XBRL filing is meant to simplify regulatory reporting, but for many SMEs in Singapore, it becomes a frustrating loop of submissions and rejections. ACRA rejection messages can feel technical, vague, and overwhelming — especially if you’re not a trained accountant.
If your XBRL filing keeps getting rejected, you’re not alone. Below are some of the most common reasons SMEs run into issues, and what’s really happening behind the scenes.
One of the most frequent causes of rejection is inconsistency between your XBRL data and your underlying financial statements.
This often happens when:
Even a minor mismatch can trigger rejection, because ACRA validates XBRL data strictly against accounting logic and internal consistency rules.
XBRL isn’t just about numbers — it’s about how those numbers are classified.
Common tagging issues include:
These mistakes are especially common when SMEs rely on manual tools or templates without a deep understanding of the ACRA taxonomy.
ACRA requires certain data points to be present, even if the amounts are zero or not applicable.
Rejections often occur when:
What makes this tricky is that these requirements may not be obvious from the financial statements themselves — but they are enforced during XBRL validation.
Some rejections are triggered not by missing data, but by logical relationships that don’t add up.
For example:
These errors are hard to spot manually, especially when working across multiple spreadsheets or versions.
ACRA updates its XBRL requirements and validation rules periodically. Using outdated templates or tools can lead to automatic rejection, even if your numbers are correct.
SMEs often run into this issue when:
The core problem isn’t capability — it’s tooling and process.
Most SMEs:
That’s why rejections feel repetitive and unpredictable.
AI-powered financial systems are changing how XBRL preparation is handled. By generating financial statements directly from structured accounting data, validating logic automatically, and reducing manual input, they significantly lower the chance of rejection.
Platforms like ccMonet support accountants by producing structured, consistent financial data that can be used to generate compliant Unaudited Financial Statements (UFS). When your numbers are already clean, aligned, and reviewed, XBRL filing becomes far less error-prone.
Instead of fixing errors after rejection, issues are caught earlier — before submission.
Repeated XBRL rejections are rarely caused by a single mistake. They’re usually the result of fragmented workflows, manual processes, and limited validation.
With the right systems in place, XBRL filing becomes what it was meant to be: a compliance step, not a recurring headache.
If you want fewer rejections, less rework, and more confidence in your financial reporting, it may be time to rethink how your financial data is prepared in the first place.
👉 Learn how structured, AI-assisted financial workflows can support compliant reporting at https://www.ccmonet.ai/
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