Risk factors identification and evolution analysis from textual risk disclosures for insurance industry
Author(s) -
Yinghui Wang,
Bin Li,
Guowen Li,
Xiaoqian Zhu,
Jianping Li
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.11.253
Subject(s) - risk management , insurance industry , business , premise , identification (biology) , risk analysis (engineering) , actuarial science , it risk management , risk pool , enterprise risk management , financial risk management , computer science , finance , key person insurance , insurance policy , linguistics , philosophy , botany , biology
In the traditional risk management of insurance industry, companies mainly focus on business risks related to insurance products in their daily operation, but ignores the harms of other risk types. There is no consensus on what are the risk factors faced by insurance companies totally, which reduces the effectiveness of risk management especially when the company is at a particular stage. By using text mining method, this paper comprehensively identifies risk factors faced by insurance companies and analyses the evolution of risks over time from companies’ own risk disclosures, which is the premise of effective risk prevention and management. Based on 1682 Form 10-K filings from 214 U.S. financial firms over 2006–2018, 49 risk factor topics are identified over the four stages, 14 of which are shared by all the four stages and risk factors specific to a stage are also identified. In addition, there are significant differences in the types of key risks faced by insurance companies at different stages. Our empirical results reveal the increasing importance of operational risk in the current risk management of insurance companies.
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