How AI Is Changing How We Process Accounts Payable

Accounts payable automation has been around for years, and we have seen how the right system transforms financial operations by cutting costs, improving accuracy, and speeding up approvals. But the truth is that not every implementation succeeds. Automation is only as good as the data it relies on. Standard AP tools are able to process invoices, capture payment terms, and match records properly when invoices are neat, structured, and predictable. But most of us in property management and other complex industries deal with messy invoices, such as invoices in different formats, missing details, and complex line items that don't line up. 

That's where basic, low-cost tools that only use Optical Character Recognition start to fail. OCR can read text, but it struggles with context, line-item prediction, and non-standard layouts. I've also noticed unrealistic expectations from businesses jumping into standard OCR automation. Some believe it'll eliminate all manual work and solve every AP headache. It's not a bad expectation, but that's not how it works. 

Most AP automation solutions using OCR need human oversight for exceptions, approvals, and those tricky invoices that don't follow standard patterns. However, the good news is that AI changes everything. When we power automation systems with artificial intelligence, they evaluate each word in context to get an accurate interpretation. They recognize patterns and correlations that even human reviewers miss. This lets AI process massive amounts of AP data efficiently and accurately, like human judgment. 

For finance teams, that translates into better analysis, stronger decision-making, and far fewer errors slipping through. In this guide, we'll walk through the ways AI is changing how we process accounts payable, from data extraction to anomaly detection.

It’s exciting to think about what the future could have in store!

How AI Is Changing How We Process Accounts Payable

At its core, ML and AI are about teaching computer systems to think and adapt like humans, and sometimes even better. It's a kind of digital brain that works like a human brain, logically analyzing data, learning complex patterns, and making independent predictions without human input. When we integrate ML and AI into AP software, the system learns from past invoices, vendor behaviors, and payment patterns. 

Over time, it starts flagging exceptions, predicting outcomes, and streamlining repetitive tasks on its own. Unlike static software that needs constant reconfiguration, AI-enabled systems adapt to new invoice formats, changing supplier rules, and evolving business needs without heavy manual input. The results we see are faster invoice approvals, fewer errors, stronger compliance, and a level of scalability that wasn't possible before. Here are the top AI use cases in accounts payable and what they mean for finance teams today.    

Top AI Use Cases:

Before we explore these use cases, let me walk you through the AP process cycle so you understand where AI makes the biggest impact. The full cycle usually looks like this once an invoice comes in:

• Receive invoices from vendors and suppliers through channels like email, mail, and portals.
• Verify that the invoice details match the goods or services delivered.
• Record the invoices in the company's accounting system.
• Route invoices for approval.
• Schedule payments.
• Issue payments through methods like checks, ACH, and EFT.
• Update the books or records in your accounting system to reflect the payment.

Traditionally, every one of these steps was handled manually. That meant paper shuffling, manual data entry, and endless back-and-forth between teams. Instead of relying on people to receive, verify, and route invoices, here's how AI-powered systems automate these steps. 

Data Extraction

Every AP process begins with capturing invoice details. As we said, the go-to tool was Optical Character Recognition. OCR converts text images into a machine-readable format, enabling AP systems to scan and extract text from invoices submitted as PDFs, Word documents, spreadsheets, emails, and scanned paper copies. The invoice data gets digitized so it's editable and searchable. 

Standard OCR tools turn pixels into text, but they struggle to structure that text in a way that finance systems actually need. You'll constantly see partial extractions, missing fields, broken outputs, and data mapped to the wrong fields. The root problem is that vendors don't stick to one format. Formats, layouts, and terminology vary wildly across industries. Totals might appear in headers, footers, or be buried in tables. 

For example, one vendor labels their total Amount Due in the header. Another vendor calls it Grand Total in the footer. Some line items sit neatly inside tables, while others hide within paragraph text. Then sometimes you have smudged scans, odd fonts, and delivery charges mixed with line items. It's not uncommon even to find papers so crumpled and faded that even humans barely make sense of them. 

That's where AI comes to help. Instead of just reading text, AI understands it. It uses context to identify what a subtotal is, what tax is, what freight is, and what the actual amount due is. It doesn't matter if the format shifts from vendor to vendor or if the invoice is half legible. It interprets documents the way humans do. Here's how AI transforms data extraction: 

• Structured data extraction: AI scans your entire document and uses intelligence to recognize different elements such as text blocks, tables, logos, and signatures. From there, it zeroes in on the details you actually need, like invoice numbers, dates, and line items. 
• Layout flexibility: It doesn't matter if the total is in the header, footer, or buried in a table. AI infers meaning from context and maps it to the right field in your accounting system. 
• Imperfection tolerance: Smudged scans, half-legible numbers and small imperfections don't throw AI off as much.

This kind of intelligence enables AI-powered AP platforms to extract details such as vendor names, amounts due, and purchase order numbers from invoices in various formats and layouts with 99% accuracy. This is high accuracy compared to the 70-80% range you see with standard OCR solutions. 

The speed difference is just as striking. Manual invoice entry can eat up 10 to 30 minutes per document, depending on complexity. AI tools that combine OCR with Natural Language Processing process those same invoices in 1 to 2 seconds each. Some AI solutions can even handle over 1,000 invoices per hour.   

Anomaly Detection

Fraud and errors have always been big headaches in AP. The ACFE estimates that businesses lose about 5% of annual revenue to fraud, and a large share of those cases occur within accounts payable. Sometimes it's a simple mistake, other times it's intentional fraud. Here are the common anomalies and fraud affecting AP:

Duplicate Invoices 

One of the most common issues is duplicate invoices. These often happen through human error, such as resubmitting an invoice without canceling the original, sending copies to the wrong department, or accidentally processing both a paper and digital version. In the worst cases, it's deliberate fraud, with someone submitting the same invoice twice to get paid twice. Either way, you pay the same invoice twice and lose money. 

The numbers show just how costly this can be. SAP Concur reports an average 1.29% duplicate invoice rate for SMBs, which works out to roughly six duplicates per month. APQC found that top performers keep duplicates around 0.8%, while weaker performers see rates closer to 2%. That means if a company processes $50 million annually, that's $400,000 to $1 million lost every year to duplicate payments alone. The worrying part is that most AP teams don't catch it until a vendor calls or an audit flags it. 

Phantom Vendors

One of the most damaging forms of AP fraud comes from phantom vendors. These are fake suppliers set up by fraudsters or insiders to siphon money out of the business without any goods or services delivered. These scams are costly. According to Medius, U.S. companies lose an average of $300,000 per year to vendor fraud. 

Fraudsters create shell companies with fabricated details like tax IDs, bank accounts, and vendor numbers. Sometimes the deception is subtle. If your valid vendor email is jared.doe@comay.com, then a fake vendor might use jared.doe@comay-finance.com. To a busy AP clerk, that invoice looks legitimate, and the team processes payments. 

Anomalies in Buying Patterns and Invoice Amounts 

Some purchasing scams need collusion between AP employees and vendor contacts. A vendor and an AP employee work together, and the vendor submits inflated and falsified invoices, then they split the fraudulent payments. 

According to EisnerAmper, it takes companies an average of 18 months to uncover this type of scheme. By then, the financial damage is significant. Anomalies in buying patterns like sudden spikes in spending with one vendor, transactions hitting late at night when no one's watching, and invoices having amounts just under the approval threshold are the common signs of this kind of fraud. 

How AI Prevents These Anomalies

What makes AI powerful is its ability to think in probabilities. It uses confidence scoring, assigning a likelihood that a match or transaction is valid. If the confidence is high, the system pushes it through automatically. If it's low, it flags the transaction for human review. That blend of speed and oversight makes fraud detection far more effective. Here's how it works: 

• Pattern Recognition:  AI looks at massive amounts of historical invoice data, such as vendor names, payment amounts, and timing, and builds a baseline of normal activity. When it sees an invoice that breaks from that normal pattern, like an unusual spike or an odd transaction time, it raises an alert in real time. That gives AP teams a chance to investigate before money goes out the door.
• Advanced Vendor Analysis: AI digs deep into vendor records such as registration data, locations, and past payment behavior, and spots inconsistencies that suggest a phantom vendor. Catching suspicious suppliers before invoices ever hit the system closes the door to one of the most common AP scams.
• Textual Analysis: One of the strengths of AI is its ability to read between the lines and analyze invoice text instead of just looking at numbers. If an invoice uses odd phrasing, adds unnecessary urgency, or threatens late fees, the system flags it. These are the same pressure tactics fraudsters use in social engineering scams.
• Smarter PO Matching: Two- or three-way matching of invoices to purchase orders isn't new. However, with traditional systems, the system flags mismatches such as wrong quantities, incorrect prices, or missing POs. But an AI-powered system can auto-correct simple human mistakes, like transposed digits, and recognize when something almost matches but doesn't line up completely. That near match might be a duplicate invoice in the making, or a sign of fraud.    

Real Estate–Specific AI Applications (Recurring Utilities)

If you run a property management firm or real estate company, you already know how much time utilities consume. Every month, you're logging into portals for electricity, water, internet, and other recurring bills. Each property has its own accounts, billing periods, usage details, and due dates. Multiply that across a portfolio, and you're quickly buried under a mountain of utility documents. AI is changing that. 

Platforms built specifically for real estate and community management can now handle utility retrieval automatically. Take LeapAP as an example. It's an AP automation solution designed for property management companies, including HOAs, condos, commercial spaces, and residential portfolios. 

One of its standout features is automated utility download. The system logs into utility portals on your behalf and retrieves the invoices directly. What takes hours of manual work now starts to happen in the background. This single feature can save hours of your team's time and cut down on late fees and missed payments. 

Risks & Ethics

AI helps overcome many of the old roadblocks in AP like manual entry, duplicate payments and fraud blind spots, but it comes with its own set of challenges. Here are some of the risks and ethics revolving around the use of AI in accounts payable.

• False Positives: AI doesn't always get it right. Sometimes it flags legitimate invoices as suspicious. This can waste valuable time chasing ghosts. In fact, Forrester reports that 70% of businesses say false positives cost them more than fraud losses themselves. The fix is to carefully train AI models and ensure ongoing human oversight to validate results.
• Data Privacy Concerns: AP systems deal with highly sensitive financial data. When AI is layered on top, it raises questions about transparency and security. How is data being used? Is it compliant with privacy regulations? It's concerning when AI doesn't provide transparency about data usage and strong user privacy practices.
• Dependence on Data Quality: AI is only as good as the data it learns from. If your invoicing data is incomplete, inconsistent, or inaccurate, the AI will produce flawed results. That's why organizations must invest in data quality initiatives. 

Future Roadmap

Companies across industries are embracing AI-driven invoice processing due to benefits like higher accuracy, less manual work, and better visibility into cash flow. Modern AI tools can automatically cross-check invoices against POs, route them for approval, and schedule payments based on pre-set rules. That means invoices move faster, costs go down, and data becomes far more reliable. The market numbers back this up. 

The AI for Invoice Management Market is projected to hit $47.1 billion by 2034, growing at a 32.6% CAGR. This growth is being fueled by businesses that are desperate to cut costs and improve efficiency. For instance, 68% of companies say they're actively pursuing AI-driven automation for better accuracy, faster approvals, and stronger fraud detection. And the shift is already visible. In 2024, only 60% of invoices were entered into ERP and accounting systems, a dramatic drop from 85% the year before. 

As AI adoption in invoice processing continues growing, your company must consider implementing automated solutions to stay competitive and improve its bottom line. For instance, if you're running a property management firm and adopt LeapAP software that's specifically tailored for PM firms and can fetch utility bills from portals automatically, you'll save up to 80% of your AP costs.  

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