AI and Machine Learning in Payment Systems: Unlocking Higher Approval Rates and Lower Fees

Abstract

This article explores the implementation of artificial intelligence and machine learning techniques in payment processing systems to optimize transaction routing decisions. In the modern payments ecosystem, merchants face significant challenges with varying approval rates across different processing paths based on factors, including card type, issuing bank, transaction geography, and merchant category. Through analyzing several machine learning approaches—ranging from decision trees to advanced neural networks and reinforcement learning—the article demonstrates how intelligent routing systems can significantly enhance approval rates while simultaneously reducing processing costs. The article evaluates the effectiveness of various ML models in identifying optimal routing paths based on historical performance data and transaction attributes, highlighting strategies such as issuer-specific routing, prevention of futile authorization attempts, dynamic fee optimization, adaptive retry mechanisms, and cross-border transaction handling. Additionally, the article addresses critical implementation challenges, including latency requirements, data quality concerns, regulatory compliance, and concept drift, offering practical frameworks for deploying these systems in high-volume production environments.