Evaluating the New AI and Data Driven Insurance Business Models for Incumbents and Disruptors: Is there Convergence?

IF 7.4 3区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Business & Information Systems Engineering Pub Date : 2021-01-01 DOI:10.52825/bis.v1i.58
Alex Zarifis, Xusen Cheng
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引用次数: 1

Abstract

AI and data technologies are a catalyst for fundamental changes to insurance business models. The current upheaval is seeing some incumbent insurers trying to do the same more effectively, while others evolve to fully utilize the new capabilities and users these new technologies bring. At the same time, technologically advanced organizations from outside the sector are entering and disrupting it. Within this upheaval however, there are signs of a convergence towards an ideal and prevailing business model. This research identifies one exemplar incumbent and one disruptor and evaluates whether their models are converging and will become similar eventually. The findings support a high degree of convergence, but some differences are likely to remain even after this transitionary period. The differences identified are firstly in the evaluation of risk and secondly that traditional insurers prioritize revenue generation from what is their primary activity, while new entrants prioritize expanding their user base.
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评估现有和颠覆者的新的人工智能和数据驱动的保险业务模式:是否存在融合?
人工智能和数据技术是保险业务模式发生根本性变化的催化剂。在当前的剧变中,一些现有的保险公司正试图更有效地做到这一点,而另一些公司则在不断发展,以充分利用这些新技术带来的新功能和新用户。与此同时,来自行业外的技术先进组织正在进入并扰乱该行业。然而,在这场剧变中,有迹象表明,一种理想的、盛行的商业模式正在趋同。本研究确定了一个典型的在职者和一个颠覆者,并评估他们的模式是否正在趋同,最终是否会变得相似。研究结果支持高度的趋同,但即使在这个过渡时期之后,一些差异可能仍然存在。所确定的差异首先是风险评估,其次是传统保险公司优先考虑从其主要业务中获得收入,而新进入者优先考虑扩大其用户基础。
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来源期刊
Business & Information Systems Engineering
Business & Information Systems Engineering Computer Science-Information Systems
CiteScore
13.60
自引率
7.60%
发文量
44
审稿时长
3 months
期刊介绍: Business & Information Systems Engineering (BISE) is a double-blind peer-reviewed journal with a primary focus on the design and utilization of information systems for social welfare. The journal aims to contribute to the understanding and advancement of information systems in ways that benefit societal well-being.
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