scholarly journals Application of the Oriented Fuzzy Numbers in Credit Risk Assessment

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 535
Author(s):  
Aleksandra Wójcicka-Wójtowicz ◽  
Krzysztof Piasecki
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Fuzzy Numbers ◽  
Scoring Function ◽  
Decision Makers ◽  
Credit Risk Management ◽  
Credit Risk Assessment ◽  
Simple Additive Weighting ◽  
Credit Standards

Over the years, banks have faced many difficulties, related mainly to lax credit standards for borrowers and counterparties. The goal of credit risk management is to maintain the volume of credit risk at acceptable level as it is a vital feature in risk management. Credit analysts take into consideration factors of a wider spectrum, e.g., the prospects of the line of business, the experience of board members, credibility of suppliers, etc. Those factors are often considered on the linguistic scale, which includes such imprecise and inaccurate phrases, for instance, such as: more/less experienced, better/worse prospects, etc., which, for the experts and decision makers, are justified and result from their personal experience, preferences and human nature. The paper presents the approach of supporting methods in the credit risk decision-making process. It presents evaluation scales of imprecise phrases commonly used during the process of credit risk assessment based on experts’ preferences. Due to the imprecision, the oriented fuzzy numbers are a useful tool. For such described evaluation scales, we use a scoring function determined with the use of an adapted Simple Additive Weighting (SAW) method.

Credit Risk Management of Commercial Banks in Nepal

2019 ◽  
Vol 4 (1) ◽  
pp. 27-37
Author(s):  
Shreya Pradhan ◽  
Ajay K. Shah
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Management Practices ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Credit Risk Management ◽  
Loan Repayment ◽  
Credit Risk Assessment

The study is primarily focused on credit risk assessment practices in commercial banks on the basis of their internal efficiency, assessment of assets and borrower. The model of the study is based on the analysis of relationship between credit risk management practices, credit risk mitigation measures and obstacles and loan repayment. Based on a descriptive research approach the study has used survey-based primary data and performed a correlation analysis on them. It discovered that credit risk management practices and credit risk mitigation measures have a positive relationship with loan repayment, while obstacles faced by borrowers have no significant relationship with loan repayment. The study findings can provide good insights to commercial bank managers in analysing their model of credit risk management system, policies and practices, and in establishing a profitable and sustainable model for credit risk assessment, by setting a risk tolerance level and managing credit risks vis-a-vis the prevailing market competition.

scholarly journals The Risk Management of Commercial Banks——Credit-Risk Assessment of Enterprises

2016 ◽  
Vol 8 (9) ◽  
pp. 69
Author(s):  
Na Luo ◽  
Jiayi Yang ◽  
Yuanfeng Zhu ◽  
Yu Zhang
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Assessment System ◽  
Assessment Model ◽  
Good Prediction ◽  
Credit Risk Management ◽  
Credit Risk Assessment ◽  
Credit Risks

With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises’ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks’ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is established on the basis of GRNN neural network model, to make an empirical analysis with the selected sample data. And the results suggest that the hit rates of identifying high quality enterprises and low quality enterprises are 92.16 percent and 93.75 percent, respectively, indicating that the model has realized a good prediction.

scholarly journals Improved forecast assessment for the expected credit losses in credit risk monitoring in commercial banks in the context of international and Russian practices

2020 ◽  
Vol 15 (3) ◽  
pp. 445-457
Author(s):  
Elena Vladimirovna Travkina ◽  
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Banking Sector ◽  
Assessment Model ◽  
Financial Instruments ◽  
Credit Risk Assessment ◽  
Ifrs 9 ◽  
Credit Losses

Current banking sector’s performance raises the issues connected with the IFRS 9 Financial Instruments driven transformation of the forecast assessment for the expected credit losses during monitoring and credit risk assessment in commercial banks. In this regard, it becomes important to conduct a comprehensive systematization of the existing Russian and international practices for monitoring and evaluating credit risk in commercial banks. The purpose of the study is to develop a comprehensive approach to the use of an effective model for the impairment of expected losses in banking activities. The novelty of the study includes the enhancement of the tools for the forecast assessment of the expected credit losses among the commercial banks’ clients to improve the credit risk management efficiency. The results from the implementation of IFRS 9 Financial Instruments in the banking area show that modern conditions maintain the uncertainty of the long-term impact of the credit risk on the commercial banks’ performance. What is more, a huge amount of additional information gives significant difficulties, which contributes into the sophisticated calculations of the future credit losses of the banks. It has been justified that a forecast assessment model for the expected credit losses of the clients during the monitoring and bank’s credit risk assessment should be based on the collective or individual ground. The efficient application of the expected losses impairment in the banking performance has been described as a fundamental tool to simulate the expected credit losses to provision for impairment. This model has been shown to be determined by the features of the credit activities and bank portfolio, types of its financial tools, sources of the available information, as well as the applied IT systems. The proposed model validation algorithm for the expected impairment losses could reduce the expected credit losses, decrease the volume of the created assessed reserves, as well as improve the overall commercial bank performance efficiency. Theoretically, the study develops the credit losses risk management in the context of the transformations in the global and Russian banking practices. From the perspective of the practical value, the research gives an opportunity to create an efficient forecast assessment model for the expected credit losses of the commercial banks’ clients, this model contributing into the cost effectiveness of the bank’s credit activities. A promising further research is considered to be aimed at developing the tools for the assessment of the commercial banks’ credit activity results in the context of the adopted changes connected with the introduction of IFRS 9 Financial Instruments in the Russian banking sector.

Credit Risk Assessment by Ordered Fuzzy Numbers

2019 ◽  
Author(s):  
Aleksandra Wójcicka-Wójtowicz ◽  
Anna Lyczkowska-Hanckowiak ◽  
Krzysztof Maciej Piasecki
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Fuzzy Numbers ◽  
Credit Risk Assessment

Retail Credit Risk Management in Indian Public Sector Banks

2012 ◽  
Vol 3 (8) ◽  
pp. 31-37
Author(s):  
Nayan J. Nayan J. ◽  
◽  
Dr. M. Kumaraswamy Dr. M. Kumaraswamy
Keyword(s):  
Risk Management ◽  
Public Sector ◽  
Credit Risk ◽  
Credit Risk Management ◽  
Public Sector Banks ◽  
Retail Credit

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.

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