Author:
Hanafy Mohamed,Ming Ruixing
Abstract
The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively.
Subject
Strategy and Management,Economics, Econometrics and Finance (miscellaneous),Accounting
Reference52 articles.
1. A proposed model to predict auto insurance claims using machine learning techniques;Abdelhadi;Journal of Theoretical and Applied Information Technology,2020
2. Integrating multispectral reflectance and fluorescence imaging for defect detection on apples
3. Different Ways to Compensate for Missing Values in a Dataset (Data Imputation with Examples)https://towardsdatascience.com/6-different-ways-to-compensate-formissing-values-data-imputation-with-examples-6022d9ca0779
4. The use of the area under the ROC curve in the evaluation of machine learning algorithms
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