Do Capital Inflows Relieve Banks’ Credit Constraints and Boost Credit Growth? Evidence from Credit Conditions and Bank Credit Risk

2021 ◽  
pp. 35-44
Author(s):  
Nombulelo Gumata ◽  
Eliphas Ndou
Keyword(s):  
Credit Risk ◽  
Credit Constraints ◽  
Capital Inflows ◽  
Bank Credit ◽  
Credit Growth

scholarly journals Modeling of Bank Credit Risk Management Using the Cost Risk Model

2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Credit Risk ◽  
Risk Model ◽  
Bank Credit ◽  
Credit Risk Management ◽  
Loan Default ◽  
Increased Risk ◽  
Credit Portfolio ◽  
Cost Risk

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

Bank credit risk events and peers' equity value

2021 ◽  
Vol 75 ◽  
pp. 101668
Author(s):  
Ana-Maria Fuertes ◽  
Maria-Dolores Robles
Keyword(s):  
Credit Risk ◽  
Bank Credit ◽  
Equity Value

scholarly journals ANALISIS LIQUIDITY CREATION PADA PERBANKAN DI INDONESIA TAHUN 2007-2013 (STUDI KASUS PADA 10 BANK BESAR DI INDONESIA TAHUN 2013)

2016 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Muhammad Mirajudin ◽  
Prasetiono Prasetiono
Keyword(s):  
Credit Risk ◽  
Commercial Bank ◽  
Banking Industry ◽  
Third Party ◽  
Bank Credit ◽  
Bank Capital ◽  
Banking Supervision ◽  
Earnings Volatility ◽  
Negative Effect ◽  
Income Instability

Problems related to banking in Indonesia today is the problem of liquidity. It is shownfrom a commercial bank credit grew 23.03% but not matched by growth in depositswhich only reached 16.56% in 2012 (Report of Banking Supervision, 2012). Therefore,this study aims to determine the liquidity creation in Indonesia as well as to analyze theinfluence of bank capital, credit risk and income instability towards liquidity creation.The samples includes 10 major banks in Indonesia with total assets of more thanRp120billion in 2013. The reason for choosing this sample because of the 10 largestbanks reflects the state of the banks in Indonesia which accounted for 65.2% of totalassets, 65.6% of total loans, and 66% of total deposits or deposits in the banking industry(PEFINDO, 2014). The results of this research note that the bank's capital and earningsvolatility is significant negative effect on liquidity creation. While the credit risk of anegative but insignificant effect on liquidity creation. In the determination coefficient testshowed that 43.6% dependent variable is the liquidity creation can be explained by theindependent variable is the capital of banks, credit risk and earnings volatility. While56.4% is explained by other variables outside the model of this study.Keywords: liquidity creation, capital of banks, credit risk, third-party funds, banks inIndonesia.

Bank credit risk and structural credit models: Agency and information asymmetry perspectives

2009 ◽  
Vol 33 (8) ◽  
pp. 1520-1530 ◽  
Author(s):  
Hsien-Hsing Liao ◽  
Tsung-Kang Chen ◽  
Chia-Wu Lu
Keyword(s):  
Credit Risk ◽  
Information Asymmetry ◽  
Bank Credit

Bank Credit Risk Analysis and Bank Credit Rating

2020 ◽  
pp. 275-348
Author(s):  
Terence M. Yhip ◽  
Bijan M. D. Alagheband
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Credit Rating ◽  
Bank Credit ◽  
Credit Risk Analysis

Bank credit risk, grain production and the Indian economy

2020 ◽  
Vol 52 (47) ◽  
pp. 5188-5202
Author(s):  
John Nkwoma Inekwe
Keyword(s):  
Credit Risk ◽  
Bank Credit ◽  
Grain Production ◽  
Indian Economy
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