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Title:	產險公司破產預測之分析：運用新類神經網路方法 Solvency Prediction of Property-Casualty Insurance Company - A New Neural Network Approach
Authors:	魏佑珊 Wei, Yu Shan
Contributors:	蔡政憲魏佑珊 Wei, Yu Shan
Keywords:	產險業清償能力類神經網路 Property and casualty insurers Insolvency Neural network
Date:	2002
Issue Date:	2016-05-09 16:31:03 (UTC+8)
Abstract:	保險業的清償能力一直是保險監理機關關心的重點，保險公司一旦失卻清償能力，所影響的將不只是該公司，還有龐大的保戶及社會大眾。自西元1988年開始，即有許多學者提出早期預警模型，針對保險公司的清償能力作預測，希望可以及早發覺問題保險公司，直到西元1994年，開始有學者以類神經網路作為預測工具，結果發現，其預測準確度較過去多篇文獻所認為的邏輯斯迴歸來的精確。　　本論文的目的在利用新的類神經網路建構保險公司失卻清償能力的早期預警系統，並將其結果與邏輯斯迴歸之結果作比較，樣本為美國產險公司，實證結果顯示，若以類神經網路作為預測的工具，在預測破產公司方面，其結果較邏輯斯迴歸好；但若是在預測健全公司方面，則為邏輯斯迴歸較好。另外，就整體的預測準確度而言，則以類神經網路的預測結果較好。　　The solvency of insurance industry plays an important role in society and has been the focus of insurance regulation. The insurer insolvency will affect not only company itself, but also the policyholders and society. The better method up to 1994 to identify insurer insolvencies in most prior researches is logistic regression. Some scholars use neural networks to predict insurer insolvencies. The result showed that neural network performed better than logistic regression model. 　　The purpose of this paper aims to construct an early warning system for property and casualty insurer insolvencies prediction and to compare the predictive ability of neural network and logistic regression model. The results show that neural network performs better than logistic regression model in classifying insolvent insurers. On contrast, logistic regression model performs better in classifying solvent insurers. Overall, the neural network performs better than its counterpart based on all sample firms.
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Description:	碩士國立政治大學風險管理與保險研究所 89358007
Source URI:	http://thesis.lib.nccu.edu.tw/record/#A2010000372
Data Type:	thesis
Appears in Collections:	[Department of Risk Management and Insurance] Theses

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