Comparing SMOTE Family Techniques in Predicting Insurance Premium Defaulting using Machine Learning Models

Default in premium payments impacts significantly on the profitability of the insurance company. Therefore, predicting defaults in advance is very important for insurance companies. Predicting in the insurance sector is one of the most beneficial and important study areas in today's world, thanks to technological advancements. But because of the imbalanced datasets in this industry, predicting insurance premium defaulting becomes a difficult task. Moreover, there is no study that applies and compares different SMOTE family approaches to address the issue of imbalanced data. So, this study aims to compare different SMOTE family approaches. Such as Synthetic Minority Oversampling Technique (MOTE), Safe-level SMOTE (SLS), Relocating Safe-level SMOTE (RSLS), Density-based SMOTE (DBSMOTE), Borderline-SMOTE(BLSMOTE), Adaptive Synthetic Sampling (ADSYN), and Adaptive Neighbor Synthetic (ASN), SMOTE-Tomek, and SMOTE-ENN, to solve the problem of unbalanced data. This study applied a variety of machine learning (ML)classifiers to assess the performance of the SMOTE family in addressing the imbalanced problem. These classifiers including Logistic Regression (LR), CART, C4.5, C5.0, Support Vector Machine (SVM), Random Forest (RF), Bagged CART(BC), AdaBoost (ADA), Stochastic Gradient Boosting, (SGB), XGBOOST(XGB), NAIVE BAYES, (NB), k-Nearest Neighbors (K-NN), and Neural Networks (NN). Additionally, model validation strategies include Random hold-out. The findings obtained using various assessment measures show that ML algorithms do not perform well with imbalanced data, indicating that the problem of imbalanced data must be addressed. On the other hand, using balanced datasets created by SMOTE family techniques improves the performance of classifiers. Moreover, the Friedman test, a statistical significance test, further confirms that the hybrid SMOTE family methods are better than others, especially the SMOTE-TOMEK, which performs better than other resampling approaches. Moreover, among ML algorithms, the SVM model has produced the best results with the SMOTE-TOMEK.