Implications of Compulsory Car Accident Insurance Comparative Study

Yas, Nadia

doi:10.17762/turcomat.v12i2.2052

Cited by 1 publication

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To facilitate analysis, the "Nominal to Numerical" operator is employed to convert categorical feature into numerical values. For example, in Table I, "private" is denoted as (1) and "commercial" as (0) for car usage. This conversion is applied to all categorical features in the analysis and in integral subprocess step within the AIRA-ML model, as depicted in Fig.…”

Section: Data Preparationmentioning

confidence: 99%

See 1 more Smart Citation

AIRA-ML: Auto Insurance Risk Assessment-Machine Learning Model using Resampling Methods

Elbhrawy,

Belal,

Hassanein

2023

IJACSA

View full text Add to dashboard Cite

Predicting underwriting risk has become a major challenge due to the imbalanced datasets in the field. A realworld imbalanced dataset is used in this work with 12 variables in 30144 cases, where most of the cases were classified as "accepting the insurance request", while a small percentage classified as "refusing insurance". This work developed 55 machine learning (ML) models to predict whether or not to renew policies. The models were developed using the original dataset and four data-level approaches resampling techniques: random oversampling, SMOTE, random undersampling, and hybrid methods with 11 ML algorithms to address the issue of imbalanced data (11 ML× (4 resampling techniques + unbalanced datasets) = 55 ML models).Seven classifier efficiency measures were used to evaluate these 55 models that were developed using 11 ML algorithms: logistic regression (LR), random forest (RF), artificial neural network (ANN), multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB), decision tree (DT), XGBoost, k-nearest neighbors (KNN), stochastic gradient boosting (SGB), and AdaBoost. The seven classifier efficiency measures namely are accuracy, sensitivity, specificity, AUC, precision, F1-measure, and kappa. CRISP-DM methodology is utilisied to ensure that studies are conducted in a rigorous and systematic manner. Additionally, RapidMiner software was used to apply the algorithms and analyze the data, which highlighted the potential of ML to improve the accuracy of risk assessment in insurance underwriting. The results showed that all ML classifiers became more effective when using resampling strategies; where Hybrid resampling methods improved the performance of machine learning models on imbalanced data with an accuracy of 0.9967 and kappa statistics of 0.992 for the RF classifier.

show abstract

Section: Data Preparationmentioning

confidence: 99%

“…Insurance is a vital risk management tool that protects individuals and businesses from unforeseen occurrences that could cause financial losses. Car insurance is one of the most important types of insurance, as it protects owners and drivers financially from a variety of dangers and uncertainties [1].…”

Section: Introductionmentioning

confidence: 99%