退休金
业务
订单(交换)
背景(考古学)
政府(语言学)
财务
精算学
古生物学
语言学
哲学
生物
作者
Pei‐Hsuan Tsai,Ying‐Wei Wang,Wen‐Chang Chang
标识
DOI:10.1016/j.seps.2022.101460
摘要
The issue of financial problems among the elderly has garnered the attention of multiple generations. In 2018, Taiwan formally attained an ageing society status. In order to address the financial difficulties elderly people face, an annuity system reform was launched by the Taiwanese government. Reverse mortgage lending, which is performed on the basis of house-for-pension, is an alternate method for enhancing their standard of living and sustaining their financial stability. The elderly applicants often mortgage their properties to financial institutions in order to maintain a steady cash flow for the fulfilment of their daily needs and live their remaining years in the mortgaged properties without vacating. Prior studies on house-for-pension in the Taiwanese context primarily concentrated on institutional implementation analysis but limitedly explored the uncertainty risks banks face when implementing the house-for-pension scheme. First, a literature review on the risks associated with house-for-pension reverse mortgage financing and comprehensive interviews with banking industry professionals were conducted. Subsequently, in the Taiwanese banking sector, assessment criteria for important risk factors in house-for-pension reverse mortgage financing were devised. The decision-making trial and evaluation laboratory (DEMATEL) was utilised to assess the interdependence of the assessment criteria. Next, the DEMATEL-based analytic network process (ANP) or DANP were used to calculate the weights of evaluation criteria. By employing modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), a gap analysis was undertaken on the assessment criteria and sub-criteria. The study's findings serve as a guideline for the Taiwanese banking industry in formulating, improving, or amending the risk exposure mitigation measures for reverse mortgage lending.
科研通智能强力驱动
Strongly Powered by AbleSci AI