Predicting Trends in Islamic Banking & Finance: A Big Data Analysis Using Google Trends and AI
Abstract
This study employs big data analysis and AI forecasting to predict global public interest trends in Islamic Banking and Finance over the period 2020 to 2025, using Google Trends data and the artificial intelligence (NeuralProphet) model. Five key terms—Islamic finance, Islamic banking, sukuk, takaful, and halal investment—were analyzed to capture diverse sector dynamics. Forecasts reveal varying trends: takaful shows the highest predicted interest (85.6) with strong seasonal fluctuations, Islamic finance and halal investment exhibit steady growth with moderate seasonality, while sukuk and Islamic banking display irregular, event-driven patterns. Model evaluation via MAE (0.41–5.03), RMSE (0.49–6.95), and R² (0.16–0.67) reflects differing predictive accuracies, highlighting stable sectors versus more volatile markets. These findings underscore the potential of integrating big data and AI to enhance strategic planning and responsiveness in Islamic finance, supporting tailored, data-driven decision-making aligned with evolving market behavior.
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DOI: https://doi.org/10.52620/jseba.v2i2.199
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