Checking creditworthiness, verifying incoming payments and writing reminders: the effort required by companies to ensure their own liquidity via accounts receivable accounting is high - and therefore costs a lot of money. To make accounts receivable management more efficient, more and more companies are turning to artificial intelligence (AI).
There is a reason for that. The McKinsey Global Institute already found in 2017: companies that use AI in accounting can save up to 40 percent of the costs incurred for this. A 2020 report by Capgemini concluded: companies that implement AI-supported automation in accounting reduce their costs by 25-45 percent.
We show what tasks an accounts receivable department takes on, what this looks like in practice - and where AI can simplify or accelerate these processes.
This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.
Accounts Receivable Definition
Accounts receivable accounting is an essential part of corporate accounting. It records and manages receivables from customers. It is responsible for systematically posting incoming payments, monitoring outstanding receivables and carrying out dunning procedures. Accounts receivable accounting is often used synonymously with receivables management.
This means that accounts receivable maintains an efficient cash flow and ensures a healthy financial position of the company.
By recording incoming payments in real time, Accounts Receivable can keep track of outstanding trade receivables and take early action to avoid payment delays.
To prevent cash flow problems from occurring in the first place (ideally), accounts receivable management also checks and monitors the creditworthiness of customers. This includes analyzing credit applications, checking references and creditworthiness. Based on this information, Accounts Receivable Management decides whether to grant credit and set payment terms.
In addition, the accounts receivable process plays a central role in communication with customers. It creates invoices, sends payment reminders and dunning letters, and responds to inquiries about open items. Effective communication with customers is of great importance in minimizing payment delays and building long-term customer relationships.
Accounts Receivable vs. Accounts Payable
Accounts receivable and accounts payable are two essential aspects of a company's financial accounting. They differ in their functionality and the tasks associated with them.
Accounts receivable accounting deals with a company's outstanding receivables from its customers. It keeps track of customers' financial obligations and ensures that incoming payments are posted correctly.
The main tasks of Accounts Receivable include invoicing, monitoring incoming payments, dunning and debt collection.
It ensures that the Company receives its outstanding receivables on time and in full.
In contrast, accounts payable focuses on a company's payables to its suppliers. It records and monitors incoming invoices and ensures that they are properly booked and paid. In other words, it ensures that companies pay their liabilities on time and in full. Various companies offer specialized solutions for invoice receipt, here is an example of the system iflow of Ritter technology.
Accounts Receivable Tasks
Accounts receivable accounting plays a crucial role in a company's financial management. Its tasks include receivables management, dunning, accounts receivable scoring and information management:
Receivables management manages the company's outstanding receivables. This includes invoicing customers, monitoring incoming payments and reconciling payment differences. The aim is to realize the receivables promptly and in full.
With the out-of-court dunning process, Accounts Receivable monitors payment deadlines and carries out dunning procedures for defaulters. It has the task of collecting outstanding receivables and ensuring the flow of payments. It includes communicating with customers, creating and sending reminders, and coordinating with internal or external collection service providers.
Debtor scoring is an instrument for assessing the risk of customers. Customers are evaluated on the basis of various criteria, such as payment history and creditworthiness or payment behavior. Scoring helps companies decide which customers they can supply on account and which payment terms they should grant.
Information management comprises the organization and preparation of relevant data and information on individual debtors. Data on customer accounts, open items, incoming payments and other relevant information is recorded, updated and evaluated. This information serves as the basis for receivables management, dunning and debtor scoring.
Accounts Receivable Example
What do the tasks of accounts receivable management look like in practice? Let's assume the following case: Your company produces and sells lighting systems. A customer who wants to completely re-light his company locations orders various, complex lighting units from you.
Your accounts receivable department then checks the customer's financial situation and payment history as a first step.
If it finds no anomalies, your company concludes a supply contract with the customer in a second step. This contract specifies the delivery method and the dates for invoicing and outstanding payment. Partial invoices are sent out on a monthly basis, with an agreed payment period of 30 days. Your accounts receivable management enters and sends the partial invoices. It also monitors incoming payments.
However, the new customer is not reliable. He does not meet payment deadlines. Your accounts receivable department therefore sends appropriate payment reminders and informs the sales department about the situation. The sales department holds talks with the customer, but the situation does not improve at first. For this reason, your accounts receivable management sends a reminder with the threat of a delivery stop. The customer then reacts and accelerates his outstanding payments. Your accounting department documents the payment difficulties in its analysis and saves this information for possible future business with the customer.
Possibilities of AI in Accounts Receivable Accounting
Companies can use AI to automate various accounts receivable processes:
Invoice receipt and processing
Companies can leverage AI to automatically manage and process incoming invoices. For example, the AI can extract relevant information such as invoice number, invoice date and amount and transfer it to the accounting system.
Review of payment terms
Companies can use AI to automatically check the payment terms in invoices. In doing so, software can determine, for example, whether the specified payment deadlines are being met or whether there are any discrepancies.
AI can be used to automate the dunning process. AI applications can automatically generate and send reminders to customers based on defined rules and payment delays.
AI-based chatbots can automatically answer customer queries related to invoices and payments. For example, you can answer frequently asked questions, update the payment status, or forward the respective customer to the right contact person.
Credit Risk Assessment
AI can assist in assessing the credit risk of individual customers. By analyzing customer history, payment behavior, and other relevant data, AI can create risk profiles and make recommendations for credit limits or payment terms.
Benefits of AI in Accounts Receivable Accounting
If companies use AI software in accounts receivable, they benefit from these advantages in particular:
Automation of routine tasks
Artificial intelligence can automate repetitive tasks such as checking incoming payments, creating invoices or assigning payments to open items. In this way, companies have less effort and make fewer errors.
Earlier detection of payment defaults
By using AI algorithms, companies can identify patterns and trends in payment behavior and history. As a result, payment defaults can be detected earlier. This makes it possible to take timely action to minimize the risk of payment defaults.
Improved accuracy in credit scoring
AI can help analyze customer information to better assess the creditworthiness of potential customers. Through the use of machine learning algorithms companies can make more accurate predictions about customers' ability to pay and thus reduce the risk of non-payment.
Efficient receivables management
AI software enables companies to determine suitable collection measures. To do this, it analyzes data such as customer history, payment behavior and creditworthiness and suggests possible, personalized collection methods.
Better cash flow forecasts
With the help of AI tools, companies can improve their cash flow forecasts. By analyzing past transactions and recognizing patterns, AI can help make accurate predictions about future cash inflows. This enables companies to better plan and manage their finances.
Process Accounts Receivable more efficiently with Konfuzio
With Konfuzio businesses can optimize their invoice management. For this purpose, the German software company provides an all-in-one tool which automatically captures, organizes, and analyzes unstructured data, such as from invoices and receipts.
In order to be able to process documents efficiently, Konfuzio has developed its software on Deep Computer Vision and trained it on over 100,000 documents. Machine and Deep Learning help extract and classify data and pass it to downstream workflows in a qualified manner.
Accounts receivable is the financial recording and management of a company's due receivables. It includes tracking incoming payments, monitoring open invoices and communicating with customers regarding payments. This ensures the liquidity of a company.
Companies can leverage AI in the accounts receivable process to reduce non-payments and automate the credit decision process. For example, AI can help monitor payment dates, analyze payment patterns, and automate communication with customers to make the accounts receivable process more efficient.
Companies benefit from AI in accounts receivable, for example, through automated payment reconciliations, more accurate credit scoring, and cash flow optimization. Through machine learning, companies can identify payment patterns, predict payment delays, and minimize payment defaults. In this way, they increase the efficiency and accuracy of their accounts receivable.