How Do You Review AI-Extracted Data in Whitevision?
Quick Summary
Whitevision B.V. Declaraties is a Dutch document-processing platform. It uses OCR and artificial intelligence to automatically extract data from invoices, receipts, and other business documents. The platform does not book this extracted data blindly. The data passes through a visual review step before final processing. Reviewing AI-extracted data is therefore a distinct and important skill for anyone working with Whitevision. It determines whether reviewers catch recognition errors early or let them flow into the financial system. This article shows how to review AI-extracted data in Whitevision effectively. It covers how the review screen is structured and what to check before approving a booking proposal. It also explains how to correct inaccurate fields and how these corrections help the system improve over time.
Why Does AI-Extracted Data Still Need Human Review?
How Accurate Is Whitevision’s Recognition, Realistically?
While Whitevision’s OCR and AI engine is designed to interpret invoice and receipt data in a human-like way rather than relying on rigid templates, no automated recognition system is perfect. Because documents vary in quality, layout, and format, particularly scanned paper receipts or unusual invoice designs, a small percentage of fields may be misread or left incomplete. Therefore, a review step exists specifically to catch these occasional errors before they affect the organization’s financial records.
What Is at Stake If Extracted Data Is Not Reviewed Carefully?
If incorrect data passes through without review, this can lead to a range of downstream issues, including incorrect VAT calculations, misallocated costs, or, in more serious cases, a duplicate or inflated payment to a supplier. Consequently, treating the review step as a genuine checkpoint, rather than a formality to click through quickly, protects both the accuracy of financial reporting and the organization’s relationship with its suppliers.
How Is the Review Screen in Whitevision Structured?
What Do You See When Reviewing a Document?
When reviewing a document in Whitevision, the recognized header and line data are displayed alongside the original document image on a single screen. Because the visual and the extracted data sit next to each other, this layout allows the reviewer to compare each field directly against the source document without switching between separate windows or applications.
What Types of Information Appear on the Review Screen?
Generally speaking, the review screen presents several categories of information at once:
| Element | Purpose |
|---|---|
| Original document image | Reference point for verifying extracted values |
| Header data | Supplier, invoice number, date, total amount |
| Line-item data | Individual products, services, quantities, and VAT |
| Suggested booking fields | Cost center, project, or general ledger account |
| Matching status | Indicates whether the document aligns with a related order or receipt |
Since all of this information is visible together, reviewers can typically confirm or correct a document in a fraction of the time it would take to manually cross-check a printed invoice against a separate accounting entry.
What Should You Check Before Approving a Booking Proposal?
Which Header Fields Deserve the Closest Attention?
Before approving a booking proposal, it is worth paying particular attention to a handful of header fields that carry the greatest financial or compliance risk. These typically include:
- The supplier name and bank account details, since errors here can result in a payment sent to the wrong party.
- The invoice number, which is also used to detect potential duplicate invoices.
- The total amount and VAT breakdown, since inaccuracies here directly affect financial reporting.
- The invoice and payment dates, which influence cash flow planning and payment terms.
Because these fields are the most consequential if misread, they generally warrant a quick visual confirmation even when the rest of the document appears to have been recognized correctly.
How Should You Review Line-Item Data?
For documents with multiple line items, reviewing every single line in detail is not always necessary, particularly once a supplier’s documents have been processed accurately many times before. However, it remains good practice to scan line items for unusual patterns, such as a quantity that seems inconsistent with a typical order or a price that differs noticeably from previous invoices. Consequently, a quick scan of line-level data, rather than a full line-by-line audit, is usually sufficient once trust in the recognition engine has been established for a given supplier.
How Do You Correct Inaccurate AI-Extracted Data?
What Are the Steps for Correcting a Misread Field?
When a field appears incorrect during review, the recommended process generally involves:
- Comparing the extracted value directly against the document image shown on the same screen.
- Editing the incorrect field so it reflects the accurate value from the original document.
- Confirming that any dependent fields, such as VAT amounts tied to a corrected total, update accordingly.
- Saving the correction before proceeding with approval or further processing.
Afterward, because the software is designed to learn from these corrections, similar documents processed in the future are more likely to be recognized accurately without requiring the same manual fix again.
What Should You Do If Matching Flags a Discrepancy?
If a document’s matching status indicates a discrepancy, such as a price or quantity mismatch against a related purchase order or delivery receipt, this typically appears as a clear visual indicator during review. In this situation, it is advisable to investigate the specific flagged line before proceeding, rather than approving the document and addressing the issue after payment has already been made. Since discrepancies are often easier to resolve with a supplier before payment than afterward, catching them at the review stage offers a meaningful advantage.
How Does Reviewing Data Differ Across Document Types?
How Does Reviewing an Invoice Differ from Reviewing a Receipt?
While the general review principles apply across document types, invoices and receipts often warrant slightly different attention. Invoices, particularly those with multiple line items or complex VAT structures, benefit from closer scrutiny of line-level detail and matching status. Receipts, on the other hand, are often simpler in structure but more prone to image quality issues, such as fading or crumpling, which means reviewers should pay particular attention to whether the total amount and expense category have been recognized clearly.
How Should You Approach Reviewing E-Invoices and Structured Documents?
Since structured e-invoices, such as those received through XML formats, are extracted directly from labeled data fields rather than interpreted visually, they generally carry a lower risk of misreading compared to scanned paper documents. Therefore, review time for structured e-invoices can often be reduced, focusing primarily on confirming that the booking fields, such as cost center or project allocation, have been applied correctly rather than re-verifying every individual value.
How Does Reviewing Data Contribute to Long-Term Accuracy?
Why Should Every Correction Be Treated as a Training Opportunity?
Whitevision’s recognition engine improves through use. Every correction made during review therefore teaches the system to recognize similar patterns more accurately going forward. Organizations that review documents consistently and correct errors promptly tend to see extraction accuracy improve more quickly over time. They don’t work around errors or ignore minor inaccuracies. Organizations that treat review purely as a bottleneck to move past see slower improvement.
How Can Teams Monitor Whether Review Quality Is Improving Results?
To track whether the review process is genuinely contributing to better recognition over time, it can help to periodically monitor a few simple indicators:
| Indicator | What It Suggests |
|---|---|
| Number of corrections per document | Declining trend suggests improving recognition |
| Time spent per review | Shorter review times may indicate growing trust and accuracy |
| Recurrence of the same error type | Persistent issues may need a configuration adjustment |
| Number of flagged discrepancies resolved before payment | Reflects the review process catching issues early |
As a result, tracking these indicators over several months can help finance teams confirm that their review efforts are translating into measurable efficiency gains rather than remaining a constant, unchanging task.
Conclusions
Reviewing AI-extracted data in Whitevision B.V. Declaraties is a straightforward but important step. It sits between automated recognition and final approval. Reviewers check header fields, line-item details, and matching discrepancies against the original document before they confirm a booking proposal. The review screen makes this process quick. It displays the extracted data and the source document together. Reviewers still need to give proper attention to key fields such as supplier details, amounts, and invoice numbers. These fields remain essential for accurate financial records. By treating each correction as an opportunity to train the system further, organizations using Whitevision B.V. Declaraties can expect their review workload to decrease steadily as recognition accuracy continues to improve.
Frequently Asked Questions
While some organizations choose to apply a lighter review process to low-value or routine documents, it is generally advisable to at least confirm the supplier, amount, and invoice number for every document, since even small errors in these fields can create disproportionate downstream issues, such as duplicate payments.
If a particular error recurs repeatedly for the same supplier, this often indicates that the corrections made during earlier reviews were not fully applied or that the document layout is unusually difficult to interpret. In this case, it is worth flagging the issue to an administrator so that recognition settings for that supplier can be reviewed and adjusted directly.
Yes, depending on how the workflow is configured. Since documents can be routed through more than one approver, it is possible for one person to review the extracted data for accuracy while another focuses on approving the document from a budgetary or authorization standpoint, allowing responsibilities to be divided according to an organization’s internal controls.
