Forecasting the Financial Soundness of Indonesia’s National Board of Zakat (BAZNAS) Using Artificial Neural Network Models

Abstract

The potential value of zakat in Indonesia in 2021 was estimated at RP 13.529 trillion; however, the actual amount of zakat collected was only RP 571 billion. Thus, this study aims to develop a precise financial soundness forecasting model for the National Board of Zakat (BAZNAS) in Indonesia using multiple linear regression (MLR) and artificial neural networks (ANN) with three algorithms to investigate, compare, and interpret the obtained results for better forecasting. The information was extracted from the financial statements of six BAZNAS for the period spanning year 2017 to 2021. The study determined unique and best models for each current ratio, cash ratio, and quick ratio. The results highlighted two models deemed best for forecasting Indonesia's BAZNAS financial soundness: MLR and ANN with Scaled Conjugate Gradient (SCG) training algorithm and MLR and ANN with Bayesian Regularization (BR). The research implications could help decision-makers strategise for the financial health of other Zakat organisations accordingly.