Artificial Intelligence as a Catalyst for Economic and Financial Development in Emerging Asian Economies: Dual Econometric and Machine Learning Approaches

Abstract

This study examined the role of Artificial Intelligence (AI) as a catalyst for economic and financial development in emerging Asian economies using a dual-method framework that combined econometric estimation with machine-learning prediction. Drawing on longitudinal data from 2000 to 2024, the research analyzed how AI adoption—measured through innovation intensity, patents, digital infrastructure, and investment—shaped GDP growth, total factor productivity, and financial inclusion. Panel cointegration results confirmed stable long-run relationships between AI and key macroeconomic indicators, while FMOLS and DOLS estimations demonstrated that AI adoption exerted strong and positive long-run effects on growth, productivity, and digital financial access. Granger causality tests indicated bidirectional causality between AI and financial development, highlighting the centrality of fintech-enabled inclusion channels. Complementing econometric results, machine-learning models (Random Forest, Gradient Boosting, LSTM) revealed high predictive accuracy, with LSTM emerging as the strongest performer (R² = 0.89). Feature-importance analysis showed that digital infrastructure, fintech usage, and institutional quality were the most influential predictors of economic outcomes. The findings suggested that AI reshaped development pathways by enhancing forecasting precision, improving decision-making efficiency, and enabling broader access to financial services. However, disparities in digital readiness and governance limited the uniform diffusion of benefits across countries. The study concluded that AI represents a transformative driver of structural growth in emerging Asia, provided that complementary investments in digital infrastructure, regulatory modernization, and human capital development are strengthened.