Conference Agenda

We construct a unique dataset by collecting macro-financial commitments data using textual analysis of the Memorandum of Economic and Financial Policies (MEFPs), a document outlining, inter-alia, policy commitments by member countries, in the context of an IMF-supported program. We combine this data with information on structural conditionality. Using a staggered difference-in-differences methodology, we show that IMF-supported programs with macro-financial policy commitments are followed by periods of lower non-performing loans and in some cases lower credit-to-GDP ratios, relative to IMF-supported programs without macro-financial commitments, mostly for the post global financial crisis (GFC) period before the COVID-19 pandemic. The results point to stronger and more abrupt declines in credit-to-GDP following ex-post macro-financial policies, those implemented after a crisis occurs (e.g., restructuring), and milder and more gradual declines following ex-ante policies, those implemented before risks materialize (e.g., regulatory requirements). The responses are also larger when countries have positive credit gaps at the start of the program than when credit gaps are negative. These results point to the importance of considering the country’s position in the credit cycle in program design and in addressing vulnerabilities preemptively to reduce the need for abrupt corrections when risks materialize. Finally, macro-financial policies targeting financial inclusion tend to increase credit-to-GDP ratios in low credit-to-GDP program countries.

Artificial intelligence has long been an integral part of the functioning of financial and banking institutions, which has significantly improved the quality of their services, the ability to meet the various requirements and desires of their clients, who are consumers of these services. At the same time, with all the advantages and positive aspects of using artificial intelligence, it also creates additional risks, depending on who uses it and for what. In the hands of fraudsters, it becomes a tool with which financial institutions and their clients are inflicted significant damage, and not only financial. At the same time, in scientific literature, this issue has been studied mainly from the technical, technological and financial sides, with insufficient attention to the legal risks of this issue. That is why the article focuses on the risks and legal aspects of protecting financial institutions, taking into account the key aspects of using artificial intelligence to commit fraudulent actions against financial and banking institutions and their clients.

Purpose: This study investigates the influence of Artificial Intelligence (AI) on millennials' investment decision-making processes, focusing on the relationship between trust in AI systems and the adoption of robo-advisory services for wealth management. Additionally, it explores how AI affects millennials' behavioral finance decisions based on their financial knowledge levels.

Design/Methodology/Approach: The research employs a descriptive survey technique, utilizing structured questionnaires to collect primary data from 754 millennials from India. Multi-Stage sampling ensures representation across various institutions in the capital market, providing a comprehensive view of millennials' investment behaviours. Used RStudio for detailed statistical analysis.

Findings: The findings indicate a significant positive relationship between millennials' trust in AI systems and their intention to adopt robo-advisory for wealth management. Moreover, AI's impact on millennials' behavioral finance decisions varies based on their levels of financial knowledge, with higher knowledge levels positively influencing perceptions of AI's impact.

Originality/Values: This research contributes to the understanding of how AI shapes millennials' financial choices, highlighting the role of trust in AI systems and financial literacy in influencing adoption patterns and perceptions. The study underscores the significance of AI in navigating behavioral biases and enhancing financial decision-making processes among millennials. The results offer valuable guidance for financial service providers, policymakers, and technology developers aiming to improve AI integration in financial markets globally.

The Nelson-Siegel, Svensson, and De Rezende-Ferreira models are the most common approaches currently used for modeling the term structures of the risk-free interest rates. However, when markets become turbulent, they may not provide reliable results. Given the importance of the term structure in determining the time value of money and thus in pricing all securities, this study aims to improve the statistical performance of the above mentioned models using more advanced approaches, starting from evolutionary algorithms, such as genetic algorithms and particle swarm optimization, to a more comprehensive hybrid approach, combining the above mentioned artificial intelligence-based methodologies with the traditional Levenberg-Marquardt method. When also this latter approach is unsatisfactory, we rely on machine learning techniques, specifically using Gaussian Process Regression. This study considers the currencies of eight countries belonging to the Pacific area, in addition to the US dollar, as a reference currency. Results show that the use of artificial intelligence-based approaches improve the performance of traditional parametric models currently used in the field.