LHDN’s E-Invoice Data Uncovers RM3.5 Billion In Unreported Income

Personal Finance
28 Apr 2026 • 4:30 PM MYT
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A review of e-invoice data by the Inland Revenue Board (LHDN) has led to 38,906 taxpayers voluntarily declaring a combined RM3.5 billion in previously unreported income, with RM760.7 million in taxes now payable as a result.

LHDN said it had been sending reminder notices, referred to as “nudges”, to taxpayers where its review of e-invoice records identified discrepancies or missing declarations. The notices were designed to prompt voluntary disclosure before any formal enforcement action was taken. Taxpayers who received reminders but have yet to file or correct their returns may be subject to audits and further enforcement measures.

Alongside the income review, LHDN conducted a national e-invoice compliance operation from 20 to 24 April 2026, finding 108 taxpayers who had not yet implemented e-invoicing despite being required to do so under Phase 1 or Phase 2 of the rollout. Phase 1 covered businesses with annual turnover above RM100 million, while Phase 2 brought in those above RM25 million.

How E-Invoicing Changed Tax Visibility

The e-invoice system, which went live on 1 August 2024, creates a digital transaction trail that LHDN can cross-reference against declared income. Since launch, 225,604 taxpayers have adopted e-invoicing and issued a combined 1.299 billion e-invoices, giving LHDN considerably more visibility into business activity than was previously possible.

What Business Owners And The Self-Employed Should Know

If you run a business, work freelance, or earn income outside of employment, the gap between what your e-invoices record and what you declare is now easier for LHDN to detect. The 39,000 voluntary declarations that followed the nudges show how much income was sitting in transaction records without a matching tax filing.

If you received a nudge and have not yet acted on it, filing now is likely a better outcome than waiting for LHDN to follow up. The board’s approach this round was to flag discrepancies before escalating, but that sequence is unlikely to repeat indefinitely as the data pool grows.

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