Applied AI for Finance and Accounting · Module 7

Anomaly Detection: 6 Key Terms

By Devon Coombs, CPA, MBA · Teaching Professor of Finance, Santa Clara University · Reviewed August 2026

Anomaly detection in finance data means searching transaction populations for entries that warrant further review, such as duplicate payments or round-dollar entries that often signal estimates rather than actual amounts. Exception-based testing narrows the work to the items most likely to matter, while terms like false positive, disposition, and audit trail describe how flagged items are judged and documented. Practitioners generally rely on this vocabulary to keep a review defensible, and the free Applied AI for Finance and Accounting course uses these terms throughout its data review module.

These terms are taught in Module 7: Data Review and Anomaly Detection of the free Applied AI for Finance and Accounting course; the full course glossary collects every chapter in one place.

Audit trail

A record of what was tested, the thresholds used, the findings, and the disposition of each, so a review can be reperformed.

Disposition

The proposed next step for a flagged entry, such as investigate, reclassify, or confirm with the vendor.

Duplicate payment

The same vendor and amount appearing more than once, a common accounts-payable error that exception testing flags.

Exception-based testing

Reviewing a data set by applying rules that surface only the entries needing attention, rather than reading every line.

False positive

A clean entry that a test flags incorrectly. Minimizing false positives keeps an exception report trustworthy.

Round-dollar entry

An amount posted in exact whole thousands, which can signal an estimate or a manual entry worth reviewing.

Put the vocabulary to work: the free calculators and decision guides apply these terms, and the free Applied AI for Finance and Accounting course teaches them in context.