AI-Driven Credit Risk Prediction using PSO-Optimized SVM

Abstract
In the rapidly expanding eCommerce ecosystem, credit risk from Buy Now Pay Later (BNPL) and installment payments poses significant challenges to platform sustainability and its cash flow. This study adapts a hybrid PSO-SVM model—optimized via Particle Swarm Optimization (PSO) for hyperplane tuning and feature selection —to predict buyer defaults using transactional data patterns to banking data. Leveraging real-world dataset processing, the model excels at nonlinear pattern detection in high-volume eCommerce transactions, outperforming baseline Support Vector Machine (SVM) in dynamic retail environments. It supports platforms in real-time risk scoring, fraud prevention, and personalized financing, enhancing customer retention in the era of digital transformation. In this research the proposed PSO-SVM offering scalable improvements in comparison to the baseline.
Keywords

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