The Effectiveness of the Risk Committee, Political Relations and Company Performance in the Banking Industry in Indonesia

This study aims to analyze the relationship between the effectiveness of the risk committee, political relations and company performance in the banking industry in Indonesia which is listed on the Indonesia Stock Exchange (BEI). The data were obtained using purposive sampling, so that 41 companies were obtained for 2017 to 2019 using the Stata analysis tool. The results of this study indicate a significant positive effect between the total risk committee members on company performance as proxied by ROA. In addition, the existence of a political relationship as a moderator indicates a negative influence between the number of meetings and concurrent positions in the risk committee on company performance with the proxy of ROA. In order to improve company performance, the risk committee must increase the number of members in the risk committee so that they can exchange ideas to solve company problems. Furthermore, political relations have a negative impact on company performance. Spending more money to win business projects and government licensing can increase costs which can directly reduce company performance.


Introduction
Business developments in the financial industry continue to produce increasingly complex business activities. Companies need to process business activities with good management and establish the right policies. Minimizing business failure can be done by implementing good corporate governance to monitor risks that may occur for financial companies (Aldhamari et al., 2020). Company failure can be caused by many factors such as failure in risk monitoring. Based on research Lukason & Laitinen (2019), found that failure risk factors contributed 73 percent to the company's bankruptcy. Generally, prevention of company bankruptcy can be done through risk management involving the audit committee. However, there are many special tasks carried out by the audit committee, so companies are required to form an organizational structure that carries out risk management tasks specifically through the risk committee (Aldhamari et al., 2020). The presence of an effective risk committee (RC) in this study will be analyzed to see its effect on company performance. The company's performance indicators will be analyzed based on internal factors, namely return of assets (ROA) and external factors, namely Tobin's Q. There are two contradictory results of previous studies. First, the formation of a risk committee in the organizational structure will improve the company's financial performance (Jia & Bradbury, 2020). Second, companies that have a separate risk committee (RC) will form communication problems and cause conflicts within the company, which will reduce the company's financial performance (Elamer & Benyazid, 2018).
Furthermore, this study also wants to see the effect of political relations (Politically Connected Firms/PCFs) as a moderating variable of the effectiveness of the risk committee on company performance. Research conducted by Wang, Yao, & Kang (2019), found that the political relationships owned by top management can have a positive influence on financial performance for the company. Top management is considered to have closer access to obtaining investment funds and loans from banks, as well as easily getting strategic information related to regulations related to a business's going concern. Otherwise, Chong, Ong, & Tan (2018) said that company directors with political ties to the Government could worsen the company's performance. This is because the company has its own vision that is different from government regulations, especially in the social and political world. Previously, research on the effect of the effectiveness of the risk committee and political relations on financial performance in Malaysia has been conducted (Aldhamari et al., 2020). Meanwhile, the data of this study uses financial companies in the banking sector listed on the Indonesia Stock Exchange (BEI) for the 2017-2019 period. This object was chosen because it was based on a regulation issued by the Otoritas Jasa Keuangan Number 55 / POJK.03 / 2016 which requires banks to have a risk committee separately from the audit committee.
Company performance is measured based on the ROA value and Tobin's Q value (Aldhamari et al., 2020). ROA is calculated based on the comparison of the value of net income and total assets (Elamer & Benyazid, 2018). While the value of Tobin's Q is defined as the sum of the total market value of equity and book value of total debt divided by the value of total assets (Aldhamari et al., 2020). Company performance uses ROA as a representative of the company's financial performance. Meanwhile, Tobin's Q is used to describe the market performance of the company (Aldhamari et al., 2020). A large ROA value indicates good company performance, because the company is able to process company assets effectively (Al-dhamari & Ku Ismail, 2015). The calculation of the Tobin's Q value can describe the company's performance through the potential development of stock prices (S. Bambang, 2010).
Research conducted by Aldhamari et al. (2020) stated that the effectiveness of a committee must be measured from several characteristic values. The collection of characteristics of a committee will provide a good picture of a risk committee and if there is one characteristic that is not considered, it will reduce the performance of the committee's effectiveness. (Bhatt & Bhatt, 2017). The characteristics of the effectiveness of the risk committee can be in the form of risk committee independence, number of risk committee members, activeness in conducting meetings, overlapping positions on the risk and gender committee (Aldhamari et al., 2020;Elamer & Benyazid, 2018). Independent risk committee members can be seen through the relationships they have with management or shareholders (Aldhamari et al., 2020). Overlapping in the risk committee is defined as a risk committee member who has another position within the company (Aldhamari et al., 2020). Finally, the existence of its own risk committee in the banking world, especially in Indonesia, has been regulated in the OJK regulation since 2016 which is stated in Number 55 / POJK.03 / 2016 concerning the implementation of governance for commercial banks.
The board of directors and commissioners of a company can hold concurrent positions outside the company organization such as holding positions in the government or politics. Political relations are defined as positions or positions held by the board of directors or commissioners in government such as members of parliament, members of the ministry, and other government-related institutions (Al-dhamari & Ku Ismail, 2015;Aldhamari et al., 2020). This study will match the members of the board of directors and commissioners with members of the government in Indonesia.
Based on the facts and the application of literature, the problems of this research are expressed in the form of questions, namely: 1) is there any influence between the effectiveness of the Risk Committee (RC) and the Company's performance? and 2) is there any influence from politically connected firms (PCFs) on the effectiveness of the Risk Committee (RC) and the Company's performance? This research is expected to contribute to the government as a financial regulator or company. This research will also evaluate the risk committee (RC) parameters and their attributes / characteristics and the political relationship to the company's financial performance.

Methods
The sampling method in this study is to use secondary data in the form of panel data consisting of time series and cross section data. This study uses company data objects in the banking sector listed on the IDX for the 2017-2019 period. The banking sector was chosen because in this industry it is more closely related to the risk committee (Ames et al., 2018;Elamer & Benyazid, 2018). Furthermore, purposive sampling technique was used to obtain samples with several criteria: 1) banking companies listed on the IDX 2017-2019 and having complete financial reports; 2) financial reporting period December 31; and 3) Not being acquired or merged during the research period. Thus, the final sample data obtained is 123 samples consisting of 41 companies and 3 observation periods 2017-2019. The variables and measurements used in this study are to determine the effect of the independent variable and control variable on the dependent variable, as well as moderation, each of which is the following measurement.
In the panel data regression test, it is necessary to test the model specifications to determine the model to be used. According to Winarno (2015) the model used in this panel data regression is the common effect model which is shown as follows: Perfit = ß0 + ß1RCFactorit + ß2Sizeit + ß3Levit + ß4Liquidit + ß5BodSizeit + eit Perfit = ß0 + ß1RCFactorit * PConit + ß2Sizeit + ß3Levit + ß4Liquidit + ß5BodSizeit + eit In estimating using the panel data method, to get the best Linear Unisex Estimation (BLUE) estimation results, then a classical assumption test is carried out: Multicollinearity Test, Heterokedacity Test, Autocarrelation test. Descriptive statistical test is a test that is used to provide a description of each variable whose results are seen based on the mean (mean), standard deviation, and middle value (median).

Model Analysis
Panel data regression model estimates can use three models, namely the common effect model (CEM), the fixed effect model (FEM), and the random effect model (REM). The model in this study was carried out by model testing to get the most appropriate model. This study uses 2 equation models, where each model has 2 dependent variables, namely ROA and Tobins'Q.
Based on the

Classic Assumption Test
Based on the model selection in table 2, models 1 and 2 of the dependent variable Tobins' Q were selected as the random effect model. According to Gujarati, Damodar & Porter, Dawn (2012) by using the Random Effect Model (REM) method, the data fulfills the classical assumption equation. Therefore, it is not necessary to perform a classical assumption test. Whereas in models 1 and 2 which are proxied by ROA, the Pooled OLS model is selected. Therefore, we tested the classic assumptions with the results in table 3 below.
In a good regression model, there should be no correlation between the independent variables. In the panel data regression model in this study, a multicollinearity test was carried out with a VIF value of 2.00 and 32.00 respectively in models 1 and 2. Model 1 is declared to have no multicollinearity (Vif <10) and model 2 has multicollinearity (Vif> 10). The second assumption is the absence of heteroscedasticity. The results of the heteroscedasticity test show the value of prob> Chi = 0.00 or less than 0.05 in both model 1 and model 2. The result is that there is heteroscedasticity. Next, autocorrelation testing. Based on the test results in models 1 and 2 the value of prob> Chi = 0.00 or less than 0.005. As a result, both models have autocorrelation. Both models 1 and 2 with the dependent variable ROA have symptoms on the classical assumption test, namely model 1 occurs heterocedacity and autoclasm, while model 2 occurs multicollinearity, heterodexity and autocorrelation. Eliminating the problem with the classical assumptions, the regression model uses a robust method, the results of which are shown in table 6.

Descriptive Statistics
This descriptive statistical analysis is used to describe or provide an overview regarding data so that information can be easier to understand. The descriptive statistical analysis conducted to calculate the variation of the average (mean), maximum value, minimum value, and standard deviation. The mean is the average value for each variable in the study. The maximum value is the highest value for each variable while the minimum value is the lowest value for each variable in research. Standard deviation is a variation of the data distribution. Here are the results Descriptive statistics for each variable in the study:  Table 4 also reports that the average RC size is 5.27 and the average number of RC meetings is 7.30. We also report that the mean proportion of RCs with overlapping directors is 0.24 and RCs with female members is 0.17. Furthermore, companies that have political relations are 0.87 from the entire sample of banking companies. As shown in Table 4, the coefficients in the correlation matrix do not show any concern for the high correlation between the independent variables.

Panel Data Regression Simultaneous Test
The results of the F test shown in Table 5 produce a probability value of 0.000 which is smaller than 0.05, which means that the regression model can significantly explain the relationship between the independent variable the proportion of independent directors, number of members, number of meetings, director of multiple positions and the gender of women in the risk committee on the dependent variable of company performance as measured by ROA and Tobin's Q with the control variable firm size, leverage, liquidity, and board size with political relations moderation.

Significance Test
The test which is usually done to test the hypothesis about individual regression slope coefficients can be done using probability values. The results are as follows:  Based on the results of statistical testing research on the first model, the probability value for model 1 is p = 0.764 and the coefficient is 0.008 for ROA and p = .123 and the coefficient is 0.660 which shows there is no influence between independent directors on company performance. The results of this study support the existing theory that the existence of independent directors can avoid conflicts of interest and protect stakeholder interests. According to Pratiwi & Wahyu (2013) stated that the appointment of an independent board of directors may only be done for regulatory compliance, but not intended to uphold good corporate governance in the company. While the research results are not in line with Aldhamari et al. (2020) where the proportion of independent risk committee directors is significant and improves the company's performance.
Based on the results of the regression test in Table 6, there is a significant and positive influence between members of the risk committee on company performance. This is evident through the probability value of 0.044 <0.05. This means that the increasing number of members of the Risk Committee can work effectively. In addition, the large number of members in the risk committee who have different experiences and knowledge can be a forum for knowledge exchange among committee members in understanding the problems and risks faced by the company. With the number of members of the risk committee, it is hoped that the monitoring will be more focused and the various deficiencies in risk management can be resolved more effectively. Other studies have also stated that a risk committee with a large number of members can improve company performance (Aldhamari et al, 2020). Therefore, a large risk committee member tends to improve the quality of internal control in improving company performance.
The insignificant effect of the number of meetings in the risk committee on the company's performance is shown in Table 6 with a probability value of 0.589 for ROA and 0.700 for Tobins'Q. This means that the meetings held by the risk committee do not have a direct influence on the achievement of company profits which shape the value on ROA or market performance on Tobins'Q. In addition, the quality of the meetings is still low where the meetings are only used as a formality and do not discuss in detail about developments or the latest company information. Thus, as a result, supervision and decision making are not going well and the performance achieved by the company cannot be maximized. While the research results are not in line with Aldhamari et al. (2020) where persistence of the risk committee team is significant and reduces the company's performance.
Based on Table 6, it can be seen that the probability value is 0.659> 0.05. This indicates that there is no effect of multiple positions on company performance. This has the same idea as the OJK institution, which states that the financial services authority institution considers that concurrent positions on the board of commissioners do not cause a big loss effect in the company. This can be seen in the absence of detailed and firm regulations regarding concurrent positions on the board of commissioners. In fact, the OJK here allows the board of commissioners to hold concurrent positions as proven by the issuance of regulation Number 33 / POJK.04 / 2014 concerning the Board of Directors and Board of Commissioners of Issuers or Public Companies which allows the board of commissioners to hold concurrent positions with several existing regulations, The insignificant influence shown by female gender on company performance. This can be proven by the probability numbers in table 6 of 0.412 for ROA and 0.809 for Tobins'Q. This is because in the Indonesian context the number of women on the board of the audit committee is largely based on family or close relatives factors rather than expertise and work experience (Darmadi, 2011). Thus, it is difficult to measure whether women who occupy company board positions are only due to family factors or actually have good competence so that they can improve the company's financial performance. While the research results are not in line with Aldhamari et al. (2020) where the proportion of female gender in the risk committee team is significant and improves company performance. Table 6 in model 2 explains that politically connected companies have a significant probability value and can reduce the influence between risk committees (RCSIZE ρ = 0.001 (-0.002) and RCOVLP ρ = 0.035 (-0.068)) on the company's performance. This means that a large number of risk committee members and overlapping risk committees will reduce corporate performance for companies that are politically connected to government. Previous research by Osazuwa, Che-Ahmad, & Che-Adam, (2016) that is, there is a negative influence between political connections and company performance which has implications for auditors, shareholders, and management. Companies that are included in political connections, pay a lot for funding and licensing fees for the convenience and smoothness of contracts or to win government project tenders so that the impact reduces the company's profitability and performance. Having a close relationship with the government is not always beneficial for the company as it can negatively impact the company's performance (Wong & Hooy, 2018). According to agency theory, the government seeks different goals that may conflict with company goals. Not in line with research Aldhamari et al. (2020) generating political relations has significant value and can increase the influence between the risk committee and financial performance.

Conclusion
Based on the findings and analysis conducted, several conclusions can be drawn: there is a significant positive influence between the total risk committee members on company performance as proxied by ROA. In order to improve company performance, the risk committee must increase the number of members in the risk committee. Given that the higher the number of risk committee members, the more effective the committee's performance will be in improving company performance. In addition, it was also found that there was a negative influence between total risk committee members on company performance, including multiple positions held by members of the risk committee with political relations as moderator. The existence of political linkages that are owned by members of the risk committee can have an impact on large expenses to obtain business projects to obtain licensing facilities. Large expenses should be considered by the company before spending large costs. It is necessary to consider the extent to which the ROA generated from the project increases the company's performance compared to the costs incurred.