Sequential Audit Sampling with Statistical Guarantees

TL;DR

This paper develops a statistical framework for sequential audit sampling, enabling auditors to make decisions with controlled error probabilities while collecting additional evidence adaptively.

Contribution

It formulates a finite-population sequential testing approach for audit sampling, providing exact error control and practical calibration via Monte Carlo simulation.

Findings

Exact sequential boundaries control decision error probabilities.

Simulation-based calibration approximates the theoretical design.

Framework is applicable to attribute and deviation-rate auditing.

Abstract

Financial statement auditing is conducted under a risk-based evidence approach to obtain reasonable assurance. In practice, auditors often perform additional sampling or related procedures when an initial sample does not provide a sufficient basis for a conclusion. Across jurisdictions, current standards and practice manuals acknowledge such extensions, while the statistical design of sequential audit procedures has not been fully explored. This study formulates audit sampling with additional, sequentially collected items as a sequential testing problem for a finite population under sampling without replacement. We define null and alternative hypotheses in terms of a tolerable deviation rate, specify stopping and decision rules, and formulate exact sequential boundary conditions in terms of finite-population error probabilities. For practical implementation, we calibrate those boundaries by Monte Carlo simulation at least-favorable deviation rates. The exact design yields ex ante control of decision error probabilities, and the simulation-based implementation approximates that design while allowing the computation of expected stopping times. The framework is most naturally suited to attribute auditing and deviation-rate auditing, especially tests of controls, and it can be extended to one-sided, two-stage, and truncated designs.