Introduction
The banking industry has not changed much since its inception, but in the last decade, the emergence of new technologies, especially artificial intelligence, has brought about profound changes in this field. The rapid development of these technologies has presented bankers with a challenging choice: should they remain faithful to their traditional methods or should they align themselves with the new wave of technology? This dilemma was initially ambiguous for many and met with much resistance. But as time passed and the significant benefits of this technology became clear, today it is rare to find a bank that is not seriously considering implementing artificial intelligence in its processes. Artificial intelligence will soon become one of the banking processes and will create evolution and innovation. Artificial intelligence will help banks to use human resources and computers in interaction with each other and optimally, in order to reduce costs and increase efficiency and productivity. All these issues show that a bright future and a broad perspective await the banking industry to achieve and realize it with the help of artificial intelligence. Therefore, designing a model for improving marketing performance in the banking industry with a focus on artificial intelligence is very important, which will be examined in this research. Therefore the aim of this study is to present a model for improving marketing performance in the banking industry with a focus on artificial intelligence.
Methodology
This research is qualitative in terms of methodology and applied in terms of purpose and is classified as descriptive-analytical research. The statistical population of this research includes 18 academic elites in the fields of commerce and banking sciences and related fields, of whom 11 were selected as samples using snowball sampling and theoretical saturation. The instrument used in this research is a semi-structured interview that is designed based on theoretical foundations. Data analysis was also conducted using grounded theory.
Results and Discussion
The descriptive statistics of the interviewees are as follows: out of the 11 experts, specialists, and professors in the relevant field who were interviewed, 73 percent were male and 27 percent were female. In terms of work experience, the statistics are as follows: there was no work experience between 5 and 10 years, 56 percent of the interviewees had between 11 and 20 years of work experience, and 45 percent of them had more than 20 years of work experience. In terms of educational status, 91 percent had a PhD and 9 percent had a master's degree. The findings of the study were explained in the form of six main categories and subcategories. The results showed that the causal factors include “ethical development in the modern banking industry”, “service provision through chatbots and robots”, “customer satisfaction with modern banking”, and “the need for information security in modern banking”. The strategic factors include “creating relationship marketing with customers as a digital roadmap”, “developing an online financial user platform for customers”, “automation based on banking rules”, and “developing innovative capabilities in the banking field”. The contextual factors include “ensuring the security of customers’ financial information to gain their initial trust”, “utilizing blockchain technology to create secure and transparent banking systems”, “improving the customer experience of banking services”, and “identifying individuals’ credit scores with the help of artificial intelligence”. The core factors include “customer risk analysis”, “customer data analysis and pattern recognition” which help banks predict customer behavior and provide personalized services and products. The intervening factors include “banking regulations and ethical rules in the use of AI”, “identification and prevention of cyber attacks” and “development of effective strategies for more effective investment and management of banks’ investment portfolios with the help of AI”. The outcomes include “automation of various banking services”, “application of marketing methods tailored to customer demand”, “increase in bank profitability” and “increase in customer satisfaction” and also help in assessing risks, identifying and preventing fraud and combating money laundering.
Conclusion
Based on the results of this study, it was suggested that in order to improve the performance of these measures, it is necessary to form specialized teams in banks with the cooperation of data and artificial intelligence experts. These teams should continuously evaluate and update their models and strategies according to the analysis of customer demand and needs. This approach can not only lead to increased efficiency of banking services but can also improve customer experience. Finally, the use of artificial intelligence in banking marketing processes can act as a key tool for creating healthier competition, improving service quality, increasing customer satisfaction and ultimately increasing the profitability of banks.