Small and Medium-sized Enterprises (SMEs) are critical drivers of economic growth and job creation globally but often face significant barriers in accessing timely credit and efficient payment systems. Traditional credit underwriting processes are typically manual, time-consuming, and reliant on limited financial data, resulting in inefficiencies, higher default risks, and exclusion of many SMEs from formal financial services. Similarly, SME payment systems encounter challenges such as transaction delays, fraud risks, and cumbersome reconciliation processes, which hinder smooth cash flow management and operational performance. This explores the transformative potential of AI-driven financial automation models to enhance credit underwriting and payment systems for SMEs. AI technologies, including machine learning algorithms and natural language processing, enable the analysis of vast and diverse data sources—ranging from traditional financial statements to alternative data such as mobile money transactions and social media activity. These advanced models improve credit risk assessment accuracy by capturing nuanced financial behaviors and reducing subjective biases inherent in manual evaluations. Furthermore, AI-powered automation in payment systems facilitates real-time transaction monitoring, fraud detection, and intelligent reconciliation, thereby enhancing operational efficiency and reducing financial losses. Integrating AI-driven credit underwriting with automated payment systems creates synergies that enable dynamic credit limit adjustments, automated loan disbursement, and continuous risk monitoring based on payment behaviors. This integration supports more adaptive and responsive financial services tailored to the evolving needs of SMEs. Despite these benefits, challenges remain, including data privacy concerns, regulatory compliance, technological infrastructure limitations, and the need for ethical AI deployment.