ROLE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL DECISION MAKING

Abstract

This study has explored the role of artificial intelligence (AI) in financial decision-making within the Kathmandu Valley, focusing on how AI’s social and emotional capabilities, such as self-awareness, empathy, motivation, self-regulation, and social skills, affect investment decisions. The primary problem has been the challenges related to the reliability, ethical implications, and systemic risks of AI in finance. The main objective has been to examine the current status of AI use, analyze its relationship with investment decisions, and evaluate its overall impact on financial decision-making. The study has utilized both descriptive and causal-comparative research designs to assess AI’s role in financial decision-making. The population has included investors and AI users in Kathmandu Valley, with a sample size of 400 selected through convenience sampling. Data collection has been based on quantitative methods, using a structured questionnaire survey designed according to the green investment decision-making model and employing a five-point Likert scale. Statistical analysis has been conducted using Microsoft Excel and SPSS, employing descriptive statistics, correlation, and multivariate regression models. The research framework has delineated the relationships between independent variables (self awareness, empathy, motivation, self-regulation, and social skills) and the dependent variable (investment decisions), guiding the analytical process. The results have shown that all AI variables positively influence investment decisions, with motivation and social skills demonstrating the strongest impacts. The study has confirmed significant positive relationships and impacts of AI’s emotional and social attributes on financial outcomes, with no multicollinearity concerns affecting the results. Practically, the study highlights the importance of integrating AI’s emotional and social capabilities into financial decision-making processes to enhance investment strategies. Theoretically, it contributes to understanding the influence of AI attributes on financial decisions and recommends further exploration of these AI characteristics in varied financial contexts to validate and extend these findings.