Data Mining in Actuarial Science: Theory and Applications
A special issue of Risks (ISSN 2227-9091).
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 42373
Special Issue Editors
Interests: Copula models and dependencies; elliptical distributions and their applications; managing post-retirement assets; longevity risks and annuitization; risk measures and capital requirements; applications of financial economics in actuarial science; competing risks models; survival analysis
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; actuarial science; computational finance
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Insurance companies continue to gather increasing volumes of information that are being used for improved data-driven decision making. In the past few years, this has generated an increasing interest and need for data mining tools and techniques to analyze data, especially big data, in insurance and actuarial science. Data mining has potential applications in all personal and commercial lines of insurance: life, non-life, health and pensions. Although data mining is clearly useful in insurance and actuarial science, it faces many challenges during its implementation.
This Special Issue aims to collect recent developments of applying data mining techniques in insurance and actuarial science. We welcome original research articles that develop data mining techniques and case studies that showcase applications. We also encourage data mining work derived from collaborative efforts between academia and the industry.
Prof. Dr. Emiliano A. Valdez
Dr. Guojun Gan
Guest Editors
Manuscript Submission Information
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Keywords
- Data mining
- Machine or statistical learning
- Actuarial learning
- Insurance big data
- Data visualization
- Predictive analytics
- Actuarial applications
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