Showing 8 results for Credit Risk
Gholam Reza . Keshavarz Haddad, Hosein Ayati Gazar,
Volume 7, Issue 4 (1-2008)
Abstract
With the continuous development and changes in the credit industry, credit products play a more important role in the economy. This has led institutions to expand the role of technology in their credit management processes. Credit scoring is a method used to estimate the probability that a loan applicant or existing borrower will default or become delinquent. There are two types of methods used for scoring: Traditional statistics models like Probit and Logistic regression and Data Mining models such as Classification and Regression Trees (CART). In spite of popularity in applying Logit model in credit assessment of applicants, it is attempted to present another method which is theoretically and empirically superior to Logit model. It is also tried to study the capability and accuracy of this method in comparison with Logit model. In this paper, we have examined the performance of different models in credit scoring on real data of a bank and the two approaches above are compared as well. After building a model using Logistic regression; we have built a model using classification and regression trees. Our aim is to emphasize on the specification of CART and testing its capability and comparing its accuracy with the Logit model. The results reveal the accuracy of CART through a bootstrap simulation. Finally it is suggested that classification and regression trees method could be used in credit scoring process instead of Logit model.
Mohammad Vaez Barzani, Leila Torki, Naeimeh Jelvehgaran,
Volume 13, Issue 1 (4-2013)
Abstract
With globalization getting momentum, capital inflow has been an instrument for economies to grow fast in recent decades. Hence, identifying the factors that affect capital inflow and outflow - net international capital mobility- would be desirable to achieve economic stability. As usual, one of the factors that influence on capital inflow is high return of capital. New experiments explore the crucial role of risk and liquidity intensive on net international capital mobility. So, the purpose of this study is to analyze the analytical impact of credit risk scoring on the net international capital mobility in Iran within the period of 1980-2009. To achieve credit risk scoring, the Fink's scoring model has been used to identify the determinants of credit risk. Then, the rank of each factor has been appeared separately and finally the country's credit risk scoring has been estimated. Then, the final model using time series data and ordinary least squares method are analyzed. The impact of liquidity, different return of inside and outside and credit risk on net international capital mobility in Iran are discussed at the end of the paper. The results show that all mentioned variables have an anticipated effect on net capital inflow.
Volume 14, Issue 4 (3-2011)
Abstract
This research has been done with the aim of identification of the effective
factors that influence credit risk and designing a model for the credit rating
of the legal clients of Tejarat Bank in 2003-2004 by using Data Envelopment
Analysis. For this purpose, the necessary sample data on financial and nonfinancial
information of 146 companies (as random simple) was selected. In
this research, 27 explanatory variables (including financial and non-financial
variables) were identified and examined. Finally, with the application of
factor analysis and Delphi method, 8 variables, which had significant effect
on credit risk, were selected and entered into the DEA model. Efficiency of
the companies was calculated by using these variables. Then the model
validity was measured by regression analysis. The DEA credibility scores
represented the dependent variables while the 8 ratios used were considered
as independent variables. The findings of the research showed that 25
companies stand on the border of efficiency. Also with one exception
(owners equity/ total asset), ’all variables had the expected direction
α = %5 .
Research conclusions confirmed the hypothesis of DEA model’s
efficiency on credit rating of the companies who have taken credit facilities
from branches of Tejarat Bank in Tehran city.
Volume 17, Issue 4 (1-2014)
Abstract
Credit institutions to provide variety of facilities to their customers, need to comprehensive studies by qualitative and quantitative aspects of their applicants. By this way, accomplish a complete evaluation of repay ability measure and calculate the refund facilities probability and finance services by them , these reviews generally validation name. The purpose of this study was ranking customer groups and specifies the best part of them until brokerage firm do its credit allocation process mechanically. Here, after the preprocessing of the data, they are processes in the RFM model. Then SOM neural network as one of the clustering algorithms will change customers to 10 cluster. Using the proposed model, the clusters will rank. The top clusters, identification and facilities grant operations to the members of these clusters will do. Finally, three clusters 5, 1 and 7 defines as top clusters that they are the target customers. Coefficient facilities granted to the top three clusters respectively are 0.271, 0.173 and 0.556.
Volume 19, Issue 1 (7-2015)
Abstract
All of the financial institutions for gaining the best profit of their investment are always looking for the best investors, consulters, and borrowers. Besides, different sciences attempt to represent accurate methods for the separation of the customers. For that reason, sciences such as psychology, management sciences, mathematics, financial and etc…seek to achieve this aim. The subject that comes into consideration in this paper is the necessity of using the new methods in data mining in mixture with artificial intelligence techniques in order to deal with the sophisticated issue and answer to this question that do the usage of combined approach predict the customer rating well? If we want this process occurs, another dimension must not be forgotten that is the select measurement criteria and in this regard, the researcher has used judging journalist and non-parametric analysis in order to rank criteria thatfinally, select the number of indicatorsin order to implement the hybrid model will lead the researcher to answer this question: do the journalist’s ideas selection criteria result in a good prediction of the credit status of customers? The three indicators “age”, “previous relationship with the bank”, and “credit”to implement a fuzzy neural hybrid model are chosen. The model has been implemented in three layers and results suggest that 89.67% times the system can accurately estimate the proportion of customers provide ratings.To optimize the fuzzy neural network, the ant colony algorithm was used which results in improved performance of the model was 90.5%.
Mr. Mehdi Bakhtiar, Dr Rozita Moayedfar, Dr Mohammad Vaez Barzani, Dr Ramin Mojab,
Volume 23, Issue 1 (3-2023)
Abstract
Aim and Introduction
Credit risk is the possibility of a loss resulting from a borrower's failure to repay a loan or meet contractual obligations. Traditionally, it refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection. Although it is impossible to know exactly who will default on obligations, properly assessing and managing credit risk can lessen the severity of a loss. Interest payments from the borrower or issuer of a debt obligation are a lender's or investor's reward for assuming credit risk.
When the borrower remains financially healthy and pays the agreed instalments and interest as scheduled, the loan is said to be performing. But there is always the risk that the company or individual will not be able to repay within the agreed timespan. If this happens or looks likely to happen, the bank must classify the loan as “non-performing”. A bank loan is considered non-performing when more than 90 days pass without the borrower paying the agreed instalments or interest. Non-performing loans are also called “bad debt”. To be successful in the long run, banks need to keep the level of bad loans at a minimum so they can still earn a profit from extending new loans to customers. If a bank has too many bad loans on its balance sheet, its profitability will suffer because it will no longer earn enough money from its credit business. In addition, it will need to put money aside as a safety net in case it needs to write off the full amount of the loan at some point in time.
Methodology
This study with a new approach examines the determinants of credit risk in Iranian banks from 2006 to 2019. Province, banking groups and time are three dimensions used in the modeling of this study as explanatory variables of credit risk. Furthermore, a three-dimensional panel data model is used to measure the coefficients of independent variables. In the case of two-dimensional panels, each observation is typically a vector of values of a dependent variable and one or more independent variables, and comes with two labels attached, one is frequently time and the other an individual person, business or nation. When the panel is multi-dimensional, each observation comes with many labels, for example, time, individual employee, firm, and industry. An observation could consist of values of multiple endogenous variables and multiple exogenous or predetermined variables, labeled with at least time and one other label. All of the problems and issues which arise for two-dimensional panels also exist for multi-dimensional panels.
Findings
The results of the study indicate that access to provincial credit has a positive effect and the size of the provincial banking sector has a negative impact on the provincial credit risk. In addition, among the variables of the regional economics, the provincial unemployment rate and the provincial real economic growth rate affect positively the provincial credit risk, and the provincial Gini coefficient variable affect negatively the provincial credit risk. The index of road network accessibility as a sensitive variable has a negative influence on the credit risk of the province, which means that in regions where the index of road network accessibility is larger, the cost of access for economic enterprises is reduced, so the profit margin and the ability to repay facilities by the enterprise increases and less default occurs.
Discussion and Conclusion
The banking system is subject to some risks in attaining its goals; one of the most important of which is encountering non-performing loans and ultimately write-offs. The emergence and accumulation of NPLs can become a systemic problem when this affects a considerable part of the financial system, threatening its stability and/or impairing its core function of facilitating financial intermediation. A significant increase in NPLs throughout the system can have a negative impact on the resilience of the banking sector to shocks, thus increasing systemic risk. NPLs may also be associated with higher funding costs and a lower supply of credit to the real economy. This may result from negative market sentiment towards banks with high levels of NPLs, which decreases banks’ ability to access liquidity and capital markets (potentially leading to credit supply constraints). In order to reduce credit risk, the necessary policies should be adopted to take into account the considerations of the regional economics in payment of facilities.
Volume 29, Issue 1 (3-2022)
Abstract
Changing the business model of banks, entering new markets, changing the nature of traditional and classic systems to digital banking and the emergence of fintechs and startups in the banking sector on the one hand and the lack of a comprehensive view in the field of identification and Risk control, on the other, have increased the concern and risk of banks. In this paper, using the standards of the Banking Supervision Committee, the effect of intra-bank and extra-bank risk factors by data panel econometric model on capital adequacy as an indicator of bank risk management in the period 2012-2018 in listed banks (10 banks) tested and analyzed. The results of the assumptions showed that all risk indicators studied have a significant effect on the capital adequacy of banks, in addition to the risk of balance sheet structure as shown in the financial crisis of 2007-2008, also threatened by macroeconomic risk.
Volume 31, Issue 1 (9-2024)
Abstract
Changes in banking business model, entering new markets, switching traditional and classical systems' nature to electronic banking and entering digital banking, as well as the emergence of FinTechs and startups in the banking industry on the one hand and the lack of a comprehensive view and inclusive in the field of risk identification and control, on the other hand, increased the concern and risk of banks. What is certain is that the process and manner of change do not indicate a secure future. Therefore, the present study aims to provide a comprehensive classification of types of risks in the Iranian banking industry. The statistical sample includes the number of thirty selected experts and risk experts in the banking industry who selected by sampling method based on systematic elimination. Twenty final indicators determined for risk classification in the banking industry from among 68 extractive components obtained from literature review, obtained by repeating the Delphi method three times in 1399-1400 period. The results showed that the proposed classification of banking risk includes financial risk, operational risk, economic risk, socio-political risk, compliance risk, and knowledge and technology risk. The validating results through the Delphi technique showed that Cronbach's alpha coefficient for the third round was equal to 0.899 and indicated that all indicators were significant and valid and there was a high level of consensus among experts.
