Optimizing Revenue and Pricing on Upi Transaction Using Ai and Dynamic Pricing Models

Background : India’s Unified Payments Interface (UPI) has revolutionized global e-commerce by enabling seamless, low-cost, and real-time transactions. This study explores the integration of AI with a dynamic pricing optimization model to enhance revenue management (RM) and pricing strategies within UPI-driven transactions. Methods : We analyze UPI transaction data from 2021 to 2024, covering 13 billion transactions. A multi-disciplinary approach, integrating data analytics, consumer behavior, economics, and marketing, is employed. AI-driven predictive analytics are applied to identify key factors influencing Person-to-Merchant (P2M) dynamics, such as transaction reliability, merchant categories, regional factors, and fraud risks. A dynamic pricing model is proposed that adjusts prices based on these factors, with AI enhancing decision-making. Results : AI analysis reveals that reliable payments (40%), merchant categories (25%), and regional factors (20%) are the primary drivers of P2M growth. Fraud concerns (15%) impact pricing stability. The dynamic pricing model, incorporating transaction reliability, consumer sentiment from social media, and AI-driven fraud risk scores, is shown to optimize revenue. Groceries (2.32 billion transactions) and restaurants (1.25 billion) are leading categories, with urban areas dominating (65%). Conclusion : The study highlights the potential of AI in optimizing pricing, improving fraud detection, and fostering rural inclusion. Despite data limitations, these findings offer actionable insights for advancing sustainable e-commerce growth through UPI and AI-driven strategies.