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Euroports - Direct/Indirect Bridging - 2025-10-27

Metadata

  • Date: 2025-10-27
  • Company: Euroports
  • External Participants: Matthias Depoorter (Treasury Manager), Evelyn A. (Group Treasury Analyst)
  • Palm Participants: Emma, Rodel
  • Type: Lead Call
  • Domain Areas: Direct and indirect forecasts bridging, Cash forecasting, Categorization

Summary

Context

Lead call with Euroports treasury team to discuss their needs around bridging direct and indirect cash flow forecasts. Matthias (new Treasury Manager since May) is reevaluating existing practices and was referred to Palm through industry contacts at the Working Capital Forum in Amsterdam.

Key Discussion Points

  • Currently using Cash Analytics with all manual input - no ERP integration
  • Doing variance analysis in Excel comparing direct cash flows to indirect budget
  • Want entity-by-entity forecasting across multiple countries (Belgium, Spain, France)
  • Planning to consolidate bank connections through BMG Bank portal
  • Need better drill-down into working capital movements (AR/AP)
  • 60-70% of receivables go through factoring (ABN), losing customer-level data

Pain Points

  • Manual classification is time-consuming and error-prone
  • Classification errors lead to "pollution of other categories"
  • Cash Analytics lacks drill-down and variance analysis capabilities
  • Can't easily reclassify historical transactions (requires support tickets)
  • Bridging direct vs indirect forecasts is cumbersome - "a lot of time asking different local entities what does this mean"
  • ERP bookings only updated monthly, not useful for within-month analysis

Feature Requests & Needs

  • Auto-classification of transactions with ability to correct/retrain
  • Waterfall charts and visualization for bridging analysis
  • Entity-by-entity forecasting with group consolidation
  • Corporate "top-up" adjustment lines (not visible to local entities)
  • Intercompany flow detection and matching

Jobs & Desired Outcomes

Job: Bridge direct and indirect cash flow forecasts bi-weekly to explain variance drivers to management

Desired Outcomes: - Minimize the time required to identify deviations between direct cash flows and indirect budget - Reduce the frequency of having to ask local entities for variance explanations - Increase visibility into working capital movement drivers (AR vs AP breakdown)

Job: Classify bank transactions into budget categories for variance analysis

Desired Outcomes: - Minimize the time spent on manual transaction classification - Reduce the frequency of classification errors that pollute category reporting - Increase the ability to reclassify historical transactions without support tickets

Job: Forecast cash flows entity-by-entity across decentralized group structure

Desired Outcomes: - Minimize the effort required to produce entity-level forecasts for Belgium, Spain, France - Increase the granularity of cash visibility at entity level (not just consolidated) - Reduce dependency on infrequent budget/forecast cycles (currently 1-2 per year)

Job: Understand AR/AP movements driving working capital changes (inferred from discussion of customer-level drill-down needs)

Desired Outcomes: - Minimize the time required to identify which customers or payment terms are driving AR changes - Increase the ability to drill down into working capital by customer segment (e.g., freight forwarding vs terminal)

Domain Insights

  • Bridging process: Classify direct flows → assign GL accounts based on budget → aggregate in Excel → compare to budget EBITDA → derive working capital movement
  • Categories used: EBITDA (indirect), interest, capex, treasury, working capital (AR/AP movements)
  • Factoring: 60-70% of receivables through ABN factor - could potentially get data from factor to maintain customer-level visibility
  • ERP limitations: Bookings only updated monthly, not useful for within-month analysis
  • Decentralization: Multiple entities across Belgium (5), Spain (2), France - want country-level consolidation

Action Items

  • [ ] BMG Bank onboarding needs to complete first (provides transaction data)
  • [ ] Matthias to build internal use case for CFO approval (early next year)
  • [ ] Palm to schedule demo showing categorization capabilities

Notable Quotes

"I really don't want to go back to Excel for nothing. Everything needs to be done through the tool." - Matthias

"I haven't seen anyone yet that has a great setup, and I think that's why they're talking to us." - Emma

"We want to know the deviation of the short term forecast versus the budget... I want to understand the drivers." - Matthias

Full Transcript

Meeting Info: Mon, Oct 27 2025 · 10:30 AM | 33 min | 3 participants

Participants: matthias.depoorter@euroports.com, rodel@usepalm.com, emma@usepalm.com

Emma S. Hello, Bernie. Rodel R. Hello morning. Matthias D. 0:00 Morning. Emma S. 0:01 Hello. Hi. Emma S. 0:03 Sorry, I'm Matthias D. 0:03 Hey, hello. Emma S. 0:04 Gonna record this. So this year where I'm starting here recording, right? Matthias D. 0:09 No worries. Emma S. 0:13 Nice to meet you. Matthias D. 0:16 Nice to meet you, too. Emma S. 0:30 There we go. Matthias D. 0:33 All right. Emma S. 0:34 Hello. Matthias D. 0:38 I think that's everybody, right? Rodel R. 0:42 Yeah, I think, let's let's kick it off. I think from our side, so graditus, unfortunately, not able to join. I think he also mentioned the email season AFP Rodel R. 0:52 in Boston and but I think we love to get more details. I think on your like Rodel R. 1:01 Direct and indirect bridge. Rodel R. 1:05 I think what we can do first is, I think Emma, you Rodel R. 1:11 Probably haven't, haven't met the years Evelyn yet? So maybe we can do just a very short round of intros. I already introduced myself last time, but Matthias D. 1:17 Sure. Rodel R. 1:21 just, Just to give away. Emma S. 1:25 All right, thank you Rodel. Hi Matthias and Evelyn. I'm Emma. Emma S. 1:31 I lead product at Palm. Emma S. 1:34 I've been with Palms since early days and I'm I have a background in software, engineering and business. Emma S. 1:41 So, yeah, that's the short story. I'm really just deep dive into into the world of direct and indirect vocals, and bridging them. And learn more about you to be honest. Matthias D. 1:55 All right, I'll do a quick one, as well. So Matthias Depoorter, so Treasury Emma S. 1:56 Yeah. Matthias D. 2:01 manager, you reports, I joined the company since May this year, so pretty new Matthias D. 2:07 and cash focus is pretty important for us. So that's why one of the reasons were A Matthias D. 2:12 Trying to reevaluate existing practices and I spoke with Gurjit previous company or also looking at cash flow forecasting before I left. So that's how I Emma S. 2:25 Mmm. Matthias D. 2:25 I got in touch. I am about the product because I went to the Working Capital Forum in Amsterdam and a contact of mine. Matthias D. 2:33 Basically recommended palm. Matthias D. 2:36 Because he heard about it, like a conduct of his, which use it for another Emma S. 2:36 that's, Matthias D. 2:41 company, based on that about five years of transactions, and told me at the results. Very good. So that peaked my initial interest and now I think it's it's Matthias D. 2:53 At your reports. It's pretty good to have this type of thing as well. Because we're Matthias D. 2:56 Somewhat decentralized, we don't have that frequent amount of forecasts, some budget of forecast. So we have one budget and maybe one or two forecast year, Matthias D. 3:06 So, it's not really that useful for short-term planning. Matthias D. 3:11 And there's also not a lot of stuff we can get out of the ERP within the month at least a month, and that's something else. But so I think from that point of Evelyn A. 3:18 Yeah, and Evelyn. I'm a group Treasury analyst. And I'm, yeah. Helping Matthias, Matthias D. 3:21 view, Matthias D. 3:23 Palm can help as well. And there's a lot of time spent on classification so auto classification can be good. We also do some checks from time to time and because it's all manual input that leads to classification errors which then looks to pollution of other categories. Matthias D. 3:40 um, Matthias D. 3:41 So trying to trying to find some some benefits here. Some Matthias D. 3:46 Some yeah, efficiency, gains and accuracy gains, basically. So that's about it so Evelyn, maybe you can quickly introduce yourself as well. Evelyn A. 4:01 I only joined the Euroports in July, so it's all new for me and I'm helping Evelyn A. 4:06 Matthias for the forecasting and yeah. Evelyn A. 4:09 Yeah, everything we do. Emma S. 4:13 Gotcha. Emma S. 4:15 Super nice interest. Thank you. I gave a really good picture of where you're at. Emma S. 4:21 so, Emma S. 4:23 Just super quick. Just question. You're not getting a lot of stuff out of your ERP. What do you mean by that? Emma S. 4:29 Just like, it's not. Matthias D. 4:30 It's not that the ERP, it's already currently using cash analytics, right? So Emma S. 4:33 Yeah. Matthias D. 4:34 it's all manual input, basically, everything's manually, input it, not even uploaded through an Excel upload. Emma S. 4:43 Okay, so it Matthias D. 4:44 and it's not, you know, usually you would say, if you integrate ERP Matthias D. 4:49 Into a cash flow forecast solution, you would upload arap. I don't know interest schedules that that's sort of thing. So that's not possible in the month, right? I mean a month that maybe, but Matthias D. 5:03 currently, I mean, obviously we did, we're not working on integrations if we're not going to use that, Matthias D. 5:08 Casually, they're gonna learn. Yeah, I mean you honest you don't want to spend time on that, right. And I think what we're seeing from cash analytics. Matthias D. 5:15 Capabilities are not maybe what we're looking for, especially on drill down, especially on variance analysis. So currently we're just using cash laser input, Matthias D. 5:24 we do a data dump and then everything is basically done in Excel for analysis, purposes. Comparing that to Matthias D. 5:31 The indirect forecasts, etc. Etc. So it's, it's It's a lot of time that we spend a lot of time asking the different local Emma S. 5:35 Got you? Matthias D. 5:39 entities. What does this mean that? What does that mean? Also for them, it's difficult, right? It's not an easy exercise. Bridging Matthias D. 5:45 indirect versus direct Emma S. 5:47 Mmm. No. Matthias D. 5:49 So, yeah, we're looking for for some efficiency gains here. Emma S. 5:53 Amazing. And do you get four calls from across all your entities that you look now? Sorry, I'm just deep timing a little bit into the topic already, but that's like my interest. It's it like one big top down that you typically get, or is it Emma S. 6:06 about like both breaking like between the entities and sanity checking across Matthias D. 6:12 Yeah, so it's Matthias D. 6:14 I wouldn't say it's not we don't bridge entity by entity but we like some group consolidations, right? So for example, we're on the Belgium, we have like five Emma S. 6:19 Yeah. Matthias D. 6:21 entities but we look at Belgium separately. Matthias D. 6:24 And then we have, for example, Spain, as two entities you look at Spain and on the total but I think what we're trying to aim here is then to have that bridge Emma S. 6:29 Culture. Matthias D. 6:33 entity by entity, right? I mean now I think now would be it would be just too much time to do entity by entity. What we're planning to do is to link all all bank transactions to BMG Bank which were on boarding now currently and Amsterdam. So once everything and fort is all the day is into that portal, the Matthias D. 6:56 goal would be that portal that connects to Palm and everything is just one connection to BMG to Palm and all the bank balance is and all the transaction information comes in like that. Matthias D. 7:06 um, and obviously we want to, we want to have A forecast entity by entity because that entity will be will be in that pool as well. So be important for us to understand. Okay, where does that entity go? And Matthias D. 7:18 then obviously you want to have some consolidation. For example, for Belgium, we considerate for France, we consulate a day for Spain, whatever. But the goal would be to have that forecast at any buy entity though. I mean, and Matthias D. 7:30 It's also a budget. It's forecasted entity by entity. So, Emma S. 7:34 Makes sense. Emma S. 7:35 So I'm really curious to this in your words here. A little bit more about high level first. What are these specific outcomes? You're looking to drive with the Emma S. 7:45 bridging. Matthias D. 7:47 Okay, I think in. So, in the bridging, I think I send over some screenshots, right? So, what we're basically, what we basically do is Matthias D. 7:54 So we classify the, the flows and cash analytics, those direct flows, we sign in jail account. Matthias D. 8:03 Based on the budget and then we basically use some kind of fair enough, some a formals or whatever in Excel just to aggregate that data. And basically we have that we have the budget data or forecast data on one hand. Then we have the Matthias D. 8:15 let's say the reclassified cash analytics, direct flows and then we're looking for for deviations basically. And so for some of them, it's easy, right? I mean for Matthias D. 8:29 For example, the budget, you have a ebda, that's an indirect measure. You don't have that in direct cash flows, right? Matthias D. 8:35 so, how it's usually done now is Matthias D. 8:38 What we do is we look at all those, what we can categorize like interest capex, a treasure etc, that's pretty clear. And then anything else like like custom receipts or supplier payments or that sort of thing that's done and that working capital slash ebda budget bucket, right? And now we basically have the Ebda from the budget. Look at these operational, cash flows, we subtract evda. Matthias D. 9:04 And then that's basically the derived movement and working capital. Matthias D. 9:09 Which we're trying to still. Matthias D. 9:13 Make it more concise on I guess. In the future will probably do instead of moving movement and working capital, We'll just probably do movement and AR movement and AP and then link those also with the direct cash flows, but that's how we're currently doing it. Emma S. 9:30 And why are you looking to achieve? It's about like aligning better in terms of like the cash and the budget and trying to drive like accuracy across both? Or is it is anything like that? Matthias D. 9:42 Yeah, what we basically need to explain on at least we try to do that on a bi-weekly basis but it's it has worked out so far because it's it's sometimes too cumbersome to get in information what's causing those deviations. But basically want to know, what's the what's the change in? Matthias D. 9:58 Working capital, that's the big one. And obviously then if we see a big change on any other categories, like capex or interest or something like that. Matthias D. 10:07 That is well but we want to know the deviation of let's say, the short term forecast, which is direct based versus the budget on the forecast, right? From the indirect forecast, I want to understand the drivers. Basically, that's the thing. That's at least that's that's the goal of this and direct bridge, right? Matthias D. 10:22 Overall home. That's one thing for indirect bridge as well, but also to improve Emma S. 10:23 Yeah. Matthias D. 10:27 the accuracy reduce the work. Matthias D. 10:30 Code, that sort of thing. Yeah. Emma S. 10:32 Yeah, that sounds very much aligned with what we've heard. Emma S. 10:37 And from other customer conversations as well. Emma S. 10:40 Or we've heard we've had different, a different types of outcomes that they would like to drive, right? This being there, I'd say biggest type of outcome that we're also looking into most. We also have like Emma S. 10:52 Companies that are very like top-down driven in terms of their forecasting. Emma S. 10:58 Yeah, so, but I think you're you belong more in India in the first kind of bucket. Emma S. 11:05 and what I'm hearing is, essentially, you want to, if you were to use palm and our categories, for all your forecasts, all your variants and analysis everything, all your visibility, Emma S. 11:18 Something like we're rolling out those categories into your DL corresponding categories or accounts. Emma S. 11:27 You know, understanding correctly, that, that would at a high level. Now, I'm just like, very like specific apologies, but that would provide you with a level of drill down into the actual underlying cash that drive the Emma S. 11:43 Yeah, the actuals essentially and also the forecast. Yeah. Matthias D. 11:46 Yeah, and I think I will, what we send over was what we have currently today, but I think we will do, we would check again all the cash flows and in the indirect cash flow budget. Matthias D. 11:58 And probably expanded a little bit. Matthias D. 12:00 Because the more you spend, the more accurate it is, the more you can drill down and some some items for example. Matthias D. 12:07 If you would say working capital, you have the accounts receivable movement. Matthias D. 12:12 Probably would. Matthias D. 12:14 With using palm drill down, a little bit more saying. Okay, this is the AR movement for example, because of, I don't know. Matthias D. 12:23 Fried forwarding revenues, or this is from thermal revenues just to have that more than that split up, right? I think with Paul me probably Could classify based on customer name, If you look at master data, this customer is, for example, a fry, forwarding customer this customers, eternal customer, right? Basically, just, we would probably look at that master data as well and try to see how we can, How can we dive a little bit deeper, right? Because Matthias D. 12:51 that's always that discussion first. Where is that ar difference coming from? And it's like, Oh yeah, it's because the revenue has shifted right now. We're Matthias D. 12:58 shifting more to customers that have different payment terms. That explains it. Yeah, but we want to know that beforehand, right? So, it's with the Matthias D. 13:06 classification being more granular. That's probably something we wouldn't be looking for as well. So, Matthias D. 13:14 Now, but you're an absolutely right? So basically would be tying, those direct Matthias D. 13:18 cash flows, directly to the indirect cash flows that we have. Yeah, Emma S. 13:25 So this might be in the degree to rodel. Let me know if there's stuff on your side as well, but, in terms of those Emma S. 13:33 Customers. Emma S. 13:35 Do you typically receive funds in the same accounts by customer? Matthias D. 13:43 we we mean in the same bank account, Emma S. 13:45 Yes. Matthias D. 13:49 yeah, I think the only the only class the only complication model complication that we have is then Matthias D. 13:54 We have what we, we also factor our friends, so, I mean our receivables. So, there's a party in between there for most of them, not for all of them. So, we have direct cash flow, direct customers. Emma S. 14:04 Yeah, I think we'll definitely dive into that a bit more. Matthias D. 14:04 But then some let's say. Matthias D. 14:07 More than half at least 60, 70 percent is through the factory, then the factoring sends it out. So from that point of view, Matthias D. 14:17 You do lose some of that data, I guess on the customer side. Because basically then you only see Matthias D. 14:24 You only see that the receipts, right? So let's Matthias D. 14:29 Maybe something to think about. Matthias D. 14:32 if we can get the data from the factor as well because obviously they also have Matthias D. 14:39 On their own bank accounts. They also have the data when the customers pay to their bank accounts or factors ABN, right in an elements. So, Matthias D. 14:45 That's something we could do as well. We can get the data there, and then maybe see or see with the with ABN what's possible on their side if they want to Matthias D. 14:55 If they could link up some data as well, I don't know. Emma S. 15:05 Product. Rodel R. 15:05 yeah, maybe just to explain a bit on on how Rodel R. 15:10 I guess the two main Rodel R. 15:14 Ways that we we get this type of gray like granular information to to derive. Those categories is either we extract them from an existing system so that can be existing TMS that can be like, it can be a certain source where we have to transactions, the balances and a transactions have already been off information to make sure that we can do the categorization. One option, and I think what I think, and that's the thing, which was mentioned with the previous option is, we can also get the bank statement files from banks. Either, we can get them or they can also just be submitted to our system Rodel R. 15:57 that can be through email etc. And this can just be the bank statement formats that are most typical and empty 940 accounts, different formats. And usually they have the granary information to make sure that we can do the categorization up to the level that you wanted to be. But I think definitely a topic that we Rodel R. 16:21 can go further into Matthias D. 16:23 Yeah, and I think we can we can we can help with that, right? I mean, Obviously, Emma S. 16:23 Yeah. Matthias D. 16:27 that would be let's say the going concern would be using those transaction information from from the Mp94 to the camp files. Matthias D. 16:36 But initially, I think we can see. Matthias D. 16:40 there's probably some categorization, for example, this type of vendor, it's a It's a Matthias D. 16:44 Mostly used for capex or whatever or we have some kind of capexclosure. We can use some data out of or we have probably also from the accounting side, I think they do use some Matthias D. 16:56 Modernization rules where basically, when transaction comes in they have probably some some classification rules as well in their system or they basically classified already. Some stuff for automatic automatic bookings. So if I, if we could copy those rules or basically present you with those rules, you already know beforehand, right? Basically. Saying What's already in the system. Matthias D. 17:16 So I think from that point of view, we can already probably provide some data Matthias D. 17:21 and anything else would probably have to then be based on a transaction information. And I guess initially, it will be some setup work, right? We're basically Matthias D. 17:33 Palm does some suggestions and then the user have to confirm those or or change those, right? So, Matthias D. 17:41 Please send in the first couple months. Rodel R. 17:44 Yeah no exactly. I think during onboarding we typically work very closely together to get like a baseline and we typically want at least the customer to feel confident that the transactions like categories look look good. But we do offer the capabilities inside the application to Rodel R. 18:06 Re-categorize anything and that can be like you can Multi-select you can do Evelyn A. 18:08 It okay. Rodel R. 18:12 single transactions. And based on the information that we get from that, we categorization the system also adjusts any future transactions coming in so, but yeah, definitely happy to life. I've further into that need be Matthias D. 18:25 Okay. Matthias D. 18:29 So it could also be possible. For example, if we do a wrong classification Matthias D. 18:33 initially that we can correct it and then easily because yeah, you know, Emma S. 18:37 100%. Matthias D. 18:38 Okay. Yeah. Emma S. 18:39 Yes. Rodel R. 18:39 Yeah. Yeah. And any historical ones you can read categorize? And what we Evelyn A. 18:44 Okay. Rodel R. 18:45 typically ask is a bit of context, Why? And then the system would also know kind of alright, then in the future, Rodel R. 18:52 This needs to, let's say we categorized as something else, but what I mentioned in terms of onboarding, these rules which you mentioned but also any context that might be relevant, that is information that I think is, is very helpful to set like a good baseline categorization. But Matthias D. 19:14 Okay. Rodel R. 19:14 that we typically again work, very close, I think with customer set up like the categorization at first Matthias D. 19:22 and and practically speaking, then you would ask us to send you around six months of historicals initially due to Matthias D. 19:30 To launch that. Matthias D. 19:32 To train palm basically or something like that. Rodel R. 19:36 Yeah, so I think it's Rodel R. 19:39 What we typically ask for is as long data, history as relevant. Also for forecasting, six months I think is Rodel R. 19:50 I think for categorization, could be sufficient for forecasting, we typically want a bit more. But what we then do, is if we get six months, then we categorize, that would help I think of your context and rules things that you, that you can provide us, we categorize those transactions. Then in the application, you can see the categories for every transaction. And then, I think Rodel R. 20:15 you can get feedback on that and either you can do it directly. So you can reconnect the garage. It's on your own or Rodel R. 20:22 And we like we do sessions where we just go through things and be like this was incorrect because of this. And then I think we can proactively adjust that. Matthias D. 20:31 Yeah. Okay. Matthias D. 20:33 Yeah. Matthias D. 20:36 And from from what I sent through the email right that's usually what you see with other customers and as well that that type of categorization direct versus indirectly. There's something if there's something a genius way to do this very quick, please let me know, right? Emma S. 20:53 I haven't seen anyone yet the house, like a great setup, and I think that's why they're talking to us, you know? Matthias D. 20:59 Yeah. Yeah. Emma S. 21:01 I think we're really keen to solve it. In a good way though and that's why I would also love to at some point keep talking a little bit more about that customer level insight like the what customers are driving what they are movements. Emma S. 21:18 and and payment schedules and so on because I think that's something that we're quite Emma S. 21:22 Exploring as well. Emma S. 21:25 But again, yes, of course, it will depend on what data is available. Matthias D. 21:31 Because in, I mean, obviously, what I'm Matthias D. 21:34 Ideally speaking, right? If we would have the ERP connection, Matthias D. 21:38 But yeah, I mean, I think ERPs or updates. Matthias D. 21:42 From bookings at our company. Usually that happens from if I understand correctly more at a month. And so the within the month of positions are not correctly, booked Matthias D. 21:51 Ideally speaking. What would be basically that we have that working capital movement for example, that Matthias D. 21:56 For us it's basically ar and ap not not so much. Matthias D. 22:01 Inventory because we're not a manufacturing company but those those bookings and if they would be, if you could connect the ERP and then have that information available to Palm then basically. Yeah. You you basic you will be able to to calculate all those working capital Mac directs, like DSO, DPO, etc. On the Matthias D. 22:19 weekly basis and you can track that out right seeing, okay? There's a spike here, collection is going up. Collections go collection is going down or paying too early, but we don't have that, right? We don't, we don't have that Matthias D. 22:29 otherwise, I mean, Matthias D. 22:31 That would that would the best thing, right? We've done. We have the band transaction formation of ERP date. I can schedule it out, but I don't think Matthias D. 22:37 maybe you. And at some point in future would have that. But now we don't because obviously we want to understand working capital as much as we can seeing. Is there a driver or are we being paid too early and etc but Matthias D. 22:51 maybe we can we can provide some information, we can maybe upload to master data and Matthias D. 22:56 With the payment terms, right? That that palm get, and then could use or I mean, Matthias D. 23:01 you, you would see the incoming payment and say, Okay, this customers paying usually on 30 days and then do some masses on that as well, right? I mean, that's not my initial thought, but any data that we can have, we'll try to provide, right? Emma S. 23:15 Yes, I think that sounds great. Matthias D. 23:18 Now, okay. Matthias D. 23:22 And then also on that bridge, because we're obviously, once we're using poem, we don't want to go back back to Excel, right? So on visualization purposes. I think you have the table but then maybe also you Palm can provide some, some waterfall chart, or something like that, I don't know. Matthias D. 23:39 Is that something that's possible visually? Emma S. 23:42 Yes, 100%. How we typically do it is we have this embedded analytics capabilities in our platform and Emma S. 23:52 It's quite flexible. We can build new components as needed. Emma S. 23:57 If you want a specific type of chart or anything that's missing, we have the ability to just build it for you. Matthias D. 24:03 Okay. Emma S. 24:04 And we're also going to have a self-serve function in place, which allows you to just play around with your data very easily and build any report or any just a lot of insights, you know, stuff like that. So, Matthias D. 24:20 Okay. Emma S. 24:21 Yeah. Emma S. 24:23 100%. Matthias D. 24:24 All right. Emma S. 24:25 And yeah. Matthias D. 24:28 Okay, good. Good. Matthias D. 24:32 Yeah, I don't know if I have any, any more questions from my side? No Evelyn, maybe you have any more questions from your side. Evelyn A. 24:41 No, because Evelyn A. 24:43 What I had was about a categorization, so that's fine. Yeah. Matthias D. 24:48 Yeah, currently currently in our cash analytics. It's not really that easy Evelyn A. 24:52 Yeah. Matthias D. 24:53 classified, right? That Evelyn A. 24:54 No. Emma S. 24:56 What is the problem? Passion analytics, can you tell me like Evelyn A. 25:01 Yeah, for the like, For example, we noticed that a dividend was not placed on the correct category, so we yeah, we had to change the past but we were not able to open that category in the past. So we had to make a ticket with education analytics and then they could only open. It's for us, we were not able to do it yourself. Emma S. 25:27 Okay. Evelyn A. 25:27 To make new categories and into. Yeah. Evelyn A. 25:31 To put it in the correct category. That's what's the problem. Matthias D. 25:35 Yeah. Matthias D. 25:37 That that's that. I mean, that's a, that's a time consumer. I just having this Evelyn A. 25:40 Yeah, indeed. Yeah to always make tickets and yeah. Matthias D. 25:40 since Matthias D. 25:46 One other thing is on the maybe just for us to know on the intercompany piece, how I think. That's pretty easily detected by by a poem than that. These are for example to Intercomplete flows, if they're let's say both entities are part of the Palm Cash flow forecasting software. Matthias D. 26:03 then I assume Palm would easily detect the flow between Matthias D. 26:08 I would say to to intercompany entities paying out right one receiving. One paying Emma S. 26:16 Yes, we are. Yes, we are. We are almost there. But it's so we have the Emma S. 26:22 capability to identify in the company transactions and we are exploring a best way to to make that counterparty connection right now. So I think we just want to really make sure that it's it's an accurate safe implementation, you know? Emma S. 26:40 And on the forecasting side, you know, they come in pairs. Emma S. 26:44 Like there's always an inflowing and outflow. So so that's something that we're also currently looking into. Emma S. 26:50 So yeah, it will be there but yeah, we to be completely transparent. It's still Matthias D. 26:50 Okay. Emma S. 26:55 working process. Matthias D. 26:57 Okay. All right. Yeah, but it's something that you at least manually can reclassify, right? I think Emma S. 27:03 Yes, hundred percent. Yes, it is. Matthias D. 27:06 Okay. Because of what, what we're seeing now? I mean sometimes I mean, obviously it works because it's manually input, sometimes what we have is Matthias D. 27:15 A payment that's done on a Friday and then, obviously, it only arrives on the Monday and then we do the weekly forecasting and it's like a mismatch issue, right? So then we have a category Matthias D. 27:27 Intercompany to be paid or something. So yeah, I mean I would call some kind of mismatches. That's the that's the usual hiccup when, you know, when you make a payments on the at the end of the week, right? That's a special. Matthias D. 27:37 Payments, not outside of Europe. So, Emma S. 27:41 Yeah. Matthias D. 27:42 Yeah, or for example, that could be for I don't know, the payment is a hundred Emma S. 27:43 Yeah. Matthias D. 27:47 is a hundred dollars or something and Matthias D. 27:51 There's some fees out of it. So basically running the incoming flow will be maybe eighty dollars, but on the payment side is a dollars. You have 20, 20 dollars mismatch because of that bank fees depending on how the bank's basically treat it, right? Some banks. They wouldn't they wouldn't deducted directly from Matthias D. 28:08 transaction amount and basically separate transaction. But a lot of banks, basically, deducted, which that causes these big concentration, differences. So that's Emma S. 28:15 Exactly. And he saw exactly the kind of challenges and complexities that we are quite aware. We need to really dive deeper into Matthias D. 28:23 Okay. Emma S. 28:24 Yes, it's very much. Evelyn A. 28:28 Okay. Rodel R. 28:28 And maybe a very small comment. I think from my side. So in terms of catheterization, we definitely already support like intercompany like Emma S. 28:37 Yeah. Rodel R. 28:37 transaction categorization. It's I think where we would love to have your input eventually is on Rodel R. 28:47 Indeed like what Emma mentioned around forecasting around how can we make sure that we 100% certain? No, that transaction X and transaction? Y are the counterparts transactions and and that that is like between customers. This is Rodel R. 29:06 indeed like it's it's also different. Rodel R. 29:09 so we definitely want to get it right and make sure that it's generic enough that Rodel R. 29:14 Every customer can use the functionality. Matthias D. 29:18 Yeah, I think yeah and I think that from speaking from my, from my past, when I did it, what did they? I did the categorization myself using a Python script. I wrote, I do. I do recall from from the past. That's always, for example, if you, Matthias D. 29:32 if you had company a paying Company B Matthias D. 29:36 And obviously the name of Company B is Company B, but if they and their own system and that using calling A Company B, would be calling code, B, or something. And obviously, the name, even though the beneficiary is the same in the name is different, right? And that, that's the issue with them. Matthias D. 29:51 The master data, not being 100% clean, right? I mean, that's always the Matthias D. 29:56 So that's something that we would probably have to. Matthias D. 29:59 Have to check, but I think based on historical information, right? If you have like, a year's worth of data, Matthias D. 30:06 You'll probably recognize those flows more or less, right? Emma S. 30:11 Yeah. Matthias D. 30:12 the only difference would be if we would open new bank accounts and then Matthias D. 30:15 Yeah, that's that's something. I know it's causing some some of those issues, right? Yeah. Rodel R. 30:23 But I did again, in terms of categorization, we already support this and happy to show that in the upcoming demo. Rodel R. 30:33 Let's see. We're overtime. Rodel R. 30:36 I think we touched upon everything at least from our perspective, Emma. Emma S. 30:41 Yes, for sure. Emma S. 30:43 I feel I have a clear picture of what you're looking to achieve, and it's quite well aligned with other customers as well. Matthias D. 30:50 Okay. Matthias D. 30:51 No, that's good. That's good. Emma S. 30:52 Yeah. Matthias D. 30:55 Yeah, and then you, yeah, I think the only other question I think, but that was that was possible. I mean, even though we do this deep analysis, etc, etc, when the CFO reviews, it, it could be. That's that's not happy with what the country's predicts, right? And then that's usually like, we have to have this Matthias D. 31:10 top-up adjustment. That's why I wrote an initial specs as well, if you would Evelyn A. 31:13 Yeah. Matthias D. 31:15 have like a separate line, basically, Matthias D. 31:17 Imagine we do consulate for Belgium and we should do we have a different forecast flow or whatever you want to call that. Matthias D. 31:24 Calling a Belgium top-up line where we basically add up x amount on certain category. Matthias D. 31:30 Which is something up from corporate adjustment and it shouldn't be visible to the countries but something that we take into account. It's not, it's not the ideal solution and I don't want to use it, but I want to prepare in case we need to do such Matthias D. 31:42 Top-up adjustments, basically. Emma S. 31:44 All right. Yes. Emma S. 31:46 I have another meeting, but I would love to speak more about that as well. Emma S. 31:53 It sounds. Emma S. 31:55 It sounds doable. One way or another, for sure. Emma S. 31:57 But maybe you said, like you said, maybe there's yeah. Matthias D. 31:58 Yeah. Emma S. 32:01 Let's export together, I think. Emma S. 32:05 To start some. Matthias D. 32:06 Yeah, it's just something I had in mind because obviously, I really don't want to go go back to excel for nothing. Everything needs to be done through the tool because it's always a lot of work. And then there's there's changes and it's not properly cracked and everything and Matthias D. 32:21 So that's just Matthias D. 32:25 Me being being future-proof, right? Okay, set. Your requirement would be just Emma S. 32:25 Yeah. Matthias D. 32:29 just to stop here. Anything you ask require from us and general information or, or set up information I think, from from my, from our, from my side basically. So I have the BMG project which will come first, I'll be all the data and then because we need to have the data, anything else you want to have beforehand, as I told initially to courage it as well? I don't know rodel, if you were there. Matthias D. 32:53 What I need to do is build a use case, right? I mean you have we have Matthias D. 33:09 build out, presented the CFO and then Matthias D. 33:13 Move forward this, right? So that would be something for Matthias D. 33:17 For early next year thinking. But anything we can do beforehand to help with that. Matthias D. 33:22 Would be good. So anything you need from information wise or anything? Matthias D. 33:27 Let me know. Emma S. 33:29 Will do. Matthias D. 33:30 Okay. Matthias D. 33:32 All right, very good. Emma S. 33:34 thank you so much, it was very Matthias D. 33:35 All right. Rodel R. 33:36 Thanks a lot. Matthias D. 33:38 All right. Evelyn A. 33:39 My. Matthias D. 33:39 Yeah. Emma S. 33:39 Hope to see you soon again. Matthias D. 33:40 Yeah. Evelyn A. 33:42 I,