Predicting participant risk profiles in private pension funds using machine learning techniques

Individuals in the private pension system can voluntarily participate in the risk profile assessment survey before they decide to direct which funds are eligible for their savings. Thus, the system proposes suitable funds in accordance with the result of the questionnaire. Nevertheless, participants who do not fill out this questionnaire are in the majority within the private pension system. The aim of this thesis is to predict the risk profile level of participants who do not fill out the survey by utilizing the information of others ones who have already filled out the survey. In this scope, the model has been built by using machine learning techniques through data including financial, demographics, and other features which belong to the customers who completed or did not complete the questionnaire. It has been shown that the XGBoost algorithm (F! Score: 59%, Accuracy: 60%) which has been applied to four risk categories distributing almost balanced is the best one among the machine learning model for prediction. The built model has proven its usability as a supplementary tool by testing both the private pension company and the fund advisors.