AI-driven blockchain lending for sustainable development: a machine learning framework for loan risk and eligibility classification

Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.