Purpose: 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.