علی بیات

Background: Reporting delinquency as an important and effective moral behavior can be influenced by the moral values ​​that govern the organization and personality traits of individuals. Therefore, given the importance of the role of reporting in identifying and preventing organizational delinquency, it is necessary to gain a better understanding of the factors that affect it. The purpose of this study is to investigate the effect of self-control and social desirability on the ethical reporting of banking delinquency of bank employees, with emphasis on demographic characteristics. Method: This descriptive-survey study was conducted and its sample size consisted of 306 employees of public and private bank branches. The research variables were measured using standard questionnaires and finally structural equations, confirmatory factor analysis method and PLS software were used to analyze the data and test the hypotheses. Results: The findings indicate that the effect of self-control and social desirability on the ethical reporting of banking delinquency is confirmed. Social desirability also affects the relationship between self-control and ethical reporting of banking delinquency. On the other hand, work experience, type of job position and gender of employees have a direct impact on the ethical reporting of banking delinquency. Conclusion: The results of the study show that people with higher self-control, seek more social desirability and are more inclined to report delinquency. It also seems that bank employees, in proportion to job promotion and increasing work experience, better understand the value of reporting delinquency and with a greater sense of commitment, try to identify and report violations in order to improve the position of the bank. Therefore, by formulating ethical guidelines, incentive policies and accountability, positive personality traits (self-control and social desirability) and inherent reporting motivations of bank employees can be strengthened. Article number: 37

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.