Ekstrakcija podatkov iz računa: popoln vodnik

Ekstrakcija podatkov iz računa: popoln vodnik

Časovni žig: 8. januar 2024 5
Izvorno vozlišče: 3051173

Predstavitev

In the modern business environment, accounts payable teams must be able to process invoices and payments as quickly and efficiently as possible. As the organization grows, the number of invoices that need to be processed also grows, requiring a larger team size and, longer processing times. In addition to this, manual invoice data extraction and processing is also quite error-prone leading to a greater investment of resources than is required. One of the most important steps in invoice processing is invoice data extraction. If done manually, this step is not only the most time-consuming but also the most error-prone. The solution, hence, is not to hire a larger team to do this manually but rather to invest in automated invoice data extraction. In this blog post, you will learn what is invoice data extraction, how to go about it, and some of the popular methods of invoice data extraction.

Before we get into invoice data extraction, let’s first understand what is an invoice.

Račun je dokument, ki opisuje podrobnosti transakcije med kupcem in prodajalcem, vključno z datumom transakcije, imeni in naslovom kupca in prodajalca, opisom opravljenega blaga ali storitev, količino artiklov, ceno na enoto in skupni dolgovani znesek.

Invoices contain important information, such as customer and vendor details, order information, pricing, taxes, etc. Information that needs to be extracted and matched to other documents like order forms, bill of goods, etc. before payment is processed.

Although it sounds simple, extracting data from invoices can be very time-consuming since invoices come in different formats. Additionally, invoices also contain both structured and unstructured data which can be difficult to extract manually and would require automated invoice data extraction software such as Nanoneti to be able to quickly process invoices.

Automate manual data entry using Nanonet’s AI-based OCR software. Capture data from invoices instantly. Reduce turnaround times and eliminate manual effort.

Invoice data extraction presents a host of challenges for AP teams because invoices come in various templates and can contain a range of information some of which may or may not be important for the AP team to process the invoice. Some of the challenges are listed below:

  • Different invoice formats – Invoices come in various formats including paper, PDF, EDI, etc. which can make it difficult to extract and process invoices.
  • Invoice template styles – In addition to the formats, invoices come in various templates as well. Some invoices may contain only the most essential information while others may have a lot of unwanted information as well. In addition, data points might be present in different places on the invoice thus making it highly time-consuming to extract data manually.
  • Kakovost in točnost podatkov – Manual invoice data extraction can lead to delays and inaccuracies in the extracted information.
  • Large volume of data – Usually organizations have to process a huge number of invoices daily. Doing this manually is extremely time-consuming and costly for these companies.
  • Različni jeziki – International vendors usually share invoices in different languages which could be difficult for the AP team to process manually if they are not versed in the language. These invoices are difficult to process for simple automation software as well.

Getting the data ready before extraction constitutes a crucial phase in invoice processing. This step is pivotal in guaranteeing the accuracy and reliability of the data, especially when handling substantial amounts of data or dealing with unstructured data that might encompass errors, inconsistencies, or other factors capable of affecting the precision of the extraction process.

Ena ključnih tehnik za pripravo podatkov o računih za ekstrakcijo je čiščenje in predprocesiranje podatkov.

An important method in readying invoice data for extraction is through data cleaning and preprocessing. This process entails recognizing and rectifying errors, inconsistencies, and various issues within the data before initiating the extraction process. Various techniques may be employed for this purpose, encompassing:

  • Normalizacija podatkov: Pretvorba podatkov v skupno obliko, ki jo je mogoče lažje obdelati in analizirati. To lahko vključuje standardizacijo oblike datumov, ur in drugih podatkovnih elementov ter pretvorbo podatkov v dosleden tip podatkov, kot so številski ali kategorični podatki.
  • Čiščenje besedila: Vključuje odstranjevanje tujih ali nepomembnih informacij iz podatkov, kot so zaustavitvene besede, ločila in drugi nebesedilni znaki. To lahko pomaga izboljšati natančnost in zanesljivost tehnik ekstrakcije besedila, kot sta OCR in NLP.
  • Validacija podatkov: This involves checking the data for errors, inconsistencies, and other issues that may impact the accuracy of the extraction process. This can involve comparing the data to external sources, such as customer databases or product catalogs, to ensure that the data is accurate and up-to-date.
  • Povečanje podatkov: Dodajanje ali spreminjanje podatkov za izboljšanje natančnosti in zanesljivosti postopka ekstrakcije. To lahko vključuje dodajanje dodatnih podatkovnih virov, kot so socialni mediji ali spletni podatki, za dopolnitev podatkov o računih ali uporabo tehnik strojnega učenja za ustvarjanje sintetičnih podatkov za izboljšanje natančnosti postopka ekstrakcije.

There are many different methods of data extraction. Picking the right method of invoice data extraction is very important for an AP team to be able to function effectively.

Manual Invoice data extraction: Manual invoice data extraction involves a human physically going through the invoice and manually and enter the relevant information in the accounting software where it can then be further matched and processed before the payment is made. This process is extremely time-consuming and can be prone to human errors. Usually, manual invoice data extraction can cause delays and payments and introduce unnecessary vendor friction.

  • Online data extraction tools: If you need to extract information from a particular document type where the information and format largely remain the same, there are many tools available that can help in addressing a particular use case. For example, if you need to convert PDF to text many online tools can help the AP team streamline this process. Conversion software provides a more reliable and accurate extraction method. However, they provide little-to-no automation capabilities for routine or complex invoice data extraction processes.
  • Template-based invoice data extraction: Template-based invoice data extraction relies on the use of pre-defined templates to extract data from a particular data set the format for which largely remains the same. For example, when an AP department needs to process multiple invoices of the same format, template-based data extraction may be used since the data that needs to be extracted will largely remain the same across invoices.

    Ta način pridobivanja podatkov je izjemno natančen, če ostane format enak. Težava nastane, ko pride do sprememb v formatu nabora podatkov. To lahko povzroči težave pri pridobivanju podatkov na podlagi predloge in lahko zahteva ročno posredovanje.
    programska oprema

  • Automated invoice data extraction using OCR: If you have multiple invoice types or a large number of invoices to extract data from, AI-based OCR programska opremakot Nanoneti, provide the most convenient solution. Such tools provide OCR (Optical Character Recognition) technology to recognize text from scanned documents or images.

    Ta orodja so izjemno hitra, učinkovita, varna in razširljiva. Uporabljajo kombinacijo AI, ML, OCR, RPA, prepoznavanje besedila in vzorcev ter številne druge tehnike za zagotovitev točnosti in zanesljivosti ekstrahiranih podatkov. Ne samo to, te orodja za pridobivanje podatkov lahko podpira ekstrakcijo besedila iz več virov, kot je npr ekstrahiranje besedila iz slikin celo pridobivanje ročno napisanega besedila iz slik.

zaključek

In conclusion, automating invoice data extraction is crucial for all AP teams to be able to effectively and efficiently process invoices. It is important to be able to process invoices within a set time frame so that vendor payments can be made in the promised time and avoid unnecessary friction.

The technique and type of invoice data extraction that is used by the AP team depends on the input sources and the specific needs of the business and needs to be carefully evaluated before implementation. Otherwise, it can lead to unnecessary wastage of both time and resources.

Eliminate bottlenecks created by manual invoice data extraction processes. Find out how Nanonets can help your business optimize invoice data extraction easily.

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