Applications of Machine Learning in Islamic Finance: A Review

Abstract

Moreover, given the Islamic financing requirements it may seem that despite their unique ethical operational mandates, Islamic finance may be less prepared compared to other sectors when it comes to predictive models and advanced analytics requirements for risk assessment and compliance. Thus, Islamic finance could serve as a testbed for the ef- fectiveness of the proposed applications in reducing complexity and enhancing efficiency and innovation by adopting artificial intelligence (AI) tools to its systems. It would cre- ate structure on the richness of material based on the gaps identified above and provide a foundation for implementing ML in a real application: supporting Islamic banks in their adoption of AI and ML in improving the way they serve the customer. The review assesses the survey of the current relevancy of machine learning in Islamic finance as well as the potential benefits it presents for Shariah compliant audits, customer service and financial product development. Practical con- straints related to the absence of a standardized Shariah-compliant algorithm and lack of data resources, while their alternatives include synthetic data generation as well as the adoption of explainable AI models are also displayed. This research gears towards suggest- ing guidelines for future works that will develop LR that align with Shariah principles with particular application to NLP for Arabic texts. Such findings will be useful to students and academicians working in the field, that is, for financial institutions to understand the possi- ble benefits and drawbacks of using machine learning in Islamic finance. The significance of this productive closure in multibranc interoperability and ongoing facility and control implementation of ML technologies as aligned with ethics and providing for changes in the expectation bases of Islamic finance by making sure that, eventually, the same will be in line with the business and political goals of policymakers who desire but tend to keep security and reputation issues at the same time.