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Authored By: Shivendra Pati Tripathi, & Co-Authored By: Prof. Dr. Ashish Chandra, Assistant Professor, ABS, Amity University Lucknow Campus, Uttar Pradesh, India,
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ABSTRACT:
“In an effort to enhance service delivery, eliminate operational inefficiencies, and meet the evolving expectations of customers, the banking sector has increasingly adopted digital advancements, particularly in the realm of artificial intelligence (AI). While AI-powered services such as chatbots, predictive analytics, and automated loan processing have gained popularity, many customers remain unaware of or do not fully comprehend their capabilities. This study seeks to explore the current level of customer awareness regarding AI services in banking and to examine the factors that influence their perceptions and utilization. By collecting primary data and conducting statistical analyses, this article aims to bridge the knowledge gap and provide recommendations for improved customer engagement and informed AI implementation within the banking industry”.
Keywords: Artificial Intelligence, Banking Technology, Customer Awareness, Fintech.
I. INTRODUCTION:
The development of artificial intelligence has the potential to transform many aspects of banking, non-banking, and financial markets. Certain AI technologies were invented almost 50 years ago. Artificial intelligence is used in industries such as asset management, algorithmic trading, credit underwriting, and blockchain-based financial services. Artificial intelligence is expected to disrupt the financial services industry by promoting the development of new goods and services, extending markets and industries, and opening up fresh possibilities of innovation. Other fields where AI technologies have been widely accepted and used include fraud detection, IT operations optimisation, and digital marketing. Banks may benefit from these apps by utilising resources more efficiently, which enhances customer satisfaction. Scheduled commercial banks in India use artificial intelligence for data analytics, fraud detection, and predictive analysis. In addition, banks have used chatbots or virtual assistants to improve customer satisfaction. One of the most significant technological developments in the financial sector is the way artificial intelligence (AI) is changing banking. AI is becoming more and more. Integrated into the contemporary banking experience, from virtual assistants that respond to standard enquiries to algorithms that evaluate creditworthiness in a matter of seconds. Customers’ interactions with their banks are evolving due to a cultural shift as well as a technological one. AI is already being used by Indian banks such as HDFC, ICICI, Axis Bank, and SBI for chat-based customer service, fraud detection, credit scoring, and personalised banking experiences. However, consumer awareness and trust are just as crucial to the success of these innovations as technology deployment. Customers may be reluctant to embrace AI if they are unaware of how it will affect their banking or if they have reservations about data security and the fairness of automated decisions. The purpose of this study is to determine the degree of customer awareness of AI services offered by banks as well as the main factors influencing their comprehension and acceptance.
II. REVIEW OF LITERATURE:
Rana, V., and S. Bharti (2025). To explore the degree of consumer knowledge regarding the use of AI-supported banking services. 123–132 in National Journal of Commerce and Management, 12(2). According to this study, public sector bank customers and older age groups have much lower awareness of AI, even though AI adoption is widespread in Indian banks. It placed a strong emphasis on ethical AI use and customer education.
Bankers’ Survey, FICCI-IBA, 2024 The Framework for the Ethical and Responsible Enablement of Artificial Intelligence (FREE-AI). To increase consumer confidence in AI, the survey recommended increased transparency and consumer education as top priorities. It suggested making disclosures to customers when AI is used in services.
Lpinraj K. & Madasamy, S., 2024. Exploring the relation between the AI technology implementation in banks and work stress among bank employees: A comprehensive analysis.
This study analysed how AI technology reduced the workload of employees in banks and assessed the relationship between AI technology implementation and work stress in private and public banks. Convenience and random sampling techniques were used; the respondents filled out a total of 110 questionnaires. Statistical tools used in this study included Cronbach’s alpha and correlation. The results were analysed using the SPSS tool. The findings of this study indicated that AI technology helped in reducing the stress of employees in banks.
Oyeniyi, L.D., Ugochukwu, E.C., and Mhlongo, N.Z., 2024. Implementing AI in banking customer service: A review of current trends and future applications. To explore the historical advancements of AI within the banking industry, to examine the extent and effectiveness of AI applications in contemporary banking, to investigate the hurdles, including ethical dilemmas, data privacy concerns and regulatory challenges and to forecast emerging trends in AI within the banking sector. The thematic analysis approach was employed in this study, which utilised qualitative research for analysis. The findings of this study indicated that Artificial Intelligence (AI) in the banking sector has significantly enhanced the efficiency and effectiveness of customer service.
Angreani, C., Afifah, N., & Barkah, 2024. The Impact of Artificial Intelligence Banking and Personal Interaction Quality towards Customer Retention with Customer Satisfaction as an Intervening Variable. This study aimed to explore the effect of artificial intelligence banking and personal interaction quality on customer retention and customer satisfaction. The Research design used in this study was quantitative. The time period covered by this research was from October 2023 to December 2023. Purposive sampling was done in this research. This research included 215 respondents, with data collected through questionnaires and interviews. The findings of this study indicate that the quality of personal interaction has decreased.
Effect on customer retention directly.
Muhammad Alshurideha, Barween Al Kurdia, Samer Hamadneha, Khireddine Chatrab, Thouraya Snoussic, Haitham M. Alzoubid, Nidal Alzbounf,b and Gouher Ahmed, 2024. Utilising Artificial Intelligence (AI) in enhancing customer-supplier relationship: An exploratory study in the banking industry. This study focused on two objectives: first, to identify the relationship between the supplier and customer using AI in the banking sector, and second, the role of AI in customers’ interaction, communication, engagement, learning, experience, and feedback. The results showed a positive relationship between the use of artificial intelligence and customer experience, with a direct link between providing personalised customer service, after-sales customer support, and the use of artificial intelligence-based technology.
Sankar, M., Deivasigamani, S., Khan, S.D., Pradeep, S.V., Prakash, O., & Janaki, L. (2023). Artificial Intelligence as a Game Changer Tool to Reshape the Banking Services in Digital Transformation. This study examines how banks interact with customers after using AI tools and helps determine the associated risks, fraud, and compliance with regulatory frameworks. Challenges faced after the implementation of AI. Random sampling technique, 250 sample size, descriptive statistics, and ANOVA. The study concluded that AI is not just a tool but a transformative force in banking services. It empowers financial institutions to operate more efficiently, deliver better customer experiences, and stay competitive in a digital and data-driven world.
Nagarajan, G., Arunadevi, R., Banu, R., Umesh, U., A. Mohideen, S. Lakshmi, M.R., 2023. Artificial Intelligence (AI) in the Banking Industry and Customers’ Perspective. To analyse the client’s perspective on the adoption of artificial intelligence in Asian international locations. Sample size was 799. This Study concludes that AI technologies help enhance client offerings, in addition to the normal increase, by producing greater revenue.
Dariusz Piotrowski, Witold Orzeszko, 2023 Artificial intelligence and customers’ intention to use robot-advisory in banking services. This paper examines the factors influencing bank customers’ adoption of Robo-advisory and their attitudes towards AI in banking. Primary data (CATI – Computer-Assisted Telephonic Interview) were used for data collection. Chi-square was applied to assess variable relationships. A multi-level logit model was employed in the study. The sample size of the study was 911 respondents. Current usage of Artificial Intelligence was minimal but expected to grow in future. Consumer attitudes towards Artificial Intelligence significantly affect robot-advisory acceptance. Investment advisory showed statistical significance in the logit model.
Gupta, M., Dhanawade, S., & More, S. (2020). The authors addressed the need to combine customer education programs with advances in technology like artificial intelligence (AI) to ease resistance and elevate satisfaction.
OBJECTIVE OF THE STUDY
To study the level of Customer Awareness about the use of Artificial Intelligence-supported services in banks.
III. RESEARCH METHODOLOGY:
Using both qualitative and quantitative methods, the study employs a descriptive research design. The study included 109 bank customers in India, from both public and private sector banks. Data were gathered through Google Forms, face-to-face interviews, and telephonic surveys. Specific Districts of Haryana (Panchkula, Ambala, Gurugram) were taken for data collection. Convenience sampling technique were used in the study for data collection. Chi-Square test, Descriptive Statistics, Percentage methods were used for data analysis.
IV. DATA ANALYSIS:
Descriptive Statistics
Descriptive statistics shows a profile of the respondents selected from public and private banks located in Ambala, Panchkula and Gurugram. The researcher collected information like Gender, marital status, occupation, age, education, annual income.
Respondents Profile
Respondents profile located in Ambala, Panchkula and Gurugram
| 1.Marital Status | Frequency | Percentage (%) |
| Married | 32 | 29.4% |
| Unmarried | 77 | 70.6% |
| N= | 109 | 100% |
| 2. Gender | Frequency | Percentage (%) |
| Male | 56 | 51.4% |
| Female | 53 | 48.6% |
| N= | 109 | 100% |
| 3. Age | Frequency | Percentage (%) |
| 18 to 25 years | 45 | 41.3% |
| 26 to 35 years | 46 | 42.2% |
| 36 to 45 years | 16 | 14.7% |
| 46 to 55 years | 2 | 1.8% |
| N= | 109 | 100% |
| 4. Occupation | Frequency | Percentage (%) |
| Business | 33 | 30.3% |
| Profession | 23 | 21.1% |
| Services | 53 | 48.6% |
| N= | 109 | 100% |
| 5. Annual Income | Frequency | Percentage (%) |
| >3 lakhs | 54 | 49.5% |
| 3-6 lakhs | 23 | 21.1% |
| 6-10 lakhs | 16 | 14.7% |
| <10 lakhs | 16 | 14.7% |
| N= | 109 | 100% |
| 6. Place of Residence | Frequency | Percentage (%) |
| Ambala | 35 | 32.1% |
| Panchkula | 35 | 32.1% |
| Gurugram | 39 | 35.8% |
| N= | 109 | 100% |
| 7. Customer Preference | Frequency | Percentage (%) |
| Private sector banks | 60 | 55% |
| Public sector banks | 49 | 45% |
| N= | 109 | 100% |
The sample displays a balanced gender representation, with 51.4% male and 48.6% female respondents. This balance enhances the sample’s representativeness. The majority of participants were in the 18–35 age group (41.3%), suggesting that younger consumers are more engaged with banking services. Most respondents were unmarried (70.6%), which aligns with the younger demographic. In terms of employment, service sector employees made up the most significant portion of the sample (48.6%), followed by business owners (30.3%) and professionals (21.1%). Additionally, 50% of respondents reported an annual income below ₹3 lakhs, indicating that middle-to-lower income groups are well represented. The respondents were fairly distributed across Ambala, Panchkula, and Gurugram.
Chi-Square Analysis of Awareness and Usage of AI-Supported Banking Services (N = 109)
Table 1: Awareness of AI-Supported Services
| AI Service | Yes (Observed) | No (Observed) | Chi-Square | df | p-value | Significance |
| Chatbot | 90 | 19 | 46.248 | 1 | 0.000 | Significant |
| Robo-Advisor | 65 | 44 | 4.046 | 1 | 0.044 | Significant |
| Robotic Process Automation | 62 | 47 | 2.064 | 1 | 0.151 | Not Significant |
| Fraud Detection | 74 | 35 | 13.954 | 1 | 0.000 | Significant |
| Machine Learning Applications | 65 | 44 | 4.046 | 1 | 0.044 | Significant |
There is a high level of awareness regarding Chatbots, with 90 individuals reporting they are aware, compared to 19 who are not. Similarly, awareness levels for other technologies were as follows: 65 are aware of Robo-Advisors, while 44 are not; 62 are aware of Robotic Process Automation, with 47 not aware; 74 individuals are aware of Fraud Detection, compared to 35 who are not; and 65 are aware of Machine Learning, with 44 not aware. When asked about their levels of awareness, consumers rated themselves as “Highly Aware” or “Aware” for both Chatbots and Robo-Advisors. Awareness of Fraud Detection was extreme, with 36 individuals considering themselves “Highly Aware” and 43 rating themselves as “Aware,” making it more recognisable compared to other AI tools. The chi-square results indicate that respondents’ awareness is significantly higher than expected for Chatbots, Robo-advisors, Fraud Detection, and Machine Learning (p < 0.05). However, awareness of Robotic Process Automation is not statistically significant (p > 0.05), suggesting moderate or average awareness among respondents.
Table 2: Level of Awareness Regarding AI Applications in Banking
| Awareness Level
|
Observed Frequency | Chi-Square | df | p-value | Significance | ||
| Highly aware / Aware dominant pattern | – |
|
4 | 0.000 | Significant |
The chi-square values for awareness level are highly significant (p < 0.001), indicating that respondents are not evenly distributed across categories. A larger proportion falls under “Highly aware” and “Aware,” while very few respondents are unaware or extremely unaware. This reflects a strong overall awareness of AI applications in the banking sector.
Table 3: Usage of AI-Supported Banking Services
| AI Service | Yes (Observed) | No (Observed) | Chi-Square | df | p-value | Significance |
| Chatbot | 74 | 35 | 13.954 | 1 | 0.000 | Significant |
| Robo-Advisor | 35 | 74 | 13.954 | 1 | 0.000 | Significant |
| Robotic Process Automation | 54 | 55 | 0.009 | 1 | 0.924 | Not Significant |
| Fraud Detection | 53 | 56 | 0.083 | 1 | 0.774 | Not Significant |
| Machine Learning Applications | 44 | 65 | 4.046 | 1 | 0.044 | Significant |
Chatbots were the most commonly used AI tools, with 74 users. In contrast, Robo-Advisors had very low usage, with only 35 users compared to 74 who did not use them, despite a relatively high level of awareness. Fraud detection systems had moderate adoption, with 53 users, while machine learning-based services had 44 users. The usage pattern shows that Chatbots are widely used (p < 0.05), while Robo-advisors have significantly lower usage. There is no significant difference in usage for Robotic Process Automation and Fraud Detection, indicating that respondents may not directly experience these services. Machine learning-based services show moderate but statistically significant usage differences.
Table 4: Source of Awareness about AI in Banking
| Source | Observed Frequency | Chi-Square | df | p-value | Significance | ||
| Banks | 19 |
|
|||||
| Television | 3 | ||||||
| Newspaper | 3 | ||||||
| Internet | 61 | 133.477 | 5 | 0.000 | Significant | ||
| Family & Friends | 16 | ||||||
| Other Sources | 7 |
The majority of respondents (61) learned about AI in banking through the Internet. Banks (19 respondents) and family and friends (16 respondents) were secondary sources. Traditional media, such as TV and newspapers, had an insignificant impact. The chi-square test is highly significant (p < 0.001), indicating that the distribution of information sources is uneven. The Internet is the dominant source of awareness about AI-supported banking services, far exceeding other sources such as banks, television, newspapers, or personal contacts.
| Test Statistics |
V. SUGGESTIONS:
- Although knowledge is widely accessible, the use of services like machine learning and robot-advisors remains low. To encourage adoption, banks should organise webinars, demonstrations, and digital literacy programs.
- Financial institutions should enhance their engagement on social media platforms, deploy targeted advertisements, and develop interactive campaigns, recognising that the internet is the predominant source of consumer awareness. Numerous individuals may exhibit reluctance in using artificial intelligence tools due to concerns about trust and security. Consequently, it is imperative for banks to communicate their data privacy policies transparently and to demonstrate the safe and fraud-free application of these technologies.
- The demographic profile of the sample predominantly consists of young and unmarried individuals. Banks are well-positioned to offer AI-driven personalised financial planning and savings tools specifically designed for this group.
- Given the limited influence of traditional media such as television and newspapers, banks should establish partnerships with mass media to execute awareness campaigns tailored to older and rural populations.
References:
Angreani, C., Afifah, N., & Barkah. (2024). The impact of artificial intelligence banking and personal interaction quality towards customer retention with customer satisfaction as an intervening variable. Journal of Business and Banking Studies.
Alshurideh, M., Al Kurdi, B., Hamadneha, S., Chatra, K., Snoussi, T., Alzoubid, H. M., Alzboun, N., & Ahmed, G. (2024). Utilising artificial intelligence (AI) in enhancing customer–supplier relationship: An exploratory study in the banking industry. International Journal of Information Management.
FICCI–IBA. (2024). The framework for the ethical and responsible enablement of artificial intelligence (FREE-AI): Bankers’ survey report. Federation of Indian Chambers of Commerce and Industry & Indian Banks’ Association.
Gupta, M., Dhanawade, S., & More, S. (2020). Customer education and artificial intelligence adoption in banking services. International Journal of Banking and Finance.
Lpinraj, K., & Madasamy, S. (2024). Exploring the relation between the AI technology implementation in banks and work stress among bank employees: A comprehensive analysis. Journal of Management Research and Analysis.
Nagarajan, G., Arunadevi, R., Banu, R., Umesh, U., Mohideen, A., Lakshmi, S., & Lakshmi, M. R. (2023). Artificial intelligence (AI) in the banking industry and customers’ perspective. Asian Journal of Economics and Banking.
Oyeniyi, L. D., Ugochukwu, E. C., & Mhlongo, N. Z. (2024). Implementing AI in banking customer service: A review of current trends and future applications. Journal of Financial Services Technology, xx(x).
Piotrowski, D., & Orzeszko, W. (2023). Artificial intelligence and customers’ intention to use robo-advisory in banking services. Journal of Financial Innovation.
Rana, V., & Bharti, S. (2025). To explore the degree of consumer knowledge regarding the use of AI-supported banking services. National Journal of Commerce and Management, 12(2), 123–132.
Sankar, M., Deivasigamani, S., Khan, S. D., Pradeep, S. V., Prakash, O., & Janaki, L. (2023). Artificial intelligence as a game changer tool to reshape the banking services in digital transformation. International Journal of Digital Banking.
| S. No. | Variables | χ² Value | df | Sig. (p-value) | Result |
| 1 | Awareness of AI-supported Chatbot services | 46.248 | 1 | 0.000 | Significant |
| 2 | Awareness of AI-supported Robo-Advisor services | 4.046 | 1 | 0.044 | Significant |
| 3 | Awareness of Robotic Process Automation in banking | 2.064 | 1 | 0.151 | Not Significant |
| 4 | Awareness of AI-based Fraud Detection systems | 13.954 | 1 | 0.000 | Significant |
| 5 | Awareness of Machine Learning-based AI services | 4.046 | 1 | 0.044 | Significant |
| 6 | Awareness level regarding AI applications in banking (Statement 1) | 73.615 | 4 | 0.000 | Significant |
| 7 | Awareness level regarding AI applications in banking (Statement 2) | 71.321 | 4 | 0.000 | Significant |
| 8 | Awareness level regarding AI applications in banking (Statement 3) | 78.936 | 4 | 0.000 | Significant |
| 9 | Awareness level regarding AI applications in banking (Statement 4) | 60.679 | 4 | 0.000 | Significant |
| 10 | Awareness level regarding AI applications in banking (Statement 5) | 44.807 | 4 | 0.000 | Significant |
| 11 | Usage of AI-supported Chatbot services | 13.954 | 1 | 0.000 | Significant |
| 12 | Usage of AI-supported Robo-Advisor services | 13.954 | 1 | 0.000 | Significant |
| 13 | Usage of Robotic Process Automation services | 0.009 | 1 | 0.924 | Not Significant |
| 14 | Usage of AI-based Fraud Detection services | 0.083 | 1 | 0.774 | Not Significant |
| 15 | Usage of Machine Learning-based AI services | 4.046 | 1 | 0.044 | Significant |
| 16 | Source of awareness about AI-supported banking services | 133.477 | 5 | 0.000 | Significant |
Overall, respondents demonstrate high awareness of visible and customer-facing AI services such as chatbots and fraud detection systems, while awareness of backend technologies like robotic process automation remains comparatively lower. The findings indicate that respondents are predominantly “aware” or “highly aware” of AI applications in the banking sector. Very few respondents reported being unaware or extremely unaware, confirming a generally positive awareness environment toward AI in banking. While chatbots are widely used, advanced AI services such as robo-advisors and machine-learning-based tools show lower user engagement, likely due to limited accessibility or lack of user understanding. The Internet is the dominant source of information about AI-supported banking services, highlighting the importance of digital platforms in spreading awareness. Traditional media play a minimal role in informing customers about AI innovations in banking.
Cite this article as:
Himanshi Thakur & Dipi Talwar, “Extent of Customer Awareness regarding the Utilisation of Artificial Intelligence-assisted Services in Banking”, Vol.6 & Issue 3, Law Audience Journal (e-ISSN: 2581-6705), Pages 410 to 423 (6th April 2026), available at https://www.lawaudience.com/extent-of-customer-awareness-regarding-the-utilisation-of-artificial-intelligence-assisted-services-in-banking/.
