Background: Innovative financial technologies (fintech) are gradually changing how financial transactions and processes are conducted. The adoption of fintech not only benefits the financial sector but can also have a broader impact on society. Due to their ability to provide customized services to a wide range of stakeholders, fintech is gaining traction and experiencing significant growth. Compared to traditional financial institutions, fintech companies can reach a wider audience and operate more efficiently. In addition to upending traditional financial services, fintech can also provide financial services to marginalized groups. We argue that fintech research and practice should focus on seizing opportunities and addressing challenges related to financial inclusion, especially in emerging markets. Method: We conducted a systematic literature review of 178 articles to understand the relationship between fintech and financial inclusion. Results: Our analysis highlights six fintech research themes: fintech and financial inclusion, fintech adoption and use, fintech and sectoral growth, fintech and lending, and technology shaping the fintech. We also present four future themes (basic, driving, niche, and emerging or declining research) that can accelerate financial inclusion. Conclusions: This study highlights the synergies between fintech and financial inclusion research. This study contributes to existing knowledge in three ways. First, the descriptive analysis maps existing research on fintech and financial inclusion. Second, the qualitative analysis provides a comprehensive overview of how fintech and financial inclusion topics is interconnected. Third, future research areas for fintech and financial inclusion were identified. In general, fintech democratizes financial inclusion for the unbanked and marginalized communities while reducing operating costs. Governments should promote financial inclusion among those most vulnerable and affected by global threats.

Purpose: This study aims to explore the potential of artificial intelligence with AI-powered humanoid Chatbots (AIPHC) as transformative tools to improve customer service quality in the insurance sector. The usability and efficiency of integrating advanced AI chatbots that can replicate human-like interactions in insurance services will be examined by taking into consideration customers’ technological readiness and chatbots’ anthropomorphism. Design/methodology/approach: This empirical study analysed 688 customer responses collected through purposive sampling using structural equation modelling. With the help of SmartPLS 4.0, the study determines whether anthropomorphism, that is AIPHC system-specific and customer personality-specific dimensions, can influence the acceptance of AIPHC in the insurance sector. Findings: The results show that the chatbot’s anthropomorphism positively influenced customers’ optimism and innovativeness but negatively impacted discomfort and security. Further optimism and innovativeness favourably impact AIPHC adoption. Insecurity had a significant negative impact, while discomfort was insignificant for AIPHC adoption. Research limitations/implications: The study determines how people will react to AI-powered information systems. The results could help us better understand how the technological readiness of customers can be used in emphasizing the significance of system-specific theories like anthropomorphism in sectors like insurance, where customer interactions and delivery of quality services are important. Practical implications: The results highlight chatbots’ potential to raise the quality of service, simplify processes and enhance customers’ overall experiences in the insurance sector. This study contributes to the continuing discussion on using AI technologies in customer service by considering the interplay between technology readiness and anthropomorphism. It also provides insightful information for insurance professionals and technology developers. Social implications: Anthropomorphic humanoid chatbots can increase the availability, affordability and accessibility of essential services. They have the potential to increase users’ competence, autonomy and—possibly counterintuitively social relatedness. Originality/value: This empirical research explores the link between anthropomorphism and technology readiness to enhance service quality provided by AI powered chatbots in the insurance sector.

Understanding the complexities of buying, selling, and holding stocks is crucial for institutional and individual investors to make informed decisions. Despite their significance, many investors face challenges in this area. Accurate stock price forecasting is a vital tool that aids investors in making profitable decisions. This study evaluates stock trends and patterns with an in-depth analysis of the Bombay Stock Exchange (BSE) stock data. We utilized various techniques, including time-series analysis, machine learning, and deep-learning models. This investigation spanned two distinct datasets: one with and one without COVID-19 stock price data. By comparing the outcomes, we seek to identify the most effective model for stock price prediction. Our findings indicate that each model has its strengths and limitations. Time series models accurately forecast short-term stock prices, whereas machine learning models demonstrate superior generalization capabilities. Deep learning models, however, stand out for their ability to predict long-term stock prices more accurately. Understanding each model's performance nuances is crucial for institutional and individual investors and regulators to optimize their strategies and decision-making processes.

This research article investigates the impact of using a Monopoly Board Game (MBG) in the teaching-learning process of financial statement analysis (FSA) to information technology management students, who earlier had little or no finance or accounting prior educational background. The subjects were students (N=159) in an Indian University. The study; first, narrated the process of administering MBG; second, quantitatively analyzed the learning experience through a structured questionnaire to validate the research objectives. The study resulted in the creation of three factor-clusters namely cognizance, collaboration, and enthusiasm which impacted students' MBG learning experience over the traditional teaching-learning methods. Results showed that factors relating to cognizance are more impacting than collaboration and enthusiasm. In the future, this research can be extended to advanced finance courses and can be integrated with relevant educational theories that underpin teaching-learning processes in higher education to other disciplines.