The integration of Artificial Intelligence (AI) in financial services has revolutionized traditional credit assessment practices, particularly through risk-based pricing models. Development of Artificial intelligence (AI) provides an additional horizon. With the help of risk-based pricing models enabled via AI, lenders have an opportunity to evaluate the risk of borrowers more precisely and adjust terms of loans. It allows quick decision-making process, enhances operating efficiency, and reduces defaults- thus promoting sustainable financial accessibility. The study explores the impact of AI-driven risk-based pricing on loan approval efficiency within rural non-banking financial services (NBFCs). AI systems can assess borrower risk profiles with greater speed and precision, enabling dynamic interest rate determination and streamlined decision-making processes. The approach significantly reduces manual intervention, minimizes default risks, and enhances the overall efficiency of loan disbursement in underserved rural areas. The findings highlight improvements in approval turnaround time, credit access for low-income borrowers, and operational scalability for NBFCs. The study also discusses the challenges of data privacy, algorithmic bias, and digital literacy in rural populations. The implications offer valuable insights for policymakers and fintech practitioners aiming to optimize rural credit ecosystems through AI innovation.