Islamic Versus Mainstream Bank Behavior Using Agent-Based Modelling Simulation

This study implements an agent-based modeling (ABM) framework for investigating the effects of Profit-Loss sharing (PLS) and interest-based lending (IBL) on the wealth level of the borrowers for pre-policy testing of the efficiency level of Islamic banking vs. mainstream (conventional or traditional) banking. We use agent-based modelling (ABM) on Netlogo platform for our purpose. Results of the simulation for different scenarios support our predictions in most of the cases, in particular that there are fewer negative impacts of PLS on the wealth level of borrowers against IBL. Interestingly, Islamic bank borrowers also suffer less negative impact on the wealth level compared to the borrowers of mainstream banks when they face idiosyncratic shocks. We provide novel evidence that how Islamic banks can play an important role in improving social welfare. The modeling has important implications for introducing Islamic banking within a dual banking system with mainstream banks. This further increases support for Islamic banking as an alternative financial system and its resiliency against exogenous shocks such as Global financial crisis or the recent COVID-19 outbreak. We contribute to the literature on the application of agent-based modelling in Islamic banking by comparing PLS and IBL on the welfare level, where a large research gap exists. Only recently, Polyzos et al. (Int J Islamic Middle East Finance Manag 16(4):777–801, 2023) claimed to be the first attempt at agent-based modelling in Islamic banking and assessing the welfare effects of PLS. They developed a happiness function using machine learning to study the welfare impact of PLS, while we are more focused on the risk-return criteria of PLS vis-à-vis IBL based approaches to assess the related impact on the social welfare.