Evan Ryan: In a couple of moments.

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Evan Ryan: So, I really want to get into, how the GSmart Ledger is really transforming forecast accuracy.

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Evan Ryan: So, I touched on that 30%, figure on the previous slide, and we need to delve in a little bit more

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00:09:38.250 --> 00:09:40.630

Evan Ryan: Into that figure, and what that means.

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Evan Ryan: So… Before I do that, I just want to talk about the AR and AP pain points.

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Evan Ryan: I'm sure, you know, I'm preaching to the choir here.

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00:09:52.500 --> 00:09:56.969

Evan Ryan: A lot of folks on this call, can really relate to this slide.

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Evan Ryan: But essentially, you know, we're talking about varying payment behaviors across customers.

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Evan Ryan: Really, vendors making it really difficult to, accurately predict when those customers are going to pay you.

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00:10:13.570 --> 00:10:20.499

Evan Ryan: And we know then that customers don't pay to terms. They really do have their own payment habits.

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00:10:21.840 --> 00:10:28.009

Evan Ryan: Also, An extra challenge is dealing with large volumes of data.

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00:10:28.580 --> 00:10:31.229

Evan Ryan: Be it customers or vendors.

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00:10:31.340 --> 00:10:37.720

Evan Ryan: And often this data is siloed across multiple ERP systems.

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00:10:37.920 --> 00:10:39.630

Evan Ryan: Across your organization.

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00:10:39.890 --> 00:10:47.680

Evan Ryan: And the Smart Ledger really gives you that consolidated bird's-eye view of your AR and AP payment behavior.

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00:10:48.860 --> 00:10:51.520

Evan Ryan: Again, you know, uncertainty over timing.

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00:10:51.660 --> 00:10:55.590

Evan Ryan: Really then can lead into inaccurate forecasts.

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00:10:56.120 --> 00:11:02.710

Evan Ryan: I've spoken to a number of CFOs in the past, where because of that uncertainty.

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00:11:02.900 --> 00:11:09.550

Evan Ryan: They really have to build up those liquidity buffers, and this essentially is cash that's

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00:11:10.120 --> 00:11:13.389

Evan Ryan: Not working for you, you know?

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00:11:13.510 --> 00:11:15.890

Evan Ryan: And I show how, you know.

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00:11:16.110 --> 00:11:18.459

Evan Ryan: Tying or freeing up that cash.

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00:11:18.610 --> 00:11:24.280

Evan Ryan: It's beneficial, essentially, due to the fact that you have a more accurate forecast.

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00:11:27.350 --> 00:11:35.349

Evan Ryan: So here, you know, transforming AR and AP and working capital with the Smart Ledger, is it a science? Is it an art?

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00:11:35.520 --> 00:11:36.329

Evan Ryan: It's both.

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Evan Ryan: You know, a couple of challenges I want to call out on the left-hand side, and then the solutions on the right-hand side, and how GSmart Ledger can really assist with that.

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Evan Ryan: So, firstly, you know, a lack of confidence.

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00:11:50.520 --> 00:11:57.999

Evan Ryan: Inaccuracy for cash flow forecasting. And this, as I said, is due to that variance

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00:11:58.250 --> 00:11:59.940

Evan Ryan: Of your customers paying.

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00:12:01.260 --> 00:12:10.619

Evan Ryan: And the solution there is really to integrate that AI and AP ledgers, and using that historical analysis, AI

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00:12:10.800 --> 00:12:17.789

Evan Ryan: Model, we can calculate and predict much more accurately when those customers are going to pay you.

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00:12:18.740 --> 00:12:21.520

Evan Ryan: Second piece here is around visibility.

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00:12:21.990 --> 00:12:26.789

Evan Ryan: So it really is that limited visibility into your cash flow drivers.

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00:12:27.320 --> 00:12:32.730

Evan Ryan: During the demo, I'll jump into not only the Smart Ledger itself.

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00:12:32.870 --> 00:12:42.399

Evan Ryan: but a consolidated, like, working capital view, dashboards, which are, you know, I'm excited to actually show you today.

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00:12:43.500 --> 00:12:48.009

Evan Ryan: So the second piece, so the solution to that, then, is those cash flow

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00:12:48.330 --> 00:12:58.220

Evan Ryan: forecasts, they're auto-adjusted with that learned behavior for better accuracy. So, it's not a case that you need to come in every time the

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00:12:58.540 --> 00:13:10.209

Evan Ryan: refresh of data comes in, and you need to adjust things, you know, it's really kind of set it and forget it, and I'll show you how you can utilize the rules within the Smart Ledger to then feed into your forecast.

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Evan Ryan: Final challenge, then, that people are facing at the moment is really, as I said on the previous slide, you know, dealing with large ERP extracts.

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Evan Ryan: … We can integrate with any ERP,

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Evan Ryan: pull in the data, be it on a daily basis, on a weekly basis. The data is then refreshed, can set it up on a scheduler, and then that comes in to feed into your,

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00:13:38.040 --> 00:13:42.880

Evan Ryan: the GSmart Ledger, and you can really do a deep dive analysis on a customer level.

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00:13:43.140 --> 00:13:45.709

Evan Ryan: Vendor level, and then those…

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00:13:45.950 --> 00:13:51.980

Evan Ryan: That data then feeds into your forecast, and ultimately then into those working capital dashboards.

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00:13:55.020 --> 00:14:00.389

Evan Ryan: So, how does the GSmart Ledger actually work?

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Evan Ryan: So it's an ERP integration for the automated short-term forecast.

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00:14:05.590 --> 00:14:13.159

Evan Ryan: And the historical data is analyzed with machine learning. And this one is what then generates

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00:14:13.350 --> 00:14:16.769

Evan Ryan: Those historical average days to pay.

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00:14:17.480 --> 00:14:21.349

Evan Ryan: So, for example, if an invoice comes in.

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Evan Ryan: You know, sent to a customer with terms of 45 days, but…

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00:14:27.040 --> 00:14:37.130

Evan Ryan: Smart Ledger can tell you, well, actually, on average, this customer doesn't pay within 45 days, this customer typically pays within 50 days.

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Evan Ryan: And what that due date… what actually happens to that 45-day due date?

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00:14:44.160 --> 00:14:55.080

Evan Ryan: That's auto-adjusted within your forecast to 50 days, so you have a more accurate forecast. So again, you know when cash is going to be coming in.

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00:14:55.260 --> 00:14:58.419

Evan Ryan: From your client… from your customers, more accurately.

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00:15:00.080 --> 00:15:08.980

Evan Ryan: The fourth piece here is around that consolidated ARNAP performance dashboard, so you really have a bird's eye view

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00:15:09.130 --> 00:15:09.950

Evan Ryan: …

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Evan Ryan: You know, at a very high level, and then you can quickly drill down to an invoice level, just really see, you know, from an overdue perspective, see what's outstanding at a customer level.

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Evan Ryan: So… as I said, we ran some analysis, and we saw that, on average.

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00:15:32.090 --> 00:15:40.869

Evan Ryan: The GSmart Ledger improves short-term forecasting by up to 30% for accounts receivable, compared to how…

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00:15:41.110 --> 00:15:46.270

Evan Ryan: our clients were seeing the payment terms within their ERP system.

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Evan Ryan: It was run on an individual invoice level, and compared the data within the GSmart Ledger, and essentially the due date in the GSmart Ledger, using those historical average days to pay, comparing that to the standard terms within the ERP system.

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00:16:06.280 --> 00:16:11.320

Evan Ryan: And the G Smart Ledger was up to 30% more accurate.

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00:16:15.340 --> 00:16:19.250

Evan Ryan: So, you know, what does… 30%.

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00:16:19.720 --> 00:16:23.619

Evan Ryan: increase in forecast accuracy mean for your Treasury team?

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00:16:24.310 --> 00:16:31.320

Evan Ryan: Well, so the first one then is, you know, you can really free up working capital so the business can run on

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00:16:31.670 --> 00:16:34.159

Evan Ryan: As, run on less cash.

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00:16:34.430 --> 00:16:37.789

Evan Ryan: So, I would say as, you know, as lean as possible.

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00:16:38.210 --> 00:16:41.690

Evan Ryan: Safely reduces excess cache buffers.

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00:16:42.210 --> 00:16:48.190

Evan Ryan: And then, you know, avoids that costly short-term borrowing for collection surprises.

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00:16:48.740 --> 00:16:53.739

Evan Ryan: Obviously, then you, and I'll show this within the dashboards, that excess cache.

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00:16:53.890 --> 00:17:03.179

Evan Ryan: You can place it out on a term, or use it to pay down a credit facility as well. We can actually calculate that figure as well, which is very useful.

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00:17:03.670 --> 00:17:10.419

Evan Ryan: But ultimately, We're enabling clients to make faster and more confident decisions.

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00:17:13.140 --> 00:17:18.230

Evan Ryan: I know, Shannon is going to now pop up a, poll question.

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Evan Ryan: Shannon, do we have that poll question?

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Evan Ryan: I'm not too sure what's, happening with that poll question.

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Evan Ryan: But essentially, I can, … Read out the…

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Evan Ryan: Shannon, do you have it there?

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GTreasury Marketing: Yes, the poll question, is being answered.

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Evan Ryan: Okay, sorry, I just can't see it on my screen.

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GTreasury Marketing: Can you see the results, Evan?

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00:18:52.200 --> 00:18:57.140

Evan Ryan: … I can't, actually. I don't know if… could you read them out, Shannon?

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GTreasury Marketing: Yes, I can.

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Evan Ryan: So….

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GTreasury Marketing: the… Question is, what best describes your organization's current use of AI in cash flow forecasting?

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GTreasury Marketing: So we have 38% planning to implement AI within the next 12 months. We have 46% researching AI options, but unsure where to start, and 15% haven't considered AI for forecasting processes yet.

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Evan Ryan: Thanks very much, …

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Evan Ryan: So, yeah, we, … well, actually, I'll discuss, the usage of, AI in the demo here, and, you know, we can discuss that,

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00:19:43.220 --> 00:19:44.120

Evan Ryan: So…

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00:19:44.510 --> 00:19:54.749

Evan Ryan: there were, yeah, the largest result here, 46%. People are currently researching AI options, but unsure where to start, so…

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00:19:55.090 --> 00:19:59.880

Evan Ryan: Hopefully folks have a, kind of, a good idea. …

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00:19:59.990 --> 00:20:07.540

Evan Ryan: Once I've finished the demo here, so let me just… Jump into the demo…

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Evan Ryan: I just…

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Evan Ryan: Okay…

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Evan Ryan: So, Shannon, you can see the screen here.

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GTreasury Marketing: Yes.

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00:20:34.060 --> 00:20:35.320

Evan Ryan: Perfect. Okay.

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00:20:35.670 --> 00:20:43.669

Evan Ryan: So, where we're gonna actually start off first is within the actual forecasting sheet.

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00:20:44.090 --> 00:20:53.929

Evan Ryan: Okay, so here I have my actuals, so what happened in the previous week, and here's my forecast of what's happening in the next 13 weeks.

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00:20:54.380 --> 00:21:02.800

Evan Ryan: I've jumped into the Ireland business unit here, and I can see my cash flow line items, my cash flow categories on the left-hand side.

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00:21:04.210 --> 00:21:18.759

Evan Ryan: I have my AR collections here, and then I have my AP collection, or my AP payments here, and my specific line item here, my suppliers, but I'm just going to focus on the AR collections first.

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00:21:20.620 --> 00:21:26.629

Evan Ryan: So, I can see I have a number of different weeks, … Out here into the future.

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00:21:26.870 --> 00:21:30.659

Evan Ryan: And if I double-click on the first cell here.

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00:21:30.780 --> 00:21:34.359

Evan Ryan: I can get a breakdown of that 1.2 million.

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00:21:35.100 --> 00:21:42.059

Evan Ryan: euros that's feeding into that, and that data is ultimately coming from the GSmart Ledger.

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00:21:42.960 --> 00:21:47.130

Evan Ryan: So, whatever invoices are forecasted

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00:21:47.950 --> 00:22:04.789

Evan Ryan: to be collected in week 4 are appearing in that 1.2 million amount. I can drill down to an invoice level, and then if I drill down a little bit deeper, I can see that there was an original due date on this.

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00:22:04.930 --> 00:22:08.269

Evan Ryan: But then that's actually been auto-adjusted due to that.

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00:22:08.540 --> 00:22:11.070

Evan Ryan: Historical average days to pay.

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00:22:11.970 --> 00:22:15.260

Evan Ryan: So, that's basically the starting point.

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00:22:15.430 --> 00:22:19.840

Evan Ryan: For the demo, and I'll jump into the actual Smart Ledger itself now.

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00:22:21.110 --> 00:22:26.129

Evan Ryan: So I can jump into the, Smart Ledger demo, or Smart Ledger.

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00:22:26.360 --> 00:22:27.230

Evan Ryan: itself.

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00:22:27.990 --> 00:22:30.950

Evan Ryan: So, here, within the Smart Ledger.

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00:22:31.220 --> 00:22:34.699

Evan Ryan: This is where that ERP data lives.

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00:22:35.090 --> 00:22:39.539

Evan Ryan: There's a number of different data sets that we take in from your ERP.

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00:22:39.690 --> 00:22:44.310

Evan Ryan: The open reports, so your current AR and AP aging report.

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00:22:44.500 --> 00:22:46.269

Evan Ryan: All payments.

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00:22:46.380 --> 00:22:47.680

Evan Ryan: And also…

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00:22:48.070 --> 00:22:56.680

Evan Ryan: any closed data. So, closed data being invoices that have been paid. In this case, in the last week, it could be in the last month.

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00:22:58.450 --> 00:23:08.800

Evan Ryan: So I can see I have a number of different items across here. I can see at an invoice level, you know, really what's happening at an invoice level.

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00:23:09.240 --> 00:23:15.999

Evan Ryan: I'm… I can see all of my open invoices, so this is the current aging bucket.

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00:23:16.260 --> 00:23:21.020

Evan Ryan: I have less in less than 30 days. I'm…

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00:23:21.220 --> 00:23:24.049

Evan Ryan: Projected or forecast to collect 15…

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00:23:24.400 --> 00:23:27.600

Evan Ryan: Million euros, 31 to 60.

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00:23:27.960 --> 00:23:30.569

Evan Ryan: 7 million euros, and so on and so forth.

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00:23:30.740 --> 00:23:33.949

Evan Ryan: I can also view what's overdue as well.

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00:23:34.230 --> 00:23:38.420

Evan Ryan: Also, a currency breakdown and so on. So at the moment, I just have Euros in this.

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00:23:38.540 --> 00:23:43.130

Evan Ryan: Ledger, but we can handle multiple currencies as well.

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00:23:44.840 --> 00:23:52.670

Evan Ryan: Also, I can view my paid invoices, so all the payments, or all invoices that have been paid over the past, period.

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00:23:54.620 --> 00:23:55.900

Evan Ryan: All payments…

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00:23:57.100 --> 00:24:04.690

Evan Ryan: But what I want to focus on here is the customer profiles. So if I jump into T-Mobile here…

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00:24:04.920 --> 00:24:10.220

Evan Ryan: I can see a breakdown of my customer behavior.

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00:24:10.390 --> 00:24:17.119

Evan Ryan: So these are all invoices that have been collected over the last, you know, let's say year in this case.

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00:24:17.410 --> 00:24:18.600

Evan Ryan: T-Mobile.

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00:24:19.200 --> 00:24:26.229

Evan Ryan: have, paid. Also, I have a view of the payment distribution and losses, so I can see, you know.

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00:24:27.160 --> 00:24:31.610

Evan Ryan: The majority of payments coming are in around the 60-7 day mark.

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00:24:32.290 --> 00:24:42.250

Evan Ryan: But perhaps, you know, we can take a look at orange. Orange seemed to be a bit more, let's say, well-behaved when it comes to, payment distribution.

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00:24:43.070 --> 00:24:45.680

Evan Ryan: If I roll back up here into T-Mobile.

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00:24:45.830 --> 00:24:57.189

Evan Ryan: What I want to focus on is this figure here, so your average days to pay. And that average days to pay is what's used to calculate that new due date.

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00:24:57.320 --> 00:25:02.459

Evan Ryan: for… any invoice that's associated with T-Mobile.

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00:25:03.350 --> 00:25:07.830

Evan Ryan: So when an invoice, a new invoice is sent to T-Mobile.

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00:25:08.310 --> 00:25:16.939

Evan Ryan: You know, and as I said, when I was talking through the slides, maybe the, you know, payment terms were 30 days or 45 days.

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00:25:17.080 --> 00:25:23.630

Evan Ryan: Historically, what's been calculated here using AI, And that statistical modeling is…

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00:25:23.920 --> 00:25:30.220

Evan Ryan: To calculate the historical average terms for each individual client.

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00:25:30.960 --> 00:25:43.340

Evan Ryan: So, if I jump back into my open report here, I can see I have T-Mobile. If I open this, okay, I can see my historical average days to pay for T-Mobile at an individual customer level.

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00:25:44.170 --> 00:25:46.209

Evan Ryan: And if you remember back to that

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00:25:46.480 --> 00:25:56.150

Evan Ryan: first or second slide within the presentation. One of the key pieces was then how this feeds into your forecast.

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00:25:57.230 --> 00:26:01.220

Evan Ryan: So, if I take a look at my… Rules here

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00:26:01.850 --> 00:26:06.720

Evan Ryan: Basically, I can see that I have a customer payment behavior rule here.

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00:26:07.580 --> 00:26:10.059

Evan Ryan: And what I can do is I can set

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00:26:11.550 --> 00:26:18.640

Evan Ryan: All clients, or all customers, for this ledger, to use that historical average days to pay.

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00:26:18.970 --> 00:26:20.689

Evan Ryan: And I can see them here.

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00:26:21.360 --> 00:26:26.399

Evan Ryan: But… Typically, what a lot of our clients will do is they will

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00:26:26.950 --> 00:26:41.600

Evan Ryan: Set up kind of a blanket rule, and use the historical average days to pay for all customers or all vendors, but you can then go in and start tweaking and customizing that rule at a customer level.

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00:26:41.900 --> 00:26:51.630

Evan Ryan: So it's not a case that, you know, you need to go in and it's just, you know, one blanket rule. You can really go down at a customer level and, you know, …

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00:26:52.160 --> 00:26:53.230

Evan Ryan: updates.

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00:26:53.350 --> 00:26:55.709

Evan Ryan: And, customize.

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00:26:55.820 --> 00:27:05.900

Evan Ryan: when that customer is going to pay you. So I've, in this case, I have historical average dates to pay, but I can add dates to my due date, and I can do that at an individual client level as well.

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00:27:07.660 --> 00:27:10.140

Evan Ryan: So once I've set my customers…

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00:27:10.420 --> 00:27:15.129

Evan Ryan: my customer payment behavior. Then, essentially, I can map

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00:27:15.670 --> 00:27:21.399

Evan Ryan: All of these invoices to a specific line item within my forecast.

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00:27:21.880 --> 00:27:27.029

Evan Ryan: So that was that AR collections line item that you saw just a couple of moments ago.

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00:27:29.370 --> 00:27:46.550

Evan Ryan: it's not the case that you need to map it all to one line item. You can specifically… you can, map specific, invoices or specific customers based off of, you know, keywords, descriptions, really to get a more granular level.

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00:27:48.420 --> 00:27:57.120

Evan Ryan: So… The final piece of the demo, I just want to jump into the actual, dashboards here.

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00:27:57.370 --> 00:28:05.800

Evan Ryan: So if I just, change up my… My date here… Let's see…

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00:28:12.290 --> 00:28:13.710

Evan Ryan: Second…

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00:28:18.680 --> 00:28:19.580

Evan Ryan: Perfect.

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00:28:20.130 --> 00:28:26.610

Evan Ryan: So what I can see here is my… Current…

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00:28:27.310 --> 00:28:30.529

Evan Ryan: AR balance at a consolidated level.

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00:28:30.690 --> 00:28:42.809

Evan Ryan: So again, if you have multiple entities, multiple cost centers, multiple business units within your organization, this really gives you that 10,000-foot view of, you know, what's happening.

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00:28:42.920 --> 00:28:55.049

Evan Ryan: So here I can see I have 226 million euros, but I can easily change up what business unit I want to filter here, you know, what currencies I want to view.

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00:28:55.160 --> 00:28:58.510

Evan Ryan: And also, I can actually rewind the clock.

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00:28:58.800 --> 00:29:02.889

Evan Ryan: And view the historical balances as well.

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00:29:03.650 --> 00:29:11.720

Evan Ryan: So I like to kind of divide this screen up into two halves. I have my customer AR collections analysis up top.

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00:29:12.100 --> 00:29:13.690

Evan Ryan: I can click into my…

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00:29:13.880 --> 00:29:19.930

Evan Ryan: Current outstanding, invoices at a customer level, and then drill down.

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00:29:20.460 --> 00:29:21.779

Evan Ryan: but also…

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00:29:23.300 --> 00:29:32.629

Evan Ryan: bottom half of the screen here is I can see what customers are overdue. So in this case, I can see I have 78 million overdue.

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00:29:33.310 --> 00:29:40.730

Evan Ryan: And also, again, like the top screen here, I can see overdue customer analysis,

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00:29:42.440 --> 00:29:48.150

Evan Ryan: At a customer level, but also it's broken out per bucket.

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00:29:48.510 --> 00:30:03.419

Evan Ryan: and, you know, like the top as well, the top of the screen, I can drill down into an invoice level, so you can really go from that, you know, very high bird's-eye consolidated view, down to that invoice level very, very quickly.

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00:30:03.720 --> 00:30:09.610

Evan Ryan: … So, you know, really the key takeaways here is… …

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00:30:09.850 --> 00:30:15.599

Evan Ryan: The data is taken from your ERP into that GSmart Ledger module that I just showed you.

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00:30:16.170 --> 00:30:21.349

Evan Ryan: The historical average days to pay is calculated, feeds into your forecast.

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00:30:21.500 --> 00:30:33.560

Evan Ryan: That forecast, then, is more accurate because of those… that historical average days to pay. And also, that data is feeding into your, working capital dashboards.

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00:30:34.580 --> 00:30:38.379

Evan Ryan: So, I'll, pause there.

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00:30:38.690 --> 00:30:42.380

Evan Ryan: And welcome… Any questions that folks may have?

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00:30:59.450 --> 00:31:02.050

Evan Ryan: So I'll see a couple of questions.

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00:31:03.640 --> 00:31:10.010

Evan Ryan: Coming in here… so, first one, how does Smart Ledger forecast

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00:31:12.000 --> 00:31:15.820

Evan Ryan: Forecast collections of future period billings.

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00:31:15.990 --> 00:31:22.839

Evan Ryan: That haven't been invoiced yet. Yeah, so there's, a number of different,

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00:31:23.040 --> 00:31:28.840

Evan Ryan: ways that we can take in that data. But typically what customers

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00:31:29.310 --> 00:31:33.820

Evan Ryan: You know, could do is… …

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00:31:35.270 --> 00:31:38.949

Evan Ryan: So, things like, purchase orders, you know, they can be…

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00:31:39.120 --> 00:31:45.389

Evan Ryan: loaded into the system, and they can be actually… they can actually then feed into your Smart Ledger.

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00:32:34.170 --> 00:32:41.740

Evan Ryan: Just a couple of more minutes there, folks, if anybody has any… so this module…

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00:32:42.420 --> 00:32:53.819

Evan Ryan: So is it a paid module? As far as I'm aware, It's, … Included in the, …

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00:32:54.050 --> 00:32:55.499

Evan Ryan: And the overall price.

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00:33:01.420 --> 00:33:10.440

GTreasury Marketing: We have another question, and it says, what kind of historical data do you need to get started, and how do you handle data quality issues?

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00:33:11.440 --> 00:33:15.960

Evan Ryan: Yeah, so… his… From a historical perspective.

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00:33:17.170 --> 00:33:27.170

Evan Ryan: the data is taken in, via, you know, API, or we do a file upload, and typically we take in a

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00:33:27.280 --> 00:33:29.920

Evan Ryan: Years worth of historical data.

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00:33:30.130 --> 00:33:35.500

Evan Ryan: And the implementation team then work with the client,

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00:33:35.780 --> 00:33:39.070

Evan Ryan: To essentially clean that data, …

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00:33:39.510 --> 00:33:43.909

Evan Ryan: And ultimately get the data into the correct format.

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00:33:45.050 --> 00:33:46.560

Evan Ryan: So…

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00:33:47.010 --> 00:33:59.880

Evan Ryan: just to answer that question, yeah, I'd say a year's worth of historical data to get that kind of seasonality, behavior down, and the clients then, you know, the customer success team, implementation team.

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00:34:00.090 --> 00:34:04.429

Evan Ryan: Work with the, customer to get that data in the correct format.

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00:34:23.580 --> 00:34:29.140

Evan Ryan: So just see one here on, receivables factoring.

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00:34:29.830 --> 00:34:34.090

Evan Ryan: Yeah, so we have a number of different ways we can, model that.

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00:34:34.350 --> 00:34:40.660

Evan Ryan: It's either, we have a special line item, which sits outside of the Smart Ledger.

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00:34:40.810 --> 00:34:50.019

Evan Ryan: Also we have a budget forecast tool, where we can model breaking down, you know, a budget amount into specific weeks as well.

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00:34:54.330 --> 00:35:04.519

Evan Ryan: I just shot one of the sales guys a quick message on that pricing, so he actually confirmed it's an additional model that would be, …

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00:35:04.700 --> 00:35:07.660

Evan Ryan: An additional paid model, or module.

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00:35:16.300 --> 00:35:22.109

Evan Ryan: Yeah, so another question here, can the forecast be shown by day, or by week?

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00:35:22.320 --> 00:35:33.559

Evan Ryan: So, yeah, we have a daily view model, weekly view model, monthly views, and those, you know, can roll up, into.

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00:35:33.670 --> 00:35:39.960

Evan Ryan: So you can have a daily model that rolls up into the weekly model, and a weekly that rolls up into

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00:35:40.210 --> 00:35:41.889

Evan Ryan: The daily model as well.

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00:35:42.260 --> 00:35:52.610

Evan Ryan: how many systems can be connected to, GSmart Ledger, really depends on how many, you know, ERPs, you have as a client.

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00:35:52.770 --> 00:35:55.259

Evan Ryan: You know, we've claimed such.

‍

00:35:55.410 --> 00:36:01.399

Evan Ryan: operate with one or two ERPs, and, you know, we can connect to, multiple ERPs.

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00:36:06.300 --> 00:36:08.999

Evan Ryan: Okay, folks, I think we can, …

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00:36:09.190 --> 00:36:13.970

Evan Ryan: Leave it there. If anybody has any other, questions, please,

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00:36:14.250 --> 00:36:17.879

Evan Ryan: Reach out to us, we'll be happy to answer them offline.

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00:36:19.550 --> 00:36:32.430

GTreasury Marketing: Thank you, Evan. And just as a reminder, the recording of this webinar will be sent out in a few days, and once again, thank you for joining us, and please reach out if you have any questions. Have a great rest of your day, everyone.

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00:36:34.460 --> 00:36:35.529

Evan Ryan: Bye, folks.

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