Presenting a Model for Evaluating the Accuracy and Stability of Various Methods in Predicting Auditors' Behavior in the Face of Fraud and Financial Misconduct

One of the significant topics in the research literature has been auditing issues related to fraud and fraud detection. The main objective of this study is to identify and predict auditors' behavior when encountering fraud and misconduct during the auditing process. Since fraud can have considerable negative impacts on the accuracy and credibility of financial reports, it is essential to predict and identify the factors influencing auditors' behavior in such situations. Using a qualitative approach and a meta-synthesis method, this study identifies and extracts patterns regarding factors affecting auditors' behavior as well as methods for predicting these behaviors. This study is exploratory in nature, and its data were extracted from reputable articles published in credible databases related to the topics of fraud and fraud detection in auditing. The sampling method employed was systematic elimination, meaning that articles scientifically and content-wise relevant to the research topic were selected and analyzed. In the end, 17 articles were used as the basis for data analysis and extraction. Data analysis and component extraction from the articles were performed using MAXQDA software, which facilitated the effective identification of relationships and various factors influencing the prediction of auditors' behavior. In this study, 12 factors influencing auditors' behavior and 10 methods for predicting these behaviors in the face of fraud were identified. These factors include auditors' individual characteristics, past experiences, training received, as well as environmental and organizational characteristics that affect how auditors make decisions and respond to fraud. Additionally, methods such as risk analysis, continuous assessment, and the use of new technologies for fraud detection were introduced as effective methods for predicting auditors' behavior. The results of this study can serve as a scientific and practical foundation for improving auditing processes and detecting fraud within organizations and financial institutions. Identifying influencing factors and prediction methods for auditors' behavior can enhance the accuracy and efficiency of auditors when dealing with fraud and misconduct, thereby increasing transparency and public trust in the auditing process. These results can also provide a basis for policymakers and auditing managers in developing guidelines and strategies for combating fraud within auditing processes.

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Journal
Business Marketing and Finance Open
Published
2025-01-01
DOI
https://doi.org/10.61838/bmfopen.2.4.3
Primary Topic
Economic and Technological Systems Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Presenting a Model for Evaluating the Accuracy and Stability of Various Methods in Predicting Auditors' Behavior in the Face of Fraud and Financial Misconduct

Azar Moslemi, M. Karami Khorramabadi, Reza Talebi
Business Marketing and Finance Open
Economic and Technological Systems Analysis
article

Presenting a Model for Evaluating the Accuracy and Stability of Various Methods in Predicting Auditors' Behavior in the Face of Fraud and Financial Misconduct

Azar Moslemi, M. Karami Khorramabadi, Reza Talebi
article en

Abstract

One of the significant topics in the research literature has been auditing issues related to fraud and fraud detection. The main objective of this study is to identify and predict auditors' behavior when encountering fraud and misconduct during the auditing process. Since fraud can have considerable negative impacts on the accuracy and credibility of financial reports, it is essential to predict and identify the factors influencing auditors' behavior in such situations. Using a qualitative approach and a meta-synthesis method, this study identifies and extracts patterns regarding factors affecting auditors' behavior as well as methods for predicting these behaviors. This study is exploratory in nature, and its data were extracted from reputable articles published in credible databases related to the topics of fraud and fraud detection in auditing. The sampling method employed was systematic elimination, meaning that articles scientifically and content-wise relevant to the research topic were selected and analyzed. In the end, 17 articles were used as the basis for data analysis and extraction. Data analysis and component extraction from the articles were performed using MAXQDA software, which facilitated the effective identification of relationships and various factors influencing the prediction of auditors' behavior. In this study, 12 factors influencing auditors' behavior and 10 methods for predicting these behaviors in the face of fraud were identified. These factors include auditors' individual characteristics, past experiences, training received, as well as environmental and organizational characteristics that affect how auditors make decisions and respond to fraud. Additionally, methods such as risk analysis, continuous assessment, and the use of new technologies for fraud detection were introduced as effective methods for predicting auditors' behavior. The results of this study can serve as a scientific and practical foundation for improving auditing processes and detecting fraud within organizations and financial institutions. Identifying influencing factors and prediction methods for auditors' behavior can enhance the accuracy and efficiency of auditors when dealing with fraud and misconduct, thereby increasing transparency and public trust in the auditing process. These results can also provide a basis for policymakers and auditing managers in developing guidelines and strategies for combating fraud within auditing processes.

Business Marketing and Finance OpenVol. 2(4)
Islamic Azad University, Tehran (IR), Islamic Azad University of Khomeynishahr (IR), Islamic Azad University, Khoy Branch (IR), Payame Noor University (IR)
Openalex Percentile: Top 82%
Economic and Technological Systems Analysis
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