TY  - BOOK
AU  - 
TI  - Antecedents of risk : : establishing a model on credit risk management towards organizational resiliency of non-bank financial institutions
PY  - 2022///
CY  - [S.n.]
PB  - 2022
N1  - Thesis (Ph.D.)-- Pamantasan ng Lungsod ng Maynila, 2022; A dissertation presented to the faculty of the Graduate School of Business PLM Business School in partial fulfillment of the requirements for the degree Doctor of Business Administration; 600-699
N2  - Abstract:

This study aims to formulate a model on credit risk management towards organizational resiliency of Nonbanking Financial Institutions without quasi-banking function in NCR. Credit risk management plays a major role for NBFI firm performance and to its sustainability by having a highly dependent model assessment and effective management risk. Furthermore, the goal of this research is to evaluate the performance of chosen nonbank  financial institutions using credit risk, efficiency, liquidity, and profitability as indicators. In addition to these indicators, based on the review of related literature there are other different factors that affects credit risk management such as credit risk policies, credit collection management practices, non-performing loans, corporate governance, and capital adequacy ratio which this research aims to analyze its impact as variables to credit risk management.
This research will use a descriptive design to examine the relationship between the independent and dependent variables. The descriptive research approach is effective in this study since it gives descriptive information of the measurement of the variables through quantitative data, thus the correlative research tries to identify correlations between two or more variables and the magnitude of these associations. A 4-point Likert scale survey will be conducted and answered by Credit sampling using cluster sampling. Luster sampling is a type of non-probability sampling that is a form of random and unbiased sampling technique in which research sample ae drawn from a cluster. The intended model will be established through Mean and Standard deviation and Multiple regression/ Multivariate Analysis.
Key findings of this study shows that the overall finding exhibits a good level of credit risk policy  \ x=3.43,s=0.649). Documentation x=3.43,s=0.649),  corporate loan standards (x=3.4,s=0.658), strategic plan (x=3.37,s=0.675), credit portfolio supervision (x=3.36,s=0.638), and individual loan standards (x=3.35,s=0.696) is the hierarchical order of credit risk policy indicators.
In addition, the findings show that, the overall level of credit risk management (x=3.46, s=0.603) is deemed “Good”. Additionally, the hierarchical ladder of the indicators starts with risk monitoring (x=3.49,s=0.628), risk assessment and analysis (x=3.47,s=0.628), risk management practices (x=3.44,s=0.638), and risk identification (x=3.43,s=0.673).
Lastly, the data gathered indicates that Credit risk management has a significant relationship with organizational resiliency t(126)=14.09, p =<0.001. Based on the findings, the effect of credit risk management on organizational resiliency is positive β=0.7835
ER  -