Every decision is a person’s.
Findings wait for a decision: confirm, reject or ask the client. Signing is blocked while any finding is open, and each decision records who and when.
Journal entry testing
Test the whole journal population against the risks you choose.
Upload the GL export, the trial balance and your instructions. Deixio proposes the tests, code runs every filter, count and total, and each flagged entry names its rows and the rule it broke.
A population nobody can read in full.
Large journal exports, several risk criteria and filters rebuilt in a spreadsheet every year. The criteria are clear; applying them to every line, and showing it, is the slow part.
From the plan to the findings, every decision is yours.
02Tests · The AI proposes, you approve
Tests drafted from the evidence and your intent.
The AI looks through the evidence and proposes each test with what it proves, its population, its sources and what counts as an exception. It lists what it noticed and asks its questions. Nothing runs until you approve the plan.
- Completeness of the population
- Imbalance
- Period and cut-off
- Weekend or out-of-hours posting
- Unusual users
It reads the audit’s instructions and proposes tests for the rules they state.
03Run · Code checks every figure
Every number from code. Every figure printed there.
Only the approved tests run, over every row. Each number comes from a read-only query on the engine or is quoted from a document line, and each cited figure is read again at its place.
- Every filter, count and total.
- Each flagged entry cites its rows.
04Results · You decide each finding
Every finding decided by a named person.
Confirm, reject or ask the client about each finding. Signing is blocked while any finding is open, and the conclusion is yours.
- You choose the risks, approve the plan, decide each flagged entry and sign the workpaper.
- Flagged entries per test
- Findings
At a glance: Journal entry testing
- Test the whole journal population, not a sample, against the risks you choose.
- Each flagged entry names its rows and the rule it broke.
What you upload
- The journal population (GL export)
- The trial balance
- Policies or instructions
Tests the AI may propose
- Completeness of the population
- Imbalance
- Period and cut-off
- Weekend or out-of-hours posting
- Unusual users
- Missing approval
- Round or just-below-limit amounts
- Repeats and reversals
- Suspense accounts
- Unusual descriptions
The AI proposes tests from your evidence and intent; these are examples, not a fixed list.
What you get
- Flagged entries per test
- Findings
- The workpaper
What the auditor sees.
Every figure explains itself.
Open a figure to see the page it was read from, the box around it, how sure the AI was and who accepted it.
Watch one procedure run, figure by figure.
In 30 minutes we run a procedure end to end on sample data and show you where every figure comes from.
- A source for every figure
- Human in the loop
- Append-only, hash-chained audit trail
Questions about journal entry testing
Can we use our own risk criteria?
Yes. Describe them in the intent or in the audit’s instructions. The AI reads the instructions and proposes tests for the rules they state.
Is the whole population tested?
Yes. The approved tests run over every entry. Sampling only happens if you add it as an explicit step.
Can I see why an entry was flagged?
Each flagged entry names its rows and the rule it broke, and opens on those rows.
Who does the arithmetic?
Code. Every filter, count and total comes from a read-only query on the engine; the AI never adds up.
Can we reuse the tests next year?
Yes. “Run again next period” starts the next audit from the plan you approved. The AI fits each test to the new period’s data and keeps your wording.