Transforming inclusion: role of Fintech in reshaping financial accessibility in emerging Indian economy

Financial inclusion is crucial for economic growth, particularly in developing countries like India, as it provides access to improved financial services, including savings, credit, insurance and payments, thereby empowering low-income households, stimulating entrepreneurship and reducing inequality (Demirguç-Kunt et al., 2020). Fintech, or financial technology, harnesses advanced innovations, including mobile apps, peer-to-peer lending, digital payments, robo-advisors and cryptocurrency platforms, to enhance this inclusion by making services more efficient, affordable and accessible (Oliveira et al., 2016; Thakor, 2020; Singh et al., 2024; Ozili, 2024). This rapidly evolving sector is expanding globally, with transaction volumes exceeding $10tn in 2024, driven by practices emphasising security, regulatory compliance and seamless user experiences (Ryu, 2018).

Fintech services use data analytics, artificial intelligence (AI) and blockchain to create user-centric products. These products prioritise accessibility and efficiency, making financial management intuitive for broader audiences and reducing reliance on traditional banking (Hu et al., 2019). For instance, in India, over 100 billion UPI transactions are processed each year. It cuts costs by up to 90% and extends reach through 1.2 billion mobile connections (Reserve Bank of India Report, 2018). Despite these benefits, fintech adoption remains uneven in developing economies, constrained by challenges like digital divides, cybersecurity concerns, technological resistance and low financial literacy. To dissect these, this study uses the Technology Acceptance Model (TAM) framework, which offers a robust lens for understanding technology adoption through core constructs, Perceived Usefulness (PU), Perceived Ease of Use (PEU), Social Influence (SI) and our novel external factor, Financial Literacy (FL) (Davis, 1989; Venkatesh and Davis, 2000). Addressing these will unlock fintech potential to bridge financial access gaps and foster inclusive growth.

This study positions FL as a pivotal cognitive variable that directly influences adoption intentions while mediating users’ perceptions of fintech-related risks, trust and decision-making. Although abundant literature exists on fintech adoption, most studies target urban, digitally literate consumers or general behaviours in emerging economies, relying solely on PU, PEU and SI without tackling cognitive-contextual barriers in underserved communities (Zhou et al., 2022; Singh and Tewari, 2021). We extend TAM by integrating FL, providing a context-specific model for low-income groups where it shapes risk assessment and platform reliance.

India exemplifies the need, despite a sizable underbanked/unbanked population and 89% adult bank account ownership, around 16% of accounts were inactive in 2024, leaving over 190 million unserved (Maity and Sahu, 2022). Fintech can capitalise on high mobile penetration to deliver affordable services, with digital platforms offering secure transactions that empower vulnerable economies (Dakduk et al., 2020). By incorporating FL, this research uniquely captures India’s socio-cultural dynamics, collectivism amplifying SI, linguistic diversity demanding tailored designs and influencing adoption.

Using Partial Least Squares Structural Equation Modelling (PLS-SEM) on data from 550 low-income respondents in Northern India, we empirically validate paths and identify barriers such as digital exclusion and cyber fears. The findings call for decisive action; FL programs via gamified apps, socially influenced outreach and user-centred innovations must be advanced to boost adoption among the underserved. This multidimensional model advances TAM theory within developing country contexts while establishing fintech as an indispensable catalyst for India’s inclusive and equitable digital financial future.

In light of this discussion, the current study tries to address the following question:

Fintech is rapidly growing worldwide, using technology to optimise financial services (Thakor, 2019). This field enhances security, compliance, efficiency and user experience (Ryu, 2018; Hu et al., 2019). While some view fintech as a threat to traditional institutions, others see it as an opportunity for greater flexibility (Romānova and Kudinska, 2016; Shiau et al., 2020; Rafat et al., 2025).

Traditional institutions must innovate by adopting technologies like AI, data analytics and blockchain to create user-centric products. For instance, robo-advisors leverage AI to provide personalised investment advice, making financial management more accessible (Hu et al., 2019). Despite the advantages of Fintech applications, such as ease of use, they also introduce regulatory and security challenges that require compliance to protect user data. As financial institutions develop Fintech innovations to stay competitive, they are reshaping the financial landscape with flexible, personalised products (Romānova and Kudinska, 2016; Shiau et al., 2020; Rafat et al., 2025). These advancements revolutionise traditional banking models, providing better customer service and personalised financial products (Singh et al., 2020). Fintech companies merge IT with finance, creating innovative business models that enhance user engagement through convenient and reliable apps (Khatri et al., 2020).

The TAM is a widely accepted framework in Information Systems, aiding in the adoption of financial technology innovations (Shakir, 2022). It focuses on understanding technology adoption in financial transactions and has been validated by numerous studies (Granić and Marangunić, 2019; Zhou et al., 2022; Al-Adwan et al., 2023; Musa et al., 2024).

Research by Yan et al. (2021), Daragmeh et al. (2021), Shiau et al. (2020) and Stewart and Jürjens (2018) emphasise the significance of information and communication technologies in fintech. These studies use TAM to assess the acceptance of new technologies (Zhang and Kim, 2019; Yan et al., 2021; Al-Adwan et al., 2023; Musa et al., 2024). Developed by Davis in 1989, TAM reliably predicts the acceptance of financial technologies (Singh and Tewari, 2021).

TAM is considered one of the most robust models for studying technology acceptance behaviour (Yang et al., 2023; Pavlou, 2003; Davis, 1989). This study uses TAM to investigate low-income individuals’ intentions to use fintech applications, aiming to provide insights for future researchers, businesses and government agencies on promoting fintech among low-income groups. An extensive literature review explores the factors influencing user intentions to adopt fintech services.

The TAM provides the theoretical foundation for examining fintech adoption, emphasising PU, PEU and SI as key predictors of behavioural intention (Davis, 1989; Venkatesh and Davis, 2000). This study extends TAM by incorporating FL to address cognitive barriers in underserved populations as (Figure 1).

The subsequent hypotheses test these direct relationships with intention to use fintech.

The TAM defines PU as the belief that current technology will enhance performance. Studies have consistently found a positive relationship between PU and the intention to adopt financial technology (de Luna et al., 2019; Lara-Rubio et al., 2020; Liébana-Cabanillas et al., 2020; Singh et al., 2020). Users see fintech as a reliable tool to achieve their financial goals, improving efficiency in financial transactions (Iman, 2020). PU significantly influences the willingness to use financial technology in various contexts (Akbar et al., 2022; Kumar et al., 2025):

PEU refers to an individual’s belief that using a technology will be effortless (Veríssimo, 2016). It involves making systems user-friendly and eliminating complexities (Daragmeh et al., 2021). PEU reflects the comfort and confidence individuals feel while using and learning Fintech applications (Hu et al., 2019). Many studies suggest a strong connection between PEU and the intention to use innovative technological services like Fintech applications, mobile payments and digital transactions (Kim et al., 2010; Nguyen et al., 2016; Shankar and Datta, 2018). However, some studies did not find a significant impact of PEU on fintech adoption.

This study uses PEU to illustrate how comfortable customers feel using fintech applications (Hu et al., 2019). Previous research shows that PEU significantly influences users’ perceptions of the technology’s trustworthiness, both directly and indirectly (Teka, 2020; Singh et al., 2020):

Social influence (SI) impacts technology adoption behaviour (Yee, 2015). Both subjective and descriptive norms shape user behaviours and perceptions (Singh et al., 2020;Khuong et al., 2022). Subjective norms are about others’ expectations, while descriptive norms are about behaviour patterns influenced by others (Chawla and Joshi, 2020; Singh and Tewari, 2021). SI boosts technology acceptance, especially with significant societal benefits (Hassan et al., 2020).

Research shows SI positively affects attitudes towards fintech (Carter and Yeo, 2016; Singh et al., 2020) and significantly influences fintech adoption, particularly in developing nations (Silva et al., 2023; Najib et al., 2021). Fintech adoption is often influenced by social circles and community perceptions (Carter and Yeo, 2016):

Studies show that FL is crucial for fintech adoption, impacting economic stability and effective financial management (Adil et al., 2021; Herawati et al., 2020). FL reflects an individual’s ability to handle finance-related tasks (Remund, 2010; Herawati et al., 2020) and positively influences fintech adoption and financial behaviour (She et al., 2022). Conversely, lack of financial knowledge can lead to issues like excessive borrowing and scams (Sevim et al., 2012; Kakinuma, 2022).

Recent studies highlight that FL significantly affects fintech adoption (Hasan et al., 2021; Kakinuma, 2022; Khan et al., 2022). Higher financial literacy lowers information costs when using new financial products, encouraging their adoption (Long et al., 2023). Technological knowledge also plays a role in fintech adoption, making services more reliable and efficient (Lim et al., 2019):

Present literature on fintech adoption predominantly applies the core TAM constructs, PU, PEU and SI to urban, digitally literate populations in emerging economies like India (Zhou et al., 2022; Singh and Tewari, 2021; Al-Adwan et al., 2023). These research studies overlook cognitive barriers like FL and other contextual challenges in underserved, low-income rural areas, where unbanked rates remain high despite high mobile penetration (Maity and Sahu, 2022). While FL’s direct impact on general financial behaviour is noted (Hasan et al., 2021; Long et al., 2023), its mediating role in shaping perception about risk, trust and TAM factors among low-income groups is under-explored. Few studies integrate FL into extended TAM framework for fintech in developing nations, ignoring socio-cultural dynamics like digital divides and technological resistance (Rafat et al., 2025). This leaves a critical gap in context-specific frameworks for equitable financial inclusion.

This study fills the identified gaps by extending TAM with FL as both a direct predictor and mediator of intention to adopt fintech among low-income users in rural Uttar Pradesh. Using PLS-SEM on a robust sample of 800 respondents, it empirically tests FL’s interactions with PU, PEU and SI, providing a tailored framework for underserved populations. By considering, India’s high mobile penetration rate amid banking inactivity (16% dormant accounts in 2024), the research offers actionable insights for policymakers, fintech firms and rural development initiatives. This approach not only advances theoretical understanding but also promotes inclusive growth through user-centred strategies.

This research used a well-structured open-ended questionnaire adopted from past research studies, tailor-made as per the requirement, of the current study. The focus of this study was on users of financial services living in remote areas of Uttar Pradesh.

Firstly, a pilot study was conducted with 50 respondents to check the reliability and validity of the measurement scale. For the main study, a well-crafted questionnaire was distributed online and offline to 850 respondents who were availing financial services and living in remote areas of Uttar Pradesh using purposive sampling technique. This region was selected because of its high population density along with geographical and cultural diversity which considered appropriate for conducting research with big sample size and varied economic and social conditions. In this study, purposive sampling was used to ensure inclusion of respondents pertaining to the research objectives. This sampling technique allows intentional selection of respondents capable of providing deeper insights. Although this technique limits the generalisation of the findings, it enhances internal validity and relevance of the targeted population. This study was conducted for the duration of eight months, from June 2024 to January 2025.

A total of 800 duly filled questionnaires were received at a response rate of 94%. All 800 questionnaires were considered for final analysis and interpretation. This study consists of 20 items, so the minimum sample required to conduct this study was 200 based on the criteria of 10 responses per item, proposed by Kline (2011). Hence, the sample size of 800 respondents was adequate.

The purpose of the study was explained to potential respondents and after getting due acceptance from participants, data was collected. The study ensured adherence to anonymity, to keep the respondent’s identity confidential. All procedures performed in this study involving human participants were under the ethics of the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study uses both offline as well as online questionnaires that were addressed to the general population of India; hence, no specific ethical approval was obtained.

In this study, measurement scales were developed using the research work of many national and international scholars who have worked in similar field. The study used seven-point Likert scale adopted from previous studies. Four items of PU were adopted from Singh et al. (2020); Iman (2020); Akbar et al. (2022); and Kumar et al. (2025). Four items of PEU were adopted from studies conducted by Veríssimo (2016); Teka (2020); and Singh et al. (2020). Furthermore, four measurement items of SI and four items of FL are adopted from Hassan et al. (2020); Singh and Tewari (2021); and Lim et al. (2019), and Herawati et al. (2020); Adil et al. (2021); She et al. (2022); and Kakinuma (2022), respectively. Table 1 shows various measurement items and their sources.

The demographic data of the respondents reveal that the majority of respondents are male, 62%. In addition, a significant population availing financial services are literate (94%) and have completed graduation and post-graduation (49%). The age range shows that most of the respondents are between 18 and 30 years old, 45% have bank account and are regular users of fintech platform.

Initially, to ascertain common method bias, the study used CFA and used SPSS. Factor loadings of the model (Table 2) were verified, and they were statistically significant as each value is higher than the threshold value of 0.5 (Becker et al., 2023). Then, hypothesis testing was performed using Smart PLS 4 and bootstrapping of 5,000 random subsamples. During the first step of hypothesis testing, 5,000 random subsamples were bootstrapped using Smart PLS 4. A 0.05 significance threshold was used (Hair et al., 2016; Becker et al., 2023). Furthermore, the validity and reliability of the constructs are tested using the PLS algorithm’s output. According to Table 3, all of the Cronbach’s alpha and composite dependability are greater than 0.70. As a result, the study confirms that the measurement model has a high level of construct dependability (Leong et al., 2018). The convergent validity of the items is supported by the average variance extracted (AVE), which is higher than 0.50. As a result, construct validity is established (Nordman and Tolstoy, 2016; Leong et al., 2018).

Fornell and Larcker (1981), in their study, mentioned that construct distinctiveness is proven by a higher value of the square root of AVE relative to the factor correlation value. Table 4 depicts that all factors are different and distinctive because the square root of AVE (diagonal value) is larger than the correlation values for all components. Furthermore, they stated that construct uniqueness is proven by a higher value of the square root of AVE relative to the factor correlation value. Table 4 shows that the square roots of the AVE are greater than the inter-correlation coefficients (Alalwan et al., 2017), which indicates that constructs have discriminant validity.

Furthermore, based on the cross-loadings (Table 5), it is clear that all items strongly load onto their constructs, supporting discriminant validity. Furthermore, as stated by most of the studies (Hair et al., 2016), acceptable range for a VIF is considered to be below 5. As shown in Table 3, VIF value of all the items is below 5, indicating no multicollinearity issues among the variables. The result of R Square is 0.820, suggesting that the measurement model can illustrate 82% of the variance in Fintech adoption.

The bootstrapping of the structural model was conducted; Figure 2 depicts the model. In Table 6, path analysis findings reveal that four out of five paths were relevant based on the bootstrapping of the structural model. We used 95% confidence intervals as well as t-statistics to determine the significance of the relationship. Table 4 shows that FL (ß = 0.240; p-value 0.000) and PEU (ß = 0.216; p-value 0.000) are significantly related to fintech adoption. Similarly, SI (ß = 0.270; p-value = 0.000) and SI (ß = 0.233; p-value = 0.001) are also significantly associated with the INT to adopt fintech.

Fintech is a new concept that focuses on improving the financial sector via technological advancement. The current paper studies potential factors that influence adoption of fintech in emerging nations like India. This study uses the TAM framework to study key constructs – PEU, PU, SI, FL and INT – that affect the willingness to adopt fintech platforms.

PEU and PU were found to be the strong predictors of fintech adoption. Finding states that users perceive fintech services as useful (β = 0.270, p < 0.001) and easy to use (β = 0.216, p < 0.001), they are more likely to integrate fintech services into their daily financial transactions. These findings suggest that simplicity of the platform and benefits provided significantly influence, Furthermore, SI also emerged as a strong influencing factor (β = 0.233, p < 0.001). These findings are mostly applicable to close-knit communities where word-of-mouth suggestions and recommendations influence users’ willingness.

Similarly, findings also suggest that having trust in peer group advice also drive decision-making process. Furthermore, FL was also found to be a significant factor (β = 0.240, p < 0.001) that influences the intention to use fintech platforms. Study suggests that individuals with good financial understanding are more likely to adopt fintech services whereas, people with limited financial knowledge are sceptical about fintech services due to risk factors and security breaches associated with these platforms.

Findings indicate that barriers to fintech adoption include unreliable internet, a digital divide, cybersecurity threats, resistance to change, distrust in modern banking and the complexity of services. The study recommends that the government promote financial literacy campaigns targeting all socio-economic groups. A stronger regulatory framework is essential to enhance trust and security in digital transactions. Fintech providers should improve user experience with consumer-friendly platforms that address varying digital proficiency levels and offer multilingual support and financial resources. Social influence significantly affects user preferences and can be harnessed to build trust in fintech. Therefore, fintech companies and banks should develop initiatives to raise awareness and promote services among underprivileged groups.

This study highlights key predictors of the intention to adopt fintech platforms: PEU, PU, SI and FL. It shows that PEU and PU are crucial predictors, with SI and FL also significantly impacting adoption. Fintech has revolutionised financial inclusion, but its success is hindered by trust issues and security concerns. By addressing barriers such as ease of use, reliability, social acceptance and FL, fintech providers, financial institutions and regulators can create an environment that encourages the use of technology-based financial services. Strategic interventions and innovations can empower individuals, reduce poverty and drive economic growth.

However, the study has some limitations. The study’s findings are not generalisable to urban regions, as different behaviours and trust dynamics may exist, especially among low-income earners. Geographic location can also affect users’ perceptions of risk, trust and usability due to varying local cultures and regulations. Furthermore, the reliance on self-reported data may introduce bias, and the cross-sectional nature of the study limits insights into changes in user perceptions over time. Future research should explore additional predictors of the TAM framework, such as habit, self-efficacy, attitude, perceived security and perceived risk, which may significantly influence fintech adoption intentions. Researchers could also use other models, like UTAUT or TRA, to examine these intentions.

The authors received no financial support for the research, authorship and/or publication of this article.

The data collection period spans eight months, from June 2024 to January 2025. The survey was conducted using a questionnaire that respects participant confidentiality by avoiding questions that disclose personal information. Questionnaires were distributed only after securing participants’ consent:

• Data collection sources: Primary data collected using structured questionnaire. Data was collected from low-income earners.

• Data collection time period: From 3/6/2024 to 5/1/2025

• Data collection methodology: Structured questionnaire

• If any copyright is required for data collection: NA

This manuscript has been edited with the assistance of GPT-40 for grammar correction and stylistic improvements. All content, intellectual contributions and final interpretations remain the responsibility of the authors.