How do you efficiently tackle data extraction in finance?

‘Data extraction’ sounds like a pretty complex concept. But did you know that this is actually what you do in finance all the time? Because it means nothing more or less than collecting and distilling meaningful data: from a sea of information, you select the data that is meaningful to you. In financial administration, this means extracting data that will help you convert an invoice into a booking proposal. How you do that is another story. We distinguish between manual and automated data extraction. What exactly is the difference? And what is the next step in data extraction?

Manual data extraction: lots of work, lots of risk

With technological innovations sprouting like mushrooms, the paper era seems to be increasingly behind us. But in practice, there are still quite a few companies that go through certain processes – at least in part – manually. Suppose you receive an invoice by mail. Or it arrives by e-mail, but you print it out for processing. In either case, you have to manually type the invoice data into your accounting system. Then you are, in effect, engaging in manual data extraction. And that process carries quite a bit of risk. For example, because 100 percent of the work is yours, it costs you an enormous amount of time. In addition, the risk of human (over-typing) errors is quite high, especially if you are processing a large volume of invoices. In addition, paper can get lost and it is difficult to keep track. How do you solve such problems?

Automated data extraction: smart software goes smart

An excellent solution is OCR software. This searches independently for words and word combinations that have meaning, such as an invoice date, a VAT number or a Chamber of Commerce number. So with this you are doing automated data extraction. If you choose a smart OCR solution, you also make use of the knowledge of the community. In other words, the software collects invoice data from multiple customers and uses it anonymously to refine the process. As a result, you benefit from each other’s knowledge and recognition gets even better over time. Of course, a good OCR supplier complies with the General Data Protection Regulation (AVG) by not using traceable information. How can this be done? An example: if customer A and customer B both receive an invoice from a certain supermarket giant, the two invoices most likely have the same layout. The software uses these to improve recognition – and it does so anonymously, because this obviously does not require the business data of either customer.
Translated with DeepL.com (free version)

Why automated data collection? 4 benefits

1.
Gebruikers profiteren van een aanzienlijke efficiëntieboost

A good OCR solution brings considerable relief to financial administration. You work faster and easier and the purchase invoice processing process is less error-prone (because you eliminate over-typing errors and maintain an overview!).

2. Recognition only increases over time

If you choose a smart OCR solution, you often start with a very high recognition rate already. But data extraction gets even better over time – you can safely go from 90 to 93 percent, for example.

3. You can use data for more than recognition

Collecting objective data automatically is a good start. But if you really want to work more efficiently, use the extracted data to automatically prepare a booking proposal. That’s exactly what good OCR software does for you!

4. Capabilities extend beyond invoice recognition

Extracting data with smart OCR software is HUGE time-saving. So why use such a solution only for purchase invoices? The principle is applicable to all kinds of incoming documents. Think of order confirmations, declarations, packing slips and received transport documents. Choose an OCR vendor that enables this type of solution through continuous development!

Looking ahead: what is the (AI) future of data extraction?

We wrote it above: technological innovations follow each other in rapid succession. Things are not standing still in the field of data extraction either. A “near-future question” is, “What is Artificial Intelligence (AI) going to mean in this? With AI, you look for patterns in data you extract from documents. Based on those patterns, you try to discern an algorithm in the historical data. Based on that, you can then make predictions. In the not-so-distant future, OCR software may be able to tell you to book a particular purchase invoice to canteen expenses – purely because patterns in past data point that way. AI may also make it possible to bring down the error rate by continuing to analyze meaningful data as well as errors made. Finally, an example that extends beyond financial accounting (but which illustrates well how AI can boost data extraction): if you throw a box of random documents into an “AI bin” and have an algorithm for it, AI technology should be able to sort the documents. Even if this is a mix of invoices, order confirmations and swimming certificates. Does this seem like a distant concept? It’s not! A good OCR vendor is already hard at work on this. Because this is the world we are moving into with the collective AI train.