This study explores the implementation of Artificial Intelligence (AI) technologies in financial forecasting, aiming to improve prediction accuracy and enhance strategic financial decision-making. Traditional forecasting methods, such as ARIMA and linear regression, often fall short in modeling complex, nonlinear financial data, especially in volatile markets. In response, this research investigates the comparative performance of machine learning (ML), deep learning (DL), and hybrid AI-big data models. A qualitative exploratory approach was adopted, involving a systematic literature review and semi-structured interviews with financial practitioners and experts. The analysis revealed that hybrid models integrating Random Forest with big data analytics achieved the highest predictive accuracy (93.2%) and operational adaptability. LSTM models also demonstrated strong performance in handling time-series data but were limited by their lack of interpretability. Compared to traditional models, AI-based approaches significantly reduced prediction errors and offered real-time responsiveness, aligning with the dynamic needs of financial environments. The findings support the hypothesis that AI technologies can bridge the gap between accurate forecasting and strategic financial planning. However, challenges such as high computational requirements and low model transparency persist. Therefore, the study concludes that while AI models present a transformative potential for financial forecasting, successful implementation requires balancing model performance with organizational capabilities and regulatory considerations. These insights provide valuable guidance for financial managers and policymakers seeking to adopt AI-driven forecasting systems in increasingly complex and data-rich financial landscapes.
Sulistiani et al. (Tue,) studied this question.
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