Machine Learning Applications in Customer Segmentation and Profit Optimization for Digital Payment Vendors

The evolution of electronic payment systems, re-defining fraud detection, vendor profitability, and customer loyalty. This study examines how machine learning models improve profit maximization and customer segmentation in electronic payment systems. Based on a mixed-methods design, data were collected through structured surveys from 205 participants and statistical analysis and predictive modelling. Machine learning algorithms such as K-Means Clustering, Multiple Linear Regression, Random Forest, and Autoencoder-based Anomaly Detection were used to assess transaction patterns, retention patterns, and patterns of fraud risk. The findings suggest that payment frequency, platform diversification, and digital payment adoption are the most powerful drivers of vendors’ profitability. The study establishes that customer segmentation and targeted offers propel customer retention while deep learning algorithms significantly enhance the accuracy of fraud detection. Vendors, policymakers, and financial institutions have implementable facts from these studies to increase digital payment adoption, anti-fraud, and customer engagement. Future studies should be directed toward fraud detection models in real time as well as adaptive reinforcement learning to ensure maximum digital transaction security and efficiency.