Inconsistent financial data is one of the most common reasons XBRL filings fail or require correction. For Singapore SMEs, inconsistencies across income statements, balance sheets, and supporting schedules often surface only during XBRL validation — when it’s hardest to fix.
Improving XBRL data consistency starts long before filing.
XBRL relies on the same figures appearing consistently across statements. This breaks down when accounts are used differently or reclassified late.
A strong foundation includes:
AI accounting platforms like ccMonet help enforce these standards automatically.
Manual edits are a major source of inconsistency. When numbers are adjusted in one statement but not reflected elsewhere, XBRL validation issues follow.
AI-driven bookkeeping reduces manual intervention by keeping records accurate and aligned from the start.
Regular reconciliation ensures balances match across bank accounts, ledgers, and reports. Automated reconciliation flags mismatches early, before they affect multiple statements.
XBRL consistency isn’t achieved at year-end. It’s built through structured, disciplined financial workflows.
If your SME wants smoother XBRL filings with fewer corrections, improving data consistency through AI accounting is a practical first step.
👉 See how ccMonet helps Singapore SMEs maintain consistent, XBRL-ready financial data: https://www.ccmonet.ai/**
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