The principal objective of this article is to gain insight into the biases that shape decision-making in contexts of risk and uncertainty, with a particular focus on the prospect theory and its relationship with individual confidence. A sample of 376 responses to a questionnaire that is a replication of the one originally devised by Kahneman and Tversky was subjected to analysis. Firstly, the aim is to compare the results obtained with the original study. Furthermore, the Cognitive Reflection Test (CRT) will be employed to ascertain whether behavioural biases are associated with cognitive abilities. Finally, in light of the significance and contemporary relevance of the concept of overconfidence, we propose a series of questions designed to assess it, with a view to comparing the various segments of respondents and gaining insight into the profile that reflects it. The sample of respondents is divided according to gender, age group, student status, professional status as a trader, status as an occasional investor, and status as a behavioural finance expert. It can be concluded that the majority of individuals display a profile of underconfidence, and that the hypotheses formulated by Kahneman and Tversky are generally corroborated. The low frequency of overconfident individuals suggests that the results are consistent with prospect theory in all segments, despite the opposite characteristics, given the choice of the less risk-averse alternative. These findings are useful for regulators to understand how biases affect financial decision making, and for the development of financial literacy policies in the education sector.

A comprehensive survey was conducted in 2012 and 2020 to assess the financial culture of Hungarian higher education students. The findings revealed that financial training effectiveness had not improved over time. To address this, a conative examination of financial personality was initiated by the Financial Compass Foundation, which gathered over 40,000 responses from three distinct age groups: Children, high school students, and adults. The study identified key behavioral patterns, such as excessive spending and financial fragility, which were prominent across all age groups. These results informed Hungary’s seven-year strategy to enhance financial literacy and integrate economic education into the National Core Curriculum. The research is now expanding internationally with the aim of building a comparative database. The study’s main findings highlight the widespread need for improved financial education, with more than 80% of adults demonstrating risky financial behaviors. The implications of these findings suggest the importance of early financial education and tailored interventions to foster long-term financial stability. The international expansion of this research will allow for the examination of country-specific financial behaviors and provide data-driven recommendations for policy development.

This study investigates how corruption impacts sustainability in African countries. Using public databases, the research draws on the African Development Bank’s corruption indicators and the World Bank’s financial inclusion metrics. The findings reveal that as financial inclusion increases, particularly through the use of digital financial services, perceptions of corruption decrease. However, economic growth paradoxically correlates with an increased perception of corruption due to rising consumption demands. The study concludes that promoting financial literacy, along with robust governance, is essential for combating corruption and fostering sustainable development.

Purpose: The purpose of this paper is to explore the impact of Artificial Intelligence on the performance of Indian Banks in terms of financial metrics. The study focused specifically on the NIFTY Bank Index. The paper also advocates that a greater transparency in disclosing AI related information in a Bank’s annual report is required even if it is voluntary. Design/Methodology/Approach: The paper uses a mixed method approach where quantitative and qualitative analysis is combined. A dynamic panel data model is used to understand the impact of AI of Return on Equity (RoE) of 12 Indian Banks in the NIFTY Bank Index over a five-year period. In addition to that, Content analysis of annual reports of banks was conducted to examine AI related disclosure and transparency. Findings: The paper highlights that the integration of Artificial Intelligence (AI) significantly influences the financial performance of sample banks of India. Return on Equity the specific parameter positively influenced with adoption of AI. The profitability of banks is positively impacted by reduced errors and improved operational efficiency. The content analysis of annual reports of the banks indicates different approach for AI disclosure where some banks give detailed information and some are not transparent about AI initiatives. The findings suggest that a higher level of transparency could enhance confidence of all stakeholders. Theoretical Implications: The positive relation between adoption of AI and financial performance, specifically ROE, gives a foundation for academic research to explore the dynamics of emerging technology and financial systems. The study can be extended to explore the impact on other performance indicators in different sectors. Practical Implications: The findings of this study emphasize the importance of transparent AI related disclosures. A detailed reporting about integration of AI helps in enhanced stakeholders’ confidence in case of banking industry. The regulatory framework of banks may also consider making mandatory AI disclosure practices to ensure due accountability to maximize the benefits of AI in banking.