What Should SMEs Do When AI Accounting Categorisation Looks Wrong?

AI accounting is designed to reduce manual work—especially around transaction categorisation. But in the real world, SMEs will occasionally notice something unsettling:

A transaction is categorised incorrectly.

Maybe a supplier invoice shows up under “Marketing” instead of “COGS.”
Maybe a software subscription is posted as “Office Supplies.”
Or worse—revenue appears in the wrong income category.

So what should SMEs do when AI accounting categorisation looks wrong?

The answer isn’t to panic—or to turn off automation.
It’s to treat categorisation issues as a normal part of early optimisation and build a clear correction workflow.

Here’s a practical step-by-step guide.

Why AI Categorisation Errors Happen (Even in Good Systems)

First, it helps to understand that “wrong categorisation” usually comes from one of these causes:

• limited historical examples (especially early adoption)
• unclear or inconsistent vendor names in bank feeds
• transactions that look similar but have different business meaning
• new vendors or new transaction types
• missing supporting documents
• overly broad categories or messy chart of accounts

In other words: categorisation issues are often data + structure issues, not “AI failure.”

Step-by-Step: What SMEs Should Do When Categorisation Looks Wrong

Step 1) Confirm Whether It’s Truly Wrong (or Just Unexpected)

Not every “surprising” category is incorrect.

Before changing anything, ask:

• Does this transaction relate to a different business activity than usual?
• Is the vendor name correct?
• Is it a refund, reversal, or split payment?
• Is it a duplicate transaction?

This prevents SMEs from making unnecessary changes that introduce inconsistency.

Step 2) Check the Supporting Document First

Documentation is the best truth source.

If categorisation looks wrong:

• open the invoice/receipt
• confirm what was purchased
• check tax details (if relevant)
• confirm the service period (important for subscriptions)

Many “AI errors” disappear once the document is reviewed.

If there is no document attached, that’s already a red flag—and should trigger follow-up.

Step 3) Correct the Category — But Do It Consistently

If it’s truly wrong, correct it—but avoid “random fixes.”

Best practice:

• use the correct standard category (not a new one)
• apply the same logic as prior similar transactions
• avoid dumping into “Miscellaneous”

Consistency matters more than micro-precision because it affects trend analysis and reporting.

Step 4) Check Whether It’s a One-Off or a Pattern

This is the most important step.

Ask:

• Is this the first time we’ve seen this vendor/transaction type?
• Has this vendor been miscategorised before?
• Are similar transactions affected?

If it’s one-off, correction is enough.

If it’s recurring, you should treat it as an optimisation opportunity.

Step 5) Create or Update a Rule for That Vendor/Transaction Type

SMEs should not manually fix the same thing every month.

When categorisation errors repeat:

• create a vendor rule (Vendor X → Category Y)
• add notes/tags for context
• confirm the correct mapping with your accountant (if needed)

This turns “wrong categorisation” into a learning loop that improves automation.

Platforms like ccMonet are designed to support this AI + review workflow—where corrections help strengthen future accuracy rather than creating ongoing manual burden.

Step 6) Recheck High-Impact Reports After the Fix

After correcting categorisation, SMEs should quickly review whether it affects:

• P&L structure (especially gross margin)
• tax-related categories
• monthly management accounts
• segment/customer profitability reporting

Even a small misclassification can distort:

• margins
• cash planning
• compliance reporting

This is why SMEs should prioritise review of high-impact accounts.

Step 7) Set a Review Threshold to Prevent Bigger Errors

To reduce future errors, SMEs should implement simple review controls:

• transactions above $X require review
• first-time vendors always reviewed
• tax-related categories reviewed monthly
• COGS-related categories reviewed monthly

This prevents automation mistakes from scaling silently.

Common Categorisation Issues (And What They Usually Mean)

1) “COGS is too low”

Often means inventory/purchase costs are being posted as operating expenses.

2) “Marketing looks unusually high”

Often means tools/subscriptions or contractor costs are misposted.

3) “Miscellaneous is growing”

Usually means categories aren’t defined clearly—or AI lacks enough structured rules.

4) “Revenue looks inconsistent”

Could be timing issues, refunds, or incorrect income mapping.

What SMEs Should Not Do

When categorisation looks wrong, avoid these reactions:

• ❌ turning off automation entirely
• ❌ creating too many new categories
• ❌ changing categories randomly each month
• ❌ ignoring the issue because it’s “small”
• ❌ relying on memory instead of documentation

Wrong categorisation is a signal.
Ignoring it creates long-term reporting and compliance risk.

Practical Tips to Reduce Wrong Categorisation Over Time

• Standardise vendor naming

Avoid duplicates like “Meta,” “Facebook Ads,” “FB.”

• Keep categories stable

Frequent changes confuse both AI and humans.

• Review exceptions weekly

Small reviews prevent big month-end surprises.

• Track recurring corrections

Patterns reveal what to optimise.

Frequently Asked Questions (FAQ)

Is wrong categorisation normal in AI accounting?

Yes—especially during early adoption or when new vendors/transaction types appear. The system improves through structured corrections and rule refinement.

Should SMEs correct every miscategorised transaction?

Focus on high-impact areas first (revenue, COGS, tax-related items). Minor low-risk items can be handled through periodic review.

Can wrong categorisation affect compliance?

Yes. Misclassification can distort tax reporting, financial statements, and audit readiness.

How does ccMonet help SMEs handle categorisation issues?

ccMonet supports structured categorisation workflows, audit trails, and expert oversight—helping SMEs correct and optimise AI categorisation without losing control.

Learn more at https://www.ccmonet.ai/.

Key Takeaways

• Wrong categorisation is normal and fixable
• Start with documentation and consistency
• Treat recurring errors as optimisation opportunities
• Review high-impact categories and set thresholds
• Automation improves when corrections are structured

Final Thought

AI accounting isn’t about never seeing mistakes—it’s about fixing them once and preventing them from recurring.

With the right workflow, SMEs can keep automation fast and reporting reliable—without becoming accountants themselves.

👉 Discover how ccMonet helps SMEs stay in control while using AI accounting at https://www.ccmonet.ai/.

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