Kamati, Nelson Mitchell.2026-01-272026-01-272025-10-07Kamati, N.M. (2025).Predicting Lapse Rate in Life Insurance Using Machine Learning Algorithms: A Case Study of Sanlam Namibia [Master’s thesis, Namibia University of Science and Technology].http://hdl.handle.net/10628/1112The prediction of lapse rate in the life insurance industry holds significant importance for insurers aiming to maintain profitability and retain a strong customer base. Accurate lapse prediction will enable insurers to deploy personalised retention efforts, effective risk management, and financial planning depending on the model’s results. The study explored the use of Machine Learning (ML) techniques to predict lapse rates in life insurance. The dissertation's main contribution was to empirically compare and benchmark 4 machine learning classifier models (Logistic Regression, Gradient Boost, Random Forest, Support Vector Machine). The study utilised secondary data that was obtained from Sanlam Namibia’s database, including policyholder demographics, policy features, premium amounts, payment frequency, lapse status, and other relevant information that could influence the lapse rate. Different types of machine learning algorithms were used on the acquired data to assess their performance in forecasting lapse rate. The study's findings showed that machine learning methods can accurately predict the lapse rates in life insurance. The findings reveal the ensemble model’s high predictive capacity. Gradient Boosting is the best overall classifier, with a 94% and F1-Scor of 94% respectively. Furthermore, some key policy and clients’ characteristics were found to have substantial predictive potential for the lapse rate. However, the limitations of the study must be considered. Finally, the study recommends using ensemble models rather than single model classifiers because they are more effective at predicting life insurance lapses.enSupport Vector MachineGradient BoostRandom ForestLogistic regressionlapsemachine learninglife insurancelapse risk.Thesis