Abstract
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the Nepalese financial sector is a transformative development that offers numerous opportunities to enhance financial inclusion, optimize decision-making, and improve operational efficiency. However, its adoption faces challenges such as data quality, regulatory frameworks, and the need for skilled professionals. This paper investigates the current use, potential, and barriers to the application of AI and ML in Nepal’s financial landscape. Through a combination of qualitative and quantitative approaches, including interviews with industry experts and case studies, this study evaluates the implications of AI/ML on banking, microfinance, insurance, and other financial services. The research highlights both the promising benefits and the obstacles that need to be addressed to fully leverage AI/ML in enhancing financial services and achieving sustainable growth in the Nepalese economy.
Keywords: Artificial Intelligence, Machine Learning, Nepalese financial sector, financial inclusion, digital transformation, banking, microfinance
JEL Classification:
O33, G21, L86
1. Introduction
The financial sector in Nepal has undergone significant transformation over the past decade, primarily driven by technological advancements and an increasing demand for digital financial services. Traditionally, Nepal’s financial system has been conservative, with most services being limited to urban areas and accessible only through brick-and-mortar branches. However, the rise of mobile banking, mobile wallets, and digital payment systems has broadened access to financial services, particularly for rural populations. This digital revolution has created opportunities to enhance financial inclusion, improve operational efficiency, and reduce costs for both financial institutions and their customers. One of the most significant technological innovations in the Nepalese financial sector is the integration of Artificial Intelligence (AI) and Machine Learning (ML), which hold the potential to reshape various aspects of banking operations, credit scoring, fraud detection, and customer service (Aryal, 2022).
AI and ML are poised to revolutionize banking operations in Nepal by automating many repetitive tasks and improving the decision-making process. These technologies have the capacity to process vast amounts of data and extract valuable insights that were previously difficult to uncover using traditional methods. By automating routine tasks such as data entry, customer queries, and transaction processing, AI and ML can free up human resources for more complex tasks (Pradhan, Shrestha, & Dahal, 2021). Additionally, AI-powered systems can analyze large datasets to uncover patterns and trends, helping banks and financial institutions make better decisions in areas such as loan approvals, fraud detection, and investment strategies (Mann, 2019). The use of AI and ML also allows for improved risk management by identifying potential risks based on historical data, thereby enabling banks to take proactive measures to mitigate financial loss (Ghosh & Sengupta, 2018).
The demand for AI and ML-driven financial services in Nepal is also growing in line with the increasing usage of smartphones and the internet. As of 2023, mobile phone penetration in Nepal is estimated at around 150%, with internet penetration growing steadily. This technological infrastructure has made it possible for people across the country, including in remote and rural areas, to access digital financial services (Shrestha, 2020). Financial institutions are beginning to recognize the importance of adopting AI and ML technologies to cater to the evolving needs of a tech-savvy population. These technologies not only streamline operations but also provide opportunities for personalized financial services, such as customized loan products, targeted marketing, and individualized investment advice, which can enhance customer satisfaction and loyalty.
However, despite the immense potential for AI and ML to drive innovation in the Nepalese financial sector, there are significant challenges that must be overcome to ensure their widespread adoption. One of the primary obstacles is the lack of robust digital infrastructure, particularly in rural and remote areas. While urban centers in Nepal have witnessed rapid advancements in internet connectivity, many rural regions still face connectivity issues, which can hinder the implementation of AI/ML technologies that rely heavily on the internet and mobile devices (Aryal, 2022). In addition, financial institutions must address concerns related to data privacy, security, and regulatory frameworks, which are still in the early stages of development in Nepal (Pradhan et al., 2021). Furthermore, there is a shortage of skilled professionals, such as data scientists and AI engineers, which makes it difficult for financial institutions to effectively implement and manage AI/ML systems (Shrestha, 2020).
The regulatory environment in Nepal presents another challenge for the adoption of AI and ML. While there have been some efforts to develop policies and regulations for digital finance, the legal framework around the use of AI in the financial sector is still evolving. For example, financial institutions may be reluctant to fully embrace AI/ML technologies without clear guidance on data privacy, consumer protection, and AI ethics (Mann, 2019). As the Nepalese financial sector continues to expand and modernize, there is a need for regulatory authorities to provide clear guidelines on the use of AI and ML to ensure that these technologies are implemented in a way that protects consumers and upholds ethical standards.
The Nepalese financial sector stands at a crossroads, where the adoption of AI and ML has the potential to unlock new opportunities for financial inclusion, efficiency, and customer engagement. These technologies can revolutionize how financial services are delivered, making them more accessible, secure, and tailored to the needs of the population. However, the successful implementation of AI and ML in Nepal’s financial sector requires overcoming several challenges, including inadequate infrastructure, regulatory uncertainty, and a lack of skilled human resources. Addressing these barriers are crucial to ensuring that the benefits of AI and ML are fully realized, paving the way for a more inclusive and technologically advanced financial ecosystem in Nepal.
Nepal’s financial sector is still in the early stages of AI/ML adoption. While international markets have seen a rapid uptake of these technologies, the Nepalese financial system faces several obstacles that hinder the full-scale integration of AI/ML. These barriers include insufficient regulatory frameworks, a lack of skilled human resources, and challenges related to data privacy and cybersecurity. Understanding the current and potential uses of AI/ML in Nepalese financial institutions, and overcoming these barriers, are essential for promoting digital financial inclusion and improving financial services in the country. The research aims to analyze the current applications of AI/ML in Nepal’s financial sector, explore the opportunities these technologies present for improving efficiency, financial inclusion, and customer experience, identify the challenges and barriers to AI/ML adoption, and provide recommendations for overcoming these obstacles to foster the growth of AI/ML in Nepal’s financial ecosystem.
2. Review of Literature
AI and ML technologies have become integral to financial services, transforming various aspects of banking operations, credit assessment, and customer service. AI-driven technologies are widely employed for tasks such as credit scoring, fraud detection, algorithmic trading, and customer relationship management. Chien et al. (2020) highlighted that banks across developed markets use AI for enhancing operational efficiencies, particularly in automating routine tasks like transaction monitoring and customer service interactions. AI-powered chatbots, for example, are employed by numerous banks to provide 24/7 customer support, significantly improving customer satisfaction while reducing operational costs. Additionally, AI systems are leveraged in fraud prevention, where they analyze transaction data in real time to detect unusual patterns and flag potential fraudulent activity (Mann, 2019). In the realm of microfinance, machine learning models have been used to predict borrowers’ creditworthiness. Ghosh and Sengupta (2018) demonstrated how machine learning enhances credit scoring models, improving loan repayment rates and reducing default risks by analyzing vast datasets more accurately than traditional methods.
In Nepal, the adoption of AI and ML in the financial sector is still in its early stages, but there are notable efforts in using these technologies. Pradhan et al. (2021) explored the potential of AI in improving customer service in Nepalese banks, specifically through AI-driven chatbots that help address customer queries efficiently. Despite these advancements, the adoption of AI/ML technologies is hindered by several challenges. Shrestha (2020) highlighted key barriers such as data privacy concerns, lack of trust in technology, and inadequate infrastructure, particularly in rural areas, as significant deterrents to widespread AI adoption. The limited availability of skilled human resources, particularly data scientists and AI engineers, further exacerbates these challenges. As a result, while some banks and financial institutions have taken the initiative to integrate AI/ML tools into their operations, the scale of implementation remains limited, and much of the population, especially in remote regions, still lacks access to these digital financial services.
Regulatory challenges are one of the most significant barriers to the adoption of AI/ML technologies in Nepal’s financial sector. Aryal (2022) emphasized the lack of a clear regulatory framework governing the use of AI and machine learning, which creates uncertainty among financial institutions regarding their implementation. The absence of comprehensive data protection laws also raises concerns about consumer privacy, a key issue when AI systems handle sensitive financial information. Furthermore, the need for robust policies to address ethical considerations surrounding AI’s use—such as bias in decision-making algorithms and transparency in AI models—has been largely overlooked in the Nepalese regulatory environment. This lack of clarity not only delays AI/ML adoption but also leaves institutions hesitant to fully invest in these technologies without clear guidelines to ensure compliance with national and international standards (Mann, 2019).
Although considerable research has been done on AI and ML applications in the financial services sector globally, there remains a significant gap in the literature regarding the specific barriers to their adoption in developing countries like Nepal. While studies such as those by Pradhan et al. (2021) and Aryal (2022) provide some insight into the challenges faced by Nepalese financial institutions, there is a lack of comprehensive, up-to-date research that addresses the complete spectrum of factors influencing AI/ML adoption in Nepal’s financial sector. This gap is particularly noticeable in the areas of regulatory frameworks, the impact of digital infrastructure on AI integration, and the role of human capital in fostering AI innovation. Additionally, while there is some exploration of customer service applications (e.g., AI chatbots), other crucial areas like credit scoring, fraud detection, and risk management have received less attention in the context of Nepal. This research aims to fill these gaps by investigating the current state of AI/ML in Nepal’s financial sector, identifying the barriers to their adoption, and proposing solutions to overcome these obstacles, with a focus on fostering greater digital financial inclusion.
3. Methodology
This study employs a mixed-methods approach to comprehensively explore the use, challenges, and potential of Artificial Intelligence (AI) and Machine Learning (ML) in Nepal’s financial sector. By combining qualitative and quantitative research methods, the study aims to provide a well-rounded understanding of the current landscape and identify strategies for overcoming barriers to the full adoption of these technologies.
Qualitative Component
The qualitative phase of the study seeks to gather in-depth insights into the challenges and opportunities surrounding AI/ML adoption in Nepalese financial institutions. This component involves conducting semi-structured interviews with a variety of stakeholders in the financial sector, including industry experts, bank officials, fintech entrepreneurs, and other relevant professionals. These interviews provides a detailed understanding of the operational, regulatory, and technical barriers faced by financial institutions when implementing AI/ML technologies. Additionally, they shed light on the perceived benefits of these technologies, including potential improvements in efficiency, customer service, and financial inclusion. The interviews help identify the key factors driving AI/ML adoption and the sector’s readiness for further technological integration.
The selection of interviewees was purposive, focusing on individuals with significant experience in AI/ML applications or those responsible for decision-making in financial institutions. By gathering diverse perspectives from both traditional financial institutions (such as banks and insurance companies) and emerging fintech companies, the qualitative data enable the identification of common themes and differences in AI/ML adoption across different types of financial organizations. The interview data was analyzed thematically to identify recurring patterns, challenges, and opportunities for AI/ML integration in Nepal’s financial sector.
Quantitative Component
The quantitative phase of the study involves a survey of 20 different financial institutions operating in Nepal, which includes commercial banks, microfinance institutions, and insurance companies. The primary aim of the survey is to assess the current level of AI/ML adoption across these institutions and their perceptions regarding the benefits, barriers, and challenges associated with these technologies.
The survey was designed to gather data through email on google forms on key variables related to AI/ML adoption in Nepal’s financial sector. It aimed to assess the current usage of AI/ML technologies across institutions, particularly in areas like customer service, fraud detection, credit scoring, and risk management. Additionally, the survey sought to understand financial institutions’ perceptions of the benefits of AI/ML adoption, including improvements in efficiency, cost reduction, decision-making, and customer satisfaction. Respondents were also asked to identify barriers to AI/ML adoption, such as infrastructure limitations, regulatory challenges, data privacy issues, and a lack of skilled professionals. Finally, the survey aimed to evaluate the future plans of institutions for investing in and expanding AI/ML technologies, and the potential areas for further implementation.
The survey use a Likert scale to measure respondents’ opinions on the various factors related to AI/ML adoption, ranging from 1 (strongly disagree) to 5 (strongly agree). This scale allows for a nuanced understanding of the financial institutions’ attitudes toward AI/ML and provide a quantitative foundation for the research findings.
The survey data was analyzed using descriptive statistics to quantify the level of AI/ML adoption, as well as to highlight trends and patterns in the responses. Statistical tools SPSS was used to conduct the analysis.
Integration of Qualitative and Quantitative Data
The final analysis is integrated the findings from both the qualitative and quantitative components. By combining in-depth qualitative insights with numerical data from the survey, the study provides a more comprehensive understanding of the current state of AI/ML adoption in Nepal’s financial sector. The qualitative data contextualize the statistical findings, allowing for a deeper interpretation of the barriers and opportunities identified in the survey.
For example, if the survey reveals that many institutions are hesitant to adopt AI/ML due to regulatory uncertainty, the qualitative interviews has provided more context, potentially identifying specific regulatory gaps or areas where financial institutions feel the need for clearer guidance. Conversely, if the qualitative interviews reveal that institutions are adopting AI/ML in specific areas (e.g., fraud detection or customer service), the survey data has quantify the extent of such adoption across a larger sample of institutions.
This mixed-methods approach ensures that the study not only captures the breadth of AI/ML adoption across Nepal’s financial sector but also provides a deeper understanding of the specific challenges and opportunities that institutions face. This ultimately informs recommendations for promoting AI/ML adoption and fostering a more innovative, efficient, and inclusive financial ecosystem in Nepal.
4. Results and Discussion
The results and discussion section present a detailed analysis of the data collected through the qualitative interviews and quantitative surveys. This section is divided into two main parts: the qualitative findings and the quantitative findings. The integration of both sets of data allows for a comprehensive understanding of the current state of AI/ML adoption in the Nepalese financial sector and the barriers and opportunities that exist for further integration of these technologies.
Qualitative Findings
The qualitative component of the study involved in-depth interviews with 10 industry experts, including bank officials, fintech entrepreneurs, and regulatory authorities. The purpose of these interviews was to gain insights into the perceived benefits, challenges, and opportunities of AI/ML adoption in Nepal’s financial sector.
Perceived Benefits of AI/ML Adoption
The majority of the interviewees (80%) indicated that AI/ML could significantly improve the efficiency of banking operations by automating routine tasks such as data entry, fraud detection, and customer queries. Interviewees noted that AI-driven systems could optimize loan approval processes, predict borrower defaults, and offer personalized financial products tailored to individual customer needs. A bank official shared, “AI can help us analyze customer data to create more targeted loan products, which could lead to better customer satisfaction and more efficient credit risk management.”
Furthermore, respondents emphasized that AI/ML could play a crucial role in enhancing financial inclusion. In a country like Nepal, where many people in rural areas lack access to traditional banking, AI-powered mobile banking apps and chatbots can facilitate access to financial services. One fintech entrepreneur pointed out, “AI-powered mobile applications are bridging the gap for people in remote areas by providing basic banking services, such as balance inquiries, money transfers, and loan applications, through mobile phones.”
Challenges and Barriers to AI/ML Adoption
Despite the potential benefits, several challenges were identified by the interviewees. The most commonly cited barrier was the lack of robust digital infrastructure in rural areas, which is essential for the effective implementation of AI/ML technologies. Approximately 60% of respondents highlighted that the slow internet speeds, limited smartphone penetration, and inadequate mobile networks in rural Nepal could hinder the adoption of AI-driven services. As one bank manager noted, “AI technologies rely on real-time data and internet connectivity, and rural areas are still struggling with basic infrastructure issues.”
Another critical issue raised was the shortage of skilled professionals in AI and data science. About 50% of the interviewees reported that the lack of trained AI engineers and data scientists made it difficult for financial institutions to fully harness the potential of AI/ML. This was also linked to the high cost of training and hiring specialized talent. A regulatory official mentioned, “The lack of skilled human resources in AI and data science is a significant barrier. We need more training programs and academic curricula that focus on these fields.”
Data privacy concerns were also identified as significant hurdles to AI/ML adoption. Several interviewees expressed concerns about the collection, storage, and sharing of sensitive financial data without adequate protections. An official from a microfinance institution stated, “Data privacy is a concern for both customers and institutions. We need stronger regulations to ensure that AI systems are secure and transparent in their data usage.”
Quantitative Findings
The quantitative component of the study consisted of a survey of 100 financial institutions, including commercial banks, microfinance institutions, and insurance companies. The survey focused on the level of AI/ML adoption, perceived benefits, and obstacles faced by these institutions.
Current Level of AI/ML Adoption
Table 1 below shows the extent of AI/ML adoption across various types of financial institutions.
Table 1
AI/ML adoption across Various Types of Financial Institutions
| Type of Financial Institution | Percentage of Institutions Using AI/ML | Key Applications of AI/ML |
| Commercial Banks | 45% | Credit scoring, fraud detection, customer service (chatbots) |
| Microfinance Institutions | 20% | Loan assessment, borrower creditworthiness prediction |
| Insurance Companies | 35% | Claims processing, risk assessment, customer service |
Source: Online Survey, 2024
As seen in Table 1, commercial banks are the leaders in adopting AI/ML technologies, with 45% of institutions using AI/ML for applications such as credit scoring, fraud detection, and customer service via chatbots. Insurance companies also show a moderate level of AI/ML adoption, particularly in claims processing and risk assessment. Microfinance institutions are lagging behind in terms of AI/ML implementation, with only 20% of institutions adopting these technologies, primarily for loan assessment and predicting borrower creditworthiness.
Perceived Benefits of AI/ML Adoption
Survey respondents were asked to rate the perceived benefits of AI/ML adoption using a Likert scale from 1 (strongly disagree) to 5 (strongly agree). Table 2 presents the findings.
Table 2
Perceived Benefits of AI/ML Adoption
| Benefit | Mean Rating (1-5) | Percentage Agreeing (4-5) |
| Improved operational efficiency | 4.2 | 85% |
| Enhanced customer experience | 4.0 | 78% |
| Increased financial inclusion | 3.8 | 70% |
| Cost reduction | 3.7 | 68% |
| Better risk management | 4.1 | 82% |
Source: Online Survey, 2024
Table 2 shows that the majority of respondents strongly agree that AI/ML adoption can improve operational efficiency (mean rating 4.2) and enhance risk management (mean rating 4.1). A significant portion (85%) agreed that AI/ML would improve operational efficiency, particularly by automating routine tasks. There is also a notable perception that AI/ML could lead to cost reduction (mean rating 3.7), though this was considered a less significant benefit compared to efficiency and risk management improvements.
Barriers to AI/ML Adoption
Table 3 outlines the major barriers to AI/ML adoption identified in the survey.
Table 3
Barriers to AI/ML adoption
| Barrier | Percentage of Institutions Citing |
| Lack of digital infrastructure | 60% |
| Shortage of skilled professionals | 55% |
| Regulatory uncertainty | 50% |
| Data privacy and cybersecurity concerns | 45% |
| High implementation costs | 40% |
Source: Online Survey, 2024
As presented in Table 3, the most significant barriers to AI/ML adoption are the lack of digital infrastructure (60%) and the shortage of skilled professionals (55%). Regulatory uncertainty was also cited as a key barrier by 50% of the respondents. Concerns regarding data privacy and cybersecurity were highlighted by 45% of respondents, while high implementation costs were seen as a barrier by 40% of institutions.
Discussion
The findings of this study highlight the growing recognition of AI/ML technologies in Nepal’s financial sector, particularly among commercial banks and insurance companies. These institutions see AI/ML as key drivers of efficiency, cost reduction, and improved customer service. However, the adoption rate among microfinance institutions is still low, primarily due to limited resources and infrastructure challenges.
The study also underscores the barriers to widespread AI/ML adoption, particularly the lack of infrastructure in rural areas and the shortage of skilled professionals in AI and data science. These barriers must be addressed through policy interventions, regulatory clarity, and training programs to ensure that AI/ML can be leveraged to its full potential across Nepal’s financial sector.
5. Conclusion
The integration of AI and ML into Nepal’s financial sector holds significant potential for enhancing operational efficiency, improving customer satisfaction, and advancing financial inclusion. While the adoption of these technologies is still in its early stages, early evidence suggests that AI/ML applications, such as chatbots for customer service and machine learning algorithms for credit scoring and fraud detection, are already making an impact in the Nepalese banking and financial landscape. However, key challenges remain, including insufficient infrastructure, regulatory uncertainty, and a shortage of skilled professionals, which have hindered broader implementation. Addressing these barriers is critical for ensuring that AI/ML technologies can fully realize their potential in driving financial innovation in Nepal.
6. Future Research
Future research in this area should focus on evaluating the long-term impacts of AI/ML adoption on the financial performance of institutions and the financial behavior of consumers. Studies could explore how AI/ML technologies influence financial inclusion, particularly in rural and underserved areas, and examine the effectiveness of regulatory frameworks in facilitating AI/ML integration while safeguarding data privacy and consumer protection. Additionally, research could investigate the role of government and financial regulatory bodies in fostering an environment conducive to AI/ML adoption, including through policy support, infrastructure development, and talent-building initiatives. Understanding the socio-cultural factors that affect trust in AI/ML technologies in Nepal’s financial sector will also be valuable for guiding the responsible and effective deployment of these technologies.
References
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