Examining Data Mining in Improving the Audit Process

• Published in: پژوهش‌های تجربی حسابداری
• Main Authors: Mohammad Hossein Setayesh, Mina Sadeghi, Younes Masoudi, Elias Dehdari
• Format: Article
• Language: Persian
• Published: Alzahra University 2025-09-01
• Subjects: auditing; data mining; process improvement
• Online Access: https://jera.alzahra.ac.ir/article_8874_d886635e060ac64b382df62a9f082699.pdf

Auditing has become an essential field worldwide due to the increasing evidence of intentional manipulations in financial reports. This study examined the role of data mining in improving the audit process. The research was conducted using a qualitative descriptive method. Data collection involved reviewing books, theses, journals, and related studies on the topic. Given the breadth and abundance of sources, both printed and digital materials relevant to the subject and accessible were selected for sampling. Additionally, receipts were used to gather information from the sample.The study’s results showed that data mining techniques for fraud detection begin with feature selection. The core principle of data mining is that data can reveal valuable insights. Although data mining is a relatively new field, it has proven to be widely applicable in financial decision-making processes. Applications of data mining methods, based on relevant studies and their nature, span a broad range, including bankruptcy prediction, credit risk assessment, going concern evaluation, financial distress analysis, business unit performance forecasting, and management fraud detection.