10 AI Applications in Accounts Payable (AP) Processes for 2024
Interest in accounts payable automation shows a increasing trend over the years (Figure 1) Gartner’s report suggests that accounts payable market is expected to grow strongly, rising to $1.9B by 2025 with a CAGR of 17%.1
We’ve written about accounts payable automation and invoice automation before, where we highlighted that AP processes can be mostly automated and shared criteria to select the right vendor. Automation is necessary for accounts payable because manual processing of accounts payable is
- expensive since manual processes require labor which is costly.
- prone to excessive payments: A company can lose up to 4% of the amounts of money paid through invoices because of various errors such as duplicate invoices, fraud, missing early payment discounts and not noticing price hikes.
- leads to dissatisfied employees and suppliers: No one likes a slow manual process.
You can also check out our comprehensive and data-driven list of:
Among the ways to automate accounts payable, machine learning makes sense when a process are not reduced to rule-based processes. This article explains tasks within AP where businesses can leverage AI and the benefits of implementing AI into account payable processes:
AI applications in Accounts Payable
1- Data capture:
Given the high volume of invoices that enterprises receive on a regular basis, OCR technology may not be enough to make AP teams’ job easier in dealing with ever-changing data; human intervention may be required. Machine learning models, when trained, can dynamically comprehend relevant data and speed up the automation process. Using models with continuous learning capacity, especially trained with company-specific data, makes the automation process faster and more efficient.
- Traditional processing methods like OCR and rules-based programming can have hard time dealing with non-PO invoices such as in handwritten or those with unexpected details. They often fail to capture the nuanced invoice data, undermining the goal of end-to-end automation.
- Machine learning presents dynamic solutions in the complex landscape of Non-PO invoice processing. It becomes better at identifying and interpreting variations as it processes more invoices. It can accurately process diverse invoice formats and details.
- Machine learning models can extract quantity and SKU information from invoices, POs and delivery notes to ensure that the correct goods have been delivered.
Enterprises have complex sets of cost centers and categories. Rules-based systems are a fragile solution for a mapping like this where cost categories evolve with reporting requirements and market changes. By using historical data and continual learning, ML models can map costs to the right categories even as these categories evolve, offering an adaptive solution.
For an invoice to be approved, AP teams or softwares need to identify the person to approve the document. This means that approver identities change in each process. Rule-based solutions struggle to adapt to this dynamic process.
Machine learning offers a flexible solution. With its ability to analyze historical data and learn patterns, machine learning models can predict the correct approver for new invoices, even with constantly changing data. This adaptability ensures that invoices are consistently routed to the right personnel for approval, streamlining the process.
4-Categorization of documents sent along with invoices
Invoices can be sent bundled with contracts and invoice recipients can also receive documents like credit notes or payment reminders. Combination of Optical Character Recognition (OCR), Natural Language Processing (NLP), and machine learning enable businesses to extract relevant data, understand the context, and categorize these documents. Automation of this process enables businesses to transform their documents into digital systems and ease search functionality.
5- Three way match:
The three-way match is a significant accounts payable process. In three-way match, the AP team looks at the paperwork, check the purchase order (PO), and the goods receipt note. Thus, they check if the amount on the invoice is the same as the amount on the PO and if the right item has been delivered and payment is released.
AI tools can make this process more efficient. RPA bots can check the company’s email for invoices from vendors. OCR bots can read the content of the invoice to figure out where it came from. Later, AI can compare the details on the invoices and the PO numbers. It can flag the exceptions, and it can control whether delivery receipts match and payments are made.
Hypatos’ continual learning makes it easy to get new data fields from documents since the model can learn from what the user does and function autonomously. Its’ deep learning model is a specialized one that offers accounts payable automation (Figure 2) including:
- coding & document classification
- processing PO and non-PO documents
- document validation
- data matching
6- Other repetitive tasks
There are various daily manual tasks accounts payable teams handle such as matching invoices to supplemental documents and invoice filing. These tasks are typical AP tasks and they hardly require human judgment due to their rule-based structure. However, each company may have different compliance requirements. Therefore, AI can make a difference in automating these processes.
Machine learning algorithms with the support of business rules can
- identify and extract required data from documents
- input data into required documents
- link data for exceptions resolution
- route documents to the appropriate individuals for validation and exceptions handling
7- Inputs to balance-sheet forecasts
Forecasting future revenue and expenses is critical for businesses’ financial planning. Accounts payable balance sheet entries are connected to data from operations and cash cycle. AI-powered analytics enables businesses to balance their company’s cash flow based on the analysis of historical data.
8- Sanctions screening
Despite increasing regulatory scrutiny and sanctions risk exposure, businesses continue to use traditional methods in screening. Processing an excessive number of transactions and papers manually is an error-prone process because as PwC puts forward, data analysis, screen alert review and record keeping are complicated processes.2
Integration of AI in sanction screening can lead to a more reliable process:
- RPA can automate the online screening process where in the traditional way, an analyst enters data manually.
- With evidence gathering, AI can save the analyst the trouble of researching in alarm review
- NLP technologies can automate paper-screening
9- Fraud detection
Any company can be challenged via fraudulent actions. Accounts payable fraud is mostly attempted in the form of fraudulent invoices being submitted for payment. Types of accounts payable fraud are:
- Invoice fraud: Involves a fraudster sending an invoice as one of the companies existing suppliers or pretending to be a new supplier. The invoice includes the bank account details of the fraudster.
- Billing scheme: The fraud type that involves employees generating false payments that will be paid to themselves.
- Check tampering: Check fraud involves a person attempting to make a transaction using a check that has been faked, stolen, altered, or invalid.
- Expense reimbursements: A fraudulent payment scheme in which an employee makes a claim for reimbursement of false or exaggerated business expenses. Expense reimbursement fraud can be occurred due to
- Mischaracterized expenses
- Overstated expenses
- Fictitious expenses
- Double claims
- Automated clearing house (ACH) fraud: ACH fraud involves any unauthorized funds transfer that occurs in a bank account.
- Kickback Schemes: A form of negotiated bribery in which a commission is paid to the bribe-taker in exchange for services rendered.
Artificial intelligence can analyze patterns in invoices to identify any non-standard behavior that may indicate a fraudulent document. Once the AI detects frauds, it flags fraudulent transactions and informs necessary decision-makers. In addition, master data management (MDM) best practices help companies detect changes in payment details and help identify common invoice fraud attempts.
10- Identification of errors
Human error is one of the most common accounts payable problems. Though it seems simple, human-made errors may result in serious losses that could have been easily preventable. Some human errors include lost or misplaced invoices, duplicate data, and poor data entry. Sophisticated AI algorithms can process invoices to catch invoice errors and duplicate payments.
Though fraud transaction detection and identification of errors are important AI applications in audit, they are not the only ones. Feel free to check our article where we examined AI applications in the audit industry.
Benefits of AI in AP
Common benefits of artificial intelligence in account payable process are:
- Faster resolution cycles & increased focus on more value-added activities: APautomation enables organizations to handle invoice processing much faster than an employee would do manually. Faster invoices resolution frees the accounts payable team’s time so that they can focus on more value-added tasks.
- Improved financial planning: AI makes forecasting faster and more accurate than humans. Insights from historical data such as recurring invoices help businesses decide when to release cash or take early-payment discounts.
- Reduced errors & improved compliance: Manual processing of invoices involves various compliance and security risks. Appointing machines to handle these processes reduces the number of people who access the document and reduces the likelihood of human errors that may lead to compliance issues.
- Cost savings: Due to all reasons we listed above, along with the elimination of high paper storage and retrieval costs in account payable processes, organizations that fully automate accounts payable processes can save significant amounts. Full automation can save on average of 4% of expenses when compared to organizations that manually process invoices.
If you still have questions about AI in accounts payable processes, don’t hesitate to contact us:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
To stay up-to-date on B2B tech & accelerate your enterprise:Follow on
Next to Read
Your email address will not be published. All fields are required.