INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Demographic Profile and Adoption of Artificial Intelligence Enabled  
Banking Services: An Empirical Study of Bank Customers in  
Coimbatore District  
Dr. R. Prasanth  
Researcher, Bharathiar University, Coimbatore  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000011  
Received: 12 December 2025; Accepted: 19 December 2025; Published: 26 December 2025  
ABSTRACT  
The banking industry's quick adoption of artificial intelligence (AI) has drastically changed consumer  
interaction, service delivery, and operational effectiveness. Evaluating the uptake and acceptance of AI-enabled  
banking services requires an understanding of consumer demographics. This study looks at how certain bank  
customers in the Coimbatore district use AI banking based on personal criteria. A structured questionnaire was  
used to gather primary data from 385 respondents, and descriptive statistical methods were used for analysis.  
The results show that the majority of users of AI-based banking services are young, educated, and salaried  
consumers, especially those who work in the private sector. consumers with moderate income levels have  
considerable engagement with AI technologies, whereas female consumers exhibit slightly higher adoption  
levels than male customers. The study offers insights for banks to improve technology-driven financial inclusion  
and customer-centric initiatives while highlighting the increasing adoption of AI banking across various  
demographic categories.  
Keywords: Artificial Intelligence, Banking Services, Customer Demographics, Digital Banking, Coimbatore  
District  
INTRODUCTION  
In the contemporary banking sector, artificial intelligence has become a disruptive force that allows  
organizations to provide individualized services, automate repetitive tasks, and improve decision-making.  
Chatbots, robo-advisors, fraud detection systems, and predictive analytics are examples of AI-driven financial  
services that have enhanced client satisfaction and operational effectiveness. However, consumer acceptability  
and usage patterns play a major role in the effective adoption of AI banking. Customers' attitudes regarding the  
adoption of technology are greatly influenced by demographic factors like age, gender, income, education, and  
occupation. Regional studies are crucial to comprehending how various client segments react to AI-enabled  
financial services in a diverse nation like India. As a developing industrial and educational center in Tamil Nadu,  
Coimbatore District offers a perfect environment for researching client acceptance of AI banking. In order to  
determine who utilizes AI banking services and to what degree, this study focuses on examining the demographic  
profile of AI banking clients in particular banks in the Coimbatore District.  
Statement of the Problem  
Customer adoption of AI-driven banking solutions is still unequal across demographic groups, despite significant  
investments in these technologies. AI-enabled services are easily embraced by younger, more educated  
consumers, but elderly users and other income groups may have issues with trust, usability, and awareness.  
Banks frequently use cutting-edge technology without fully comprehending the demographics and interests of  
their clientele.  
There is little empirical data on how individual factors affect the uptake of AI banking services in the Coimbatore  
district, where banking clients come from a variety of socioeconomic backgrounds. Banks face difficulties in  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
optimizing AI implementation, enhancing client engagement, and guaranteeing fair access to digital financial  
services due to the lack of localized, data-driven insights. Therefore, to close this knowledge gap, a methodical  
demographic analysis is needed.  
Research Gap  
The majority of research on artificial intelligence in banking, according to a review of the literature, focuses on  
technological efficiency, improving service quality, customer satisfaction, and security-related issues. These  
studies offer important insights into the technological and operational advantages of adopting AI, but they mainly  
ignore how customer-specific traits affect the use of AI-enabled financial services. Additionally, empirical  
research on the demographic characteristics of AI banking users is conspicuously lacking, particularly at the  
district or regional level. The majority of current research takes a macro-orientational approach, which restricts  
our ability to comprehend localized adoption trends influenced by customer disparities in age, gender, education,  
and occupation.  
Furthermore, relatively few primary data-based research has examined the duration of AI banking usage in  
relation to consumers' socioeconomic characteristics. In order to fill these gaps, the current study provides a  
thorough demographic analysis of Coimbatore District's AI banking clients using primary data, adding region-  
specific insights and expanding the body of knowledge already available on AI adoption in the banking industry.  
Objectives of the Study  
1. To examine the age distribution of AI banking clients in particular institutions.  
2. To investigate the marital status and gender of clients utilizing AI financial services.  
3. To investigate AI banking users' educational and professional backgrounds.  
4. To determine the income levels of clients using AI banking.  
5. To determine how long bank clients have been using AI technologies  
Hypotheses of the Study  
The following hypotheses are framed for the study:  
H₁: There is a significant association between age and usage of AI banking services.  
H₂: Gender significantly influences the adoption of AI banking.  
H₃: Educational qualification has a significant impact on AI banking usage.  
H₄: Occupation and income level significantly affect the adoption of AI-based banking services.  
H₅: The duration of AI usage varies significantly among different demographic groups.  
Analysis and Interpretation  
Demographic Profile of The Selected Bank Customers in Coimbatore District  
Personal Factors  
Classification  
No. of Respondents  
Percentage  
Cumulative  
Percentage  
Up to 25 years  
186  
48.31  
48.31  
Age  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
26- 35 Years  
36-45 Years  
46-55 Years  
122  
48  
24  
5
31.69  
12.47  
6.23  
80.00  
92.47  
98.70  
100.00  
Above 56  
Years  
1.30  
Total  
Male  
385  
177  
208  
385  
185  
200  
385  
13  
100.0  
45.97  
54.03  
100.0  
43.6  
45.97  
Gender  
Female  
Total  
100.00  
Married  
Unmarried  
Total  
48.05  
Marital Status  
47.6  
100.00  
100.0  
3.38  
School Level  
UG  
3.38  
22.08  
69.91  
100.00  
Education Qualification  
72  
18.70  
47.53  
30.39  
100.0  
36.88  
PG  
183  
117  
385  
142  
Others  
Total  
Govt.  
Employee  
36.88  
75.58  
Occupation  
Private  
149  
38.70  
Employee  
Others  
94  
24.42  
100.0  
47.8  
33.8  
12.5  
5.2  
100.00  
Total  
385  
Below 20,000  
20,001-40,000  
40,001-80,000  
80,001-1,00,000  
Above 1,00,000  
Total  
184  
47.8  
81.6  
94.0  
99.2  
100.0  
Monthly Earning  
130  
48  
20  
3
0.8  
385  
100.0  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Less than 3 Years  
3 to 5 Years  
Above 5 Years  
Total  
138  
142  
105  
385  
35.84  
36.88  
27.27  
100.0  
35.84  
Using AI Technology in  
Your Bank  
72.73  
100.00  
Sources: Calculated & Computed from Primary Data  
The demographic profile of the personal characteristics of the AI Banking clients in the Coimbatore district is  
shown in the above table. It is evident from the age-wise classification that 48.31 percent (186 respondents) of  
bank customers are under 25 years old. Of the bank customers, 31.69 percent (122 respondents) are between the  
ages of 26 and 35, 12.47 percent (48 respondents) are between the ages of 36 and 45, 6.23 percent (24  
respondents) are between the ages of 46 and 55, and 1.30 percent (5 respondents) are between the ages of 56.  
Therefore, it can have concluded that the most of respondents are up to 25 years and over, indicating that many  
middle-aged consumers took part in the study.  
Age  
Up to 25 years  
36-45 Years  
26- 35 Years  
46-55 Years  
Above 56 Years  
Source: Calculated and Computed from the Primary Data  
When it comes to gender, 45.97 percent of bank customers were male (177 respondents), while 54.03 percent  
were female (208 respondents). When compared to male customers, it suggests that the majority of Coimbatore  
district female clients use AI banking.  
Upon classifying bank customers according to their marital status, it was found that 43.6 percent (185  
respondents) of them are married, while 47.6 percent (200 respondents) are single.  
Gender & Marital Status  
Male  
Female  
Married  
Unmarried  
Source: Calculated and Computed from the Primary Data  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
When it comes to educational qualifications, the majority of customers fall into one of the following categories:  
30.39 percent (117 respondents) are from other categories, followed by postgraduates (47.53 percent; 183  
respondents), undergraduates (18.70 percent; 72 respondents), and school-level customers (3.38 percent; 13  
respondents). Here, 70 percent of the clients are well-educated.  
The aforementioned table provides proof that the vast majority of bank clients are private-sector workers. Here,  
24.42 percent (94 respondents) belong to another occupation type, 38.70 percent (149 respondents) are private  
employees, and 36.88 percent (142 respondents) are government employees. Therefore, the table suggests that  
bank clients of private employees deal in the AI banking industry.  
Education Qualification &  
Occupation  
School Level  
Others  
UG  
PG  
Govt. Employee  
Private Employee  
Others  
Source: Calculated and Computed from the Primary Data  
According to the bank customers' monthly income classification, 48.31 percent (186 respondents) earn less than  
Rs. 20,000, 31.69 percent (122 respondents) earn between Rs.20,000 and Rs. 40,000,12.47 percent (48  
respondents) earn between Rs. 40,000 and Rs.80,000, 6.23 percent (24 respondents) earn between Rs. 80,000  
and Rs.1,00,000, and 1.30 percent (5 respondents) earn more than Rs. 1,00,000.  
Monthly Earning  
Below 20,000  
40,001-80,000  
Above 1,00,000  
20,001-40,000  
80,001-1,00,000  
Source: Calculated and Computed from the Primary Data  
Of the bank respondents 35.84% (138 individuals) reported using AI technology in banking for less than three  
years, 36.88% (142) have used it for three to five years, and 27.27% (105) have been using it for more than five  
years of AI technology using through banking.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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Using AI Technology  
Less than 3 Years 3 to 5 Years Above 5 Years  
Source: Calculated and Computed from the Primary Data  
CONCLUSION  
The demographic research of Coimbatore District's AI-enabled banking clients shows that younger users  
especially those under 35 are mostly driving adoption. Younger customers' greater digital literacy and receptivity  
to technological advancements are reflected in this area. Customers who are female exhibit a somewhat higher  
level of participation than those who are male, indicating a rise in women's financial inclusion and digital  
engagement. These results suggest a progressive change in the adoption of modern financial technologies toward  
more equitable female representation. Adoption of AI banking appears to be significantly influenced by  
educational attainment. With postgraduate and undergraduate degrees making up the largest sectors, the majority  
of users are well educated. This emphasizes how important education is to comprehending, embracing, and  
successfully using AI-driven banking services. Customers with better education are more likely to recognize the  
value, dependability, and convenience of use of AI-based systems, which will hasten their adoption.  
Private sector workers make up the majority of AI banking users in terms of occupation and income, closely  
followed by government workers. This suggests that salaried people rely more and more on AI-enabled banking  
services for time-saving, efficiency, and convenience. Customers from lower- and middle-class groups,  
especially those making less than ₹40,000 a month, make up a sizable portion of the user population, indicating  
that AI banking services are not exclusive to high-income clients.  
Overall, the results show that the adoption of AI-enabled financial services in Coimbatore District is greatly  
influenced by demographic parameters, including age, gender, education, occupation, income, and length of  
usage. Growing familiarity, confidence, and dependence on AI-driven financial services are shown in the  
sizeable percentage of clients who have been using AI banking for more than three years. Banks may use these  
findings to create inclusive, user-friendly AI apps, improve user education, and create focused awareness  
campaigns. Future studies could expand on this analysis by adding behavioral factors, customer satisfaction, and  
how AI banking affects long-term consumer loyalty and financial decision-making.  
REFERENCE:  
1. Moharrak, M., & Mogaji, E. (2025). Generative AI in banking: empirical insights on integration,  
challenges and opportunities in a regulated industry. International Journal of Bank Marketing, 43(4),  
871-896.  
2. Emmanuel Baffour Gyau et al (2024). Transforming banking: Examining the role of AI technology  
innovation in boosting banks financial performance, “International Review of Financial Analysis 96”,  
103700, pp.1-13.  
3. Anuradha Dwivedi & Khyati Kochhar (2023). Employee’s Attitude towards Artificial Intelligence in The  
Indian Banking Sector, “International Journal of Professional Business Review”, Miami, volume 8, issue  
11, pp. 01-13.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
4. Assunta Di Vaio, Rohail Hassan & Claude Alavoine (2022). Data intelligence and analytics: A  
bibliometric analysis of human–Artificial intelligence in public sector decision-making effectiveness,  
“Technological Forecasting & Social Change 174”, 121201, pp.1-17.  
5. Chandrima Bhattacharya & Dr. Manish Sinha, (2022). The Role of Artificial Intelligence in Banking for  
Leveraging Customer Experience, “AABFJ”, Volume 16, Issue 5, pp.89-105.  
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