For many Singapore SMEs, one of the most frustrating XBRL issues is inconsistency — figures that don’t match across different statements, periods, or submissions. Even when individual numbers look correct, inconsistencies can trigger validation errors, rejections, or follow-up questions from ACRA.
The good news is that most inconsistencies are preventable once you understand where they come from.
Inconsistent numbers rarely appear during XBRL tagging itself. They usually originate earlier in the workflow.
Common sources include:
When data isn’t centralized, inconsistencies become almost inevitable.
The most effective way to avoid inconsistencies is to work from one validated dataset.
This means:
When multiple spreadsheets or versions are in circulation, mismatches are hard to avoid.
Re-entering numbers manually is a major risk factor.
Manual re-keying can lead to:
XBRL is far more reliable when data flows automatically from source to output.
Frequent changes late in the process often cause inconsistencies.
Best practice is to:
Stability matters as much as accuracy.
XBRL checks relationships across statements, not in isolation.
SMEs should review:
These relationships are common failure points.
Manual controls only go so far.
Modern financial systems reduce inconsistency by:
Platforms like ccMonet support accountants by generating structured Unaudited Financial Statements (UFS) from consistent bookkeeping data, reducing inconsistency across XBRL submissions.
Inconsistent numbers don’t just affect XBRL.
They can:
Consistency builds trust, both internally and externally.
Trying to “fix” inconsistencies at the XBRL stage is inefficient and risky. Preventing them requires disciplined data management throughout the year.
When SMEs maintain clean, centralized, and structured financial data, XBRL submissions align naturally — and compliance becomes predictable.
👉 Learn how structured, AI-assisted financial workflows support consistent, XBRL-ready reporting at https://www.ccmonet.ai/
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