نوع مقاله : مقاله پژوهشی (کمی)
نویسندگان
گروه مدیریت بازرگانی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران
کلیدواژهها
عنوان مقاله English
نویسندگان English
Abstract
The purpose of this research is to examine the presentation of an artificial intelligence (AI) model at Rafidain Bank to ensure the enhancement of marketing capabilities. This study is developmental in terms of objectives, survey-based in data collection, exploratory in nature, and quantitative in execution. The statistical population consists of all employees of Rafidain Bank in Iraq, from which 450 individuals were selected using Cochran’s formula and stratified random sampling. The data collection instrument is a questionnaire. SPSS and SmartPLS software were used for data analysis. The results indicated that AI has a significant effect on the bank’s effective performance. AI capability affects AI adaptability. AI capability affects the bank’s effective performance. The experience of using AI has no effect on AI adaptability. The experience of using AI affects AI capability. The experience of using AI affects the bank’s foresight. The experience of using AI affects the bank’s effective performance. AI knowledge affects AI adaptability. AI knowledge affects the bank’s foresight. AI sustainability affects AI adaptability. AI sustainability affects the bank’s foresight. AI sustainability affects the bank’s effective performance. The bank’s foresight has no effect on AI adaptability. The bank’s foresight affects AI capability. The bank’s foresight affects the bank’s effective performance.
Introduction
The banking sector is undergoing a fundamental transformation, driven by the integration of AI into financial service processes. AI enhances operational efficiency, risk management, and customer engagement (Deloitte, 2022). As financial institutions worldwide strive to remain competitive in a digital-centric economy, AI technologies—particularly machine learning and natural language processing—have emerged as vital tools for processing massive datasets, predicting customer behavior, and automating complex processes. Simultaneously, marketing activities in banking have evolved from traditional advertising to sophisticated, data-driven strategies that leverage AI to deliver personalized customer experiences (Chaffey & Smith, 2022).
Applications such as fraud detection, credit scoring, and chatbot-based customer service rely on machine learning algorithms to analyze historical data and make real-time decisions (Fethi & Pasiouras, 2010). Researchers further note that natural language processing has enhanced conversational AI, enabling more natural customer interactions (Hirschberg & Manning, 2015). While predictive analytics have refined risk assessment models, these advancements have positioned AI as a strategic asset for banks seeking to navigate competitive and regulatory challenges (Noreen et al., 2023).
Marketing activities in banking have been equally transformed by AI’s ability to analyze consumer behavior and deliver targeted campaigns. Deloitte’s report on AI in the banking sector indicates that banks utilizing AI-driven marketing strategies achieve a 30% increase in customer retention and a 25% improvement in campaign conversion rates (Deloitte, 2022). In line with this objective, the main question of the present study is: How does the artificial intelligence model at Rafidain Bank ensure the enhancement of marketing capabilities?
Theoretical Framework
The Evolving Landscape of Banking and Finance
In 2025, banks are increasingly leveraging artificial intelligence (AI) and generative AI to enhance productivity and customer personalization, with over 75% of them planning to invest in data management and cloud infrastructure. This trend is evident in Asia, where digital banking and fintech have increased resilience following crises and strengthened financial inclusion through mobile payments and central bank digital currencies. In Europe, asset tokenization on the blockchain is on the rise, promising faster settlement and reduced costs, supported by initiatives such as the UK’s Digital Securities Sandbox. Meanwhile, the African banking sector is leading in mobile banking adoption, accounting for nearly half of global mobile accounts, which bolsters inclusion and robust brand growth, contributing an average of 22% to brand value. Globally, revenue streams are shifting; non-interest income from payments and wealth management is expected to rise, while net interest margins face pressure from declining rates (Deloitte, 2024).
AI Application in the Banking Industry
AI in digital marketing for banks not only increases customer acquisition and retention but also improves risk management, fraud detection, and overall financial performance. In banking, AI-driven segmentation utilizes machine learning to categorize customers based on behavior, transaction history, and demographic information. This enables targeted marketing, such as promoting credit cards to high-spending segments or savings programs to conservative savers (Sardjono & Perdana, 2023).
Ahmadi Alinoudehi et al. (2026) examined the dimensions and components of AI-based digital transformation management. The results indicated that AI-based digital transformation management is designed within four overarching categories: “contextual requirements,” “digital infrastructure,” “digital transformation management process,” and “organizational capital,” encompassing 12 organizing categories: “ethical requirements,” “cultural requirements,” “organizational requirements,” “hard digital infrastructure,” “soft digital infrastructure,” “digital transformation management process,” “digital transformation planning,” “prototyping,” “learning,” “human capital,” “process capital,” “structural capital,” “social capital,” and 73 basic categories.
Deshmukh (2025) investigated the impact of AI on customer relationship management practices in retail banking, focusing on data-driven personalization to enhance customer engagement. Using a mixed-methods approach, including surveys and interviews with banking professionals, this study explores how AI technologies, such as machine learning and predictive analytics, enable banks to analyze customer data, improve segmentation, and tailor marketing strategies.
Research Methodology
This study is developmental in terms of its objectives, survey-based in its data collection method, exploratory in nature, and quantitative in execution. The statistical population consists of all employees of Rafidain Bank in Iraq, from which 450 individuals were selected using Cochran’s formula and stratified random sampling. The data collection instrument used is a questionnaire.
Research Findings
Data analysis was performed using SPSS and SmartPLS software. The results indicated that artificial intelligence (AI) has a significant effect on the bank’s effective performance. AI capability affects AI adaptability. AI capability affects the bank’s effective performance. The experience of using AI has no effect on AI adaptability. The experience of using AI affects AI capability. The experience of using AI affects the bank’s foresight. The experience of using AI affects the bank’s effective performance. AI knowledge affects AI adaptability. AI knowledge affects the bank’s foresight. AI sustainability affects AI adaptability. AI sustainability affects the bank’s foresight. AI sustainability affects the bank’s effective performance. The bank’s foresight has no effect on AI adaptability. The bank’s foresight affects AI capability. The bank’s foresight affects the bank’s effective performance.
Conclusion
This study was conducted with the aim of examining the presentation of an artificial intelligence (AI) model in Rafidain Bank to ensure the strengthening of marketing capabilities. The findings of this research are consistent with those of Baffour Gyau et al. (2024), Wang et al. (2023), Eskandarany (2024), Alotaibi (2024), Byambaa et al. (2025), Svoboda (2024), Lin et al. (2024), McKinsey (2020), Abdulsalam and Tajudeen (2024), Deloitte (2024), and Khan et al. (2024). In particular, the findings of Baffour Gyau et al. (2024) on the dynamic relationship between AI technological innovation in banking and finance and banks’ financial performance across 20 countries show that AI technological innovation in banking and finance positively affects banks’ return on assets, highlighting its role in improving financial performance. The interaction term between AI innovation and economic growth also emphasizes their combined positive effect on financial performance. Mediation analysis further highlights the role of information and communication technology development in transforming AI innovation into improved financial outcomes.
Based on the research findings, the following recommendation is proposed:
· Comprehensive AI literacy training for bank employees: The findings showed that the bank should shift its focus from training employees in simple tools toward comprehensive AI literacy, enabling its workforce to move from passive users to active participants in the AI ecosystem.
کلیدواژهها English
