Data driven credit risk management process: a machine learning approach

Author(s):  
Mingrui Chen ◽  
Yann Dautais ◽  
LiGuo Huang ◽  
Jidong Ge
2018 ◽  
Vol Special Issue on Scientific... ◽  
Author(s):  
Jalil Elhassouni ◽  
Mehdi Bazzi ◽  
Abderrahim Qadi ◽  
Mohamed Haziti

Special ISSUE VSST 2016 This paper proposes an ontological integration model for credit risk management. It is based on three ontologies; one is global describing credit risk management process and two other locals, the first, describes the credit granting process, and the second presents the concepts necessary for the monitoring of credit system. This paper also presents the technique used for matching between global ontology and local ontologies.


Author(s):  
EMANUEL KRISTIJADI ◽  
UBUD SALIM ◽  
MADE SUDARMA ◽  
DJUMAHIR DJUMAHIR

The financial institution in any nation has a potential role in the economy but it can also create the risks taken by the borrowers. This study seek to test the effect of policy and credit risk management strategies, quality of human resources, information technology intensity, and moral hazard of lending staff on the credit risk management process. This is positivist approach with qualitative information to support quantitative analysis using 83 respondents of commercial banks (excluding foreign banks), collected by means of questionnaires related to respondents’ perceptions with Likert scale. The analysis was done by using Generalized Structured Component Analysis (GSCA). Results showed that credit risk management olicies can improve credit risk management strategy formulation; credit risk management strategies improves credit risk management process quality; the intensity of high IT improves credit risk management process quality; the human resource quality can less improve credit risk management process quality; moral hazard less improves credit risk management process quality; and, the high quality of credit risk management processes can reduce credit risk. It can be concluded that credit risk management process has a significant effect on credit risk. The credit risk management policy and strategy, information technology, and moral hazard are needed to support such process.Keywords: Business and Management, credit risk, Generalized StructuredComponent Analysis (GSCA), Indonesia Commercial Banks, Indonesia


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