درخت حوزه‌های تخصصی

Innovations in electronic banking in Iran have led to the development in capacities such as payment instruments and transactions by cards, which are known as electronic payment equipment in the Iranian banking network. Financial supervision is required to be increasingly based on reporting and regulatory processes to efficiently and proactively monitor risk and compliance at banks and financial institutions. Besides, the banking system needs relevant information and instruments on strategy, assessments, and policy decisions in line with the required procedure to enhance transparency. Designing a new criterion in banking innovations by combining the Electronic banking instruments, considering the Electronic banking capacity proper and scaled to the banks’ assets, equity and resources as well as distinguishing the impact of banks’ profitability and capital are attributed as the key contributions in this paper rather than other similar researches. Results indicate that Electronic Banking capacities including the pin-pad, ATM, online branches, card services, and P.O.S volumes have positively and significantly influenced transparency since scales of these innovative capacities have expanded relative to the banks' assets and capital due to their contributions in the velocity and disclosure of data collection and analysis potentials. Results also denote that the state-owned banks in Iran are less transparent than private banks and the size of the bank hurts transparency. The return on equity in the form of bank ownership is multiplied as well as results also indicate that the productivity of equity returns has a positive effect on transparency. The ratio of non-interest income to total income also has a positive impact on transparency. There would be needed to provide transparent information on fee-based services to develop non-interest income. Hence, to improve transparency, the development of fee-based and non-profit-based services are required.

Being aware of the waiting time for selling residential units is one of the important issues in the housing sector for the majority of people, especially investors. There are several factors affecting the waiting time for selling residential units. Determining the influential factors on the time period of selling real estates can lead to an informed decision making by real estate consultants, sellers as well as those seeking to buy real estates. Using a real estate database in Iran, the present paper proposes a two-module procedure. The first module deals with implementation of association rule mining. Using the well-known association rule mining techniques namely FP-Growth, several association rules have been extracted which indicate the effective factors on the waiting time for selling residential units. Generated association rules have been evaluated based on metrics such as support, confidence and lift and finally the best rules are selected. The main objective of the second module is to develop a fuzzy inference system which can determine the factors influencing the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments. In conclusion section, a number of suggestions for future studies are presented. For example, machine learning algorithms such as neural networks, decision trees, etc. can also be used to predict the duration of residential units’ sale. The main objective of the second module is to develop a fuzzy inference system which can learn about the factors that influence the waiting time for selling residential units from historical data, so that the model can be used to estimate the time it takes to sell the property for a real estate agency. Several IF-THEN rules are extracted from this module. Extracted rules can be used by real estate agencies as well as buyers and sellers of residential units to make better decisions in their investments.