Interpretive structural modeling of risk-based internal audit planning in banks listed in the Tehran Stock Exchange

Document Type : Original Article

Authors

1 Ph.D. Student in Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran

2 Assistant Prof. in Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran

3 Assistant Prof. in Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran.

10.22034/iaas.2023.401545.1482

Abstract

The purpose of this research is to interpret the structural modeling of risk-based internal audit planning drivers in banks. Comprehensive risk-based planning enables the internal audit function to focus and align its limited resources well with reassurance and informed advice on critical issues facing the organization. The rapidly changing risk landscape in today's world requires internal auditors to evaluate risks periodically or even continuously. Coding of interviews using MAXQDA2020 software led to the identification of 16 drivers, which were selected as the most important drivers according to expert feedback and the pie chart of 12 drivers. Based on the results of the research, the drivers of risk-based internal audit planning in banks include auditors' professional maturity, the challenge of documentation, insisting on outdated procedures, the complexity of banking operations and activities, the bank's functional scope, lack of separation of duties, organizational structure, time monopolies, characteristics of the board of directors. , the support of senior management and employees, the competence of internal auditors, the lack of information provided by different banking units.In order to stratify the identified drivers, the opinions of 72 internal auditors of banks and managers in bank branches were used using stratified random sampling and paired comparison questionnaires.. It is worth mentioning that, during the many researches that have been conducted in the field of risk-based internal audit planning, they have not done leveling and interpretive structural modeling, and the innovation of the present research is in the modeling of its drivers.

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