The problem, and how AuditWise works

The problem

Testing a small sample can miss what is happening across the rest of the population. It also makes it hard to see a control slowly getting worse, or to tell which cases have the weakest evidence.

AuditWise is designed to enable population-level testing rather than small-sample review, to identify deteriorating controls over time, and to focus auditors on the cases with the weakest evidence or highest risk.

Deterministic checks

Deterministic code handles the questions software should always decide, the same way every time:

• identifiers, accounts, and currencies;
• quantities and valuation dates;
• tolerances and materiality thresholds;
• deadlines and timestamps; and
• verification of expected future outcomes.

Structured AI judgments

Structured semantic judgments handle the questions that need language and context, such as:

• Do messy or incomplete records represent the same position?
• Is the break explanation plausible?
• Does the evidence support the explanation?
• Was the independent review substantive?
• Does closure appear justified?
• Is a claimed timing difference genuine?

Outcome verification

An explanation such as “subscription settling at T+2” becomes a testable assertion. AuditWise records the expected resolution date and position, checks the later reconciliation, and confirms or invalidates the original explanation.

The continuous loop

1. Detect a break.
2. Assess the explanation and evidence.
3. Extract the expected future condition.
4. Observe the later reconciliation.
5. Verify the outcome.
6. Recalibrate risk and control confidence.