banking

This research presents an approach based on customer lifetime value (CLV) and artificial neural networks (ANN) to classify bank corporate customers. CLV in banking means how much financial value each customer creates for the bank. The research is of an applied and quasi-experimental type and the statistical population includes 127,672 corporate IDs in the Tejarat Bank Corporate Customer System. Data are collected by scanning customer files over a six-month period. Sampling is by enumeration. K-means algorithms and artificial neural networks are used to cluster customers. Simulations showed that the artificial neural network algorithm provides more accurate results than the K-means algorithm. This approach can effectively classify customers into three clusters. The clusters were reviewed based on the opinions of banking experts; in order to conduct a deeper analysis. Based on the data of Tejarat Bank's corporate customers, these categories were analyzed in terms of CLV. Marketing and sales strategies were developed for each customer cluster. The approach proposed in this research can help banks improve their customer segmentation process and ultimately increase profitability and customer retention.

The main goal of the current research is to identify the success factors of technology-oriented startups in solving the challenges of Iran's fintech industry.

This research has been done in the framework of a mixed approach and using the foundational data method.  The collection tool in the qualitative section was selected from the sample by conducting a semi-structured interview.  The studied community were experts, members of scientific life and founders of startups in the fintech industry. The purposeful sampling method and the selection of the sample according to the snowball process proceeded until the theoretical saturation was reached and 22 people were selected as the sample. Strauss and Corbin method was used for data analysis in the qualitative part.  For the validity and reliability of the questions, the matching method by the members and the intra-subject agreement method were used. 

The findings of the research show that the causal conditions include (focusing on the real needs of customers, the use of advanced technologies,...), the central phenomenon including (the success of technology-oriented startups in solving the challenges of Iran's fintech industry), the background conditions including (governance support and creating appropriate business environment, development of talents and specialized human resources, ...), intervening conditions including (governance and legal support, development of specialized human resources, ...), strategies including (improving the technological capabilities of startups in the field of fintech, creating specialized capital markets for  Attracting investment,...), the consequences include (development and sustainable growth of fintech industry in Iran, improving the quality and efficiency of financial and payment services,...). AHP technique has been used to analyze the data in the quantitative part of the paired scale.

The results of this section indicate that the creation of specialized capital markets to attract investment with (0.951) has the highest score and the implementation of programs to attract and retain technologist talents with (0.147) have received the lowest score.

The purpose of this research was to present an organizational resilience model with an entrepreneurial approach in the banking industry. The current research method was a descriptive type that was implemented in the field. The statistical population of this research included all experts and knowledgeable people in the field of banking industry in Iran. In order to identify these experts, purposeful sampling was used. This sampling was done until theoretical saturation was reached, and 15 people were selected as the research sample. In the qualitative part of the research, believability, transferability and verifiability were used to check validity. Also, in the quantitative analysis part of the research, Dimtel method was used. The results of this research revealed that the changes affecting organizational resilience with an entrepreneurial approach in the banking industry include structure dynamics, opportunity recognition, management optimization, liquidity consequences, management transformation, creating social justice and structure dynamics and creating banks' flexibility. is. Also, the results of this research show that the factor of structure dynamics with a weight of 0.237 was determined to be the most important factor in organizational resilience with an entrepreneurial approach in the banking industry.

The purpose of this study is to investigate the factors influencing customer behavior in the use of electronic services of Shahr Bank. 

This study employed a quantitative research method. A total of 77 components were extracted from the literature and categorized into four dimensions: sociological, psychological, demographic, and technical services. Additionally, 10 indicators, including culture and norms, social class, social pressure, demographics, quality of technical services, ease of technical services, and behavioral, emotional, perceptual, and personality factors, were determined. After content validation, the initial research model was examined. A questionnaire was designed for data collection and distributed among the bank's customers. 

The findings, using exploratory factor analysis, revealed that the dimensions and indicators identified in the first phase of the study were sufficiently valid. Results indicate that psychological, demographic, and technical service factors have significant effects on customer behavior in utilizing electronic services. 

The final model of the study was developed based on the internal relationships between variables and statistical analyses. This model can serve as a framework for improving the acceptance of electronic technologies in banking. Based on the findings, recommendations were provided to increase the adoption of electronic services at Shahr Bank.

In today’s world, Business Intelligence (BI) tools are recognized as one of the main pillars of evaluating the performance of organizations in various sectors, including banking. This research examines the role of business intelligence in evaluating the performance of banks and its impact on improving decision-making processes and enhancing the efficiency of organizations. By using Balanced Scorecard models and BI tools, banks are able to analyze complex data and make strategic decisions. This research has shown that successful implementation of BI in banks can lead to greater transparency, more accurate data analysis and ultimately improve the overall performance of organizations. However, challenges such as implementation costs, the need for appropriate infrastructure and cultural changes in organizations are among the obstacles to the optimal use of these tools. The present study has identified these challenges and provided solutions to overcome them.

The phenomenon of service failure expresses the gap between satisfaction and expectations of customers from a service. Ignoring the causes of this phenomenon has become a problem. The purpose of the current research is to identify these factors and provide solutions to reduce customer dissatisfaction with domestic banking services based on the marketing funnel.

The data of this research is of qualitative type and its statistical population consists of employees and managers of Iran-Zemin Bank and customers of this bank. In the first population, 23 employees and managers of Iran-Zmin Bank and in the second statistical population, 13 customers of this bank were determined and interviewed and analyzed.

In total, 3 main themes and 12 sub-themes were identified. The main themes included the main reasons for the failure of banking services, the revenge of the dissatisfied customer, and the ways out of the service failure.

According to the opinions of experts, many of the reasons for the failure of services in the banking industry are the ignorance and lack of information of the customers about the banking rules and instructions, and it is necessary to take measures to educate the customers. Also, it seems necessary to improve the level of knowledge and skills of managers and employees about how to deal with customers, self-restraint and non-confrontation of bank employees with dissatisfied customers.

Global competition, dynamic markets, and rapidly shrinking innovation and technology cycles, all have imposed significant challenges on the financial, banking, and insurance industries and the need to data analysis for improving decision-making processes in these organizations has become increasingly important. In this regard, the data stored in the databases of these organizations are considered as valuable sources of information and knowledge needed for organizational decisions. In the present research, the researchers focus on the common customers of the bank and insurance industry. The purpose is to provide a methodology to predict the performance of new customers based on the behavior of previous customers. To this end, a hybrid model based on support vector machine and genetic algorithm is used. The support vector machine is responsible for modeling the relationship between customer performance and their identity information and the genetic algorithm is responsible for tuning and optimizing the parameters of the support vector machine. The results obtained from customer classification using the proposed model in this research led to customer classification with a high accuracy of 99%.

This research sought to find the determinants and consequences of non-performing loans. For this purpose, meta-synthesis method was used and 7 steps of meta-synthesis proposed by Sandelowski and Barroso (2007) were implemented. The intended sample for meta-synthesis consists of selected studies based on their relationship with the research question. In the present study, the initial sample included 194 sources related to the research topic, which were extracted from databases without time limits. After screening the sources in terms of various items such as title, abstract, content and quality, 151 sources remained, whose results were examined more precisely and combined with each other. The result of this process was the extraction of 118 codes, which were categorized into 10 categories of determinants, 11 strategies to reduce and 12 consequences of non-performing loans. Determinants include international factors, macroeconomic factors, bank-specific factors, industry-related, market, borrower, and loan, political, macro-financial and institutional factors. 36% of determinants were classified in the category of macroeconomics determinants, 31% in the category of bank-specific factors, and 33% in the other 9 categories. Another result of this study is the circular causality observed among the consequences and determinants.

This research has been done with the aim of developing Iran's banking scenarios with an emphasis on big data. The current research is practical in terms of orientation and exploratory in terms of the goal. It is also mixed in terms of its philosophical, pragmatic and methodological foundations. To carry out the research in the first stage, 20 key drivers of the research were extracted through literature review and interviews with banking and technology experts. After screening with the fuzzy Delphi method, 8 factors were removed and the rest were evaluated with the Marcus decision making technique. The findings of the research show that the two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. According to the findings of the research, considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

Financial innovations has been challenged the banking sector and can improve it. They cover a variety of financial businesses such as online lending, asset management platforms, trading management, mobile payment platforms, etc. All these services generate a large amount of data every day (Hasan et al, 2020: 1). Analyzing this volume of data is difficult, giving rise to the concept of "big data" (Munawar et al, 2020: 2). Big data as one of the important fields of future technology has attracted the attention of various industries (Raguseo & Vitari, 2018: 5206). In general, big data refers to a large volume of structured or unstructured data that is generated and stored at a high speed (Dicuonzo et al, 2019: 41). Big data has found its position in the banking industry; Because of the useful data they have stored in recent years (Rakhman et al, 2019: 1632). Recent applications of big data in banking have been for improving customer relationship management, marketing, optimizing strategic management and human resources (Parmar, 2018: 33; Hassani et al, 2018: 2). Therefore, it can be said that nowadays big data plays a major role in providing financial and banking services, and the realization of its potential benefits in banking is more from technical aspects and affects the organizational structure of banking and mobilizes a large number of different actors (Diniz et al, 2018: 151- 152). With changes in customer expectations and increased competition, the banking industry is no longer able to ignore technological innovations in the banking sector. Due to the numerous applications and benefits of big data in various industries, including the banking industry, and it's becoming more widespread in the future, this technology is becoming a prominent research topic (Phan & Tran, 2022: 6.)

What are the plausible scenarios for banking in Iran with an emphasis on big data?

Many studies conducted in the field of banking and big data deal with the role of big data in improving the performance of the banking industry (for instance: Shakya & Smys, 2021; Gonsalves & Jadhav, 2020; Hung et al, 2020; Parmar, 2018). Also, another part of the studies conducted with a future research approach in the banking sector without focusing on innovative financial technologies and specifically big data (for instance: Baumgartner & Peter, 2022; Eskandari et al,2020). The focus on innovative banking and financial technologies with a Futures Studies approach has been weak (for instance: Maja & Letaba, 2022; Murinde et al, 2022; Hajiheydari et al, 2021; Broby, 2021; Harris & Wonglimpiyarat, 2019). And the role of big data in the Futures Studies of the banking industry has been seen to be very limited due to the relatively large amount of data available in banks and its effect on performance and gaining a competitive advantage (for instance: Valero et al, 2020). Therefore, despite the studies conducted in the field of banking and big data, some of these researches have paid attention to the present time, and the researches conducted in the future of the banking industry have also been without focusing on the role of big data. Now, the most important theoretical gap in research is the lack of studies on the future of banking in Iran with an emphasis on big data.

The current research is pragmatism due to the use of qualitative and quantitative methods from the perspective of philosophical foundations. It is also exploratory in terms of purpose due to the identification of drivers and practical in terms of direction due to the application of the results in the analysis of the future of banking in Iran. In the current research, two methods of literature review and interviews with experts are used to identify drivers, both of which are qualitative methods. According to Popper, the interview tool is based on the expert dimension. The literature review is evidence-based and uses articles and scientific texts to identify factors. Fuzzy Delphi, which is semi-quantitative and evidence-based, is used to screen and determine key drivers that require great accuracy. Then, to determine the key uncertainties, the MARCOS technique is used based on the importance and uncertainty indicators of the Global Business Network (GBN) approach, which is a quantitative and evidence-based technique. Finally, interviews with focus groups are used to write the scenario, which is a qualitative method based on the expert dimension. The theoretical community of the research includes academic experts and managers of the banking sector and are aware of new banking and financial technologies (Fintechs) and specifically big data. The selection of the participants is based on their knowledge and nobility of the research topic and the importance of their presence in the research, and finally 15 people were selected by purposeful sampling using the snowball method. Experts have at least 10 years of relevant work experience and a master's degree.

This research has clarified the situation of this area by identifying the shaping factors and drivers of the future of banking in Iran. Two factors of "technology regulation" and "technology transfer costs" were chosen as key uncertainties for developing research scenarios. Based on these two key uncertainties, four scenarios were developed based on interviews with the focus group with the titles of comprehensive banking, static banking, searching banking, wandering banking. In the comprehensive banking scenario, everything is in its optimal state; Technology transfer costs have decreased and regulators are supportive of the technologies. Considering drivers, key uncertainties and alternative scenarios by managers and decision makers can improve the performance and increase the competitive advantage of banks.

This research has been done to identify and rank factors affecting the growth and development of the market share of Refah Kargaran Bank. The current research is a mixed type of research in terms of its applied-developmental goal and terms of its implementation. The statistical population in the qualitative part of this research includes 50 managers of Refah Kargaran Bank branches in Fars and Bushehr provinces. A statistical sample consisting of 25 branch managers was selected using the snowball method. The statistical population in the quantitative section includes 600 middle-level employees and managers of Fars and Bushehr Refah Kargaran Bank, among whom 234 people were selected as a sample by simple random method. To analyze quantitative data in this research, descriptive and inferential statistics have been used. The findings of the theme analysis using the TOPSIS model showed that the use of customer relationship management policies (needs assessment, responsiveness, satisfaction, and loyalty) with a coefficient of 0.842 had the greatest impact on increasing the market share of Refah Bank, and the support and facilitation services of Refah Bank. Workers with a proximity coefficient of 0.444 are in the last place. The path analysis findings showed that the variables of attracting customers with a rate of 0.504 and electronic banking with a rate of 0.449 had the greatest impact on the development of the market share of Refah Kargaran Bank. According to the investigations carried out about the planning process to increase the productivity of the Refah Kargaran Bank in a competitive environment, weaknesses and threats have the highest coefficients of 9.41 and 8.36, and strengths and opportunities have the lowest coefficients of 7.13 and 6.54. Strategies have been presented in this regard.

In order to investigate the impact of a technology on the business model, it is necessary to examine the characteristics of that technology and how to use those characteristics in order to benefit from opportunities and deal with threats, so the purpose of this research is to investigate the characteristics of blockchain on financial innovations. The statistical population of this research is more than 100 thousand people and the statistical sample using Morgan's table is 384 people. Information is collected by library, field and internet methods. The validity of this questionnaire was confirmed by experts and supervisors. Also, the measuring tool is a questionnaire. The questionnaire was designed by Latman (2016) and has 36 questions. Data analysis software is the Spss software package. A 5-point Likert scale was used for the questionnaire. The research results showed that some of the key features of blockchain technology will have a direct impact on the banking industry. These key features include dynamism, identity preservation, information security, lower cost, time saving, trust building, decentralized and impenetrable blockchain technology and financial innovations and value transfer in banking. Each of these features will affect different aspects of the business model.

Digital transformation has transformed many industries in recent years. One of the consequences of digital transformation can be mentioned the change of structures, procedures and processes. The wave of digital transformation has also caused fundamental changes in the banking industry. Digital banking is the provision of banking services based on digital technologies. Iran's banking industry has turned to digital services in recent years and this industry can be considered one of the leading industries in the country. However, there are strategic entry barriers in the way of banks to join the wave of digital transformation and sometimes it prevents them from joining this wave. The present study examines the barriers to strategic entry into the digital banking industry in Iran using the meta-composite method. In a period of ten years from 1391 to 1401, this research has examined the researches conducted in this field and has extracted the most important barriers to strategic entry into the digital banking industry. Research studies show that barriers to strategic entry into the digital banking industry in Iran are divided into two general categories: "internal" and "external" and factors such as: cultural, managerial, technological, financial, political-legal and skill barriers. They are effective.

With the expansion of technology in businesses, customers don’t want to be exposed to extensive traditional advertising. Therefore, businesses should use new advertising and marketing methods. The purpose of this research is to explain the content marketing model in the banking industry that can help increase market share in this industry. In the qualitative part, the available information was collected and codes were identified, which were categorized in the form of new concepts and categories, and it was the basis for designing the questions and the interview protocol. Theoretical saturation has been achieved after 13 interviews with experts of 7 banks. Based on 70 codes related to 23 concepts related to the research topic, the final model has been designed. In the second step, the validation of the presented model has been done with the quantitative approach of structural equation modeling. The tool for collecting data in the quantitative section was a closed-ended questionnaire with a 5-point Likert scale. Because the number of items in the questionnaire was 70items, 319 questionnaires were distributed and collected among bank employees and used in the analysis. The final categories of the model are: content planning, content production, content distribution, qualitative market share, quantitative market share and valuation, based on the findings of the qualitative stage, the model was designed, and in the second stage, equations were modeled with a quantitative approach. It has been structurally validated. The analysis of the output of the questionnaires confirms the appropriateness of the mentioned model.

With rapid environmental change, the emergence and complexity of new jobs and tasks, and competition in today's rapidly changing business environment to meet customer needs, the role of education and change in knowledge, skills, abilities and behavior of staff is tangible. Implementation of training and development of human resources causes people to be able to effectively continue their activities in accordance with organizational and environmental changes and increase their efficiency.The present study aimed to identify and explain the components and indicators of training and development of Knowledge-based human resources training and development strategy in the banking industry in the public sector. First, using theoretical literature the components of training and development strategy and knowledge management were extracted extracted, and in the next part, through Delphi method and interviews with 15 human resource management experts in Bank Mellat, using a semi-structured questionnaire , components and indicators of Knowledge-based staffing strategy were determined. After designing the pairwise comparison questionnaire, the opinions of human resources experts were collected and the components and indicators were weighted and ranked. According to the research findings, To successfully implement knowledge management in the training and development strategy in the field of human resources must be in five areas : Determining educational needs, determining the goals of education and human resource development, determining the content of courses, applying the principles and foundations of learning,Implementation and evaluation ;paid attention to a total of 24 indicators in the fields of structure, value and behavior in knowledge management.