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The Covid-19 pandemic has hit the world since the end of 2019 and has damaged the world economy, including Indonesia. Banking as a financial institution that provides funding to companies is also affected. Because the pandemic has reduced the company's performance, and this decline in performance has had an impact on banking. The aim of this research is to test whether the Covid-19 pandemic has an impact on the relationship between risk factors and bank performance. Bank performance is measured by profitability which consists of return on assets (ROA) and return on equity (ROE), while bank risk in this study consists of credit risk, liquidity risk, capital, operational risk and the Covid-19 pandemic. The population in this research is the Regional Development Banks (BPD) in Indonesia totaling 34 banks with a sample of 30 banks. The observation period was 4 years with quarterly data so that 320 observation data were collected. Hypothesis testing uses panel data regression, and to select the best regression model, the model will be tested using the Chow-test, Hausman-test, and Lagrange Multiplier test. After the model test was carried out, the fixed effect model was selected as the best model. The research results show that credit risk and operational risk have a significant and negative influence on all profitability, both ROA and ROE. Meanwhile, liquidity risk (LDR) has no effect on both types of profitability, while capital has an effect on ROE but has no effect on ROA. Another interesting result is that the Covid-19 pandemic has no impact on the influence of risk on bank performance.

Banks are a high-risk industry, which is regulated by the government through the Financial Services Authority (FSA), so bank management must be very careful in managing banks so that risks can be controlled. The purpose of this study was to examine the effect of bank risk and third party funds on bank performance. Bank performance is measured by return on assets (ROA), while bank risk consists of operating risk which is measured by operating expenses to operating income ratio (EIR), liquidity risk is measured by loan to deposit ratio (LDR), capital risk is measured by capital adequacy ratio (CAR), and credit risk is measured by non-performing loans (NPL), and third party funds (TPF) are measured by the natural log of total credit. The research population consisted of 46 banks listed on the Indonesia Stock Exchange with a sample of 24 banks taken using a purposive sampling technique. The data collection period is four years with quarterly data (2019-2022). To test the hypothesis used panel data regression. After testing the model with the Chow-test and Hausman-test, the best regression model is the fixed effect model. The results of research using the fixed effect model show that operating risk (EIR) has a significant and negative effect on bank performance, while liquidity risk (LDR) has a significant positive effect on bank performance. Meanwhile, the capital adequacy ratio (CAR), non-performing loans (NPL), and third party funds (TPF) have no effect on bank performance.

The country's macroeconomic conditions, whether directly and indirectly will influence the development of the financial and banking sector. This study aims to determine the effect of macroeconomic and internal company variables consisting of BOPO, ROA, money supply, and the unemployment rate on non-performing loans (NPL) in Indonesian banking, especially Regional Development Banks (BPD). The sampling technique used is purposive sampling technique. There are 20 Regional Development Banks (BPD) that met the requirements determined by the observation period from 2008-2021. The data processing method used is panel data regression. Model selection uses the Chow test, Hausman test and Langgrange Multiplier test (LM Test). Based on this, the appropriate model is the Fixed Effect Model (FEM). Testing the classical assumptions of FEM shows that there is a heteroscedasticity problem, then it is recovered using the Feasible Generalized Least Square (FGLS). The results of the analysis show that the variables BOPO, ROA, money supply, and unemployment rates both simultaneously and individually have a significant positive effect on the NPL of Regional Development Banks.

This study aims to examine the association between banks and firms in a loan. Our data set was pooled cross section data from 2014 to 2016, with long-term loans of firms listed in Indonesia Stock Exchange as a unit of analysis. We estimate our regression model using ordinary least square (OLS) estimator. The results revealed that lower-risk banks tend to associate with riskier but well-performing firms. Similarly, lower-capital banks tend to associate with riskier but well-performing firms. These results are quite contrary to previous studies conducted in developed countries. Lower risk banks have relationship with riskier and well-performing firms in condition that the amount of loan is small enough to be considered safe. Meanwhile, lower capital banks in this study, especially in Indonesia, may face the geographical problem which drives cost so high that makes banks charge higher interest to possibly riskier firms. This study makes contribution by providing evidence from a relative fragmented credit market. Future research on this topic is encouraged to look out more detail especially in the causality interpretation.

This study aims to examine and analyze the influence of interest rates, exchange rates and capital structure on profitability in state-owned and private banking go public in Indonesia. This research is quantitative research based on the study of empirical rational principles. Collecting data using secondary data with purposive sampling technique, the sample consists of Commercial Banks Business Group (BUKU IV) with core capital > Rp 30 trillion. The data analysis technique used panel data regression analysis using EVIEWS version 11 software. The results showed that the interest rate had a positive and significant effect on banking profitability; Exchange Rate has a negative and significant effect on Banking Profitability; Capital Structure has a positive and significant effect on Banking Profitability; Interest Rate, Exchange Rate, and Capital Structure simultaneously have a positive and significant effect on Banking Profitability

Referring to the provisions of Law Number 7 of 1992 and its amendments to Law Number 10 of 1998 concerning Banking, it is explained that a Bank is a business entity that collects funds from the public in the form of deposits and distributes them to the public in the form of credit and or other forms of to improve the standard of living of the wider community so that the banking industry greatly affects the overall stability of the economy because banking institutions are intermediary institutions that bridge people who do not need their funds (surplus) to be deposited in banking institutions as deposits and distributed by these institutions to people who need them in a productive form of distribution. The role of banking institutions is systemic; in other words, banking is an important part of the economic system which will cause a multiplier effect if it is not healthy or in this case, it becomes a failed bank. The main function of banking in macroeconomic policy infrastructure is indeed directed in the context of how to make money effectively and efficiently to increase economic value, which requires the role of regulation in the economic sector as a rule for the essence and existence of banking institutions in carrying out their operations. This study is normative legal research that uses several approaches namely: the statute approach, the case approach, the historical approach, and the conceptual approach carried out with juridical concepts of banks, failed banks, bank restructuring, macroprudential and microprudential. The objective of this study is to comprehensively explain the model and system for rescuing failed banks that meet the benefits principles.

The purpose of this study is to determine the effect of the level of banking health on the ratio of liquidity coverage in the corporate banking sub-sector included in the category of commercial banks based on business activities (BOOK) 3 during the 2017-2018 period with each period only taking 2 quarterly data. The sample in this study was a banking company listed on the IDX and included in a commercial bank based on business activities (BOOK) 3 consisting of 19 banks selected using the purposive sampling method, with a total of 76 data units. The analytical method used in this study is panel data regression analysis. The results of partial hypothesis testing indicate that the ratio of liquidity coverage is influenced by the level of health with the Altman Z-Score modification method and the Grover G-Score method.

The success of SMEs is determined by the business capital and able to give value added to the business more productively. Various financing pattern schemes for SMEs have been provided in various regions and all economic sectors. However, the development of SMEs credit is still concentrated in several regions in Indonesia this can lead to inequality in the growth of SMEs. This study aims to analyze the convergence of absolute beta and conditional beta in the Provinces in Indonesia and calculate the half-time or time required to achieve steady-state conditions of the SME credit convergence process. Convergence test used is beta conditional convergence and absolute convergence with the monthly time period during 2017.1 until 2017.7. The determinant of SMEs convergence of credit in Indonesia is a number of third-party funds. The model used is dynamic panel data regression and the estimation method used is Arellano-Bond Generalized Method of Moment (AB-GMM). The results of the study show that during the study period, there has been a convergence credit process in Indonesia for both absolute and conditional convergence models. The conditional beta-convergence model provides a smaller beta value than the absolute model. The speed of credit growth convergence between provinces in Indonesia with half-time of 6.94 months for the absolute model and the half-time of 6.96 months for the conditional model