CEO overconfidence and shadow-banking life insurer performance under government purchases of distressed assets

There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being...

Full description

Saved in:
Bibliographic Details
Published inRisks (Basel) Vol. 7; no. 1; pp. 1 - 25
Main Authors Chen, Shi, Lin, Jyh-horng, Yao, Wenyu, Huang, Fu-Wei
Format Journal Article
LanguageEnglish
Published Basel MDPI 01.03.2019
MDPI AG
Subjects
bailout
Bailouts
Bankruptcy
CEO overconfidence
Chief executive officers
Competitive advantage
Default
Economic crisis
Efficiency
Influence
Insurance industry
Insurance policies
Interest rates
International finance
Liability insurance
Life insurance companies
Policyholders
profit-sharing life insurance policy
shadow-banking transaction
United States > US
Europe
Online AccessGet full text

Cover

More Information
Summary:There is an increasing influence of machine learning in business applications, with many solutions already implemented and many more being explored. Since the global financial crisis, risk management in banks has gained more prominence, and there has been a constant focus around how risks are being detected, measured, reported and managed. Considerable research in academia and industry has focused on the developments in banking and risk management and the current and emerging challenges. This paper, through a review of the available literature seeks to analyse and evaluate machine-learning techniques that have been researched in the context of banking risk management, and to identify areas or problems in risk management that have been inadequately explored and are potential areas for further research. The review has shown that the application of machine learning in the management of banking risks such as credit risk, market risk, operational risk and liquidity risk has been explored; however, it doesn't appear commensurate with the current industry level of focus on both risk management and machine learning. A large number of areas remain in bank risk management that could significantly benefit from the study of how machine learning can be applied to address specific problems.
ISSN:2227-9091
2227-9091
DOI:10.3390/risks7010028