Changes in Insurance Contract Standards Under Artificial Intelligence Scenarios
- DOI
- 10.2991/978-94-6463-036-7_134How to use a DOI?
- Keywords
- Artificial intelligence; Insurance pricing; Algorithms; Profits; Loss ratio
- Abstract
Artificial intelligence is widely used in many ways, and people are also applying the technology to the pricing of insurance contracts. This article will apply common algorithms like the big data analysis of artificial intelligence to the predetermined incidence calculations in insurance pricing and the decrease of insurance refusal. Therefore, people can better understand the usage of artificial intelligence in the insurance pricing area, and also the increase of loss ratio through analyzing the whole pricing process. Through the research we have done, we can conclude that artificial intelligence is capable of making the predetermined incidence calculations inside insurance pricing accurate, which increases the loss ratio, as it can precisely predict the most effective and useful insurance for people. Therefore, insurance companies can enhance their cooperative image, and create more clients, in the end, providing more profits for the insurance company.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shuyi Zhang AU - Xuyan Zhang PY - 2022 DA - 2022/12/31 TI - Changes in Insurance Contract Standards Under Artificial Intelligence Scenarios BT - Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022) PB - Atlantis Press SP - 911 EP - 916 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-036-7_134 DO - 10.2991/978-94-6463-036-7_134 ID - Zhang2022 ER -