A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies
This article proposes a hybrid model to assist insurance companies accurately assess the risk of increasing claims for their premiums. The model integrates long short-term memory (LSTM) networks and convolutional neural networks (CNN) to analyze historical claim data and identify emerging risk trend...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-04-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2830.pdf |
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| _version_ | 1850201047909269504 |
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| author | Walaa Gamaleldin Osama Attayyib Mrim M. Alnfiai Faiz Abdullah Alotaibi Ruixing Ming |
| author_facet | Walaa Gamaleldin Osama Attayyib Mrim M. Alnfiai Faiz Abdullah Alotaibi Ruixing Ming |
| author_sort | Walaa Gamaleldin |
| collection | DOAJ |
| description | This article proposes a hybrid model to assist insurance companies accurately assess the risk of increasing claims for their premiums. The model integrates long short-term memory (LSTM) networks and convolutional neural networks (CNN) to analyze historical claim data and identify emerging risk trends. We analyzed data obtained from insurance companies and found that the hybrid CNN-LSTM model outperforms standalone models in accurately assessing and categorizing risk levels. The proposed CNN-LSTM model achieved an accuracy of 98.5%, outperforming the standalone CNN (95.8%) and LSTM (92.6%). We implemented 10-fold cross-validation to ensure robustness, confirming consistent performance across different data splits. Furthermore, we validated the model on an external dataset to assess its generalizability. The results demonstrate that the model effectively classifies insurance risks in different market environments, highlighting its potential for real-world applications. Our study contributes to the insurance industry by providing valuable insights for effective risk management strategies and highlights the model’s broader applicability in global insurance markets. |
| format | Article |
| id | doaj-art-adeec18bb9b349a994ec18c055dbe25e |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-adeec18bb9b349a994ec18c055dbe25e2025-08-20T02:12:07ZengPeerJ Inc.PeerJ Computer Science2376-59922025-04-0111e283010.7717/peerj-cs.2830A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companiesWalaa Gamaleldin0Osama Attayyib1Mrim M. Alnfiai2Faiz Abdullah Alotaibi3Ruixing Ming4School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, ChinaSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, ChinaDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh, Saudi ArabiaSchool of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, ChinaThis article proposes a hybrid model to assist insurance companies accurately assess the risk of increasing claims for their premiums. The model integrates long short-term memory (LSTM) networks and convolutional neural networks (CNN) to analyze historical claim data and identify emerging risk trends. We analyzed data obtained from insurance companies and found that the hybrid CNN-LSTM model outperforms standalone models in accurately assessing and categorizing risk levels. The proposed CNN-LSTM model achieved an accuracy of 98.5%, outperforming the standalone CNN (95.8%) and LSTM (92.6%). We implemented 10-fold cross-validation to ensure robustness, confirming consistent performance across different data splits. Furthermore, we validated the model on an external dataset to assess its generalizability. The results demonstrate that the model effectively classifies insurance risks in different market environments, highlighting its potential for real-world applications. Our study contributes to the insurance industry by providing valuable insights for effective risk management strategies and highlights the model’s broader applicability in global insurance markets.https://peerj.com/articles/cs-2830.pdfMachine learningDeep learningCNN-LSTMClaimsPremiumsMeasuring risk |
| spellingShingle | Walaa Gamaleldin Osama Attayyib Mrim M. Alnfiai Faiz Abdullah Alotaibi Ruixing Ming A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies PeerJ Computer Science Machine learning Deep learning CNN-LSTM Claims Premiums Measuring risk |
| title | A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies |
| title_full | A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies |
| title_fullStr | A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies |
| title_full_unstemmed | A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies |
| title_short | A hybrid model based on CNN-LSTM for assessing the risk of increasing claims in insurance companies |
| title_sort | hybrid model based on cnn lstm for assessing the risk of increasing claims in insurance companies |
| topic | Machine learning Deep learning CNN-LSTM Claims Premiums Measuring risk |
| url | https://peerj.com/articles/cs-2830.pdf |
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