Professional Auditing Research

Professional Auditing Research

Forward-Looking Auditing: Examining the Predictive Auditing Model Based on Expert Perspectives

Document Type : Original Article

Authors
Shdi Borji 1
Ali rahmani 2
azadeh maddahi 3
1 Msc.Student. Department of Accounting, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran.
2 Professor, Department of Accounting, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran
3 Assistant Professor, Department of Accounting, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran
10.22034/jpar.2026.2089871.1533
Abstract
This study aims to conceptualize predictive or forward-looking auditing. Predictive auditing is an emerging approach in the field of auditing that employs data mining techniques, machine learning, and statistical analytics to identify patterns, trends, and anomalies in an organization’s financial and operational data and to predict potential risks before they materialize. In contrast to traditional auditing, which primarily focuses on examining past events, this approach adopts a forward-looking perspective, enabling auditors to make more informed decisions and undertake appropriate preventive actions in a timely manner. The primary objective of predictive auditing is to enhance the efficiency and effectiveness of the audit process by focusing on high-risk areas and identifying potential instances of fraud. By employing advanced algorithms, this approach can rapidly process vast volumes of data and uncover complex relationships among different variables. Consequently, rather than devoting substantial time to examining a large number of individual transactions, auditors can focus on analyzing key findings and providing valuable recommendations. By generating timely and accurate analytical reports, predictive auditing enables managers to continuously monitor organizational performance and undertake corrective actions when necessary.This study adopts a qualitative research approach. To this end, semi-structured interviews were conducted with 13 experts comprising accounting faculty members, partners of audit firms, Certified Public Accountants (CPAs), senior audit managers, and specialists and researchers with professional experience in financial statement auditing. The participants (experts) were selected using the snowball sampling method, and the interviews continued until theoretical saturation was reached. The findings indicate that the implementation of predictive auditing requires the establishment of technological infrastructure, enhancement of auditors’ data mining and statistical analysis skills, development of clear standards and guidelines, and a shift in perspective from retrospective auditing toward a forward-looking approach. The experts emphasized that the use of artificial intelligence, machine learning, and big data analytics can transform the role of auditing from detecting errors and fraud to preventing them. This study is among the few domestic studies to identify the dimensions and requirements for implementing predictive auditing based on the perspectives of auditing professionals and experts in Iran. The findings provide practical insights into the role of technology, data mining, and specialized training, thereby clarifying the pathway for auditors’ transition from a traditional approach to a forward-looking approach. The results can also serve as a basis for developing modern policies and standards in the field of data-driven auditing.
Keywords
Predictive Auditing
Forward-Looking Auditing
Artificial Intelligence
Data Mining
Audit Risk
Subjects
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