Exploring the Experimental Analysis of Deep Learning Based Risk Management and Corporate Governance in Islamic Finance

The development of Islamic banking since its inception has been meteoric, and the concept is now widely acknowledged as a viable alternative to conventional banking. The Islamic financial industry has grown rapidly due to a number of factors, including indexes that do not comply with Shariah law, a lack of regulatory standards, and risk management tools. The study's over arching goal is to assess how well and how far down the path to risk management the Islamic financial system now is. In order to assess the efficacy of the suggested model, this research introduces a novel deep learning-based approach termed Neural Learning based Risk Management Protocol (NLRMP) and then cross-validates it with the traditional Random Forest Classifier (RFC) model. There are two parts to this: progress and performance. Some of the methods used to investigate these issues include building portfolios, doing econometric modelling, theoretically developing a financial risk management tool, and conducting qualitative analysis of semi-structured interviews with Islamic financial institutions to learn their perspectives on the effects of governance and regulatory standards on performance and financial innovation. Findings from this study provide credence to Islamic finance as a viable investment and risk management option, and they also demonstrate that conventional portfolios would benefit from adding Islamic indexes for both performance and variety. A Shariah-compliant Islamic call option demonstrates the benefits of creating financial risk management products based on risk-sharing rather than risk-transfer. Market players are in support of implementing central regulatory norms to promote the Islamic financial system's ongoing and steady growth, according to the study's results.