IT risk management

By Yimei Lin. 2014

1,214 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d2eb96a65e7ec6a1d6cf96d7c68216bee54f1e5c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yang Liu
2. Tianjian Chen
3. Han Yu
4. Xinwei Zhang
5. Ce Ju
6. Gangjin Wang
7. Chi Xie
8. Dashan Gao

Citations in the corpus

• Machine Learning in Banking Risk Management: A Literature Review (2019, background): “Considerable research (Van Liebergen 2017; Deloitte University Press 2017; Helbekkmo et al. 2013; MetricStream 2018; Oliver Wyman 2017), both in academia and industry, has focused on the developments in banking and risk management and the current and emerging challenges.”
• Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention (2020, background): “Recent data privacy concerns begin with an investigation of Facebook’s data privacy violations in collecting user information for uninformed use [Tuttle, 2018].”
• Risk assessment and risk management: Review of recent advances on their foundation (2016, methodology): “According to Aven and Zio (2013) , model uncertainty is to be interpreted as uncertainty about the model error, defined by g ( x ) − y , where y is the quantity we would like to assess and g ( x ) is a model of y having some parameters x .”
• Quantifying Credit Risk I: Default Prediction (2003, methodology): “More complete treatment of the KMV model from a theoretical perspective can be found in Caouette, Altman, and Narayanan (1998), Crouhy et al., and Vasicek (1984). 5.”
• Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach (2019, background): “Second, from the SME’s point of view, as a supplier the SME might sometimes fall short of the capacity to meet customer demand (Richard et al., 2007).”