Factors Influencing Non-Performing Loans in Commercial Banks: The Case of Banks in Selangor

Murthy, Uma; Kamil, Naail Mohammed; Mariadas, Paul Anthony; Devi, Dilashenyi

doi:10.5539/ijbm.v12n2p246

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…This means that even if banks offer lower interest rates during the financial crisis, the number of non-performing loans will continue to rise. The study similar with the previous research made by Mazreku et al (2018) and Murthy et al (2017), showing a significant negative correlation. However, the research of Rehman et al (2016) shows that there is a significant positive correlation between these two variables.…”

Section: The Influence Of Bir Towards Nplsupporting

confidence: 92%

Determinant Factors of Non-Performing Loans in Chinese Commercial Banks

Purwanto¹,

Sun²

2021

jmm17

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The purpose of this research is to identify the influence of GDP growth rate, bank interest rate, inflation rate, capital adequacy ratio, and return on asset towards non-performing loans in Chinese commercial banks partially and simultaneously. This study has applied descriptive statistical analysis, classical hypothesis testing, multiple linear regression, and hypothesis testing. When selecting the observation data, this research adopts the intentional sampling method and panel data, 70 units of observational data in total, one part of the data was taken from the financial reports of seven selected sample companies on the Shanghai Stock Exchange in China, and another part of the data was taken from the kyle website. The method used in a quantitative approach with the instrument is EViews 10. The result indicates that BIR and IFR have a partially negative significant influence on NPL. However, GDP growth rate, CAR, and ROA have a negative insignificant effect on NPL. Simultaneously, all of the independent variables have a significant effect on NPL which is described by the value of 63.9% and the left 36.1% is explained by another factor that is excluded in this study. Furthermore, IFR was chosen as the most significant factor which influences NPL.

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Section: The Influence Of Bir Towards Nplsupporting

confidence: 92%

Determinant Factors of Non-Performing Loans in Chinese Commercial Banks

Purwanto¹,

Sun²

2021

jmm17

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“…While the amount of NPA in public sector banks is higher than in private sector banks, both public and private sector banks' NPA levels remain high when compared to overseas banks. According to Murthy et al (2017), the rate at which non-performing loans are added to the total number of non-performing loans is mostly determined by the bank interest rate. Madhvi and Shrivastava's (2017) provided that NPA may not be a reliable or exclusive criterion for evaluating a bank's health and basing investment decisions entirely on it.…”

Section: Panel Regression Resultsmentioning

confidence: 99%

“…While public sector banks have more NPA than private sector banks do, both sectors' NPA levels are nonetheless high when compared to foreign banks. A study conducted in Malaysia examined the influence of three factor such as consumer income, the economy of the country and bank interest rate on the NPAs of banks; Murthy et al (2017) discovered that the bank interest rate has the biggest impact on the rate at which non-performing loans are added to the NPL total.…”

Section: Review Of Literaturementioning

confidence: 99%

Priority and non-priority sector GNPAS in Indian commercial banks: a comparison between sub-sectors

Kanyan,

Singh

2024

XJM

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Purpose This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks. Design/methodology/approach The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression. Findings The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result. Practical implications Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed. Originality/value There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

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“…The study by Murthy et al (2017) highlights the government's interference and support on loan recovery initiatives as done by the Malaysian government. Through the government's support, identification of loan defaulters and associated conditions for loan recovery was made possible in Malaysia.…”

Section: Inform Local Government On the Selling Of Propertymentioning

confidence: 99%

Loan Recovery Procedures in Tanzania: A Case of Selected Microfinance Institutions in Dar-Es-Salaam, Tanzania

Sikira

2021

int.jour.sci.res.mana.

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The study aimed to determine the procedures employed by MFIs, specifically in loan recovery in Tanzania. Loan recovery has been a difficult task for many financial institutions but becomes more challenging to microfinance institutions due to the nature and processes they employ. The study examined the loan recovery procedures at NFBS, SMF and AML found in Dar-Es-Salaam, Tanzania. The researcher employed a mixed- case study design, which involved 50 credit officer respondents obtained under the purposive sampling. The study used semi-structured questionnaires to gather primary data and also secondary data from documents available at NFBS, SMF and AML. Data were analysed using simple descriptive statistics with percentages and frequencies for quantitative data, while thematic analysis was used for qualitative data. The study found that NFBS, SMF and AML used loan recovery procedures such as direct contact, friendly reminder, and issuance of final demand notice. Another procedure is to inform the local government on property selling and using a debt collection agency. The study recommends for MFIs to establish and prefer formal procedures that can be used for loan recovery because the use of informal procedures creates room for conflict and leads to ineffectiveness of recovering loans.

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Factors Influencing Non-Performing Loans in Commercial Banks: The Case of Banks in Selangor

Cited by 5 publications

References 4 publications

Determinant Factors of Non-Performing Loans in Chinese Commercial Banks

Determinant Factors of Non-Performing Loans in Chinese Commercial Banks

Priority and non-priority sector GNPAS in Indian commercial banks: a comparison between sub-sectors

Loan Recovery Procedures in Tanzania: A Case of Selected Microfinance Institutions in Dar-Es-Salaam, Tanzania

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