Keypoints
- This tutorial explains how to mark invoices as paid in Lobo, either manually for all invoices in a time window or using the import wizard.
- The import wizard simplifies the process by matching payment data from files (CSV, XML, etc.) with existing invoices, ensuring accurate and efficient payment recording.
Transcript
Hi, this tutorial video is about how to mark invoices as paid. I switched to the module invoices and generally there are two options. First, I can use the import wizard or I can manually mark the invoice as paid. So let’s have a look on the second option which is called straightforward. I can simply click on this date field and set a certain payment date.
I can change this date or delete the payment again. Well, before we come to the import wizard, I want a clean start of the system. As this system is filled with test data, I want to mark all invoices as paid from the year 2000. Let’s say up to the year 2023. You only have to do this step if you haven’t marked the invoices as paid up to now.
So let’s mark all these 29000 invoices as paid to get started with the import wizard. I can open it via this button. And the first thing I need to do is to import a payments data file. You can typically receive such a file from your online banking account. There you export the incoming payments and typically they are in a CSV or in an XML format, but you see, we support all the other formats.
Let’s import such a test data as it is an XML file, we can recognize each column and by default, only positive amounts are imported. If you have the need, you can also enable the importing of negative amounts. Well, actually, as all columns are recognized correctly, I can move forward to the next step.
Lobo now tries to match the payment data with the data of the invoices that are not marked as paid yet. So what we see here is that for each line (that I can expand) I have on the left side the data that was imported on each line. The right side, I have the data that I can check or correct. So in this case, we see that Lobo found the right customer, even if it is written a little bit differently and the correct invoice. If I want to change the invoice, I can simply click here and get a drop down with all invoices that are not paid yet. But we see the matching was correct. It’s the 18. 60. and it even matches the invoice number with the booking details. So this holds true also for the next datasets. You can choose yourself if you check them in every detail or just make a plausibility check.
The first special line that comes to my attention is this steadfast engineering. You see the customer was matched, but lobo didn’t find an inovice. So maybe we open the details and we see that we can search either for the amount. So I type 61, but there are no invoices found with that amount. But I can also look for the invoice number.
So if I enter this, you see that there is an invoice with this invoice number. Well, but what we see is the amounts don’t match. So I won’t mark it as paid because there’s something wrong here. I leave this line as it is. Well, this seems to match again. And let’s have a look at the streamline logistics.
What’s wrong here? We can look for, okay, there’s only one open invoice. And what we see the invoice numbers matching with the booking details from the bank, but there seems to be two cents difference. Well, I don’t care. This seems to be just a typo. So I mark this invoices as paid. And as you see, if the amount does not match with the imported data, then it’s indicated with a red number. Next line that comes to my attention is the “Zenith Security” and what we see here is that there is no invoice with around 6 000 euros but what we see is that we can also select multiple invoices and actually the customer seems to have transferred two invoices in one payment.
And you see, if I select these two invoices, I get the sum. I can also get the details of each invoice. And now we have match. What’s wrong here? Here Lope didn’t even find a customer with the right name. That’s definitely wrong. I search for Grillage. Okay, so actually there is no customer like that. I have to handle this manually later on.
And last but not least, I have this “Consulting Fulcrum” thing and Lobo didn’t find the customer here. If I search for “Fulcrum Consulting”, I find the customer. You see, it’s just written totally different, but it obviously is the same customer. And now we are looking for this invoice, but seems to be missing.
I have to check manually later on, but we have lots of invoices that were correctly matched and so I can go on to the next step and all these nine datasets were marked as paid. Very important: the data sets that failed, you can download these via this download button. I’ll do this and finally can close the import wizard.
Well, if I reload here, you see that the “AlpineTech” invoices is correctly marked as paid and what I can also do is: if I want to go through the data that failed, I can just use the downloaded file and you see, I can go through the steps again. One more thing I want to show is, what if I want to import a CSV file.
Prepared such a file here. And as you see, in this case, Lobo could detect all the columns. It guessed some of them, but to assign the right columns, I can include show the first line, the header of the imported file. And this might give me hints about the columns. So I set the Verwendungszweck to booking details and the customer (Kundenreferenz) to payment reference.
Well, then it seems that the name and the IBAN and BIC were correctly assigned. So I can hide the first line header again and move on to the next step. Well as this is some random data, this, this can’t be matched. But anyway, the important thing is that during the CSV import, you have to make sure that all relevant columns are assigned.
The ones with star are mandatory, but I highly recommend to also select the other details to have a better matching result. Well, so much for the import wizard. I’m sure this will save lots of time. So have fun with the new payment import wizard. See you. Bye.