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The objective of this research is to contribute to the advancement of knowledge in the field of automated accounting and auditing, with a particular focus on the use of blockchain technology. Furthermore, the research will seek to establish a theoretical, conceptual and ethical framework for the aforementioned processes. This research employs a teleological approach, which is defined as a method of inquiry that seeks to identify the purpose or end result of a phenomenon. To this end, a systems approach in the modern approach of Future Technology Analysis (FTA) was used to examine the fundamental concepts underlying blockchain and its applications in accounting and auditing. The fundamental concepts underlying automated accounting and auditing on the blockchain platform were organized and explained within a scientific framework that includes theoretical, conceptual, and ethical considerations. Based on the aforementioned findings, a conceptual, theoretical, and ethical framework was developed and elucidated based on content analysis derived from an understanding of the essential elements of the topic, its underlying rationale, and its operational mechanics. This was accomplished through the use of coding in Atlas.ti v9 and ConceptDraw software. Blockchain capabilities provide a platform for automated accounting and auditing in a scientifically, practically, and logically rigorous manner. The evolution of this research was meticulously designed and presented as a conceptual, theoretical, and ethical framework. The features, alterations, and potentialities of automated accounting and auditing on the blockchain technology platform collectively reinforce the necessity for further investigation, as they demonstrate the significant advantages that this technology offers in comparison to traditional methods. This research takes a novel, forward-looking approach and envisions the use of automated accounting and auditing on the blockchain technology platform. It presents, for the first time, comprehensive scientific frameworks and a systematic model for integrating the understanding of concepts, foundations, processes, and norms.

The asymmetry of information between managers and shareholders significantly affects the company's investment decisions and exacerbates the problems of agency. It seems that these problems in developing markets such as Iran are more than developed markets. This research is an exploratory study, and it aims at reviewing the theory of agency in Iran. In the present study, based on the research paradigm, first, data acquisition strategy is selected based on a qualitative approach, and then qualitative study and analysis, quantitative approach, and factor analysis approach have been used to confirm the validity of the extracted model. Statistical analysis of research data shows that among the hypotheses of agency theory, risk aversion, supervision, and reward are revised dimensions in Iran like examples of developing countries.The results confirm the existence of assumptions; 1. Conflict of interests of manager and owner, 2. Information asymmetry, 3. Reverse choice, 4. Ethical risk, 5. Maximizing the expected utility, 6. Forcing and 7. Failure to confirm the assumption is the ability to predict the future and receive information. due to the existence of information asymmetry, higher investment risks of minority shareholders and transparency problems, the lack of ability to predict the future and receive information is necessarily confirmed.

In the past, deciding over granting loans to bank customers in Iran would be made traditionally and based on personal judgments over the risk of repayment. However, increase in demands on banking facilities by economic enterprises and families on the one side, and increased as well as extended commercial competitions among banks and financial and credit institutions in the country for reduction of facility repayment risk on the other side, have caused application of novel methods such as some statistical ones in this context. Now to predict the risk of negligence in banking facility repayment and classification of the candidates, bankers use their customers’ credit ranking. Time efficiency, cost effectiveness, avoidance from personal judgments, and further accuracy in examining the candidates who apply for various funds are of its salient merits of this new combined method. Various statistical methods including biased analysis, logistic regression, non-parametric parallelism, and also some others such as neural networks have been employed for credit ranking. In this research, given the random forest metaheuristic algorithm-based smart pattern of real bank customers’ credit risk (case study: Bank Tejarat) was presented. According to the value of skewness, the data could be stated to have a normal distribution. Based on the observed results, the lowest mean was related to the variable of type of facility and its maximum value, to the amount of facility.