Master data integrity
Duplicate vendor, customer, and employee records; missing or invalid bank, tax, and contact attributes; inconsistent naming and formatting.
RISK COVERAGE
Data Integrity & Audit Risk Analytics
Every other risk analytic depends on this one. If the master data is wrong, the findings will be too.
Data integrity is the control that determines whether any other analytic can be trusted. Duplicate vendor records inflate spend analysis. Missing tax identifiers defeat matching. Gaps in the audit trail make a finding impossible to defend.
foretale.ai profiles your master and transactional data first, reports the integrity issues that would distort downstream analytics, and tests whether the audit trail is complete enough to support a conclusion.
WHAT WE DETECT
Risks tested in this domain
Data quality and completeness
Missing mandatory fields, out-of-range values, orphaned transactions with no master record, and referential breaks between related tables.
Audit trail gaps
Transactions with no change history, deleted or amended records without a trace, and periods where logging was disabled.
Suspicious transaction patterns
Amounts clustered at approval thresholds, Benford's Law deviations, and postings at implausible times or frequencies.
Audit exception detection
Reconciliation differences, sub-ledger to general ledger breaks, and balances that do not roll forward.
Unauthorised master data change
Bank detail changes shortly before payment, changes made outside approval workflow, and changes reversed immediately after use.
HOW IT RUNS
Understand → Plan → Execute → Explain
foretale.ai maps your data and relationships, plans the analytics for this domain, tests the full population, and returns findings with evidence attached. Your team keeps every judgement.
DATA USED
What we need from your systems
- Vendor, customer, and employee master data
- Master data change logs
- Transaction and posting history
- System audit and configuration logs
- Reconciliation and sub-ledger data
- Reference data and validation rules
Related reading
Frequently asked questions
Why does data integrity testing come first?
Because it determines whether every other finding is reliable. Duplicate vendor records split spend and hide concentration, missing identifiers break matching logic, and audit trail gaps make a conclusion impossible to evidence. Profiling and correcting these issues materially reduces false positives in every subsequent analytic.
What is a bank detail change before payment?
It is a change to a vendor or employee bank account made shortly before a payment is released to that account. It is one of the highest-value single tests available, because it is the mechanism behind most payment diversion and mandate fraud. The test pairs master data change logs with payment timing.
Is Benford's Law analysis useful in practice?
It is useful as a ranking signal rather than a conclusion. Deviation from the expected leading-digit distribution indicates a population worth examining, particularly for manual entries and expense claims, but it requires corroborating tests before a finding is raised.
How are audit trail gaps evidenced?
By comparing transaction records against system change and audit logs to identify records with no corresponding history, periods where logging was disabled or incomplete, and amendments that left no trace. The gap itself becomes the documented finding.
NEXT STEP
See it on your data.
Request a focused walkthrough—usually within one business day.