Analisis Strategi Efisiensi Perbankan Indonesia dalam Menghadapi Era Masyarakat Ekonomi ASEAN

Abstract

The study is motivated by the need for more efficient Indonesian banks when the ASEAN economic cooperation scheme in the banking sector is carried out in 2020. Both quantitative and qualitative methods were deployed to answer the research questions. The quantitative analyses are stochastic frontier analysis (SFA) and panel data regression with Indonesian banking population data for 2007–2016. Analysis at the ASEAN level uses a sample of the 20 largest banks in 2014-2016. A desk study, surveys, focus group discussions (FGD), and analytic network process (ANP) were used for qualitative analysis. Both types of analysis are complementary to each other so that the results can answer questions and research objectives comprehensively. Some conclusions related to banking management and regulatory policies have been made and are expected to support Indonesia's banking industry to be more competitive in the future. Throughout the last ten years up to 2016, the efficiency of Indonesian banking has not experienced an increase. Indonesian banking efficiency is normally distributed with an average value of 67.7%, with a minimum value of 41.1% and a maximum of 93.3%. The stochastic frontier model shows that all input and output factors, as well as price variables, are significant. The magnitude of variables, from the biggest to the lowest, is operating costs (0.30), labor costs (0.26), and fixed assets (0.05) . Bank size does not affect efficiency, while differences in ownership and type of Islamic or conventional bank significantly affect efficiency. The average value of bank efficiency per country is as follows: Singapore (93.1%), Malaysia (75.2%), Philippines (75.2%), Thailand (75.0%), Indonesia (65.7%), and Vietnam (64.2%). Indonesia's banking condition is characterized by high profit and low efficiency, which is a feature of the "Quiet-Life Hypothesis." This condition makes Indonesian banks less likely to be more efficient because their profitability is high enough even in an unefficient condition. A separate study is needed to develop an effective incentive system. At the national level, determinants analysis of banking efficiency at the national level shows that BOPO, ROE, and NIM are significant at 1% level, affecting efficiency. Banks with greater NIM conditions tend to be less efficient due to "quiet-life" conditions, where in a wider margin position there is less incentive for banks to be more efficient. The determinants model of banking efficiency at the ASEAN level shows that the variable 3-month deposit rate (3MRATE) is significant in all models with a greater magnitude than many other macroeconomic variables. Another significant determinant is NPL for TOBIT and PLS models, with a negative relationship. It is certainly very logical that the banking industry, which has many loan problems, will be increasingly inefficient. This research supports previous studies that show higher deposit rates tend to make a banking system less efficient. The strategy map has a weight of 30.0% for the financial perspective, 24.4% for the customer perspective, 19.4% for the internal perspective, and for the learning and growth perspective, 26.2%. The financial perspective has a short time dimension; the results are expected in the near future. The overall strategy map model is a management tool to identify various strategic objectives in order to increase efficiency. Both the SFA model and the strategy map model provide in-line conclusions in identifying important factors for efficiency. It can be seen that the higher the weight of a strategy in the strategy map, the more linkages with input factors in the estimated banking cost function. Better management of labor costs, bank operational costs, and asset utilization is the key to improving banking efficiency. This was confirmed by both the SFA model and the strategy map model.