Indonesian Islamic banks: A review of the financial state before and after the COVID-19 pandemic

The COVID-19 pandemic has had a significant impact on small and medium enterprises (SMEs). The purpose of this study is to assess the impact of the COVID-19 pandemic on SMEs in Georgia, identify the challenges of SMEs in pandemic conditions, and analyze government economic policy measures. For this purpose, 102 small and medium entrepreneurs from different regions of Georgia were interviewed. The survey was conducted by sending a questionnaire through digital channels. The results were processed using data analysis, quantitative and qualitative research methods, and presented graphically. As a result, key challenges and development directions that are typical for SMEs operations in Georgia during the pandemic were identified. For 60.8% of surveyed SME owners, lockdown is the main challenge. Sales volume of 45.1% of SMEs decreased. In addition, 39.3% of the surveyed SME owners increased the innovative capabilities of a company and 37.2% increased the sales volume of a company through digital channels. The main challenge of the pandemic, lockdown, gave impetus to a change in entrepreneurial behavior. In a pandemic, entrepreneurial activity has become more favorable through digital channels. During the pandemic, there is a need to continue the government’s program of tax incentives and subsidies for SMEs. In addition, the government should promote education in the fields of entrepreneurship, innovation, and technology.

This study aims to investigate the impact of COVID-19 on the increase in bad credits at conventional commercial banks in Indonesia. The data used in this study are secondary data sourced from the Ministry of Health and from the Financial Services Authority (OJK), each of which consists of 50 data samples. The data analysis technique used in this study is simple regression analysis to determine the magnitude of the influence of COVID-19 on non-performing loans. The results of the data analysis show that COVID-19 has a significant effect on non-performing loans, and the COVID-19 variable can be used as an external indicator of the increase in non-performing loans for commercial banks in Indonesia. The implication of the research is that other researchers can make COVID-19 an external indicator of an emergency beyond human ability that can affect the level of non-performing loans. For banking, this study can be used as a reference when considering credit risk management policy during the COVID-19 pandemic.

Acknowledgment
The researchers are grateful to University of Sultan Ageng Tirtayasa for financial support. In addition, the authors sincerely apologize for the errors and mistakes found in this paper.

Despite the widespread use of benchmarking as an effective tool for improving the efficiency of the bank’s functioning, its implementation does not take into account the relation between comparable performance indicators, the choice of benchmark for comparison, deviations of indicators from target values with stages of the bank’s life cycle, which cause differences in the intensity and characteristics of development of financial institutions. The procedure for identifying a reference bank for comparison is also insufficiently specified, which is important in terms of adapting its experience by the recipient bank due to the possible fundamental differences in their functioning. Therefore, the article has modified the technology of benchmarking of the bank’s performance based on the life cycle concept and the DEA approach.
The research is based on the use of the DEA method to determine the most efficient bank as a reference bank in benchmarking comparison; canonical analysis – for the formation of a list of indicators of bank performance; cluster analysis – to substantiate the levels of deviations of the actual values of comparable indicators from the target ones.
The study envisages, firstly, the selection of indicators for benchmarking comparisons based on the identification of causal relationships between the indicators of subsystems “Finance”, “Customers”, “Business processes”, “Personnel development” that arise at each stage of a bank’s life cycle; secondly, the choice of a benchmark bank for comparison according to the maximum value of the performance indicator calculated through the DEA method for a set of banks that are at one and the same stage of their life cycle; thirdly, definition of the range of deviations (low, permissible, critical) of the actual values of comparable indicators of the effectiveness of management of finance, customer base, business processes and personnel of the bank from the target ones. A practical testing of the benchmarking technology was carried out on the example of Ukrainian banks, whose stage in 2016 was identified as “intense growth”.