OCR Isn’t Enough: How Human-in-the-Loop Drives Real Results in Finance

Every finance workflow starts with one critical action: capturing the data. Whether it's an invoice, a W-9, a utility bill, or a vendor packet, the accuracy of that first step determines how smooth, or chaotic, the rest of the process will be. With finance teams processing hundreds or thousands of documents monthly, even a 5% error rate can cost organizations thousands in missed discounts, duplicate payments, and wasted labor hours. 

That’s why invoice capture remains one of the most important, and frustrating, aspects of accounts payable. Many teams rely on Optical Character Recognition (OCR) to extract data from documents. It’s fast, it’s been around for decades, but doesn’t completely get the job done. 

But here's the truth: OCR alone isn’t enough anymore. 

Industry-wide, OCR delivers around 64 percent accuracy. Add AI, and you can improve that to roughly 80 percent. That helps with document classification and routing, but it still means one in every five invoices requires human intervention. 

And when your finance team is managing hundreds or even thousands of documents each month, that 20 percent adds up. It leads to missed fields, delayed payments, frustrated vendors, and wasted hours fixing preventable mistakes. 

If automation is going to live up to its promise, it has to solve the whole problem, not just most of it. 

Why OCR Alone Doesn’t Cut It Anymore 

OCR works by scanning documents and converting printed or handwritten text into digital data. It’s a foundational technology for any document automation strategy, but it has limits. For instance, OCR regularly fails with: 

• Poor image quality or low-contrast scans 
• Complex multi-column layouts 
• Tables with merged cells 
• Varying invoice formats across vendors 
• Special characters or industry-specific notation 

By itself, OCR averages around 64 percent accuracy. That means it might miss a field, misinterpret a handwritten total, or drop a critical line item entirely. 

With AI layered on top, performance improves. Machine learning can help identify patterns, tag fields more accurately, and even predict invoice coding and routing paths. That boosts accuracy to roughly 80 percent. 

But 80 percent still isn't good enough. That missing 20% isn't random. It's where the most complex, high-value, and potentially problematic transactions often hide. It means your team is manually fixing every fifth invoice. For a mid-sized organization processing 1,000 invoices per month, that's 200 documents needing specialized attention every month, requiring approximately 25-30 labor hours that could be dedicated to strategic initiatives. 

This not only wastes time. It also delays approvals, increases the risk of errors, and reduces trust in the automation that was supposed to make things easier. 

What Is Human-in-the-Loop? 

Human-in-the-Loop is an approach to automation that keeps people in the loop during key decision points. It’s not about replacing human effort. It’s about making automation smarter by including human oversight where accuracy, context, or judgment is still needed. 

For example, when a low-confidence data point is flagged for a blurry invoice total or a PO number with a formatting error, a trained expert can validate or correct it before the document moves on to the next stage. 

This concept is gaining traction across industries. As Stanford’s Institute for Human-Centered AI points out, Human-in-the-Loop is essential in building interactive systems that reflect the real-world needs of human users. 

In finance, this level of oversight isn’t optional. One incorrect value can create a domino effect that impacts approvals, reporting, and compliance. Without Human-in-the-Loop, your automation might just be speeding up the spread of bad data. 

What Human-in-the-Loop Looks Like in Real Life 

Let's look at a concrete scenario: a manufacturing business receives 500 invoices weekly with varying formats from hundreds of vendors. They’re routed through shared inboxes, scanned into folders, or sent as PDFs. OCR software ingests the files and extracts invoice numbers, PO values, totals, and due dates. 

But some invoices are poorly formatted. Others are missing required fields. A few have handwritten notes that OCR can’t interpret. So instead of moving directly to approval, those invoices stall. Someone on the AP team has to step in to fix the gaps. 

Using traditional OCR with basic AI, the team still manually corrects approximately 100 invoices weekly, spending roughly 15 hours on exception handling. This costs over $30,000 annually in direct labor alone, not counting the strategic opportunity cost. 

This delay triggers a chain reaction. Vendor calls start coming in. Discounts are missed. Reconciliation gets pushed back. And the AP team is stuck doing work that automation was supposed to eliminate. 

With a Human-in-the-Loop-enabled process, that stall never happens. Documents are automatically captured and sorted, AI does the initial scan, and a trained expert provided as part of the intelligent automation solution reviews any low-confidence or flagged values before the document moves forward. The AP team doesn’t have to fix errors after the fact because the errors never reach them. 

It’s not about adding more people. It’s about putting human review in the right place, up front, so the rest of the process runs smoothly. 

Common Misconceptions About Human-in-the-Loop 

When teams hear the phrase “human-in-the-loop,” it can raise a few eyebrows, especially for organizations looking to automate as much as possible. But much of that hesitation comes from a misunderstanding of what Human-in-the-Loop actually is (and isn’t). Let’s break down three of the most common misconceptions: 

“Adding people back into automation defeats the purpose.” 
This is a common reaction, but it misses the point. Human-in-the-Loop doesn’t mean replacing automation. It means completing it. Strategic human touchpoints aren’t a step backward. They’re how you prevent downstream issues like invoice mismatches, missing data, or delayed approvals. By resolving low-confidence or complex exceptions early, Human-in-the-Loop actually enables true end-to-end automation by reducing the need for manual rework later. 

“We can just fix our OCR with better AI.” 
AI absolutely improves OCR, but even the most advanced models still struggle with unstructured documents, handwritten notes, stamps, and inconsistent layouts. These challenges are especially common in finance workflows. AI alone can’t always recognize context, and that’s where human verification steps in. Rather than cleaning up errors after they’ve already caused delays, a Human-in-the-Loop approach resolves issues in real time, preserving both speed and accuracy. 

“It’s too expensive to scale with people in the loop.” 
It’s true that fully manual processes don’t scale well, but Human-in-the-Loop isn’t a manual process. It’s a targeted layer of oversight that supports automation, not replaces it. In fact, by reducing error rates and eliminating rework, Human-in-the-Loop often lowers the total cost of ownership. The result is a more predictable, efficient process that scales sustainably without sacrificing quality. 

Now that we’ve cleared up some of the biggest misconceptions, let’s look at what a complete Human-in-the-Loop solution actually requires, and why not all automation platforms are equipped to deliver it. 

How the Right Platform Solves the Full Problem 

To fix capture at scale, you need more than OCR and AI. The most effective solutions combine all three: intelligent document capture, machine learning for routing and classification, and trained experts who step in when confidence is low. 

This approach ensures that data is complete, correct, and ready for approval before it ever reaches your ERP or workflow. 

Here’s how it works: 

• Documents are sent to a monitored inbox 
• OCR and AI extract and organize the data 
• Supporting materials are automatically linked 
• Any low-confidence fields are flagged and reviewed by a trained specialist 
• Verified, audit-ready data flows directly into your downstream systems 

And this approach applies far beyond invoices. It can support vendor packets, W-9s, contracts, HR forms, and other document types that require high accuracy and compliance. A layered, intelligent capture strategy with human validation ensures finance teams can scale with confidence without sacrificing control. 

Why Human-in-the-Loop Is the Smart Move for AP Teams Today 

It’s Not Just Automation. It’s Assurance. 
Finance leaders don’t just want speed, they want reliability. Human-in-the-Loop solutions deliver both. 

For AP managers, that means clean handoffs and fewer exceptions. For controllers, it means better visibility and fewer reconciliation headaches. And for CFOs, it means automation they can trust with financial reporting, cash flow planning, and vendor management. 

It’s not just about eliminating manual work. It’s about delivering clean data from the start so teams can make smarter, faster decisions. 

It Builds a Stronger Business Case. 
If you're making the case for AP automation, you need to show how it reduces risk, saves time, and enables scale. 

Human-in-the-Loop strengthens that case by: 

• Decreasing exception handling 
• Reducing errors and rework 
• Improving compliance readiness 
• Supporting faster approvals and early payment discounts 
• Helping prevent fraud through human review 

According to Forbes, Human-in-the-Loop AI can act as a collaborative teammate in operations, improving decision-making, reducing errors, and accelerating ROI. 

The Cost of Doing Nothing. 
Living with 80 percent accuracy in your document capture process isn’t just an operational inconvenience, it’s a hidden cost that compounds over time. Delayed approvals, manual clean-up, and reactive exception handling don’t just frustrate your team. They quietly erode your margins. 

Here’s what that looks like in the real world: 

• Lost early payment discounts averaging 1–2% of invoice value 
• Duplicate payments occurring in approximately 0.5% of transactions 
• Compliance and audit prep challenges due to inconsistent data 
• Delayed month-end close cycles and financial reporting 
• Strained vendor relationships from preventable payment delays 

When you address these friction points head-on, the ROI of a Human-in-the-Loop-enhanced automation strategy becomes clear. For many mid-sized organizations, the gains in accuracy, efficiency, and financial control pay for themselves within the first year. 

Now Is the Right Time to Act. 
Finance & Operation teams are expected to do more with less. Manual processes are too slow, and incomplete automation leaves costly gaps. OCR alone won’t cut it. AI on its own still misses too much. 

The only way to scale operations without sacrificing accuracy or visibility is to pair automation with human judgment. 

In finance automation, the difference between 80% and 99.9% accuracy isn't just a number—it's the difference between partial automation that creates new problems and true automation that delivers on its promise. 

See How onPhase Makes It Happen 

If your team is still spending time fixing invoice errors, reconciling mismatches, or answering vendor questions about payment status, it's time to consider a more comprehensive approach to document capture and processing. 

onPhase helps finance teams capture, automate, and pay with confidence. The right automation platform combines AI with human oversight to deliver 99.9 percent accurate data from day one. No more bottlenecks, no more backlogs. Just clean data and faster processing. 

Ready to move beyond basic OCR?  
Download our mini-guide to learn how to build your business case and evaluate solutions that solve the whole problem, not just part of it.