IndiaAuditLens
Run Ledger Scrutiny checks
Use Ledger Scrutiny for deterministic exception checks over imported records. It produces review inputs—not final audit opinions. A team member must inspect the evidence and raise supported matters into the Findings…
By IndiaAuditLens product team · Updated 2026-10-10
Before you begin
Open the correct client and engagement and confirm the period under examination. Make sure the latest books, mappings, materiality, and relevant source evidence are available. Confirm who will prepare the work and who will review it. Do not conclude from a dashboard count alone; inspect the underlying records and evidence.
Run the Ledger Scrutiny review
Confirm that the engagement's books have been imported and normalised. Open Audit → Analytics and testing → Fraud and journal entries. Its own pages open under it in the menu. Confirm the dataset identity, source/import batch, period, import counters and active normalized record populations shown on Summary. Review the factual totals for checks performed, checks not performed, procedures with exceptions and affected records. These are results, not an automated audit-readiness conclusion. Work through the pages, one kind of analysis each: Journal entries, Unusual postings, Sales invoices,…
Exception-table columns are generated from the check and therefore vary. Typical fields include party, PAN/GSTIN, invoice/voucher reference, date, amount, narration, HSN/rate, payment reference and the reason the record was flagged.
Dataset provenance is not a separate work area. Summary retains the exact source, import batch, period, counters, active populations and evaluation fingerprint. Structural checks also continue to run, but appear where the CA reviews their consequence: journal, sales, purchase and payment linkages on Journal entries, Sales invoices, Purchase invoices and Payments; GL and trial-balance account references under Data reliability → Accounting; and canonical classifications under Data reliability → Master data. One evaluation still runs every procedure: the pages divide what is shown, not what…
Raise a finding or ask the client about a data exception
Expected result
The deterministic check population is reviewed; evidence supporting real exceptions is preserved; rows that are not real exceptions and data gaps are documented (a raised finding that proves not to be one is closed in Findings as Not an exception); and only human-reviewed matters progress to Findings, client requests or conclusions.