The current role of machine learning and explainability in actuarial science
Actuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitive...
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| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/125144 |
| Aporte de: |
| Sumario: | Actuarial science seeks to evaluate, predict and manage the impact of future events. Nowadays, the actuary faces the challenge of predicting and managing risks efficiently, with a universe of information growing exponentially in real-time and with a business dynamic that demands constant competitiveness and innovation. The techniques associated with data engineering and data science open a window of tools that seek, through technology, to improve the processes of product design, pricing, reserves and establishment of market niches practically and realistically, considering the pros and cons that brings the availability and constant updating of information, as well as the computational times that this implies. Therefore, this article aims to review the application of Explainable Machine Learning techniques as an alternative to the development of more efficient and practical actuarial models. |
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