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Expert Knowledge on Digitalization & Automation of Business Processes
Expert Knowledge on Digitalization & Automation of Business Processes
Due to the ongoing digitalization and through enormous leaps in development, artificial intelligence has turned into a major future topic in recent years. "Artificial intelligence" is referred to with a wide variety of terms. (xSuite offers an overview in the blog article Artificial Intelligence - the most important terms). When people talk about artificial intelligence, especially in connection with self-learning software systems they usually mean a special sub-form: machine learning. Machine learning refers to algorithms that can learn autonomously from information and data based on experience. This enables the system to improve results over time and generate solutions by recognizing patterns. The software can link data intelligently, recognize correlations, and make predictions.
Artificial intelligence is already applied in a wide variety of contexts, including chatbots and purchase recommendations in online shopping, risk analyses, and protection mechanisms against attempted fraud. AI is also used in business applications, providing companies with ways to optimize operations in invoice processing and evaluate new potentials for more efficient process control.
Many companies are still facing the challenges of manual invoice captureâ€“which is just what this article is intended to shed light on. We will discuss some options for an automated invoice capture solution and take a closer look at the future potential of artificial intelligence in invoice capture.
Manual entry of invoices into the ERP system is no longer up to date: It is time-consuming and error-prone, especially where large document volumes are concerned. When processing invoices, even in the invoice-entry phase, accounts payable clerks still have to work around numerous impediments. A recent survey examined the challenges most frequently faced in invoice processing. The encumbrance that tops paper documents, invoices gone lost or missing, manual routing for invoice verification and the non-utilization of discounts and rebates appears to be manual data entry in invoice capture. Almost half of the respondents identified manual data entry from documents and the inefficient processes resulting from them as the biggest hurdle in invoice capture.
But why is it that manual data entry is seen as the biggest challenge in invoice processing? On the one hand, however well-intentioned an employee may be, if a task is both monotonous and time-consuming, there is great likelihood that focus will momentarily be lost, which is the way errors creep in. On the macro level, it goes without saying that the larger the volume of manually processed documents, the greater the susceptibility to error. Putting aside the probability of error with manual entry, increases in document volumes simply render manual data entry unfeasible in a business sense. Automation of these kinds of activities, and in particular of data capture, clearly represents such a gain in efficiency and solution to the problem of error that there can really be no argument against it.
Automated invoice capture makes data entry easier and eliminates the need for manual processing of paper invoices. Documents are digitized, and then the data is read out using centralized extraction, after which an employee can validate the results. The extraction process uses defined fields to identify the data in the document. An automated invoice entry solution such as this can be implemented using templates which determine where certain data is found in the document. For example, an invoice number is generally expected in the header area of a document. Similarly, rules are also defined which can be used to identify the invoice number if it follows a certain character string. A due date can be identified with the anchor phrase "due on" combined with a date in the future.
In a conventional automation tool for invoice entry, if there are changes to rules for vendors, invoice numbers, etc., they must be continuously adapted in the configuration. In order to avoid having to make such adjustments, therefore, the option of supporting it with artificial intelligence can be considered.
Machine learning refers to learning in patterns of data. The relevant principle in invoice capture is that the more data the system collects, the smarter it becomes; and the more patterns it has recognized, the more quickly and accurately it can interpret them.
With machine learning, and thus also through the use of artificial intelligence, it is possible to automatically recognize invoice documents and learn from the information they contain. Learned information influences document separation, document reading, supplier recognition, and how individual field contents are read. The training, i.e. the learned results, can also be used in the long term for processing multiple documents.
In order to recognize information from recurring documents, templates are created, to be used by the software for automated invoice entry. These templates contain definitions as to where in a document which information should be extracted. For instance, an invoice number is always found in the upper right-hand area of a document. The item data, on the other hand, is usually entered in the middle area of the invoice data. Information on the vendor can be found in the sender address of a document as well as in the footer with data on account details, VAT ID or tax number.
Through artificial intelligence, the software is able to create these kinds of templates and trainings from historical data. The artificial intelligence within the software learns continuously from the changes made and adapts the template on an ongoing basis. If, for example, a vendor changes its invoice layout, the software will recognize the changes and adjust the template automatically. If an accounts payable clerk requires additional fields for a document, the characteristics for the data can be created by the employee, the software will recognize the new field and add it to the template.
Invoices arrive at a company in different ways â€“ digitally by e-mail, or physically by traditional mail. An invoice may contain anywhere between one page and hundreds of pages. The documents received by post must be digitized and separated. Conventionally speaking, two approaches are conceivable for this purpose. In the first, a barcode sticker is affixed to each document before the invoices are digitized. The software for digitizing the documents recognizes the barcode as the first page of a new document and performs the document separation accordingly. In the second, incoming documents are separated manually by an employee after the solution has digitized them.
Artificial intelligence eclipses these two approaches by automatically identifying the individual documents based on their differing layouts, page numbers and data fields.
Manual corrections can be made by employees in a subsequent validation step. The software solution then learns from the changes made, using information gained to identify future information and derive new rules.
Looking back on the leaps and bounds that development of artificial intelligence has taken over the past ten years, as well as the numerous different applications machine learning has found, it is clear that enormous potential has been tapped, and that more may still be hidden. Suppliers are stepping on the gas to develop further future-oriented software solutions that intelligently and independently meet the challenges faced at today's companies. Artificial intelligence is not an end in itself. The focus of its utilization is clearly on relieving the burden of manual tasks on employees, to allow them to concentrate on their core tasks and processes.