AP AI Applications & Tools for Accounts Payable Processes
AI eliminates the inefficiencies that plague manual AP, like fraud risk, data errors, slow payment cycles, and a lack of spending visibility. By implementing these AP AI tools, your finance team can achieve massive cost savings, boost compliance, and gain the strategic insights needed for better cash management.
Explore the top 10 AI applications that are moving AP from a cost center to a strategic function and top 5 AP AI tools:
Artificial intelligence-based accounts payable automation tools
PairSoft
PairSoft’s AI is designed to handle the core accounting task of coding:
- AI-driven GL coding: The AI learns from all past transactions and uses that history to automatically categorize new expenses. This ensures every entry follows the company’s rules and dramatically reduces the chance of misclassifying expenses in financial reports.
Choose PairSoft for AP automation
Visit WebsiteTipalti AI
Tipalti uses Generative AI to improve invoice processing:
- Intelligent coding: The AI looks at past invoices and correctly predicts and codes new invoices. This makes processing much faster and gives a clear view of where money is going.
- Easy reporting: There is no need to learn complex report settings. A simple question is typed in plain English, like “show me all unpaid invoices”, and the AI instantly builds the required report.
- Payables intelligence: The system constantly learns from payment data and uses that knowledge to keep workflows current and efficient.
Stampli (Billy the Bot™)
Stampli uses its AI assistant, Billy the Bot, to automate many routine, manual actions:
- Automated capture and coding: Billy the Bot automatically pulls data from invoices and codes them.
- Routing: It handles the complex job of sending invoices to the right people for approval.
- Fraud detection: The AI helps find potential fraud before payments are made.
Rillion AI
Rillion focuses on using AI to increase accuracy and reduce manual data entry across key processes:
- AI-powered invoice capture: The AI extracts details from invoices (even low-quality scans) and learns from every document. Unlike older scanning technology, the AI gets better and more accurate over time, adapting to new invoice layouts.
- AI for GL coding: The AI analyzes past financial data (including seasonal changes or department rules) to recommend the exact GL accounts, cost centers, and project codes for each invoice. This ensures financial reports are more accurate.
- AI approval workflows: To stop delays, the AI looks at company policy and history to recommend the fastest and most compliant path for an invoice to get approved, preventing bottlenecks.
- AI assistant (Riley): The assistant acts as a built-in guide. Simple questions, like how to fix a rejected invoice, can be asked, and the AI provides instant answers, walking users through the solution inside the tool.
Hypatos
Hypatos uses its AI “co-workers” to streamline operations and reduce manual workload:
- Reduced manual work: The AI takes over repetitive tasks to free up employees.
- Risk and fraud protection: It actively works to protect cash flow by guarding against financial risks and fraud.
AI applications in accounts payable (AP AI)
Automation
1. Data capture
Businesses are bombarded with invoices daily. Traditional Optical Character Recognition (OCR) tools often stumble over poor image quality, messy formatting, or handwritten notes, still requiring human review.
AI models solve this by learning from historical data and adapting to new formats over time. This makes data capture faster and drastically more accurate. When trained on your specific company data, these models become even more powerful, automatically identifying product codes, quantities, and other details across invoices, purchase orders (POs), and delivery notes to confirm receipt of goods.
2. Cost coding
Large organizations use complex cost categories. These categories often change with market trends and reporting needs. Rule-based systems are hard to update and easy to break.
Machine learning offers a better solution. It learns from historical entries to map costs to the correct categories, even when the categories evolve. This creates a more flexible system that requires less manual input.
3. Approver identification
Invoices require approval, but the correct approver isn’t always the same person. Traditional systems rely on fixed rules that quickly fail in dynamic team structures.
AI can analyze past approval patterns to predict and route a specific invoice to the right person. This keeps workflows flowing and eliminates constant manual intervention from finance teams.
4. Document categorization
Invoices rarely arrive alone; they often come bundled with contracts, credit notes, or follow-up reminders.
AI uses a combination of OCR, natural language processing (NLP), and machine learning to read, understand, and automatically sort these attachments into the correct categories. This effort quickly converts messy paper records into easily searchable digital files, dramatically reducing the time spent locating details later on.
5. Three-way match
The famous three-way match is where an invoice is compared against a Purchase Order (PO) and a goods receipt, if they align, payment is approved.
AI makes this process hyper-efficient. Robotic Process Automation (RPA) bots grab new invoices from email, OCR tools extract the data, and AI models instantly match the details against POs and receipt records. Any discrepancy is flagged immediately, drastically reducing delays and errors.
6. Other repetitive tasks
AP teams spend too much time on mindless, repetitive tasks like filing, attaching support documents, or manually routing files. These are ideal for AI and automation:
- Extracting key data from documents.
- Entering data into core systems.
- Spotting exceptions that need human attention.
- Routing files based on pre-set rules.
AI can be tailored to follow even the most complex, company-specific compliance rules.
Analytics
7. Forecasting inputs
Accounts payable data plays a role in cash flow planning. Using historical trends, AI-powered analytics can help finance teams estimate future spending. These forecasts support better decisions for budgeting and cash management.
Compliance
8. Sanctions Screening
Many businesses still screen vendor data manually, even though regulations are stricter now. This method is slow and prone to error1
AI can support responsible use of data by improving screening accuracy. For example:
- RPA tools can automate the input of names into watchlists
- NLP can help analyze documents for risks
- AI can store evidence to help users during review stages
This makes screening more reliable and faster.
9. Fraud Detection
Fraud in accounts payable can take many forms:
- Fake invoices sent by outsiders
- Employees creating false bills
- Altered or stolen checks
- Exaggerated expense claims
- Unauthorized bank transfers
- Kickback arrangements
AI tools can spot unusual patterns in invoices or payments. When something looks off, the system alerts decision-makers. Combined with master data management (MDM), AI can catch small changes, like new payment details, that may signal fraud.
10. Error Detection
Human errors, like duplicate entries, missing invoices, or bad data, are common in AP and costly.
AI models can scan invoices to detect errors or duplicates. By doing this early, they prevent delays and losses. AI doesn’t replace audit professionals but can support them by flagging potential issues before they grow.
Though fraud transaction detection and identification of errors are important AI applications in audit, they are not the only ones.
Benefits of AI in AP
Accounts payable market is expected to grow strongly, rising to $1.9B by 2025 with a CAGR of 17%,2 despite fluctuating interest in the market.
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.
Recommended reading
Most AP automation will take place within your ERP system. You can learn more about how AP automation can be improved within your ERP:
Leading AP solutions & their alternatives:
- Top 10 Kofax Alternatives/Competitors
- 14 Rossum AI Competitors/Alternatives
- Blackbaud Accounts Payable (AP) Automation
Data-driven lists for:
FAQ
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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.
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