faramarz fathnejad

Examining the countries' financial systems shows that this field has also been affected by technological developments and has benefited from its advantages and benefits to overcome challenges and create opportunities. Therefore, technology development in the banking sector has meaningful implicit concepts in bank marketing, especially in digital banking, because it affects the standard level of customers. This research aims to design a model for evaluating the effectiveness of the digital transformation of the banking system. Based on the purpose, this research is of developmental and evaluation type, and in terms of method, it is considered descriptive research of mathematical modeling type. In this research, in the quantitative part, two categories of statistical techniques are used using SPSS software. Also, in the second part of the research, content analysis was used to identify the model, dimensions, main categories, and subcategories. In the final part, it was used to analyze the banking system to establish digital transformation based on system dynamics and fuzzy network data coverage analysis method. The research findings showed that identifying and analyzing the practical components in the digital transformation of the banking system is essential for designing a model to evaluate the effectiveness of these transformations.

Due to the limitations imposed on financial resource allocation to research projects and also prioritization done in research processes, financial management of theoretical research processes not only presents challenges to the decision makers at defensive studies centers, but also received a lot of attention and have a wider significance for all experts. This study was an attempt to identify the appropriate criteria for financial management at Iranian army defensive centers and is also aimed at proposing a model for allocating resources to financial management through utilizing artificial neural networks. For this purpose and also in an attempt to conduct this study, at the early stages of investigation, criteria for allocating financial resources were emerged as a result of investigating the guidelines and instructions on financial resource allocation in Iranian army. To prove the selected criteria, the study was also conducted through administering a questionnaire among experts affiliated to the Iranian army research centers. Having collected the data, an appropriate model for allocating financial resources was designed in Matlab environment through applying hapfield neural networks. Having investigated the inputs and the outputs of the proposed models for financial resources allocation, three models were emerged through running artificial neural networks. The findings revealed that from among 25 identified criteria for allocating financial resources, 3 criteria were rejected and the criteria including book publication, research innovation and theory development stood at the top of prioritization. The findings also revealed that from among 3 models designed through ANN, the first model stood at the top of ranking with emphasis on military knowledge enhancement.

Nowadays customers’ role is changed from just accepting the producers, to leading investors, producers, and even researchers and inventors. Therefore, it is necessary for organizations to identify their customers well and to make plans for them. Some statistical and machine-based learning methods are used so far. However these methods alone are not without limitations. Using various methods of data mining, this research was to eliminate those restrictions as far as possible, so that a framework for identification of car insurance customers could be provided. In fact, the purpose was to categorize the most similar customers and to estimate the amount of risk in each category, according to their characteristics. Now, using this scale (i.e. amount of risk in each category) and considering the type of customer’s policy, the level of recompense could be estimated. This criterion can be helpful to identify customers and for making insurance tariff policies. For this purpose, in insurance industry the two data mining methods were been used to estimate customers’ detriment: the decision tree and clustering. Nevertheless, the decision tree method appears to give better results, although at the same, the clustering method generates a good categorization.