Decision Theory and Risk Simulation Analysis for Optimizing Profit in Pay Later Service

Pay later services may increase profitability via effective mechanisms provided by decision theory and risk simulation analysis. We have suggested using the machine learning algorithms KNN, NB, and SVM (K-Nearest Neighbor, Naïve Bayes, Support Vector Machine) as a key tool for modeling user behavior and forecasting their likelihood of payment default. Using KNN, NB, and SVM, this method divides users into discrete risk categories so that different credit limit and price methods may be evaluated via decision theory for each group. Pay later services may reduce default risks and enhance earnings at the same time using this approach. Beyond profit optimization, there are other benefits to using decision theory and risk simulation analysis with KNN, NB, and SVM. These benefits include improved customer segmentation, decreased vulnerability to fraud, and strengthened compliance measures, all of which help to create a more stable and effective pay later service ecosystem.