Why to Buy Insurance? An Explainable Artificial Intelligence Approach
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Javier Sada Bittini & Salvador Cruz Rambaud & Joaquín López Pascual & Roberto Moro-Visconti, 2022. "Business Models and Sustainability Plans in the FinTech, InsurTech, and PropTech Industry: Evidence from Spain," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
- Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
- Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
- Alex Gramegna & Paolo Giudici, 2022. "Shapley Feature Selection," FinTech, MDPI, vol. 1(1), pages 1-9, February.
- Esther Salmerón-Manzano, 2021. "Legaltech and Lawtech: Global Perspectives, Challenges, and Opportunities," Laws, MDPI, vol. 10(2), pages 1-9, April.
- Siti Nurasyikin Shamsuddin & Noriszura Ismail & R. Nur-Firyal, 2023. "Life Insurance Prediction and Its Sustainability Using Machine Learning Approach," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Dangxing Chen, 2023. "Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models," Papers 2309.13246, arXiv.org.
- Bastos, João A. & Matos, Sara M., 2022.
"Explainable models of credit losses,"
European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
- João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
- Ahelegbey, Daniel & Giudici, Paolo & Pediroda, Valentino, 2023. "A network based fintech inclusion platform," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
- Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020.
"Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds,"
LEO Working Papers / DR LEO
2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
- Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021. "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers hal-02507499, HAL.
- Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
- David Mhlanga, 2021. "Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment," IJFS, MDPI, vol. 9(3), pages 1-16, July.
- Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
- Zhang, Tianjiao & Zhu, Weidong & Wu, Yong & Wu, Zihao & Zhang, Chao & Hu, Xue, 2023. "An explainable financial risk early warning model based on the DS-XGBoost model," Finance Research Letters, Elsevier, vol. 56(C).
- Yanhui Shen, 2023. "American Option Pricing using Self-Attention GRU and Shapley Value Interpretation," Papers 2310.12500, arXiv.org.
- Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
- Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
- Mohsin Ali & Abdul Razaque & Joon Yoo & Uskenbayeva Raissa Kabievna & Aiman Moldagulova & Satybaldiyeva Ryskhan & Kalpeyeva Zhuldyz & Aizhan Kassymova, 2024. "Designing an Intelligent Scoring System for Crediting Manufacturers and Importers of Goods in Industry 4.0," Logistics, MDPI, vol. 8(1), pages 1-30, March.
- Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2022. "Explainable artificial intelligence for crypto asset allocation," Finance Research Letters, Elsevier, vol. 47(PB).
- Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021.
"Is It Possible to Forecast the Price of Bitcoin?,"
Forecasting, MDPI, vol. 3(2), pages 1-44, May.
- Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Post-Print halshs-04250269, HAL.
- Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
- Ajitha Kumari Vijayappan Nair Biju & Ann Susan Thomas & J Thasneem, 2024. "Examining the research taxonomy of artificial intelligence, deep learning & machine learning in the financial sphere—a bibliometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 849-878, February.
- Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
- Sunghyon Kyeong & Daehee Kim & Jinho Shin, 2021. "Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea," Sustainability, MDPI, vol. 14(1), pages 1-12, December.
- Alhanouf Abdulrahman Saleh Alsuwailem & Emad Salem & Abdul Khader Jilani Saudagar, 2023. "Performance of Different Machine Learning Algorithms in Detecting Financial Fraud," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1631-1667, December.
- Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
More about this item
Keywords
explainable machine learning; non-life insurance; customer profiling;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:8:y:2020:i:4:p:137-:d:461564. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.