Islamic Microfinance Institutions (IMFIs) encounter distinct difficulties with credit scoring because they need to follow Shariah principles that combine riba bans with fair financial dealings regulations. Conventional credit scoring models exhibit two shortcomings: a poor capability to incorporate non-financial behavioral data and inadequate support for Islamic Microfinance Institutions’ requirements. Researchers use machine learning coupled with blockchain technology to create an adaptive Shariah-compliant credit scoring method that solves problems found in standard evaluation systems. Using a dataset of 1275 farmers with 52 weeks of transaction data, we implemented and compared three ML models: Linear Regression, Random Forest, and Gradient Boosting. Data preparation involved addressing 53% missing transaction data, followed by summing weekly financial activity to prepare it for predictive evaluations. Our analysis shows that the Random Forest model produced the best results with an R-squared value of 0.87 and a Mean Squared Error (MSE) of 12.4. In creditworthiness binary classification tasks, Gradient Boosting delivered an F1 score of 0.91 while maintaining precision at 0.89 and recall at 0.93. Blockchain integration exists to protect data through secure mechanisms that also conserve Islamic financial integrity and promote transparency. The research shows how ML and Blockchain technology enable fundamental changes in IMFIs by delivering elevated predictive accuracy, operational enhancements, and complete transparency. The conceptual framework guides ethical financial inclusion strategy by offering a solution for marginalized communities, but remains consistent with global sustainability objectives. The research established foundational elements for implementing cutting-edge technologies within IMFIs, which will promote new economic growth and build confidence in Shariah-compliant financial systems.
Mukit et al. (Mon,) studied this question.