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Journal articles on the topic 'Banking Chatbots'
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Azevedo, Nádila, Gustavo Aquino, Leonardo Nascimento, et al. "A Novel Methodology for Developing Troubleshooting Chatbots Applied to ATM Technical Maintenance Support." Applied Sciences 13, no. 11 (2023): 6777. http://dx.doi.org/10.3390/app13116777.
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The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.
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Mahadi,, Aishu S. "Virtual Banker at Your Fingertips Using Chatbot System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40723.
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Abstract- Technology's quick development has revolutionized the banking industry and opened the door for creative solutions like chatbots driven by artificial intelligence. The creation and deployment of a virtual banking system that uses chatbot technology to increase security, simplify processes, and improve customer experience is the main emphasis of this study. The study investigates how to build intelligent and responsive chatbots using machine learning, natural language processing (NLP), and interaction models. Personalized financial services, round-the-clock accessibility, cutting down on time spent on repetitive tasks, and upholding strict security and compliance guidelines are some of the major goals. Increased client pleasure, better operational effectiveness, and greater confidence in banking services are anticipated results. The suggested solution guarantees a safe and moral method of virtual banking by taking privacy issues and legal requirements into account. According to the paper's conclusion, chatbot technology is a game-changer for contemporary banking, allowing organizations to satisfy the needs of tech-savvy clients while encouraging contentment and loyalty. To ensure continued innovation and growth, future directions include investigating trends in personalized banking experiences and combining chatbots with upcoming technology. Keywords- Virtual Banking Chatbot Technology Natural Language Processing (NLP) Artificial Intelligence (AI)
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Wiliam, Andi, Sasmoko Sasmoko, Harjanto Prabowo, Mohammad Hamsal, Elfindah Princes, and Yasinta Indrianti. "ANALYSIS OF E-SERVICE CHATBOT AND SATISFACTION OF BANKING CUSTOMERS IN INDONESIA." Asia Proceedings of Social Sciences 4, no. 3 (2019): 72–75. http://dx.doi.org/10.31580/apss.v4i3.860.
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This research was conducted to qualitatively analyze the actual opportunities of banking e-service chatbot to satisfy Indonesian banking customers and also to explore qualitatively what factors were expected to affect customer satisfaction in its implementation through the experience review of other companies as users of e-service chatbots. In this study, the authors used literature studies on the experiences of various companies when using e-service chatbots to satisfy their customers, which then contextualized into the banking industries in Indonesia. The results of the study found that the e-service chatbots had the opportunity to increase the satisfaction of banking customers in Indonesia. This opportunity occured when the customer had fully experienced digital literacy and they experienced the readiness to change a stage of banking services. The results of the study also found that the opportunities for the Indonesian banking industry that have been fully implemented since the beginning of their governance were arranged electronically, so that their customers experienced good readiness which ultimately resulted in good customer satisfaction. The implication of this research is the importance of banking governance to seriously carry out digital transformation as an excellent service through chatbots.
 
 Keywords: Chatbot, E-Service, E-Satisfactions, E-Retention
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Singh, Nidhi, Avishek Chakraborty, Susovan Paul, Soumya Bhattacharyya, and Shubhendu Banerjee. "A Pentagonal Neutrosophic TODIM Approach for MultiCriteria Decision-Making in Digital Banking Chatbot Assessment." International Research Journal of Multidisciplinary Scope 06, no. 02 (2025): 600–613. https://doi.org/10.47857/irjms.2024.v06i02.03108.
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"Chatty robots," or chatbots, are a mix of the terms "chat" (to converse) and "bots," which is short for robots. A robot is any machine that has been programmed and managed by a computer. As the banking business evolves into a 24/7 service sector, it is imperative that customers receive continuous help; nevertheless, human agents are unable to meet this demand alone. This resulted in the creation of automated systems, like chatbots, that can manage complex problems with the aid of built-in system rules or external human input. These machines efficiently do difficult, dangerous, and repetitive tasks that were previously impossible for people to accomplish. Robotic process automation, or RPA, is crucial to modern banking because it reduces human costs, minimises operational errors, and ensures continuous, high-efficiency service. Chatbots are an extension of RPA that enhances service delivery, optimises client interactions, and boosts operational efficiency. Given the increasing reliance on online banking, it is critical to understand the key factors that influence chatbot efficacy. This study examines important chatbot assessment criteria in the West Bengal banking sector using a Pentagonal Neutrosophic Number (PNN)-based TODIM technique for multicriteria decision-making (MCDM). In order to demonstrate how RPA and AI-powered chatbots are transforming digital banking and customer relationship management while giving financial institutions useful data, this study looks at usability, security, flexibility, and customer satisfaction.
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Rumetor, Meilisa, Gabriela Thea Sajow, Daniel William Niode, and Evi Rinawati Simanjuntak. "Contagion-Based Chatbot Usage Intention: Synthesizing Technology Adoption And Social Contagion Theory." Journal of World Science 3, no. 2 (2024): 222–37. http://dx.doi.org/10.58344/jws.v3i2.535.
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Chatbots have become transformative technology in the banking industry. However, there is still a knowledge gap in understanding the influence of social contagion on chatbot user behavior. This research aims to identify and analyze the intention to use chatbots based on the synthesis of technology adoption and social contagion theories. The research method used is quantitative, employing a survey approach and collecting data through online questionnaires from 300 chatbot users in private banks in Manado. Data analysis was conducted using smartPLS. The research results indicate that factors such as Perceived Effectiveness, Perceived Ease of Use, and Coercive Pressure significantly influence users' intention to continue using chatbots. However, normative pressure and mimicry do not have a significant impact. These findings provide an important contribution to theoretical understanding and practical application in the sustainable use of chatbots in the banking industry, which can assist banks in designing more effective marketing strategies and services.
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Сигида, Любов, та Євгенія Левусь. "Сhatbot and intelligent assistant: bibliometric analysis and research in banking sphere". Bulletin of Sumy National Agrarian University, № 2 (88) (28 червня 2021): 41–47. http://dx.doi.org/10.32845/bsnau.2021.2.8.
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Today, the importance of chatbots and intelligent assistants is growing, given the rapid pace of information systems' development and the desire of consumers to receive advice at any time and on a self-service basis. Accordingly, the article presents the bibliometric and visual analysis of publications from the Scopus database in chatbots and intelligent assistants. According to the results obtained, the authors can note the following.First, the total number of publications in the scientific field of chatbots and intelligent assistants is on the rise, rapid development started in 2016, and the peak was in 2019. Second, chatbots and intelligent assistants' most relevant subject areas are computer science and engineering. However, chatbots and intelligent assistants get into other spheres (social sciences, decision sciences, medicine, etc.). It means that the research sphere becomes more interdisciplinary. Third, the highest publication activity demonstrates authors from the USA.Moreover, powerful publication clusters form around China, the Netherlands, Germany. Also, four authors from the USA are in the top 10 contributing authors in the research sphere. Fourth, 13 clusters form relationships between keywords within the research problem. The most significant clusters around the following keywords “intelligent assistants,” “chatbots,” “human-computer interaction,” “artificial intelligence,” “computer software”. In addition, a comparative analysis of chatbots of three Ukrainian banks – PrivatBank, MonoBank, and Alfa-Bank, was conducted. This analysis identified the strengths and problem areas of each of the chatbots. According to the results of the investigation, PrivatBank and MonoBank chatbots are more competitive. Alfa-Bank chatbot has more “gaps,” which reduce the effectiveness of interaction for users and need to be corrected. Thus, the research theme is in trend and will develop further. All findings of this paper and their graphical presentation are relevant. Scientists and practitioners can use them to understand the latest achievements and gaps in the “chatbot” and “intelligent assistant” fields and offer helpful guidance for further research.
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Et. al., Dr C. Punitha Devi,. "Banking Chatbot (B-Bot." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 5795–804. http://dx.doi.org/10.17762/turcomat.v12i10.5394.
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Chatbots square measure intelligent systems that perceive a user's tongue queries and respond consequently during a conversation, that is the focus of this study. It's an additional sort of a virtual assistant, folks want they're talking with a real person. They speak a constant language we have a tendency to do, and will answer all queries. In banks, at customer care centers and enquiry desks, humans are lean and usually take very long time to method the only request which ends up in wastage of your time and additionally cut back quality of client service. In this paper we introduce a more efficient way to resolve customer queries. Today’s customers have high expectations and they want quick and accurate responses, complete and robust resolution, service that is available anywhere and anytime. All of these can be within well-designed chatbots. The entire experience is conversational. The aim is to implement a chatbot which may resolve client queries, search the knowledgebase for resolution and provide the solution. The chatbot can handle the queries ultimately reducing human effort.
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Kesavan, D., S. Sivaprakkash, and N. Marianand. "Chatbots Adoption Behaviour in Banking: An Empirical Study in South India." Journal of Commerce and Accounting Research 14, no. 4 (2025): 55–64. https://doi.org/10.21863/jcar/2025.14.4.006.
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The digital transformation has impacted all sectors, including banking, and has led to adopting Artificial Intelligence (AI) to improve services and operational efficiency. Driven by artificial intelligence, banks increasingly use chatbot applications to provide quick, efficient, and error-free services, resulting in increased revenues. This study aimed to investigate the determinants of Behavioural Intention to Use (BTU) chatbots in banking using an Empirical Research Design with a sample of 800 respondents from five public and five private sector banks in the South Indian states. The study found that perceived usefulness and ease of use directly influence behavioural intention to use chatbots, while perceived trust has a significant indirect effect. Awareness of Service (AWS) and perceived compatibility also have a substantial indirect impact on behavioural intention to use chatbots, and Perceived Ease of Use (PEU) is the critical factor in the adoption of chatbots in banking; consumers expect chatbots to be helpful, easy to use, compatible with their lifestyle, and accompanied by clear communication on their usage and benefits. Banks can leverage these findings to improve the design and implementation of chatbots and communicate their benefits to customers to enhance their acceptance and usage.
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Ben-Enukora, Charity Amaka, Agwu Agwu Ejem, Babatunde K. Adeyeye, Oluwafunbi F. Ikunle, and Favour E. Maduadichie. "AWARENESS, ADOPTION AND PERCEPTION OF WHATSAPP CUSTOMER SERVICE CHATBOTS IN THE BANKING SECTOR: PERSPECTIVES FROM UNDERGRADUATE STUDENTS IN LAGOS, NIGERIA." Nigerian Journal of Communication Review (NJCR) 1, no. 2 (2022): 60–70. https://doi.org/10.5281/zenodo.7928247.
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In an attempt to accentuate the role of chatbots in customer service, this study investigates awareness, use, and perceived usefulness of WhatsApp chatbots in banking operations and their performance outcome. The study participants consisted of 362 undergraduate students purposively selected from three tertiary institutions in Lagos state, Nigeria. Results of the cross-sectional survey suggested inadequate awareness and little usage of WhatsApp chatbot services among the study population. Specifically, there is a widespread negative perception of WhatsApp chatbot as a viable alternative to traditional customer service delivery. This was reflected in the low level of perceived usefulness of the chatbots (50%). These results raise concerns over human resources, time, and cost reduction in banking operations through digital customer service. Based on these findings, we recommend a substantial public awareness campaign to educate existing and potential customers about the existence and the benefits of using chatbot/virtual customer assistants and incentives as motivation for the customers to use the virtual customer support option.
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M, Ms Pushpalatha. "Virtual Banker at your Fingertips Using Chatbot System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40744.
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The Virtual Banker systemThrough AI-Powered chatbots, real-time announcements, and video call support, it will ensure an extremely smooth and interactive banking experience for its users. The improvements in this system include a responsive user interface with integration of AI and APIs to provide dynamically interactive responses and backend secure connectivity to fetch data in real time. This also includes the features dynamic announcement management, live escalation on chat, analytics-driven improvements, and more. Scalable, optimized performance, and robust security, the system is capable of offering a comprehensive digital banking solution. The platform features interactive and AI-augmented learning tools to enhance the learning process. These tools include AI chatbots, word definition features, and content understanding mechanisms, allowing users to engage with complex concepts effectively. The integration of quizzes, Q&A sessions, and other testing formats reinforces learning and allows candidates to gauge their understanding of key topics. This paper examines the implementation and impact of chatbot systems in the banking sector. It highlights their ability to provide 24/7 customer service, streamline routine banking operations, and enhance user experiences. Key benefits include cost efficiency for banks, reduced wait times for customers, and improved operational scalability. However, challenges such as data security concerns, customer resistance to AI, and the need for continuous technological upgrades remain significant barriers to widespread adoption. Index Terms -Virtual banker, chatbot systems, artificial intelligence (AI) in banking, natural language processing (NLP), financial technology (FinTech), customer service automation, banking operations, transaction efficiency, digital banking solutions, machine learning in banking, AI-powered financial assistance, banking security and privacy, user experience in banking, predictive analytics in FinTech, and voice-enabled banking chatbots
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Govindaraj, Manoj, Ravishankar Krishnan, and Jenifer Lawrence. "Analyzing the Drivers of Customer Chatbot Adoption in the Banking Industry." Journal of Technology Management for Growing Economies 14, no. 2 (2023): 51–60. https://doi.org/10.15415/jtmge/2023.142002.
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Background: The integration of Artificial Intelligence (AI) chatbots into various industries has become a significant trend, with the banking sector being one of the key adopters. AI chatbots are designed to simulate human conversation, offering automated responses to customer queries. Their use in the banking industry aims to streamline customer service and improve efficiency. However, understanding the factors that influence customers’ willingness to use chatbot services remains crucial for banks in optimizing these technologies. Factors such as perceived usefulness, ease of use, trust, privacy concerns, and customer satisfaction play vital roles in determining the acceptance of chatbot services in banking. Purpose: The purpose of this research is to identify and analyze the factors that influence customer intention to use chatbots in banks. By investigating these factors, the study seeks to provide banks with actionable insights to improve their chatbot services, enhance customer engagement, and increase customer satisfaction. The research also aims to assess the role of various technological aspects such as the chatbot interface, content, safety, and convenience in shaping customer decisions to adopt this technology. Methods: This study employs a quantitative research approach, utilizing a structured questionnaire to gather data from a sample of 250 bank customers. The questionnaire assesses several key factors, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. The collected data is then analyzed using statistical techniques, including regression analysis and structural equation modeling (SEM), to test the Technology Acceptance Model (TAM) and examine the relationships between the identified factors. Results: The analysis reveals significant relationships between customer intention to use chatbot services and factors such as perceived usefulness, trust, and ease of use. Customers’ satisfaction with the interface, content, and security of the chatbot also plays a critical role in their willingness to adopt this technology. The study confirms that perceived convenience and safety strongly influence customers’ decision to engage with AI-driven chatbots in banks. Conclusions: The findings of this research provide valuable insights into the factors affecting customer acceptance and intention to use chatbots in the banking sector. Financial institutions can use these insights to tailor their chatbot services, ensuring they address customer concerns related to trust, security, and ease of use. The results also highlight the importance of designing user-friendly interfaces and ensuring the safety of customer data. By understanding these factors, banks can improve customer satisfaction, foster trust, and promote the adoption of AI-driven services, benefiting both customers and service providers in the long term.
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Amelia, Amelia, Fani Sartika, Suryani Murad, and Nazmatul Ufra. "Determinants of Customer Satisfaction in Chatbot Use." Almana : Jurnal Manajemen dan Bisnis 8, no. 1 (2024): 46–55. http://dx.doi.org/10.36555/almana.v8i1.2345.
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Chatbots are becoming increasingly popular in business due to their ability to deliver immediate responses to customers. However, there is limited empirical evidence focusing on customers’ perspectives. This study aims to investigate the determinants of customer satisfaction in chatbot use as well as provide an overview of chatbot users in Indonesia. The research uses quantitative methods and descriptive types of research, with the number of samples being 150 chatbot users. All data collected met the criteria for validity and reliability and were analyzed descriptively and quantitatively through multiple linear regression methods. The research showed that most of the respondents were male chatbot users, aged 18–29 years, had last used chatbots between 1–3 months ago, and used chatbots to convey their complaints/problems. It is also known that banking chatbots are the most frequently used chatbots. Additionally, perceived usefulness and perceived ease of use are the determinants of customer satisfaction in chatbot use. Of these two factors, perceived usefulness has a dominant influence on chatbot consumer (user) satisfaction in Indonesia.
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Abdallah, Wael, Arezou Harraf, Osama Mosusa, and Abdalmuttaleb Sartawi. "Investigating Factors Impacting Customer Acceptance of Artificial Intelligence Chatbot: Banking Sector of Kuwait." International Journal of Applied Research in Management and Economics 5, no. 4 (2023): 45–58. http://dx.doi.org/10.33422/ijarme.v5i4.961.
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The Purpose: This study investigates the role of Artificial Intelligence- chatbot (AI chatbot) quality and AI chatbot users across various banking needs and its impact on customer acceptance of AI chatbots through the mediating role of perceived usefulness and ease of use. Design/methodology/approach – This quantitative study uses a cross-sectional time dimension. The questionnaire of this study was developed using multiple academic sources. Partial least square structural equation modeling was used to analyze the data, and the SmartPLS 4 software was used for the calculation. Finding - The findings indicated a significant positive direct relationship between AI chatbot quality and acceptance of AI chatbot (path coefficient of 0.138 and p-value of 0.022). At the same time, the direct relationship between the AI-chatbot user and the acceptance of the AI chatbot was insignificant (path coefficient = 0.0.096, and p-value = 0.246). While the results of the indirect relationship reveal that perceived usefulness and ease of use partially mediated the relationship between AI chatbot quality and acceptance of AI chatbots. The perceived usefulness and ease of use fully mediated the relationship between AI chatbot users and acceptance of the AI chatbot. Originality/value – The results of this study developed a framework for banking and other customer-oriented businesses in understanding and developing AI chatbots to address customer needs.
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Kondybayeva, Saltanat, Meruyert Daribayeva, Raffaele Fiume, et al. "A New Concept of Transforming Service: Impact of Generative Voice Chatbots on Customer Satisfaction and Banking Industry Productivity." Emerging Science Journal 8, no. 6 (2024): 2278–311. https://doi.org/10.28991/esj-2024-08-06-09.
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This study examines the impact of implementing generative AI voice chatbots on customer expectations and satisfaction in the banking sectors of Kazakhstan, Russia, and Italy. To achieve this objective, this study conducted a survey of 253 customers from 35 commercial banks in Kazakhstan, Russia, and Italy from November 2023 to early April 2024. This study employed partial least squares structural equation modelling (PLS-SEM) to assess and validate the validity and reliability of the research model. The study integrates the Expectation Confirmation Model with AI components to analyze factors influencing customer satisfaction with AI-enabled digital banking services. Key findings indicate that expectation confirmation, perceived performance, visual attractiveness, problem-solving capabilities, and communication quality significantly affect customer satisfaction with AI chatbots. However, trendiness and customization features showed minimal impact. The research also explores how customer satisfaction and corporate reputation influence chatbot adoption. Additionally, the study investigates the relationship between chatbot adoption and productivity performance in banking operations. The study provides several policy recommendations, including enhancing perceived performance, expectation confirmation, communication quality, visual attractiveness, and corporate reputation, which can improve customer satisfaction and increase confidence in generative AI voice chatbots in the digital banking industry. Doi: 10.28991/ESJ-2024-08-06-09 Full Text: PDF
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Vyas, Deval G. "An investigation on the acceptance of technology through the use of banking chabot." Journal of Advances and Scholarly Researches in Allied Education 21, no. 7 (2024): 384–401. https://doi.org/10.29070/yvvgdj72.
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The purpose of this research is to investigate the variables impact customers' propensity to utilise banking chatbots. Customer knowledge of the service, their perception of the privacy risk, and the technological acceptance model were the pillars upon which the measurement framework & hypotheses were built. There are 287 people in the sample, and 24 percent of them have used a chatbot for banking at some point. After a measurement model verified that the measure's items were accurate, PLS-SEM was used to evaluate the hypotheses. Research indicates that compatibility & perceived usefulness are the two most critical aspects of banking chatbot adoption. Knowing about the service influences how easy it is to utilise, how worried people are about their privacy, and how likely they are to use banking chatbots because of how valuable they think they are. Not only that, but one's impression of the product's utility affects their impression of how easy it is to use, and vice versa for compatibility. Neither the perceived privacy risk nor the perceived ease of use influences the propensity to utilise.
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Vyas, Deval G. "An investigation on the acceptance of technology through the use of banking chabot." Journal of Advances and Scholarly Researches in Allied Education 21, no. 7 (2024): 384–401. https://doi.org/10.29070/qhcwvc10.
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The purpose of this research is to investigate the variables impact customers' propensity to utilise banking chatbots. Customer knowledge of the service, their perception of the privacy risk, and the technological acceptance model were the pillars upon which the measurement framework & hypotheses were built. There are 287 people in the sample, and 24 percent of them have used a chatbot for banking at some point. After a measurement model verified that the measure's items were accurate, PLS-SEM was used to evaluate the hypotheses. Research indicates that compatibility & perceived usefulness are the two most critical aspects of banking chatbot adoption. Knowing about the service influences how easy it is to utilise, how worried people are about their privacy, and how likely they are to use banking chatbots because of how valuable they think they are. Not only that, but one's impression of the product's utility affects their impression of how easy it is to use, and vice versa for compatibility. Neither the perceived privacy risk nor the perceived ease of use influences the propensity to utilise.
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Baladari, Venkata. "AI-Powered Chatbots in Banking: Developer Best Practices for Enhancing Efficiency and Security." International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) 4, no. 2 (2024): 649–56. https://doi.org/10.48175/IJARSCT-15300C.
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Chatbots powered by AI technology are revolutionizing banking by enhancing customer support, streamlining processes, and increasing digital security measures. These chatbots employ artificial intelligence, natural language processing, and machine learning techniques to offer rapid assistance, execute transactions, and deliver customized financial guidance. Banks can achieve cost reductions, enhanced efficiency, and more effective fraud detection with their assistance. This study examines the primary technologies driving banking chatbots, encompassing AI, blockchain, and predictive analytics. The analysis also looks at security concerns including data confidentiality, verification processes, and adherence to financial rules. Developers can create chatbots that offer secure and efficient digital banking experiences by implementing these guidelines. With advancements in AI technology, chatbots are expected to gain more sophistication, ultimately leading to quicker, more secure, and more user-friendly banking services. This study is designed to facilitate the creation of AI-driven banking solutions in a rapidly evolving financial environment.
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Melo, Giselle Lorrane Nobre, Ronald Pinto Marques, Nicoly da Silva Menezes, Luciano Arruda Teran, and Marcelle Pereira Mota. "Inspecting the Accessibility of Chatbots and Mobile Banking for Emergent Users in the Context of People with Low Literacy." Journal on Interactive Systems 15, no. 1 (2024): 839–59. http://dx.doi.org/10.5753/jis.2024.4317.
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Digital banking services are embedded in people's daily lives. Despite this, these services are not accessible to everyone for various reasons, especially for people with low literacy. The objective of this work was to develop a method of accessibility inspection in mobile banking and banking chatbots, and the examination was directed toward the public mentioned before. For this reason, two inspection sequences were carried out on five Brazilian banking applications, mediated by accessibility considerations based on literature and case studies applied in the contexts of a riverside community and around elderly people with less experience in digital technology, as well as the mapping of design considerations with the Brazilian technical standard aimed at mobile applications and the investigation of banking chatbots made available by these applications. The results found in the inspections indicate how much the design considerations are satisfied, not satisfied and partially satisfied in the inspected applications and the comparison of the two inspections carried out. Additionally, it was possible to review and evaluate design considerations and propose improvements aimed at professionals who intend to develop accessible mobile bankings, as well as the importance of making banking chatbots.
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Kumar Asthana, Praveen. "An Analytical Study of Generative AI in Optimizing Automated Customer Support Chatbots: Reducing Resolution Time and Enhancing Customer Satisfaction in Retail Banking." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 6 (2022): 114–20. http://dx.doi.org/10.17762/ijritcc.v10i6.11171.
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The recent development of AI has had a significant impact on the customer service systems in all industries as the chatbots become integral parts in the automated elements for achieving customer ease of accessing services through such channels. Artificial intelligence chatbots are now being implemented in retail banking to solve simple customer requests, process transactions, and resolve commonly encountered problems in the banks. This paper aims at evaluating the deployment of generative AI for the improvement of these chatbots with respect to reducing resolution times towards customers and to enhance overall satisfaction compared to traditional rule-based chatbot systems (Adamopoulou & Moussiades, 2020).
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Abdul Karim Shaikh, Irfan, Sania Khan, and Shaha Faisal. "Determinants affecting customer intention to use chatbots in the banking sector." Innovative Marketing 19, no. 4 (2023): 257–68. http://dx.doi.org/10.21511/im.19(4).2023.21.
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The study aims to analyze the factors that influence customers’ inclination to utilize chatbots in banking services. The paper employed the technology acceptance model and utilized structural equation modeling to examine the factors affecting consumers’ willingness to embrace chatbot services. The survey evaluated various determinants, including perceived usefulness, perceived ease of use, trust, privacy concerns, and customer satisfaction. Data were collected from 250 bank customers in the Bombay region of India through an online survey employing a random sampling method. The collected data were analyzed using IBM SPSS AMOS. This study identifies the aspects of chatbot technology in the banking sector, such as user interface, content, security, and convenience, that influence customers’ decisions to adopt this innovative technology. The results of the analysis revealed path coefficients indicating a significant relationship between information security and perceived usefulness (β = 0.286; p = 0.005) and between perceived usefulness and intention to use (β = 0.489; p < 0.001). Additionally, the path coefficients for design, security, and facilitating conditions were β = 0.281, β = 0.193, and β = 0.136, respectively, all of which held nearly equal significance in the study. The inter-correlations among the variables ranged from 0.346 to 0.854 and were statistically significant. In the banking sector, customers’ intention to use chatbots is influenced by convenience, efficiency, trust, and personalized experiences. Customers are more likely to embrace chatbots when they provide seamless support and tailored solutions, ultimately enhancing customer satisfaction and engagement. AcknowledgmentThis study is supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1445).
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Satya Rameshwari Raghavan. "Trustworthy Chatbots in Finance: A Framework For Explain Ability, Accessibility, and AI Governance." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 314–23. https://doi.org/10.32628/cseit2511420.
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The integration of AI-powered chatbots into the banking sector has transformed how financial institutions engage with customers, streamline operations, and enhance security. While early chatbot implementations focused on automation and convenience, modern systems must now meet higher expectations in terms of transparency, accessibility, regulatory compliance, and ethical alignment. This research investigates the current limitations of banking chatbots and proposes an expanded framework for developing intelligent, inclusive, and secure conversational AI systems. Key areas explored include the application of explainable AI for decision transparency, design strategies for accessibility and inclusive interaction, mitigation of algorithmic bias, and compliance with global data protection laws. The study also examines the role of adaptive learning, voice interfaces, and blockchain in enhancing user trust and operational integrity. Through a synthesis of technical best practices and ethical considerations, this paper provides actionable recommendations for developers and banks to build chatbot ecosystems that are not only efficient but also responsible and future-ready.
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Thota, Sowmya, Sai Keerthana Bongoni, Dr L. Chandrasekhar Reddy, and Parameswar M. "Smart Banking Chatbot." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2319–23. https://doi.org/10.22214/ijraset.2025.67801.
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Abstract: This paper provides the development and implementation of a smart banking chatbot designed to enhance the user experience by providing automated responses to customer inquiries. With the increasing digitalization of banking services, the need for quick and efficient customer support has become crucial. The chatbot, developed using the RASA framework, leverages Natural Language Processing (NLP) to understand user intents and execute predefined actions. It offers services such as checking account balances, making transactions, and addressing customer queries in real-time, reducing the dependency on human customer support. By evaluating different machine learning algorithms, including Random forest, (KNN) k-Nearest neighbors, and Support Vector Machines(SVM), the chatbot ensures high accuracy in predicting user requests. RASA is an open-source (NLP) Natural Language Processing platform used to create virtual assistants and contextual chatbots. RASA has several main components: RASA Open Source and RASA Action Server. Rasa Open Source has Rasa NLU which is used to recognize the intent of communication and Rasa core to determine what to do next and how to continue the conversation. Rasa Action Server allows you to host python scripts to do certain custom tasks, like processing or modifying databases in the backend.The experimental results demonstrate that the chatbot significantly reduces response time and improves service efficiency, making it a valuable addition to modern banking systems
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Doherty, Dana, and Kevin Curran. "Chatbots for online banking services." Web Intelligence 17, no. 4 (2019): 327–42. http://dx.doi.org/10.3233/web-190422.
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Rozghon, Yuliia, and Leonid Melnyk. "DIGITALIZATION OF BUSINESS PROCESSES BASED ON CHATBOT IMPLEMENTATION." Економіка розвитку систем 7, no. 1 (2025): 119–25. https://doi.org/10.32782/2707-8019/2025-1-15.
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The main aspect of the review is a comprehensive analysis of the development and use of chatbots in the modern world. The article explores the practical aspects of using chatbots in various industries, such as retail, banking, agriculture, and tourism. The authors analyze the specifics of using chatbots in each of these areas, identifying key challenges and opportunities. Based on the analysis of scientific publications and real cases, the authors identify the key advantages and challenges associated with using chatbots. Special attention is paid to the analysis of the impact of chatbots on business processes, customer service and interaction with users. The paper also looks at the potential for chatbots to further develop and integrate with other technologies such as artificial intelligence. Also, the algorithm for successfully introducing a chatbot into the business process for proper functioning and the ROI calculation option to evaluate the integration result is considered. This study provides a comprehensive assessment of chatbots' economic impact on various economic sectors, analyzing their impact not only on efficiency but also on changing consumer behaviour and the competitive environment.
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Edwin Joshua Jone, D. "Revolutionizing Employee Well-being and Performance: Personalized Emotional Chatbots for Banking Professionals in Tamil Nadu." Shanlax International Journal of Arts, Science and Humanities 11, S2-Feb (2024): 79–81. http://dx.doi.org/10.34293/sijash.v11is2-feb.7426.
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In this article, we delve into personalized emotional chatbots and their potential to enhance the well-being and performance of banking employees in Tamil Nadu. With the banking sector in the region experiencing heightened levels of strain, it is imperative to consider the impact of employee stress and burnout. By harnessing innovative technologies and psychological principles, personalized emotional chatbots offer customized support to mitigate these hurdles. Through carefully examining relevant research and real-life examples, this article illuminates the advantages and possible implementations of these chatbots in the banking industry, ultimately promoting a more positive work culture and increased job satisfaction.
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Muhammad, Alfian. "ANALISIS SWOT TRANSFORMASI DIGITAL PADA LANSIA DALAM PENGGUNAAN JASA PERBANKAN DI INDONESIA ERA SOCIETY 5.0." Jihbiz: Global Journal of Islamic Banking and Finance 5, no. 1 (2023): 79. http://dx.doi.org/10.22373/jihbiz.v5i1.17303.
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The purpose of this study is to answer the problems that occur in the elderly in the use of banking services in Indonesia in the era of society 5.0 by using a SWOT Analysis. This research is a type of library research using a qualitative descriptive approach. After conducting a SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis regarding digital transformation in the elderly in using banking services in Indonesia in the era of society 5.0, it can be concluded that the development of technology in financial institutions has a high level of effectiveness to provide quality banking services to the elderly, but the level of use is still low, because many elderly people do not understand how to use digital services Banking. Preferably, banking companies provide chatbots services for the elderly who want to do digital banking transactions. If an elderly person wants to make transactions using digital banking services, then it can start a conversation with chatbots and all their complaints will be resolved and replied to automatically, although it is unlikely that chatbots will do so to replace banking employees.
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Wu, Haoqi. "Impact of Chatbot Service on Bank Performance Based on a Case Study of IBM Corporation." Highlights in Business, Economics and Management 40 (September 1, 2024): 858–66. http://dx.doi.org/10.54097/0y765327.
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Exploring the impact of chatbot on bank performance helps to identify the potential of this technology to revolutionize the bank's business model and enhance customer experience, while it can improve employee efficiency and provide a scientific basis and guidance for bankers to effectively respond to the increasing competitive market and achieve bank performance growth and sustainable development. This paper delineates the definitions of chatbot and business performance, provides an overview of current research, and examines the influence of chatbots on bank performance. Furthermore, it utilizes IBM's chatbot as a case study to investigate its impact on banking performance, focusing primarily on cost reduction, enhanced customer satisfaction, and improved employee efficiency. At the same time, the banking industry also faces the negative risk impact of chatbot, which mainly includes perceived risk, security threat and moral risk. The paper also concludes with recommendations on the negative risk impacts of smart customer service. This study links chatbot with bank performance to fill the research gap. It also provides an outlook and suggestions for the future development prospects of chatbot in the banking industry.
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Hashem, Tareq N. "The Future of Customer Service in Jordan: How Chatbots and Conversational AI are Shaping the Face of Customer Experience." Asian Research Journal of Arts & Social Sciences 23, no. 4 (2025): 1–10. https://doi.org/10.9734/arjass/2025/v23i4656.
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This study looks at how chatbots and conversational AI improve the consumer experience in Jordan. It focuses on the quality of chatbot interactions, how frequently people use chatbots, and the importance of conversational AI. The researcher used a simple random sample to collect the data from 500 participants who had interacted with chatbots in retail, banking, and telecommunications sectors. He conducted a survey to assess the quality of bot interactions, including how frequently individuals used them, what the AI could perform, and how customers felt about it all. He ran the numbers through SPSS v26 to examine how these things were related. The findings revealed that stronger chatbot interactions led to happier customers. People who utilized chatbots more frequently reported a better experience. Customers were particularly pleased with advanced AI technology that allowed them to personalize talks to individual needs. These findings support previous research and add new information from a burgeoning market, helping to enhance our understanding of chatbots and conversational AI. This study has practical implications for Jordanian enterprises. It demonstrates that they should invest in high-quality chatbots, encourage more people to use them, and employ clever AI to interact with clients. The study has some limitations, such as employing a convenience sample and looking at a single point in time. The researcher proposed additional studies to investigate how these technological tools affect various industries in the long run.
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Giordani, John. "Advancing Mitigation Strategies for AI-Driven Chatbots: Building Resilience Against Data Privacy Violations. A Follow-up Study." European Journal of Applied Science, Engineering and Technology 2, no. 6 (2024): 163–67. https://doi.org/10.59324/ejaset.2024.2(6).15.
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This study extends prior research on mitigating AI data poisoning risks in banking chatbots, emphasizing the development of dynamic defense strategies and advanced mitigation frameworks. This research explores the evolving threat landscape by employing grounded theory methodology and insights from IT professionals and end-users, incorporating practical applications to enhance chatbot resilience. Key findings highlight the integration of real-time anomaly detection, enhanced encryption techniques, and ethical AI governance frameworks to safeguard sensitive data and maintain consumer trust. The recommendations empower banking institutions to adopt robust strategies, aligning technological advancements with compliance and ethical standards.
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Giordani, John. "Advancing Mitigation Strategies for AI-Driven Chatbots: Building Resilience Against Data Privacy Violations. A Follow-up Study." European Journal of Applied Science, Engineering and Technology 2, no. 6 (2024): 163–67. https://doi.org/10.59324/ejaset.2024.2(6).15.
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This study extends prior research on mitigating AI data poisoning risks in banking chatbots, emphasizing the development of dynamic defense strategies and advanced mitigation frameworks. This research explores the evolving threat landscape by employing grounded theory methodology and insights from IT professionals and end-users, incorporating practical applications to enhance chatbot resilience. Key findings highlight the integration of real-time anomaly detection, enhanced encryption techniques, and ethical AI governance frameworks to safeguard sensitive data and maintain consumer trust. The recommendations empower banking institutions to adopt robust strategies, aligning technological advancements with compliance and ethical standards.
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Yuda Pramana, Maria Apsari Sugiat, and Anton Mulyono Azis. "Evaluation of Customer Satisfaction on Indonesian Banking Chatbot Services After the COVID-19 Pandemic." JKBM (JURNAL KONSEP BISNIS DAN MANAJEMEN) 11, no. 1 (2024): 1–13. https://doi.org/10.31289/jkbm.v11i1.12590.
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This study evaluates the variables influencing users' satisfaction with Indonesian Banking Chatbots after the COVID-19 outbreak. The study examined several factors, including system quality, information quality, service quality, perceived value, and situational factors. All of these were examined using purpos-ive sampling to obtain information from 161 respondents. According to the research criteria, participants had to be DKI Jakarta locals and have used Banking Chatbot services before. The sample size was calculated using the G*Power approach. The influence of each variable on customer satisfaction with Banking Chatbot services was examined, with an intention to use serving as an intervening variable. After gathering the data, the results were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM 4.0). The study's findings indicate that situational factors and perceived value elements indirectly but positively influence consumer satisfaction, while intention to use has a direct and positive influence. By focusing on these aspects and associated indications, Indonesia's banking management can utilize this model to improve customer satisfaction and intention in using Chatbot services.
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Humairoh, Humairoh, Nandan Limakrisna, and Anoesyirwan Moeins. "Intelligent Banking Chatbot: Intention to Continue Through Millennial Customer Satisfaction in Indonesia Using the TAM Method." Dinasti International Journal of Economics, Finance & Accounting 4, no. 6 (2024): 816–27. http://dx.doi.org/10.38035/dijefa.v4i6.2277.
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This study examines the impact of perceived usefulness and ease of use on the intention to continue utilizing banking chatbots in Indonesia. The sample comprises Generation Millennials enrolled in Master's degree programs in Tangerang Raya, Banten Province. These individuals have utilized the banking chatbots MITA, VIRA, AISYAH, CINTA, and SABRINA. The sample comprised 230 individuals, and the sampling method employed was simple random sampling. Data collection employs a survey methodology with a questionnaire instrument. The data underwent processing utilizing path analysis techniques facilitated by SPSS Version 26 software. The study yielded findings indicating that both partial and simultaneous judgment of usefulness and ease of use had a favorable and substantial impact on customer satisfaction and intention to continue. Customer satisfaction was discovered to impact the level of ongoing interest. Similarly, the ongoing attraction of millennials towards utilizing banking chatbots in Indonesia is driven by their perception of the chatbots' usefulness and ease of use, with customer satisfaction as a mediating factor. The coefficient of determination for customer satisfaction is 74.4%, while the coefficient of determination for intention to continue using chatbots is 83.2%.
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Walia, Dr Davinder. "Chatbots and Conversational AI in Banking: Assessing Operational Efficiency and Customer Trust." International Journal of Environmental Sciences 11, no. 2s (2025): 722–29. https://doi.org/10.64252/spqdns98.
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The integration of chatbots and conversational AI in the banking sector represents a major shift toward digital service delivery, aimed at enhancing operational efficiency and improving customer experience. This study examines the relationship between chatbot-induced operational efficiency, customer trust, and overall satisfaction from a project management perspective. Using a structured questionnaire, data were collected from 400 banking customers who have interacted with chatbot services. Reliability analysis confirmed the internal consistency of the instrument (Cronbach’s Alpha = 0.808). Pearson correlation analysis revealed a strong positive relationship between operational efficiency and customer trust (r = 0.602, p < 0.01). Further, multiple regression analysis demonstrated that operational efficiency and customer trust significantly predict overall user satisfaction and intent to use, explaining 55.7% of the variance (R² = 0.557). These findings underscore the importance of efficient system design and trust-building in project planning and execution. The study contributes to the field of project management by emphasizing that technological success in banking is not solely measured by cost and time metrics but also by user perception and adoption.
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Eren, Berrin Arzu. "Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey." International Journal of Bank Marketing 39, no. 2 (2021): 294–311. http://dx.doi.org/10.1108/ijbm-02-2020-0056.
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PurposeThis study aims to investigates customer satisfaction from the use of bank chatbots and the effect of perceived trust in chatbots and banks' reputation on customer satisfaction.Design/methodology/approachA survey is conducted in Turkey involving 240 customers who experienced banking transactions using a chatbot. Partial least squares structural equation modeling (PLS-SEM) is used to investigate the relationships between the variables. The data were analyzed using SPSS 21 and SmartPLS programs.FindingsPerceived performance, perceived trust and corporate reputation significantly affect customer satisfaction with chatbot use. Customer expectations and confirmation of customer expectations have no direct impact on customer satisfaction, but customer expectations positively affect perceived performance. Customer expectations exert an indirect influence on customer satisfaction through perceived performance. Perceived performance has a positive impact on the confirmation of customer expectations, but customer expectations do not significantly impact the confirmation of customer expectations.Research limitations/implicationsThis study relies on a limited number of participants. Moreover, its sample is not representative of the target population due to the convenience sampling technique. Even if the results may not be generalized to the entire population of Turkey, they reflect the reality of emerging markets with relatively high technology sensitivity and a young population.Practical implicationsThe results provide new insights regarding banking service delivery channels, which may be of interest to professionals, academics, banks' top management, product development teams, design teams and customer satisfaction units.Social implicationsThis study is believed to help the community make their lives easier by providing them with knowledge and awareness about chatbots.Originality/valueThis study extends expectations confirmation theory's predictions to chatbot use in banking.
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Joshi, Atharva. "Evaluation of Transformer Model for Conversational Chatbots Using Cosine Similarity Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 691–94. http://dx.doi.org/10.22214/ijraset.2021.38488.
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Abstract: The use of chatbots has seen a significant rise in the past few years, these pieces of software are seen on various Ecommerce platforms or banking platforms. These applications mostly involve the use of Rule-based chatbots, in this paper we have explored the possibility of using smart chatbots which can have a free-flowing conversation with the user. The training of these chatbots determines their ability to have a human-like conversation. The transformer model in this study has an encoderdecoder architecture, with a self-attention layer. The self-attention layer helps the model to have a context of the input, which gives it a reference of each word with respect to the other words in the sentence to the model. The model is being trained for various epochs and these outputs have been based against expected output and their similarity is calculated using Cosine Similarity Technique. The results from these tests were observed and presented in the paper. Keywords: Chatbot, Transformer Model, Self -Attention Layer, Epochs, Cosine Similarity.
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Cîmpeanu, Ionuț-Alexandru, Denis-Alexandru Dragomir, and Răzvan Daniel Zota. "Banking Chatbots: How Artificial Intelligence Helps the Banks." Proceedings of the International Conference on Business Excellence 17, no. 1 (2023): 1716–27. http://dx.doi.org/10.2478/picbe-2023-0153.
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Abstract When we speak of solutions that can be designed, developed, and implemented to solve a determined problem in a specific domain, human intelligence shows that it cannot be kept within regular bounds. In addition, it consistently manages to produce innovative elements that increase efficiency, applicability, and abstraction, with the drawback of increasing the overall underlying complexity. As technology advanced and time passed, in every system there was a visible need to automate manual, repetitive, and mundane tasks to help save time and let humans focus on more important things. A practical example of such a system is the banking system. In this paper, our aim is to present how Artificial Intelligence (AI) helps this field through the use of chatbots. In this sense, we present and analyse five examples of such applications that are already used in the international banking system, highlighting the main benefits they bring. Finally, we present some conclusions regarding the chatbot systems analysed, acknowledging that the future will increasingly belong to IT applications of this kind.
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Q. Bataineh, Abdallah, Dhia Qasim, and Mohammad Alhur. "The impact of digital banking channels and organizational culture on operational excellence in Jordanian banking." Banks and Bank Systems 19, no. 4 (2024): 163–76. https://doi.org/10.21511/bbs.19(4).2024.13.
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This study explores the impact of digital banking channels on the operational excellence of Jordanian banks. It investigates the impact of social media, chatbots, digital wallets, and mobile applications on operational excellence in Jordanian banks, focusing on the moderating role of organizational culture. The study used a quantitative research method and a stratified random sampling technique to ensure a diverse sample of the five largest banks in Jordan, including managers and IT professionals responsible for managing digital banking operations. A total of 276 completed questionnaires were analyzed using structural equation modeling. The findings show that digital banking channels significantly contribute to operational excellence. Social media banking (β = 0.155, p &amp;lt; 0.05) and chatbots (β = 0.212, p &amp;lt; 0.01) positively impact operational excellence, while digital wallets (β = 0.301, p &amp;lt; 0.001) and mobile banking applications (β = 0.22, p &amp;lt; 0.01) also exhibit significant positive effects. The structural model explained 63% of the variance in operational excellence. Additionally, organizational culture was found to positively moderate the impact of social media banking (β = 0.20, p &amp;lt; 0.01), chatbots (β = 0.10, p &amp;lt; 0.05), and digital wallets (β = 0.16, p &amp;lt; 0.01) on operational excellence. However, the interaction between mobile applications and organizational culture was not significant (β = 0.078, p = 0.065).
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Ok, SeungHyun, and Seungbeom Kim. "A Study on the Service Quality Attributes of AI Banking Chatbots: Based on the KANO Model." Korean Production and Operations Management Society 36, no. 1 (2025): 133–48. https://doi.org/10.32956/kopoms.2025.36.1.133.
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With the advancement of Large Language Models (LLMs), chatbots in the financial sector have come to play a variety of key roles, such as handling customer inquiries, providing financial consultations, and supporting transaction processes. This study analyzes the service quality attributes of banking chatbots to improve user experience and service quality, using the KANO model and Timko’s Customer Satisfaction Coefficient. The core elements of AI chatbot service quality were identified, and a survey was designed with seven dimensions and 23 detailed items. Based on the results, each element was systematically classified into attractive, one- dimensional, must-be, and indifferent quality categories. Additionally, the services provided by human bankers were examined to compare and analyze the differences between AI chatbots and human services. Among the service quality dimensions of AI chatbots, elements such as “social presence,” “perceived enjoyment,” and “empathy” are frequently identified as attractive quality (A). However, some elements are also categorized as indifferent quality (I), indicating the need for an in- depth analysis of customer needs for effective improvement. Elements related to “stability,” such as maintaining security and responding to system failures, are deemed the most fundamental aspects of service quality. Furthermore, the ability to accurately understand customer needs, provide professional responses, and ask clarifying questions in cases of uncertainty has become a basic requirement rather than a distinguishing capability. The study also revealed that even within the same quality dimension, elements may possess varying quality attributes.
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Sutantri Sutantri. "Effectiveness of Using AI-Based Chatbots in Increasing Customer Engagement." OPTIMAL Jurnal Ekonomi dan Manajemen 5, no. 2 (2025): 662–71. https://doi.org/10.55606/optimal.v5i2.6516.
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This study examines the effectiveness of AI-based chatbots in enhancing customer engagement across various industries. Against the backdrop of increasing digital transformation, the research explores how chatbots influence key metrics such as response time, customer satisfaction, and conversion rates, while identifying implementation challenges and success factors. Through a systematic analysis of recent case studies and empirical data, the study reveals that well-designed chatbots can improve customer satisfaction by 18 percentage points and reduce response times by 99.6%, though limitations persist in handling complex queries and ensuring data privacy. The findings highlight the importance of anthropomorphic design, omnichannel integration, and balanced human-AI collaboration in optimizing chatbot performance. Practical implications suggest that businesses should prioritize transparent data policies, continuous model training, and user-centric conversation flows to maximize engagement. The study contributes to the growing body of knowledge on AI-driven customer service by synthesizing actionable insights from diverse sectors, including retail, banking, and healthcare.
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Satheesh, Meganathan Kumar, Nagaraj Samala, and Raul Villamarin Rodriguez. "ROLE OF AI-INDUCED CHATBOT IN ENHANCING CUSTOMER RELATIONSHIP MANAGEMENT IN THE BANKING INDUSTRY." ICTACT Journal on Management Studies 6, no. 4 (2020): 1320–23. https://doi.org/10.21917/ijms.2020.0185.
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Customers are less satisfied with the prompt services provided by the banks. They also feel that guidance to use the bank’s products and services is not adequately explained. When they ask for information or report any issues, the process is not easy. On the other hand, handling millions of customers with limited bank employees is a tedious process. The bank employees are also exhausted to answer to the same repetitive questions for a long time. The customers are comfortable with the answers, and the bank employees are also tired of the same routine of giving the same answers to different customers. This unpleasant situation will weaken the relationship between the banks and the customers. This paper will discuss how AI-induced chatbots improve customer interaction and how chatbots play an essential role in customer relationship management in the banking industry. The AI-induced chatbot certainly enhances the customer relationship with the banks.
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Patel, Dhruv, NIhal Shetty, Paarth Kapasi, and Ishaan Kangriwala. "College Enquiry Chatbot using Conversational AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 903–15. http://dx.doi.org/10.22214/ijraset.2023.51324.
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Abstract: Chatbots are computer programs that use text or voice-based interfaces to replicate human conversation. They are often used to automate mundane processes, provide customer support, or aid in the retrieval of information. Chatbots are built with a number of strategies that enable them to interpret and respond to user inputs in a more human-like manner. They can be employed in a variety of industries, including e-commerce, healthcare, and banking. We have analysed and compared numerous chatbot strategies in this report to establish the optimal way for our own chatbot project. We reviewed twenty-six papers on chatbot development and assessed the advantages and disadvantages of various strategies. Natural language processing techniques, such as tokenization and named entity recognition, have been shown in our research to be critical for interpreting user inputs. We also discovered that dialogue management methods, such as rule-based and machine learning-based approaches, have an important influence in influencing discussion flow. Furthermore, we discovered that natural language generation techniques, such as template-based and neural network-based methods, are critical in generating effective chatbot responses. We also investigated various services on the market in order to create a functional chatbot for our college. We also emphasized the various applications of chatbots as well as the current hurdles in the industry. Based on these findings, we chose a technique for our own chatbot project that employs advanced natural language processing and machine learning techniques to create more human-like conversations and improve overall user experience.
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H V, Supreetha, and Dr Sandhya S. "Implementation of an Educational Chatbot using Rasa Framework." International Journal of Innovative Technology and Exploring Engineering 11, no. 9 (2022): 29–35. http://dx.doi.org/10.35940/ijitee.g9189.0811922.
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The growth in Artificial Intelligence (AI), Big-data,and Internet-of-Things (IOT) technologies has increased chabot’s application in many areas. Some of the applications of chatbot can be seen in areas such as social media, e-commerce, healthcare, stock market, education, banking sector etc. Most of the high-end chatbots are deployed inside e-commerce, banking and health websites. There is a need to deploy the chatbots in educational website to improve interactivity of the educational platforms. The main target users of this website is rural students. In rural areas, probability of students dropping school after some age is common because, there won’t be proper monitoring of students and also sometimes facilities will be less. With e- learning, anyone can learn everything with limited cost. The key insight of developing this e-learning website is to provide a chatbot which can motivate rural students towards education. Thus a single platform where users can learn different courses, take quizzes, and chat with the bot is developed. It also provides an additional facility of tracking the scores of the quizzes and giving personalized recommendation systems to improve the scores. The chatbot will also help users to find details aboutfaculties and help users to set an appointment with distant faculties in online mode for doubts clarification. Flask micro- framework is used for developing the website. Firebase is used to store the data. RASA framework is used in developing the chatbot. Finally a content based filtering is used to givepersonalized recommendation systems.
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Supreetha, H. V., and Sandhya S. Dr. "Implementation of an Educational Chatbot using Rasa Framework." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 9 (2022): 29–35. https://doi.org/10.35940/ijitee.G9189.0811922.
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<strong>Abstract</strong>: The growth in Artificial Intelligence (AI), Big-data, and Internet-of-Things (IOT) technologies has increased chabot’s application in many areas. Some of the applications of chatbot can be seen in areas such as social media, e-commerce, healthcare, stock market, education, banking sector etc. Most of the high-end chatbots are deployed inside e-commerce, banking and health websites. There is a need to deploy the chatbots in educational website to improve interactivity of the educational platforms. The main target users of this website is rural students. In rural areas, probability of students dropping school after some age is common because, there won’t be proper monitoring of students and also sometimes facilities will be less. With elearning, anyone can learn everything with limited cost. The key insight of developing this e-learning website is to provide a chatbot which can motivate rural students towards education. Thus a single platform where users can learn different courses, take quizzes, and chat with the bot is developed. It also provides an additional facility of tracking the scores of the quizzes and giving personalized recommendation systems to improve the scores. The chatbot will also help users to find details about faculties and help users to set an appointment with distant faculties in online mode for doubts clarification. Flask microframework is used for developing the website. Firebase is used to store the data. RASA framework is used in developing the chatbot. Finally a content based filtering is used to give personalized recommendation systems.
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Dhanya, C., and K. Ramya. "Impact of System-Level Indicators of Chatbots on Perceived Usefulness and Intention to use for Banking Services." Review of Finance and Banking 16, no. 1 (2024): 43–55. http://dx.doi.org/10.24818/rfb.23.16.01.04.
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Technology is rapidly mounting, and AI has transformed the financial industry rapidly. The primary objective of the research is to identify factors (like Design, Information Quality, Security, and Facilitating Conditions) that influence a customer to employ chatbot technology in the banking industry. A survey of 250 respondents was carried out utilizing an online questionnaire. The study’s target respondents are customers who use banking services and chatbots through a random sampling method. The structured questionnaire employed in this study comprised two modules: one detailing the attributes of respondents and enterprises and the other containing questions on research constructs. The Stimulus Theoretical Framework and Technology Acceptance Model were tested throughout the study. The model helps to analyze the factors responsible for customer propensity that influence the use of chatbots. The research first conducted an exploratory factor analysis (EFA) to determine the measured data’s structure. The internal consistency of data was tested using Cronbach’s alpha values. Finally, the research hypothesis was assessed using Structural Equation Modeling (SEM). The research findings can help banks to meet the needs of their diverse customer base. The study would help the providers of chatbot services to reduce the complexity of communication and services for the users and make the customers adopt the technology services without any intervention. This develops trust among the consumers, thus making it a valuable contribution
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Elcholiqi, Abidah, and Aina Musdholifah. "Chatbot in Bahasa Indonesia using NLP to Provide Banking Information." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 1 (2020): 91. http://dx.doi.org/10.22146/ijccs.41289.
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FAQs are mostly provided on the company's website to inform their service and product. It's just that the FAQ is usually less interactive and presents too much information that is less practical. Chatbot can be used as an alternative in providing FAQ. In this study, chatbots were developed for BTPN in providing information about their products, namely Jenius. Chatbot developed utilizes natural language processing so that the system can understand user queries in the form of natural language. The cosine similarity algorithm is used to find similarities between queries and patterns in the knowledge base. Patterns with the highest cosine values are considered to be most similar to user queries. It's just that, this algorithm does not pay attention to the structure of the sentence so that it adds checking the structure of the sentence with the parse tree to give weight to the pattern. This chatbot application has been tested by 10 users and it was found that the suitability of the answers with user input was 84%. Therefore the chatbot developed can be used by BTPN to provide Jenius product information to consumers more interactively and practically.
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Ezekiel Onyekachukwu Udeh, Prisca Amajuoyi, Kudirat Bukola Adeusi, and Anwulika Ogechukwu Scott. "AI-Enhanced Fintech communication: Leveraging Chatbots and NLP for efficient banking support." International Journal of Management & Entrepreneurship Research 6, no. 6 (2024): 1768–86. http://dx.doi.org/10.51594/ijmer.v6i6.1164.
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The convergence of artificial intelligence (AI) and financial technology (fintech) has revolutionized the way banks and financial institutions communicate with customers. This paper explores the use of AI-enhanced fintech communication, focusing on the utilization of chatbots and natural language processing (NLP) to provide efficient banking support. AI-powered chatbots have become indispensable tools for banks seeking to enhance customer service and streamline communication channels. By leveraging NLP algorithms, these chatbots can understand and respond to customer queries in real-time, providing personalized assistance round-the-clock. The integration of AI into fintech communication enables banks to offer seamless and efficient support, improving customer satisfaction and loyalty. The key to the effectiveness of AI-enhanced fintech communication lies in the ability of chatbots to interpret and respond to natural language input accurately. NLP algorithms enable chatbots to analyze and understand the intent behind customer queries, allowing them to provide relevant and contextually appropriate responses. This capability enhances the overall customer experience by reducing response times and ensuring that customers receive accurate and helpful information. Furthermore, AI-powered chatbots can handle a wide range of inquiries, from basic account inquiries to complex financial transactions. By automating routine tasks and inquiries, banks can free up human agents to focus on more complex and value-added activities. This not only improves operational efficiency but also allows banks to deliver faster and more responsive customer service. In addition to providing support to customers, AI-enhanced fintech communication can also help banks gather valuable insights into customer preferences and behavior. By analyzing interactions between customers and chatbots, banks can identify trends, anticipate customer needs, and tailor their products and services accordingly. This data-driven approach enables banks to offer more personalized and targeted offerings, leading to increased customer satisfaction and loyalty. In conclusion, AI-enhanced fintech communication, powered by chatbots and NLP, offers significant benefits for banks and financial institutions. By leveraging AI technology, banks can provide efficient and personalized support to customers, improve operational efficiency, and gain valuable insights into customer behavior. As AI continues to advance, the future of fintech communication promises even greater efficiency, personalization, and innovation in banking support. Keywords: Al- Enhanced, Fintech Communication, Leveraging, Chatbots, NLP.
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Sunitha, V., and S. Ramola Pon Malar. "Digital Banking using AI." Thiagarajar College of Preceptors Edu Spectra 7, S1 (2025): 25–36. https://doi.org/10.34293/eduspectra.v7is1-feb25.003.
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Digital banking powered by artificial intelligence (AI) is transforming the financial industry by enhancing customer experiences, improving security, and streamlining operations. AI technologies, such as Chatbots, fraud detection systems, and predictive analytics, enable banks to provide personalized services, detect fraudulent activities in real-time, and make data-driven decisions. AI also allows for efficient credit scoring, risk management, and tailored financial advice, making banking services more accessible and convenient. One of the most significant applications of AI in digital banking is customer service automation. Chatbots and virtual assistants, powered by AI, are now commonly used by banks to handle customer inquiries, process transactions, and provide realtime support. These AI-driven systems can understand natural language, offer personalized responses, and even predict customer needs based on past behavior. The result is a more seamless and efficient customer experience, reducing the need for human intervention and lowering costs for banks. This paper explores the role of AI in digital banking, its applications, challenges, and the future outlook of AI in the financial sector.
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Laksamana, Patria, Saripudin Saripudin, Suharyanto Suharyanto, and Yohanes Ferry Cahaya. "Artificial intelligence-driven brand strategy: Impact on awareness, image, equity, and loyalty." Journal of Infrastructure, Policy and Development 8, no. 15 (2024): 10084. https://doi.org/10.24294/jipd10084.
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The study investigates the impact of artificial intelligence (AI)-powered chatbots on brand dynamics within the banking sector, focusing on the interrelationships between AI implementation and key brand dimensions, including awareness, equity, image, and loyalty. Using structural equation modeling (SEM) analysis on data collected from 520 banking customers, the study tests eight hypotheses to explore the direct and indirect effects of AI-driven interactions on brand development. The findings reveal that AI chatbots significantly enhance brand awareness in banking services, demonstrating moderate positive effects on both brand equity and brand image. Notably, while brand awareness exerts a strong influence on brand image, it does not have a significant direct effect on brand loyalty. Instead, the study shows that brand loyalty is primarily developed through the mediating effects of brand equity and image, with brand image exerting a particularly strong influence on brand equity. For banking practitioners, these insights suggest a need to integrate AI chatbots within a comprehensive brand strategy that merges technological innovation with traditional relationship-building approaches. Limitations of the study and potential directions for future research are also discussed, providing avenues for further exploration of AI’s role in brand management.
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Agarwal, Harsh. "Exploring Customer Satisfaction in Indian Banking Through Artificial Intelligence: Insights, Trends, and Global Parallels." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50316.
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Abstract: This report analyzes customer satisfaction with AI-based banking services in India, examining survey data from 60 respondents across SBI, HDFC, Axis, and PNB. The study investigates customer perceptions of chatbots, voice assistants, predictive alerts, and AI-driven personal finance recommendations, focusing on factors influencing satisfaction and preferences for AI versus human interaction. The analysis reveals potential trends, including higher satisfaction with chatbots, a preference for human interaction for complex needs, and the importance of digital comfort in AI adoption. However, the small sample size necessitates cautious interpretation, emphasizing the need for future research with larger datasets. The report concludes by offering recommendations for banks and policymakers to promote responsible AI innovation in the Indian banking sector. Keywords: AI in banking, Customer Satisfaction, Indian Banking, Digital Transformation, AI Adoption, FinTech.
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Shrivastava, Dr Manish. "The Role of Artificial Intelligence in Enhancing Banking Services." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44871.
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Artificial Intelligence (AI) has transformed banking services by improving efficiency, security, customer experience, and risk management. AI-driven applications, such as chatbots, fraud detection systems, and predictive analytics, have streamlined banking operations and enhanced service quality. This paper explores the various roles AI plays in banking and its impact on financial institutions. Keywords: Artificial Intelligence, Banking Services, Automation, Fraud Detection