Artificial Intelligence Adoption, Financial Decision-Making, andOrganizational Performance in Somali Financial Institutions

Abstract

Artificial intelligence (AI) is increasingly used in financial services, but evidence from fragile and developing financial systems remains limited. This study examines the relationships among AI adoption, financial decision-making, organizational performance, and implementation challenges in Somali financial institutions. Drawing on the Technology Acceptance Model, Resource-Based View, and Dynamic Capabilities Theory, the study used a quantitative cross-sectional design. Questionnaire data were collected from 100 employees of banks, Islamic financial institutions, fintech firms, insurance companies, and investment firms in Mogadishu. The analysis used descriptive statistics, Pearson correlations, reliability analysis, and multiple regression in SPSS version 27. Respondents reported moderate to high AI adoption. AI adoption was strongly and positively correlated with financial decision-making (r = .809, p < .01) and organizational performance (r = .796, p < .01). A regression model with AI adoption as the dependent variable explained 69.9% of its variance (R2 = .699, p < .001). Financial decision-making (β = .469, p < .001) and organizational performance (β = .368, p = .009) were significant positive predictors, whereas contextual challenges were not significant (β = .035, p = .778). These cross-sectional findings show strong associations rather than causal effects. They suggest that Somali financial institutions could support responsible AI adoption through clearer regulation, stronger data governance, staff capability development, and continued investment in digital infrastructure.