Number Of Non-performing Loans Research Articles

Development of Website-based Risk Mitigation to Prevent Non-performing Loans at Pt. Prima Kencana Rejeki

Competition in the business world, especially in sales, requires people to aggressively increase sales volume. Therefore, various efforts were made by the company to further increase the level of sales. One of them is to provide convenience in the form of sales on credit even though it has credit risks, such as an increase in the number of Non-performing Loans. This research was carried out to produce a risk mitigation website that could facilitate companies in their efforts to prevent credit risk, in this case, Non-performing Loans at PT. Prima Kencana Fortune. This study used the research and development method involving collecting initial information, planning, initial product development, initial trials, and product revisions. This website consists of two user pages: the admin user page and the sales user page. The feasibility test results based on the convenience aspect have an average score of 3.18, with very good criteria. The attractiveness aspect received an average score of 2.73, a good criteria. For the usefulness aspect, the average score was 3.41, with very good criteria.
 Keywords: non-performing loan, credits, risk, website

PENGARUH FAKTOR FUNDAMENTAL DAN MAKRO EKONOMI TERHADAP PROFITABILITAS BANK: STUDI EMPIRIS PADA BANK UMUM YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2016 - 2020

Abstract— This study aims to analyze the capital adequacy ratio, non-performing loans, operating income operating expenses, loan to deposit ratio, firm size, net interest margin, DPK, inflation, and the exchange rate on bank profitability. This type of research is associative research. The data used for this research is data sourced from the financial statements of commercial banks that are listed consecutively on the Indonesia Stock Exchange (IDX) period 2016-2020. The type of data taken is banking financial statements. The data was obtained from the official website of the Indonesia Stock Exchange (IDX). The data obtained from the research results were analyzed using a regression analysis model with the help of the Eviews 10 program. The results showed that only the CAR, NPL, and NIM variables had a significant influence on the return on assets of commercial banks listed on the Indonesia Stock Exchange period 2016 – 2020. This is in accordance with the provisions because sufficient capital, a decrease in the number of non-performing loans, and a high level of bank effectiveness in managing company assets will be able to increase profitability. Then the BOPO, LDR, firm size, DPK, inflation, and exchange rate variables cannot have a significant effect on the return on assets of commercial banks listed on the Indonesia Stock Exchange period 2016 – 2020.
 Keywords: Capital Adequacy Ratio; Non Performing Loan; Operating Expenses Operating Income; Loan to Deposit Ratio; Firm Size; Net Interest Margin; Third Party Funds, Macroeconomics; Profitability

Kvalita bankovních úvěrů v koronavirové krizi

This article deals with the issue of non-performing bank loans during the time of the coronavirus crisis and provides valuable insights into the sectors, classified by the NACE (Statistical Classification of Economic Activities) system, that were most affected in terms of the quality of bank loans in the Czech Republic. The main objective is to identify the most affected economic sectors in the Czech Republic based on the number of non-performing loans. A secondary aim is to compare the number of non-performing bank loans between data for the Czech Republic and the European Union. The article begins by describing the development of the Covid-19 pandemic and the measures and compensations implemented by the government of the Czech Republic. It then defines non-performing bank loans and presents an overview of the number of non-performing bank loans in the Czech Republic as a whole. The results and discussion section delves into the detailed analysis of non-performing bank loans according to the mentioned NACE classification. The data used to achieve the stated objectives were sourced from the European Banking Authority and were compared and visualized in graphs. The most affected sector in both the Czech Republic and the European Union was found to be Sector I - Accommodation, Food Service Activities, and Hospitality.

ANALISIS PENANGANAN KREDIT BERMASALAH (NON PERFORMING LOAN) PADA PERUMDA BPR BANK GRESIK

This study aims to determine the handling of non-performing loans at the Perumda BPR Bank Gresik BPR. The reason for the research was to find out the number of non-performing loans that occurred in the Perumda BPR Bank Gresik over the last 5 years. The research uses descriptive research using a qualitative approach. The results of this study stated that there are many factors that cause non-performing loans such as internal bank factors, internal debtor factors and external factors of banks and debtors. The handling of non-performing loans is carried out by the Perumda BPR Bank Gresik with a process of rescheduling, requisition and realignment and is carried out by confiscating platforms from problem debtors.

The effect of economic growth on banking system performance: An interregional and comparative study of Sub-Saharan Africa and developed economies

As an important part of the financial sector, banking systems play a critical role in economic development as well as in improving the quality of life of the people of sub-Saharan Africa (SSA). However, little evidence exists in the literature about the performance of the banking systems in SSA compared with developed economies. This paper investigates the effects of economic development on banking performance across 23 SSA countries and 14 developed countries between 1981 and 2018. Using estimation models such as feasible general least squares, fixed-effects estimation with Driscoll-Kraay standard errors, and system and difference generalized method of moments, our findings show that in SSA as a whole, economic development has a positive impact on commercial bank lending, raises lending rates, increases the amount of domestic credit to the private sector (DCPS), and reduces the number of nonperforming loans (NPLs). In developed countries, economic development has a positive impact on lending rates, DCPS, and the number of NPLs. However, although economic development in developed economies has mostly positive effects on banking performance, SSA countries have more performance issues in banking as a whole and in regional pockets. A careful reassessment of SSA’s interregional banking system is therefore highly recommended. An increase in the number of NPLs in East and Southern Africa, a decline in deposit rates offered in Southern, West, and Central Africa, and increasing real interest rates in SSA despite economic growth are some of the concerns that require careful reassessment and policy adjustments.

Pengaruh risiko kredit terhadap permodalan dengan efisiensi sebagai variabel intervening

Capital for the banking industry as a safeguard against possible risks is very important. This study aims to analyze whether NPL, CKPN, LAR, and BOPO have a significant effect on CAR. This research uses secondary data, taken by documentation method. The data were obtained from the Bank’s financial reports (Book 3) for the 2016-2019 period from the published reports of the Financial Services Authority (OJK). The data were analyzed using a descriptive analysis and Multiple Regression Analysis (MRA). The results show that NPL, CKPN, and LAR have a significant positive effect on BOPO. The effect of NPL, CKPN, and LAR is significantly negative on CAR. The effect of the BOPO variable on CAR is positive but insignificant. Meanwhile, the effect of NPL, CKPN, LAR through BOPO as an intervening variable on CAR is negative and insignificant. The research results imply that, in the future, banks can control the number of non-performing loans so that the cost of funds reserved for problem loans does not increase.

Current methods of the enterprise’s creditability assessment

The article is devoted to the interpretation, analysis and systematization of the methods of credit assessment of enterprises. Taking into account the current state of the economy of Ukraine, as well as the financial state of most domestic enterprises, a considerable number of non-performing loans, Ukrainian banks are trying to develop, refine and improve the process of lending to customers. The number of non-performing loans in banks' portfolios indicates that banks do not cooperate with enterprises. Today, the issue of loan repayment is very painful: unstable economic situation, declining corporate incomes, devaluation of the national currency are among the main reasons for the large number of non-performing loans. In today's context, loans cannot be provided solely on the basis of trust, the lender must have other guarantees of repayment of own funds. Therefore, any financial institution examines the borrower for creditworthiness (evaluates its ability to repay debt on time). During the writing of the article, the authors noted that the problem is the determination of quantitative standards for comparison in the analysis, because there is no single clear value of the indicators. That is why it is impossible to single out the indicator, which is due to industry affiliation, and the standards given in the economic literature, the desired values of financial ratios, do not contain industry specifics and the specifics of the enterprise. The research problem is determined by the fact that there is no unified regulatory framework for a real assessment of the financial condition of the borrowing enterprise, and accordingly, there are no comparative indicators that are minimally acceptable for a particular industry in question.

Analysis of Influence Factors of Non-Performing Loans and Path Based on the Dynamic Control Theory

The balance of non-performing loans (NPLs) of Chinese banking financial institutions rebounded for the first time after 2005, and credit risk has emerged as one of the rapidly rising risks in today’s financial markets. In this study, we focus on the NPLs of financial institutions. In particular, the factors affecting their rate are studied. A dynamic control theory model is used to set up a Hamilton function for describing the effect of these factors. Moreover, the path of NPLs under the influence of various factors is obtained. It was found that improved risk control and macroeconomy factors reduced the number of NPLs. In particular, to reduce the number of NPLs, the capacity of banks to manage loans should be strengthened.

An Outline of Loans’ Trend in Albania

The early 2000`s epitomized very low levels of loans in Albania. The combination of imposition of stringent credit ceilings and the high and rising number of non-performing loans caused the commercial banks to have difficulties to meet the soaring demand for credit. Bank of Albania imposed low credit ceilings, artificially lowering the ability of the banks to provide loans despite having healthy deposit reserves. In 2001 the situation changed dramatically however, as a result of the administrative decision to transfer the bad loans from the Savings Bank to the Loan Handling Agency. This led to a changed financial picture overnight and a greatly improved NPL situation, by wiping out most of them as far as the banking sector was concerned. The Albanian economy had a boom in credit growth particularly after 2004, especially in foreign currency lending. It still remained less than what it should have been, due to low ratio of loans, compared to deposits level. The credit crisis of 2008 saw a decrease in lending, and 2009 saw an even greater deterioration. Nowadays supply has tightened, with banks making it more difficult to request loans, and the demand has decreased as well, with businesses reluctant to take on risky investments, indicating a lack of confidence in the Albanian economy and uncertainty about the future, especially in sectors such as construction, that are regarded as highly important to the overall performance of the economy. DOI: 10.5901/ajis.2016.v5n3s1p40

Credit risk assessment model for Jordanian commercial banks: Neural scoring approach

Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new family of statistical techniques and promising data mining tools that have been used successfully in classification problems in many domains. This paper proposes two credit scoring models using data mining techniques to support loan decisions for the Jordanian commercial banks. Loan application evaluation would improve credit decision effectiveness and control loan office tasks, as well as save analysis time and cost. Both accepted and rejected loan applications, from different Jordanian commercial banks, were used to build the credit scoring models. The results indicate that the logistic regression model performed slightly better than the radial basis function model in terms of the overall accuracy rate. However, the radial basis function was superior in identifying those customers who may default.

An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data

The number of Non-Performing Loans has increased in recent years, paralleling the current financial crisis, thus increasing the importance of credit scoring models. This study proposes a three stage hybrid Adaptive Neuro Fuzzy Inference System credit scoring model, which is based on statistical techniques and Neuro Fuzzy. The proposed model’s performance was compared with conventional and commonly utilized models. The credit scoring models are tested using a 10-fold cross-validation process with the credit card data of an international bank operating in Turkey. Results demonstrate that the proposed model consistently performs better than the Linear Discriminant Analysis, Logistic Regression Analysis, and Artificial Neural Network (ANN) approaches, in terms of average correct classification rate and estimated misclassification cost. As with ANN, the proposed model has learning ability; unlike ANN, the model does not stay in a black box. In the proposed model, the interpretation of independent variables may provide valuable information for bankers and consumers, especially in the explanation of why credit applications are rejected.