Machine Learning Approaches for Auto Insurance Big Data
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/9/2/42 |
| _version_ | 1797395864642125824 |
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| author | Mohamed Hanafy Ruixing Ming |
| author_facet | Mohamed Hanafy Ruixing Ming |
| author_sort | Mohamed Hanafy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:40:55Z |
| format | Article |
| id | doaj.art-adbe12ed06184aa0a7d1ebf945b611fe |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-09T00:40:55Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-adbe12ed06184aa0a7d1ebf945b611fe2023-12-11T17:49:24ZengMDPI AGRisks2227-90912021-02-01924210.3390/risks9020042Machine Learning Approaches for Auto Insurance Big DataMohamed Hanafy0Ruixing Ming1School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, ChinaThe 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.https://www.mdpi.com/2227-9091/9/2/42big datainsurancemachine learninga confusion matrixclassification analysis |
| spellingShingle | Mohamed Hanafy Ruixing Ming Machine Learning Approaches for Auto Insurance Big Data Risks big data insurance machine learning a confusion matrix classification analysis |
| title | Machine Learning Approaches for Auto Insurance Big Data |
| title_full | Machine Learning Approaches for Auto Insurance Big Data |
| title_fullStr | Machine Learning Approaches for Auto Insurance Big Data |
| title_full_unstemmed | Machine Learning Approaches for Auto Insurance Big Data |
| title_short | Machine Learning Approaches for Auto Insurance Big Data |
| title_sort | machine learning approaches for auto insurance big data |
| topic | big data insurance machine learning a confusion matrix classification analysis |
| url | https://www.mdpi.com/2227-9091/9/2/42 |
| work_keys_str_mv | AT mohamedhanafy machinelearningapproachesforautoinsurancebigdata AT ruixingming machinelearningapproachesforautoinsurancebigdata |