Reduction of Defects in Insurance Policies Through Random Forest and Total Quality Management

Abstract

In recent years, the Peruvian insurance sector has undergone significant transformation driven by digitalization and the increasing demand for more personalized services. However, micro and small enterprises (MYPEs) in the industry have faced limitations in policy management, reflected in a high rate of defects in the renewal process and a rise in customer attrition. This issue reduced operational efficiency and policyholder loyalty, directly affecting business competitiveness. In this context, the present research proposed an improvement model based on the Random Forest method and the Total Quality Management (TQM) philosophy under the PDCA approach. The model aimed to predict the probability of non-renewal and implement corrective actions oriented toward customer retention. Recent studies have demonstrated that combining predictive models with continuous improvement strategies can increase customer retention by 7% to 15%, while simultaneously optimizing service quality and managerial decision-making. It was concluded that the integration of predictive analytics and quality management constitutes an effective and scalable strategy to optimize service continuity in Peruvian insurance companies.