DIGITALIZING AUDIT SAMPLING AND EVIDENCE EVALUATION: A SYSTEMATIC LITERATURE REVIEW OF SOFTWARE-BASED STATISTICAL AUDITING TOOLS - Hana Dhayan; Reza Pahlevi; Nadya Eka Putri; Elga Yulindisti (Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE), 2026)
Digitalization is reshaping audit work through data analytics, automation, and software-supported sampling. Yet, evidence on whether such tools reliably improve audit time efficiency and auditability remains fragmented, and auditors still face pressures to over-sample under inspection risk. Building on recent advances in statistical auditing—including open-source tools for audit sampling and Bayesian methods that can reduce sample sizes substantially—this study conducts a systematic literature review and qualitative evidence synthesis of software-based statistical auditing tools (e.g., generalized audit software, technology-based audit techniques, robotic process automation, and Bayesian audit sampling workflows). Using a PRISMA-informed search strategy across major academic databases and targeted hand-searching, we synthesize findings from audit technology, sampling, and standards-oriented literature. The review identifies (i) consistent efficiency mechanisms (automation, standardization, optional stopping, and risk-focused sampling), (ii) conditions that enable time savings without degrading audit quality (data quality, auditor competencies, and methodology fit), and (iii) adoption barriers related to cost-benefit visibility, cybersecurity/privacy, and regulatory expectations regarding evidence evaluation. We develop a consolidated framework linking digital audit tools to audit efficiency and accountability outcomes, and we propose research directions for future empirical tests (e.g., fee and lag outcomes, stratified sampling effectiveness, and governance impacts). (Bierstaker et al., 2001; Bradford et al., 2020; Barr-Pulliam et al., 2023; Eulerich et al., 2022; Derks et al., 2023; Derks et al., 2024; Derks et al., 2025; Mensink et al., 2025; Meng et al., 2024).