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. Data can then be linked intelligently, correlations recognized, and predictions made.
Artificial intelligence is already applied in a wide variety of contexts, including chat bots and purchase recommendations in online shopping and risk analyses, and for protection against attempted fraud. Business applications also use AI, providing companies with ways to optimize business processes in invoice processing and to evaluate new potentials for more efficient control of processes.
Many companies are, in fact, still facing the challenges of manual invoice capture– which is just what this article is intended to shed light on. We will indicate some options with an automated invoice capture solution and take a closer look at what kind of future potential the use of artificial intelligence in invoice capture really possesses.
Automation of invoice capture without artificial intelligence
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.