In the era of rapid technological advancement, Artificial Intelligence (AI) stands as a pivotal force reshaping various industries, including banking. As digital transformation continues to revolutionize the financial landscape, the integration of AI technologies into digital banking platforms has become instrumental in delivering personalized services to customers. [1]  This research paper delves into the profound impact of AI on personalization within digital banking, emphasizing its role in enhancing customer experience and promoting financial inclusion. The convergence of AI and digital banking has ushered in an era where financial institutions can harness vast amounts of data to gain deep insights into customer behavior, preferences, and needs. [7] Through sophisticated algorithms and machine learning techniques, AI enables banks to analyze this data in real-time, allowing for the customization of services tailored to individual customers. From personalized product recommendations to customized financial advice, AI-powered personalization has redefined the way banks engage with their customers, fostering stronger relationships and loyalty. [8]

One of the key drivers behind the adoption of AI in digital banking is its potential to enhance customer experience. By leveraging AI-driven personalization, banks can offer seamless and intuitive digital experiences that anticipate and fulfill the unique needs of each customer. Whether it's providing proactive assistance, streamlining account management processes, or delivering targeted marketing campaigns, AI empowers banks to deliver highly relevant and timely interactions across various touch points, thereby elevating the overall customer experience. The integration of AI into digital banking has emerged as a transformative force, revolutionizing how banks interact with customers and driving progress towards greater financial inclusion. By harnessing the power of AI-driven personalization, banks can deliver tailored experiences that not only enhance customer satisfaction but also empower individuals to achieve their financial goals. However, realizing the full potential of AI in digital banking requires a balanced approach that prioritizes both innovation and responsible use to ensure equitable access to banking services for all members of society. [11]

Lazo and Ebardo (2023) In their study, delve into the prospective impact of AI on the banking sector, emphasizing its pivotal role in refining and augmenting financial operations for individuals and enterprises alike. They underscore the emergence of a transformative era in banking, propelled by the adoption of sophisticated self-learning AI frameworks. These advancements herald a paradigm shift, wherein AI and machine learning technologies become indispensable drivers shaping the course of financial services. [6]Chen and Huang (2021), the authors examine the mechanisms through which AI enhances customer experience in digital banking. They identify factors such as convenience, customization, and responsiveness as key drivers of customer satisfaction, facilitated by AI-powered personalization. The research underscores the importance of seamless user experiences in driving customer loyalty and advocacy. [5]Rahman et al. (2020) Several studies have examined the potential of AI to promote financial inclusion by addressing the needs of underserved populations. For instance, a study by explores how AI-powered credit scoring models can expand access to credit for individuals with limited credit history or from marginalized communities. The research highlights the role of alternative data sources and machine learning algorithms in assessing creditworthiness and mitigating bias.Zhang et al. (2020) investigates the impact of AI-driven personalization on customer satisfaction and loyalty in digital banking. The study finds that personalized interactions, facilitated by AI technologies, significantly improve customer engagement and retention rates, ultimately leading to greater profitability for banks. [2]Li et al. (2019) explores the role of AI in personalization within digital banking, emphasizing its ability to analyze vast amounts of customer data and deliver tailored services in real-time. The authors highlight how AI algorithms can enhance customer experience by providing personalized product recommendations, optimizing user interfaces, and offering proactive assistance. [4] Kumar et al. (2019) investigates the use of AI-driven robo-advisors to provide personalized financial guidance and investment recommendations to underserved individuals. The study underscores the importance of user-friendly interfaces and tailored recommendations in empowering individuals to make informed financial decisions and improve their financial well-being. [3] [9]

Research Gap While prior studies show how AI enhances personalization, satisfaction, and financial inclusion in digital banking, gaps remain. Little empirical work explores how cultural, regulatory, and infrastructural contexts especially in India shape adoption and trust in AI-driven solutions. Limited attention has been given to balancing automation with human touch, measuring inclusivity beyond credit access, and addressing long-term impacts on loyalty, fairness, and financial literacy. Moreover, ethical and socio-economic concerns such as bias, privacy, and the digital divide are underexplored. This study addresses these gaps by examining both the benefits and challenges of AI in India’s digital banking sector.

Objectives and Hypotheses

• To examine how cultural, regulatory, and infrastructural contexts in India influence the adoption and trust in AI-driven digital banking solutions.
• To evaluate the benefits and challenges of AI in enhancing customer experience and financial inclusion, while addressing ethical and socio-economic concerns.

Conceptual Framework Chart

Construct Labels for Research Model

The hypothesis model highlights trust in AI banking as the central mediator connecting contextual, ethical, and socio-economic factors with adoption and financial inclusion. Infrastructural access, regulatory clarity, social influence, transparency, data security, fairness, and affordability collectively shape trust. Strong trust enhances the intention to adopt AI services and directly improves financial inclusion, making trust the pivotal link between enabling conditions and long-term digital banking outcomes.

Reliability

The reliability test (Cronbach’s Alpha) for the 13 items gave a value of ~0.00, far below the acceptable level (α ≥ 0.70). This indicates that the items lack internal consistency and are too heterogeneous, covering different dimensions (e.g., income, infrastructure, trust). Therefore, they should not be treated as a single scale.

Factor Analysis

Component Matrix

Factor Analysis Interpretation

The data were eligible for factor analysis as the KMO = 0.895 and Bartlett’s Test (χ²= 1270.238, p < 0.001). PCA with Varimax rotation revealed 2 factors, which explained 68.017% of the variance.

Factor 1 was composed of the items trust, security, fairness, usability, transparency and adoption intention. It is called “Trust and Adoption of AI-Driven Banking.”

The largest loading of Monthly Income was identified Factor 2 and termed “Economic Capability”.

These findings suggest that AI-based banking is embraced when people trust it and income continues to drive access and adoption, which has a direct connection to the twin objectives of customer experience enhancement and financial inclusion.

Findings

The findings show that two key elements influence AI-based digital banking adoption in India: trust in AI systems and economic capability. Trust in fairness, security, and transparency increases willingness to use AI banking, while income levels affect access and affordability. This supports the study’s aim that regulatory support, social influence, and affordability drive confidence and adoption of AI in banking, helping advance financial inclusion.

This research reveals that trust in AI based systems and economic feasibility are major determinants for AI-driven digital banking adoption in India. Seen from another perspective, the perceived fairness, security, transparency and reliability improve the intention for AI banking adoption among user while the divide between digital infrastructure and affordability still limit access for the low-income groups. To build adoption as well as financial inclusion, banks need to promote transparency through explaining AI-made decisions in a clear manner, provide robust data protection and fairness in automated results. Digital access will be improved by providing cost-effective internet, easy-to-use platforms, and individualized attendance. Regulators such as the RBI have to give clear guidelines but awareness programmers and digital literacy are imperative to develop confidence in the system and safer use. With these steps, AI-powered banking will be able to enable India’s journey into a digital financial world that is more customer centric and all inclusive.

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