This proposal outlines the comprehensive implementation of an AI-powered Credit scoring and loan underwriting system for Savings and Credit Cooperative organizations (SACCO) in Uganda. The system leverages alternative data sources like mobile money transactions, utility payments and behavioral indicators to build accurate and inclusive borrower risk profiles. By integrating advanced machine learning (ML) and generative pre-trained transformer (GPT) models, this system aims to overcome key challenges in traditional credit assessments like limited credit histories, collateral requirements, and subjective decisions which have led to high default rates and financial exclusion The proposed AI model enhances operational efficiency by delivering real time risk evolutions, improving loan turnaround times and supporting proactive risk management. Human oversight is maintained through explainable AI outputs and override mechanisms, aligning with Uganda’s data protection laws and SACCO values. A dedicated AI Steering Committee will oversee ethical governance while comprehensive training and stakeholder engagement will ensure smooth adoption. Custom built for SACCOs, the system is designed for full integration with existing platforms such as the Mobis management information systems, mobile money platforms, credit bureaus and includes agentic monitoring for fraud and early warnings. Ultimately the solution strengthens financial inclusion, improves portfolio quality and empowers SACCOs to deliver faster fairer and more inclusive lending services across Uganda.