Knowledge management
Alireza Aghanoori Koupaei; Ozhan Karimi; Shahram Hashemnia
Abstract
This research aims to provide an information system based on virtual currency data in the banking industry in Iran. Data is one of the most significant assets of the organization. This research is applied in terms of purpose. The population includes university professors in marketing and experts, including ...
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This research aims to provide an information system based on virtual currency data in the banking industry in Iran. Data is one of the most significant assets of the organization. This research is applied in terms of purpose. The population includes university professors in marketing and experts, including managers of the banking industry. Sampling in the qualitative section was done purposefully, and the criteria of expertise were at least a graduate degree and ten-year of relevant work experience. Finally, 20 people were selected as samples. The method used is SWARA's quantitative prioritization method. The index of using virtual currency in Iran's sanctions situation (Q67) with a weight of 0.12 is the first. The index of entrepreneurial acceleration based on the new payment system (Q56) with a weight of 0.11 is the second priority. The index of general uncertainty about the nature of virtual currencies (Q16) with a weight of 0.09 is the third priority. The index of improving communication and interactions between customers and the bank (Q63) with a weight of 0.0859 is the fourth priority. The index of alignment of banking processes with global standards (Q61) with a weight of 0.07 is the fifth priority. Extracting useful information from the database and converting it into actionable results is the main challenge that companies face. Therefore, results show that one of the ways to the success of cryptocurrencies is the use of data-oriented information systems and data mining techniques. The data-oriented information system is one of the recent developments in data management technologies.
Data mining
Tina Malekolkalami; Khadijah Khodabakhshi Parijani; Maliheh Alifari
Abstract
The purpose of this research is to use data mining to detect accounting fraud in the database of stock exchange member companies. The combination of discrete and continuous data has increased the necessity of using data mining and machine learning methods in the field of fraud detection. This research ...
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The purpose of this research is to use data mining to detect accounting fraud in the database of stock exchange member companies. The combination of discrete and continuous data has increased the necessity of using data mining and machine learning methods in the field of fraud detection. This research is applied in terms of purpose and descriptive in terms of method. The document review method was used to collect information in the field of literature and research background. The prepared questionnaire includes 7 main indicators consisting of 48 questions for each of the variables. This questionnaire was made available by the researcher to 400 accountants of companies admitted to the Tehran Stock Exchange by sampling method. In order to fit the model, the structural equation method was used in SMARTPLS software. In the data mining section, all IB1, IBK, LWL, KSTAR, and KNN algorithms were used to simulate the proposed model in Rapidminer software. Effectiveness of internal control, compensation system, asymmetry of information, compliance with accounting rules, management ethics, and ethical principles are effective and meaningful on accounting fraud. In evaluating parameters and according to the graphs, the K-STAR algorithm has better performance than other algorithms. The proposed data mining model for financial fraud detection showed that since the amount of data creation in financial companies is increasing day by day with the development of technology, it is possible to provide early detection of fraud by reviewing and analyzing the data.