





















Contents

Asian Finance & Banking Review
Vol. 1, No. 1; 2017

Published by Centre for Research on Islamic Banking & Finance and Business

Comparative Analysis on the Sources of Credit Risk in Nepalese Commercial Banks
   Fatta Bahadur K.C and Indra Kumar Kattel 

Comparative Analysis on the Sources of Credit Risk in Nepalese Commercial Banks  
Fatta Bahadur K.C1
Indra Kumar Kattel2
1 Tribhuvan University, Nepal
2Mewar University, Rajasthan, India
Correspondence: Indra Kumar Kattel, Rastriya Banijya Bank Limited, Central Office Singhdurbar Plaza, Kathmandu, 44600, Nepal, Email: indra.kattel2024@gmail.com 
Received: September 24, 2017  
Accepted: September 26, 2017  
Online Published: September 29, 2017  
Abstract

The study was conducted to find out the significant differences between state-owned bank and private sector bank in the practice of measuring the sources of credit risk. This paper attempts to ascertain the perceptions of Nepalese bankers about the importance of identification practice of credit risk sources within the specific borrowers. The result of the study indicates that the collaterals, characters, capacity, capital, condition, legality of the business, economy of the country, business environment and industrial relation were consider as a sources of credit risk in Nepalese banking sector. The result shows that level of the credit risk sources were disparity the Nepalese commercial banks. In addition, Collaterals provided as a security by the borrower, characters of the borrower, capacity, legality of the business, economy of the country and industrial relation were found significant predictor for the sources of credit risk. Moreover, there was a positive relationship between identification practice and sources of credit risk.
Keywords:Credit, Risk, Identification, Collaterals, Characters, Capacity, Capital, Condition, Legality, Economy
1. Introduction

Credit risk is one of the most general risks that exist in the banking market and a major risk faced by financial institutions (Duffie & Singleton, 2003). Credit risk normally refers to the risk that a borrower will default on any type of debt by failing to make payments which it is compelled to do. Credit risk is the major risk that banks are exposed during the normal course of lending and credit underwriting. The credit risk is most simply defined as the potential that a bank borrower or counterparty will fail to meet its commitment in accordance with agreed terms (Basel, 2000, p. 1). Credit risk arises from non-performance by a borrower. For most banks, loans are the largest and most obvious source of credit risk, however, credit risk could stem from activities both on and off balance sheet. It may arise from either an inability or an unwillingness of the borrower to perform in the pre-committed contracted manner. In a bank’s portfolio, losses arise from outright default due to the inability or unwillingness of a customer or counter party to meet commitments in relation to lending, trading, settlement and other financial transactions. Alternatively losses may result from a reduction in portfolio value due to actual or perceived deterioration in credit quality of the banks (Nepal Rastra Bank, 2010).

Diwan & Rodick (1992), suggested that high NPLs increase the uncertainty regarding the capital position of the banks and therefore tend to limit their access to additional financing in regular banking business. The shortfall of the lending fund contributes to lower credit growth. In certain banks, governments have large amounts of non-performing loans and some commercial banks tend to finance government fiscal deficits and sustain some unprofitable government projects with large borrowings from banks. These actions increase the prospects of generating NPLs in the banks. So, non-performing loans are one of the main reasons that cause insolvency of the financial institutions and ultimately destroy the whole economy (Hou, 2007). By considering these facts, it is necessary to control non-performing loans for the financial soundness of banks; otherwise the capital can be jammed in unprofitable projects and sectors which not only damage the financial health of banks but also the economic stability of the country. 

Credit risk management is a structured approach to manage uncertainties through risk assessment, development of strategies to manage it and mitigation of risk using managerial resources. The strategies include transferring the risk to another party, avoiding the risk, reducing the negative effects of the risk, and accepting some or all of the consequences of a particular risk (Afriyie & Akotey, 2012, p. 1).

Credit risk management in modern banking industry has gained a momentum due to the high degree of non-performing assets. Credit risk is the most common cause of bank NPA, causing virtually all regulatory environments to prescribe minimum values for credit risk management. The foundation of sound credit risk management is the identification of the existing and potential risks, inherent in lending activities entire the individual borrowers.  Measure to counteract, these risks normally comprise evidently clear policies that express the bank’s credit risk management philosophy and the parameter within which credit risk is to be controlled (Greuning & Bratanovic, 2003, p. 151). The three goals of credit risk measurement when extending the business credit are 1) for the lender to limit credit risk exposure, 2) to earn adequate compensation for the level of credit relative to facility amount, and 3) to mitigate credit risk from economic loss. To fulfill these goals bank and financial institutions have made the credit policy, guideline and create the appropriate credit culture.

The assessment analysis a task is to measure the risk level and to find out what events could potentially cause harm or benefits for the banks. The risk is being assessed in terms of the sternness of the impact, likelihood of occurring and controllability (Gray & Larson, 2008, p. 215). Risk assessment is done by prioritizing the risk either by using risk evaluation or risk analysis (Williams, et al., 2006, p. 70).  This risk analysis is based on the likelihood and consequences. Likelihood depends on the probability that the risk will occur and how frequently it will take place. While, consequences on the other hand can be calculated by looking at the effects on results or on the enablers of results (Williams, et al., 2006, p. 70). For this purpose, banks adopt the various assessment and analysis tools to know the frequency of incidence of the risk and its effect. Hence, risk appraisal is then carried out when an appropriate risk analysis tools has been assumed. An assessment is done against an appropriate risk-acceptance criterion to give a risk level of the credit (Williams, et al., 2006, p. 70). Therefore, assessment and analysis is equally important for credit risk management practice on banking sectors. During the credit appraisal, banks try to find out risk level from the basic sources of credit risk. An examination of real risk assets allocation of banks conducted by McKinsey & Company (1997) reveals that credit risk exposure takes up to 60.0% of risks that banks face while market risk and operational risk take 20.0% respectively. The acknowledgment, dimension, manage and supervision of credit risk are, therefore, very significant for banks. There is no financial institution that could avoid any types of risks.

1.1 Objective of the Research
The major objective of the study is to analyze the practice of credit risk sources in some selected commercial banks operating in Nepal. The key objective of this research is to ascertain relationship between identification practice and sources of credit risk in Nepalese commercial banks. 

1.2 Hypothesis of the Research
To fulfill the predefined objectives of this study, the following hypotheses were developed and tested by using statistical tools.

H1: 
There are significant differences between state-owned and private sector banks in the practice of measuring the sources of credit risk. 

H2: 
There is positive relationship between identification practice and sources of credit risk. 

1.3 Definition of Key Variables 

The major aim of credit analysis is to find out the risk level of the borrowers for credit decision. For this purpose the following factors will be considered either implicitly or explicitly during the credit risk analysis (Joseph, 2014, pp. 24-25). For this purpose commercial banks use the Five C principle for credit granting process. 

1.3.1 Character of the Borrower
Character shows the integrity and honesty of the borrower to settle the dues in time. It is related that the history of credit has recorded several instances where the borrower had the capacity to repay but not the willingness. A banker takes an informed view about the character of the borrower through the study of ownership, management, business transactions as well as business history from the appropriate market enquires. The quality and reliability of the references will be checked for final consideration. The background and experience level of employees play the essential role to indentify the honesty and integrity of the borrowers.

1.3.2 Capacity
It is the borrower’s capacity to meet their debt service obligation studied during the credit decision. In the case of business lending this is done through the financial statements analysis. Financial statements including interpretation of financial ratios that indicates the ability to pay. Capacity to repay is the most serious of the five factors; it is the primary source of repayment–cash inflows and cash generated by the borrower. The prospective lender wants to identify exactly how the borrower intends to repay the loan. The bank will judge the cash flow from the business, the timing of the repayment of loan, and the probability of successful repayment.  Payment history on existing credit relationships - personal or commercial is considered an indicator of future payment performance. The business is influenced by external factors such as government policy, economic policy, national income etc; it is necessary to assess the capacity of the borrower. So, bank will also want to know about other possible sources of repayment.

1.3.3 Capital 
A bank would ensure that the borrower has a sufficient stake in the business. Higher the capital contribution by the owner in the business is considered the better capacity, because a large involvement by the borrower will reduce the chance of default. 

1.3.4 Collaterals
Collaterals means the asset offered by the borrower to secure the loan, such as a property, to the lender with the agreement that it will be the repayment source in case the loan is not pay back from the established sources as per terms and conditions agreed for the financing. Some lenders may require a guarantee in addition to collateral as security for a loan. Collateral is considered the subsequent way out by the lender in case the credits become default.

1.3.5 Conditions 
Conditions cover terms and covenants included in the loan or credit facilities agreement letter. It describes the intended purpose of the loan and the conditions under which the credit is being granted. The bank must ensure that the loan agreement clauses are legally enforceable.

Instead of five C principle, legality of the business, economy of the country, business environment and industrial relation are also considered as key components for credit risk assessment.  Lack of the proper assessment, bank faces the risk from these area. So that collaterals, characters of the borrower, capacity, capital invested by the borrower in the business, condition, legality of the business, economy of the country, business environment and industrial relation are support to produce the credit risk in the bank. Hence, it is considered sources of credit risk as independent variables. 

2. Literature Review 

Credit risk can be raised due to variety of reasons of both internal and external sources. According to Nijskens (2011) and Breuer, Jandacka, Rheinberger, & Summer, (2010), the sources of credit risk includes poor governance and management control, inappropriate laws, limited institutional capacity, inappropriate credit policies, volatile interest rates, low capital and liquidity levels, directed lending, massive licensing of banks, poor loan underwriting, reckless lending, poor credit assessment, poor loan underwriting, laxity in credit assessment, poor lending practices, government interference, and inadequate supervision by the central bank. The literature has recognized these sources that could lead to credit risk in commercial bank. The levels of credit risk incurred fluctuate across sectors as well as countries also. 

Credit risk is often considered as a consequence of systemic risk derived from the macroeconomic perspective. The factors influencing the systematic credit risk are macroeconomic factors, changes in economic policies, political changes and the goals of leading political parties Macroeconomic factors include the inflation rate, the employment rate, growth in gross domestic product, stock index and exchange rate movements, and conjuncture fluctuations in the economy (Aver, 2008, p. 318). Changes in economic policies are signified by changes in monetary and fiscal policies, economic legislation changes, as well as trade policies of the country. The political changes or changes in the goals of leading political parties also influence the systematic risk. All these factors can have an important influence on the likelihood of borrowers debt servicing capacity, but as changes in economic policies and political changes are difficult to examine, the literature has mainly focused on the macroeconomic factors only.

There are also internal factors that can cause credit risk of bank and financial institutions. Deficiencies of techno economic appraisal of loan proposal, inadequately define lending policies, lack of post sanction monitoring system, inadequate value of the securities, over optimistic assessment, liberal loan sanctioning power, lack of knowledge and skills of credit relationship officers, lack of adequate and reliable information, lack of proper coordination of various department, lack of well defined organization structures, lack of proper credit scoring and rating system and lack of reliability and integrity of the data etc. are the major factors related to the bank that influence to increase the credit risk (Bidani, 2010, pp. 31-32).

According to Koopman and Lucas (2005), making a distinction between idiosyncratic and systematic risks, the individual risk factors are intrinsic to individual characteristics of borrowers. The systematic risk is most significant at the loan portfolio level, and the systematic credit risk issues are usually thought to correlate with macroeconomic conditions. The loan portfolios are generally exposed to a counterparty credit risk and asset value risk, which is provisional in the occurrence of credit default events (Mileris, 2012, p. 86). Credit default and asset value risk are highly interdependent. During economic declines the asset values decrease and credit default events increase; this enlarges the realized loss rates (Rosch & Scheule, 2010). One of the major indicators of credit risk is ratio of NPLs to total loans portfolio. If a bank can establish a link between the macroeconomic environment and systematic credit risk factors, this knowledge may help in assessing and managing the portfolio credit risk over time and may prove useful in dynamic credit risk management circumstances in which default scenarios can occur over a variety of economic conditions (Koopman & Lucas, 2005; Wang, 2013).
According to Wang (2013), it is necessary to the bank and financial institutions to have well-capitalized, service to a wide range of customers, sharing of information about borrowers, stabilization of interest rates, reduction in non-performing loans, increased bank deposits, increased credit extended to borrowers, increased service quality and maintained adequate level of corporate governance to minimize the impacts of credit risk factors. Nonperforming and default loans need to be reduced for sound performance of the bank. Because credit risk is inherent throughout the entire credit process, the nature and sources of credit risks have to be identified and measured to prevent losses.

Bagchi (2003) scrutinized the credit risk management in banks in India. He examined risk identification, risk measurement, risk monitoring, and risk control and risk audit as basic considerations for credit risk management in banks. He concluded that proper credit risk structural design, policies, procedures and framework of credit risk management, credit rating system, monitoring and control contributes in success of credit risk management system in banking industry.

Al-Tamini & Al-Mazrooei (2007) conducted the comparative study on risk management of UAE national bank and foreign bank. They found that foreign exchange risk, credit risk and operating risk were facing by the UAE commercial banks. They also found that the UAE banks were somewhat efficient in managing risk, and risk identification and risk assessment and analysis were the most influencing variables in risk management practices. Finally, the results indicated that there was a significant difference between the UAE national and foreign banks in the practice of risk assessment and analysis, and in risk monitoring and controlling.

Das & Das (2007) evaluated the credit risk management practices in Bangladesh. The study identified the importance of Credit Risk Management of  commercial banks and then tries to find out the existing procedures for credit risk management that were followed by the different commercial banks in Bangladesh. The future of banking depends on the risk management dynamics. Those banks that have effective risk management mechanism survive in the market in the long term. The effective management of credit risk is a serious component of comprehensive risk management essential for long term success of a banking institution. From the result they found that the existing procedures of credit management were not adequate to compete with the complex financial and economic environment. 

Alam & Masukujjaman (2011) examined the risk management practices of commercial banks in Bangladesh based on five commercial banks operating in Bangladesh.  The research revealed that credit, market and operational risk are the major risks in commercial banks which are managed through three layers of management structure. The Board of Directors performs the responsibility of the main risk oversight; the Executive Committee observes risk and the Audit Committee supervise all the activities of banking operations. In the circumstance of views regarding use of risk management techniques, it was found that internal rating system and risk adjusted rate of return on capital are comparatively more significant techniques used by commercial banks in Bangladesh.

Abdelrahim (2013) conducted the research on effectiveness of credit risk management of Saudi bank in light of global financial crisis with the objective of examine the determinants, challenges and developing means of credit risk managements. The study recommended that  an overall strategy for effective credit risk management of Saudi Banks based on enhancing capital adequacy, improvement asset quality, intensification management soundness, increasing earnings, having adequate liquidity and dropping sensitivity to market risk besides hedging credit risk; having adequate provisions for downgraded loans; renegotiating loan terms and conditions, transferring credit risk to a third party, extending credit maturity by rescheduling  and lowering interest rate on insolvent loan.

Poudel (2013) investigated the macroeconomic determinants of credit risk in Nepalese banking sector by using time series modelling. Secondary data were used between the periods of 2001-2011 from the annual financial statements. This study found that the credit risk of banks was significantly affected by inflation and foreign exchange fluctuation negatively. However, other macroeconomic variable GDP growth, Broad Money Supply growth, Market Interest Rate had no any influence in credit risk in the Nepalese banking sector during the analysed period. 

Imbierowicz & Rauch (2014) investigated the relationship between the two major sources of bank default risk: liquidity risk and credit risk. Result of the research showed that both risk categories do not have an economically meaningful reciprocal contemporary or time-lagged relationship. Though, they do influence banks’ probability of default. This effect is two folded: whereas both risks separately increase the PD, the influence of their interaction depends on the overall level of bank risk and can either aggravate or mitigate default risk. These results provided new insights into the understanding of bank risk, as developed by the body of literature on bank stability risk in general and credit and liquidity risk in particular. 

Timsina (2014) examined the impact of commercial bank credit to the private sector on the economic growth in Nepal from supply side perspectives. The empirical results showed that bank credit to the private sector had positive impacts on the economic growth in Nepal only in the long run. However, in the short run, it observed a feedback effect from economic growth to private sector credit. 

Research method and Materials

In order to find answers to the research questions useful different methods and instruments were used to collect data. The researcher has chosen the survey as the appropriate research design for the study, and as such, questionnaires were used as research instruments. A sample of 6 commercial banks randomly chosen was used in this analysis. Ten questionnaires were used to gather data with about two categories of banks like State owned and Private sector banks.  a chosen. Descriptive statistics, ANOVA and regression used to analyze the data.

To ensure accuracy, internal consistency and completeness, reliability of the instrument was established using Cronbach’s alpha coefficient test (Cronbach, 1946). The choice of this indicator was influenced by the simplicity and its prominence in banking risk literature. The higher generated score is more reliable. Nunnaly (1978) has indicated 0.7 to be an acceptable reliability coefficient to measure the reliability but lower thresholds are sometimes used in the literature. In this case, the alpha (α) coefficients were 0.86, which is acceptable level.

3. Result and Discussion 

This section presents the findings obtained from the data analysis. This result is presented in two sub sections: descriptive statistical analysis and regression analysis. 

3.1 Descriptive Statistical Analysis

As shown in the given table, there was found difference of mean value of the sources of credit risk such as collaterals, characters, capacity, capital, condition, legality of the business, economy of the country, business environment and industrial relation in State-owned and private sector banks in Nepal. The result indicates that sources of credit are different level in the Nepalese commercial banks.

Table-1: Descriptive statistics of credit risk measuring techniques


      Source: Survey data 2015,               SOB= State-owned Banks, PSB = Public Sector Banks,  

The one way ANOVA has been used to see the any differences between State-owned banks and Private sector banks in the analysis of the collaterals against the bank loan. It demonstrated the model was significant (p<0.05) with F value 148.216 at one degree of freedom.  Similarly, there was significant differences (p<0.05) in the analysis of the characters of the borrower between state-owned bank and private banks with F value 30.428 at one degree of freedom. 

The analysis of variance (ANOVA) of capacity of the borrowers shows that F value is 23.913 at significant level (p<0.05) suggesting that there was a significant differences between two group of banks.  Similarly, ANOVA of capital of the borrower demonstrated that there was significant (p<0.05) differences with F value 22.682 at one degree of freedom. 

Table 2 Analysis of variance

	 
	Sum of Squares
	df
	Mean Square
	F
	Sig.

	Collaterals 
	Between Groups
	20.754
	1
	20.754
	148.216
	0

	
	Within Groups
	52.93
	378
	0.14
	 
	 

	
	Total
	73.684
	379
	 
	 
	 

	Characters
	Between Groups
	5.275
	1
	5.275
	30.428
	0

	
	Within Groups
	65.525
	378
	0.173
	 
	 

	
	Total
	70.8
	379
	 
	 
	 

	Capacity
	Between Groups
	4.318
	1
	4.318
	23.913
	0

	
	Within Groups
	68.258
	378
	0.181
	 
	 

	
	Total
	72.576
	379
	 
	 
	 

	Capital
	Between Groups
	4.301
	1
	4.301
	22.682
	0

	
	Within Groups
	71.685
	378
	0.19
	 
	 

	
	Total
	75.987
	379
	 
	 
	 

	Condition 
	Between Groups
	4.629
	1
	4.629
	26.207
	0

	
	Within Groups
	66.768
	378
	0.177
	 
	 

	
	Total
	71.397
	379
	 
	 
	 

	Legality 
	Between Groups
	4.326
	1
	4.326
	24.602
	0

	
	Within Groups
	66.474
	378
	0.176
	 
	 

	
	Total
	70.8
	379
	 
	 
	 

	Economy
	Between Groups
	4.96
	1
	4.96
	35.111
	0

	
	Within Groups
	53.398
	378
	0.141
	 
	 

	
	Total
	58.358
	379
	 
	 
	 

	Environment 
	Between Groups
	5.958
	1
	5.958
	20.706
	0

	
	Within Groups
	108.768
	378
	0.288
	 
	 

	
	Total
	114.726
	379
	 
	 
	 

	Industrial Relation
	Between Groups
	53.731
	1
	53.731
	77.057
	0

	
	Within Groups
	263.574
	378
	0.697
	 
	 

	
	Total
	317.305
	379
	 
	 
	 


The analysis of variance (ANOVA) of condition of borrower shows that F value is 26.207 at significant level (p<0.05) symptomatic of significant differences between two group of banks.  Similarly, ANOVA of legality of the business demonstrated that there was significant (p<0.05) differences with F value 24.602 at one degree of freedom. The analysis of variance (ANOVA) of the economy, environment and industrial relation demonstrated that the model was significant (p<0.05) with F value 35.11, 20.76 and 77.057 at one degree of freedom respectively. 

From the above statistical explanation, we conclude that there are significant differences between State-owned banks and private sector banks in the analysis the sources of credit risk.  Hence H1 is accepted. 

3.2 Regression Analysis

Table 3: Model Summary

	Model
	R
	R Square
	Adjusted R Square
	Std. Error of the Estimate

	1
	.519a
	0.269
	0.251
	0.38


a. Predictors: (Constant), Industrial relation, Collateral, Capital, Environment, Clients’ characters, Economy of the country, Legality of business, Condition, Capacity

In the model shows that when the independent and dependent variables interact, the model has been Pearson's correlation coefficients (R) is 0.519 and coefficient of determinates (R square) of 0.269, signifies positive and strong connection between two.  
Table 4 :ANOVA

	Model
	Sum of Squares
	df
	Mean Square
	F
	Sig.

	1
	Regression
	19.683
	9
	2.187
	15.118
	.000b

	
	Residual
	53.525
	370
	0.145
	 
	 

	
	Total
	73.208
	379
	 
	 
	 


a. Dependent Variable: identification practice   

b. Predictors: (Constant), Industrial relation, Collateral, Capital, Environment, Clients’ characters, Economy of the country, Legality of business, Condition, Capacity

The analysis of variance (ANOVA) shows that F value is 15.118 at .00 significant level ( p<0.05) suggesting that the relationship identification practice  and  its explanatory variables is positive.  Hence, H2 is accepted.

While going through the every variable given in the above table, it was found out that capital condition and environment were not significant in the model. Similarly, no any variable was excluded from the model due to the absence of homogeneity. 

Collaterals provided as a security by the borrower, characters of the borrower, capacity, legality of the business, economy of the country and industrial relation were found significant variables during the analysis.

Industrial relation is variable that makes the significant contribution to explaining the identification of risk level when other remaining variables are controlled for with Beta coefficient of 0.224. Industrial relation plays an important role to categorize the credit risk. Similarly, significant contribution also found to make by economy of the country with the beta coefficient of 0.345, and condition of the borrower with the beta value of 0.171, while keeping all other variables constant.

Table 5: Coefficients

	Model
	Unstandardized Coefficients
	Standardized Coefficients
	t
	Sig.

	
	B
	Std. Error
	Beta
	
	

	1
	(Constant)
	0.941
	0.106
	 
	8.9
	0

	
	Collaterals 
	-0.187
	0.047
	-0.187
	-3.937
	0

	
	Characters
	-0.128
	0.16
	-0.126
	-0.8
	0.024

	
	Capacity
	0.097
	0.206
	0.097
	0.473
	0.037

	
	Capital
	0.022
	0.086
	0.022
	0.253
	0.8

	
	Condition
	0.173
	0.189
	0.171
	0.914
	0.361

	
	legality
	0.007
	0.158
	0.007
	0.045
	0.016

	
	Economy
	0.387
	0.102
	0.345
	3.805
	0

	
	Environment 
	-0.113
	0.058
	-0.142
	-1.954
	0.051

	
	Industrial Relation
	0.108
	0.023
	0.224
	4.609
	0


a. Dependent Variable: identification practice  

More important, capacity of the borrower was the variable with beta coefficient (0.97), collateral was the variable with beta coefficient (-0.187) and character with beta coefficient (-0.126566) were found significant predictor for credit risk measurement.

4. Conclusion
An identification source of the credit risk is very important phenomena in the risk management practice. It helps to evaluate the credit risk level of the every borrower as well as portfolio level of the banks. Sources of the credit risk unbounded terms. The most common sources like collaterals provided by the borrower as a security, character of the borrower, capacity, capital, and condition, legality of the business, economic condition, environment and industrial relation are grouped and discuss in this paper. These sources are differently utilized by the banks on the basis of market strategy, credit culture and philosophy. Hence, the identification practice of risk sources was significantly different in State-owned and private sector banks in Nepal. 

In the credit appraisal processes, the banks shall be determined any untrustworthy activities on the part of the borrower to know the risk level. The bank is always trying to improve their credit risk measuring tools and techniques in their credit policy for the quality of lending and various measures are undertaken to follow the effective credit management system. This requires adequate training to the employees to enhance the skills. More risk analyst may be recruited improve the quality of the credit exposure. 

In this research, the limited source of the credit risk is considered as a primary data.  The statistical result obtained through primary data analysis is not correlated using the secondary data. This may be the prospective area for future researcher in banking sector. 
References 
Abdelrahim, K. E. (2013). Effectiveness of Credit Risk Management of Saudi Banks in the Light of Global Financial Crisis:A Qualitative Study. Asian Transactions on Basic and Applied Sciences , 3 (2), 73-90.

Afriyie, H. O., & Akotey, J. O. (2012). Credit Risk Management and Profitability of Selected Rural Banks in Ghana. Retrieved June 17, 2013, from http://www.econrsa.org/system/files/workshops/papers/2012/afriyie-credit-risk-management.pdf

Alam, M. Z., & Masukujjaman, M. (2011). Risk Management Practices: A Critical Diagnosis of Some Selected Commercial Banks in Bangladesh. Journal of Business and Technology (Dhaka) , 6 (1), 15-35.

Al-Tamini, H. A., & Al-Mazrooei, F. M. (2007). Banks' risk management: a comparison study of UAE national and foreign banks. Journal of Risk Finance , 394-409, DOI 10.1108/15265940710777333.

Aver, B. (2008). An Empirical Analysis of Credit Risk Factors of the Slovenian Banking System. Managing Global Transitions , 6 (3), 31-334.

Bagchi, S. K. (2003). Credit Risk Management – A Panacea or Conundrum? SBI Monthly Review , 42 (10), 497-504.

Basel. (2000). Principles for the Management of Credit Risk. Basel Committee on Banking Supervision.

Bidani, S. N. (2010). Banking Risk: Management and Audit. New Delhi: Vision Books Pvt.Ltd.

Breuer, T., Jandacka, M., Rheinberger, K., & Summer, M. (2010). Does adding up of economic capital for market-and credit risk amount to conservative risk assessment. Journal of Banking & Finance , 34 (4), 703-712.

Das, S., & Das, S. (2007, July- December ). Credit risk management practice - An evaluation of commercial banks in Bangladesh. ASA University Review , Available at SSRN:http://ssrn.com/abstract=1849144.

Diwan, I., & Rodick, D. (1992). Debt Reduction, Adjustment Lending, and Burden sharing. NBER working Paper No. 4007 (Cambridge MA, March).

Duffie, D., & Singleton, K. J. (2003). Credit Risk: Pricing, Measurement and Management. NewJersey: Oxford: Princeton University Press.

Gray, C. F., & Larson, E. W. (2008). Project Management:The Managerial Process (3rd ed.). New Delhi:Tata MacGraw-Hill.

Greuning, H. v., & Bratanovic, S. B. (2003). Analyzing and Managing Banking Risk: A Framework for Assessing Corporate Governance and Financial Risk. (2. edition, Ed.) Washington DC, World Bank Publications.

Hou, Y. (2007). The non-performing loans: some bank_level experiances. 4th AFE-QASS Conference,INEAG.

Imbierowicz, B., & Rauch, C. (2014). The Relationship between Liquidity Risk and Credit Risk in Banks.Journal of Banking and Finance , 40 (1), p.242-256, doi.org/10.2139/ssrn.1884917.

Joseph, C. (2014). Advance Credi Risk Management : Analysis and Management. New Delhi: Wily India Pvt. Ltd.

Koopman, S. J., & Lucas, A. (2005). Business and default cycles for credit risk. Journal of Applied Econometrics , 20, 311–323.

Mckinsey & Company. (1997). Credit Portfolio View. New York: MacKinsey Inc.

Mileris, R. (2012). The Effect Of Maccro Economic Coditio Of Macroeconomic Conditions On Loan Portfolio Credit Risk And Banking System Interest Income. Economica , 91 (3), 85-100.

Nepal Rastra Bank. (2010, July). Risk Management Guidelines. Nepal Rastra Bank, Bank Supervision Department.

Nijskens, R. (2011). Credit risk transfer activities and systemic risk: how banks became less risky individually but posed greater risks to the financial system at the same time. Journal of Banking & Finance , 35 (6), 1391-1398.

Poudel, R. P. (2013). Macroeconomic Determinants of Credit Risk in Nepalese Banking Industry. 21st International Business Research Conference 10 - 11 June. Ryerson University, Toronto, Canada.

Rosch, D., & Scheule, H. (2010). Downturn credit portfolio risk, regulatory capital and prudential incentives. International Review of Finance , 10 (2), 185–207.

Timilsina, N. (2014). Bank Credit and Economic Growth in Nepal: An Empirical Analysis. NRB Working Paper series, NRB-WP-22 . Kathmndu: Nepal Rastra Bank, available at www.nrb.org.np.

Wang, Y. (2013). Credit Risk Management in Rural Commercial Banks in China( Doctoral dissertataion),Retrived from http://researchrepository.napier.ac.uk/id/eprint/6659.

Williams, R., Bertsch, B., Dale, B., Wiele, T. v., Iwaarden, J. v., Smith, M., et al. (2006). Quality and risk management: what are the key issues. The TQM Magazine , 18 (1), 67-86.

Copyrights
Copyright for this article is retained by the author(s), with first publication rights granted to the journal.














17
27

