A Comparative Study of Banking Financial Performance Before and After the Bank Digitalization in Indonesia
Akbar Triazriel Ramadhan1*, Oktofa Yudha Sudrajad1
1 School of Business and Management, Institut Teknologi Bandung, Bandung, Indonesia
*Corresponding Author: [email protected]
Accepted: 15 September 2022 | Published: 1 October 2022
DOI:https://doi.org/10.55057/ijaref.2022.4.3.12
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Abstract: In recent years digital banks' presence has become increasingly widespread in Indonesia. Digital banking has become one of the most essential and modern innovations. This innovation aims to improve the efficiency and quality of bank operations. This strategy raises the question of whether the digitalization strategy carried out by banks has a significant positive effect on bank financial performance. This study uses the Difference-in-Differences method, where digitalized Banks are the Treatment Group and non-digitalized Banks are Control Groups. Bank digitalization is not carried out simultaneously, and it indirectly creates an ideal treatment and control group for conducting impact evaluation research. This study uses 3 banks that have adopt digitalization as treatment group and 12 commercial banks that are part of the Bank Group based on Core Capital (KBMI) 2 and 3 as control group. The empirical analysis was performed on a data panel of 15 KBMI 2 and KBMI 3 banks' financial performance. The observation period of pre-treatment is from 2012 to 2015, and post- treatment is from 2018 to 2021. The results of this study found that the bank's financial performance significantly did not increase after digitalization. The results of this study can be used as a source of information for banks in Indonesia to consider digitalization strategy to increase banking financial performance.
Keywords: Digitalization, Digital Bank, Bank Financial Performance, Difference-in- Differences
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1. Introduction
Since entering the second decade of the 21st century, technology and information development have experienced rapid growth. The change in this era is marked by the rapid increase in digital device technology by personnel and households, such as cell phones. In Indonesia, there has been an increase in the ownership of mobile devices.
The growth of cellular phone users reflects the high need of the community for mobile communication devices. This era has changed the way people live, work and relate to one another, including changes in people's behavior patterns in using services provided by financial services institutions. The role of information technology is crucial considering the increasing use of cellular devices as a medium for financial transactions. This is also supported by the increasing use of internet networks in Indonesia, followed by the expansion of internet network infrastructure development.
Along with the development of the device technology, customers accompanied by the internet and various gadgets such as cellular phones, causing various business companies that aim to survive to adapt themselves to accommodate this reality. This makes the decision-makers in the company have been widely developed and adopted by organizations in their business operations (Legner et al., 2017), no exception in the banking sector.
Digital banking services in modern days banking activity have become a trending topic in the financial industry. The study conducted by Jaubert et al. (2014) about the banking transformation roadmap reveals that by 2020, 80% of the market share will be dominated by cellular phone users, and cellular phones will become the forerunner of the epicenter of digital banking. The development of digital technology that has entered the bank customer segment has made several banks compete to create digital bank services. Entering 2022, there are already several digital banks in Indonesia. This digital bank is present as a new form of service, and this strategy is taken as a form of adjusting the business model to the latest situation. This phenomenon can be studied more deeply to review whether presenting a digital bank is the right strategy for bank performance.
1.1 Problem Statement
Several banks in Indonesia carried out the strategic bank digitalization policy as a form of business transformation. This strategy aims to increase the efficiency of operational activities and the quality of bank services. The increasingly widespread presence of digital banks in Indonesia raises the question of whether the digitalization strategy carried out by banks has a significant positive effect on bank financial performance. It is necessary to prove whether the digitalization strategy that is made and implemented is running under the goals and expectations to be achieved. The perspective used in this study is to find out whether the bank's financial performance and stability worsen, improve, or remain the same as before the digitalization strategy.
2. Literature Review
Digitalization
The term digitalization describes the increased use of information or digital technologies in a firm/entity to enhance corporate processes. The digital transformation fundamentally alters business operations, goods and services production, and marketing (Kriebel & Debenner, 2020). According to Bresnahan et al. (2002), investments in technology with structured complementary organizational structures positively impact firm productivity. Kleis et al.
(2012) discovered that digital investments increase innovation productivity. While digitalization can increase efficiency in some regions of the process and address some of the most pressing customer issues, it will never provide a seamless experience. To handle end-to- end customer orientation, the digitalization team requires assistance from every function involved in the customer experience. Furthermore, the end-user customer must be highly engaged (Markovitch & Willmott, 2014).
Dedrick et al. (2003) conclude that the impact of information technology on productivity varies by industry, with no clear evidence for banks. Because bank outputs are notoriously difficult to quantify, the researcher argue that banks should be examined separately in this regard.
According to Lee et al. (2021), financial innovation through technology in banking relates to quality improvement, which can increase bank performance by improving bank cost efficiency and technology. Evidence also reveals that market support service innovations play the most critical impact in boosting bank efficiency and technology adoption.
Digital Bank in Indonesia
Digital banking services are activities using electronic or digital facilities owned by the bank or digital media belonging to prospective customers or Bank customers, carried out independently. While most banks have made a foray into digitalization, not all online banks are digital. According to Indonesian Financial Services Authority (POJK No.12/POJK.03/2021), a digital bank in Indonesia is a financial institution that effectively manages the customer lifecycle from onboarding to withdrawal while having a certain level of security similar to that of a conventional bank. Digital bank operates without any physical branches and relies on the online process for the entire customer life cycle, starting from account opening. Digital bank allows prospective customers or bank customers to obtain information, communicate, register, open accounts, banking transactions, and close accounts, as well as obtain other information and transactions outside of banking products, such as financial advisory, investment, electronic-based trading system transactions, and various customer needs. This service aims to improve the efficiency of operational activities and the quality of bank services to its customers.
Digital bank has the same function as conventional banks, but because it has been supported by technology, they can be run without having a branch office (branchless). The establishment of a digital bank in Indonesia can be done through 2 options:
1) Digital bank as a result of the transformation of traditional banks; or 2) Digital bank through the formation of a new bank (fully digital bank)
Bank Financial Performance
Many models and techniques have been used in other research to determine the determinants financial performance. Banking is a service business that belongs to the "trust" industry and has distinctive financial performance determinants. A bank's financial performance can be assessed using several assessment indicators. Banking financial performance determinants can be classified into five groups, which are liquidity, profitability, business risk, capital, and efficiency (Sawir, 2005). Ferrouhi (2018) states that increased bank performance can be realized through increased profitability. Profitability refers to a firm's ability to profit from all available capabilities and resources, such as sales activities, cash, capital, the number of employees, branches, and so on (Harahap, 2010). Internal and external factors can influence profitability. Several studies (Ali et al., 2022; Derbali, 2021) examined internal and external factors as determinants of bank profitability. Several previous studies tested the determinants of bank profitability from internal factors only, such as in Hayati & Musdholifah (2014).
Internally, profitability can be determined by calculating various relevant benchmarks. One of the benchmarks is the financial ratios as one of the tools for analyzing the financial condition, results of operations, and the level of profitability of a company (Brigham & Houston, 2019).
This ratio can provide an overview of the performance and effectiveness of the company's management.
There are several ratios used as determinants of profitability. The ratios commonly used are return on assets (ROA) and return on equity (ROE). Syamsudin (2004) explains that ROA describes management's ability to gain profit. The higher the ROA, the higher the company's profits, so the better the management of company assets or the better the productivity of assets in obtaining net profits. Likewise, Syamsudin (2004) explains that Return On Equity (ROE) is a measurement tool of income available to company owners, both ordinary shareholders and preferred shareholders, for the capital they invest in the company. The higher the level of Return On Equity (ROE) of a company, the better the rate of return on the funds that have been invested. Several studies (Abdullah et al., 2014; Bougatef, 2017; Derbali, 2021) used return on
assets as a performance measure for commercial banks, while (Albulescu, 2015; Kohlscheen et al., 2018) employed return on equity as a bank performance measure. Other studies (Reddy, 2011; Tan & Floros, 2012) have used net interest margin (NIM) as the bank performance measure. In this study, the researcher used ROA, ROE, and NIM as the determinants of profitability.
Another aspect of seeing bank performance is through the efficiency of operational activities.
Fathony (2013) shows that an efficient bank is an essential aspect of improving the performance of a healthy bank so that profitability increases. Efficient banks will tend to be able to reduce costs, so loan interest rates will tend to be lower. This is in line with (Muljawan et al., 2014), which show that the adaptive ability and level of bank efficiency reflect a bank's competitiveness. The more efficient the bank, the more competitive it will be. A bank comparison of costs with income can be seen by reviewing the CER ratio because CER compares operating expenses to operating income (Sawir, 2005). If a bank's CER in a year decreases from the previous year, the bank's operations will be more efficient. On the other hand, if a bank's CER increases in one year from the previous year, then the bank's operations are increasingly inefficient.
Several previous studies used different approaches to examine the determinants of bank efficiency and showed inconsistent results. (Lutfiana, 2015) conduct research about the efficiency of Islamic banks showing that CAR affects efficiency. Contrary to (Nisa, 2018), who found that an increase in the core capital, which is the aspect of CAR of a bank, is not always followed by an increase in the bank's relative efficiency. (Muljawan et al., 2014) show that capital, LDR, NPL, and NIM significantly affect efficiency. In this study, the researcher used CER as the determinant of efficiency.
Referring back to the background of banks in Indonesia carrying out digitalization strategies.
This strategy aims to increase the efficiency of operational activities and improve the bank's ability to generate profits in order to improve the quality of the bank. Therefore, in this study will explore efficiency and profitability as the main comparison between banks. As well as liquidity, business risk, and capital as covariate elements.
3.1 Data
The data used in this study is quantitative. Research data are bank and digital bank ratio presented in panel data from banks in Indonesia, both from the control and treatment groups.
3.1.1 Samples
The population in this study is all Commercial Banks in Indonesia in KBMI 2 and 3, in addition to Regional Development Banks (BPD) and Foreign Branch Bank Offices (KCBLN). The researcher has used purposive sampling to determine the sample used in this study. Purposive sampling is a method where the researcher determines the criteria for selecting a sample based on the study's objectives (Sugiyono, 2013).
The sample selection is based on the character of banks that have digitized all of them in the KBMI bank group 2 and 3 to ensure data parallelism and accept the assumptions of the Difference-in-Differences method. In addition, no KBMI 1 and 4 banks and Foreign Branch Bank Offices have digitized in the 2016-2017 period, so if KBMI 1 and 4 Banks are included, all of them will be included in the control, and there will be no treatment group. Regional Development Banks were excluded, apart from the same reason, it is also because the scope of
regional bank services was limited to the region, so the implications of using digitalization would not have much impact on bank services.
The treatment group in this study is all Commercial Banks that have to apply a digitalization strategy (adopt digital banking) from 2016 to 2017. Three banks Commercial Banks have applied a digitalization strategy. The Banks on KBMI 2 and 3, apart from Regional Development Bank and Foreign Branch Bank in Indonesia, are 12 in total and will be all used as control group.
3.1.2 Sources
This study uses data sources published by the Indonesian Financial Services Authority (OJK) and the annual report of each bank in the control and treatment group from the bank's official website.
3.1.3 Hypothesis
Electronic banking services reduce average operational costs for banks (Shahabi & Faezy Razi, 2019). This is in line with Dias et al. (2012), which explain that digital platforms increase interaction with customers and deliver their needs more quickly. Digital platforms also provide methods to make internal functions more efficient and cost savings with automated applications. This indicates that digitalization causes a significant decrease in the CER from a decrease in operating expenses [H1: The CER is decreasing after bank digitalization]
Scott et al. (2017) states the use of technology has a positive and significant impact on banking profitability over nine years as measured by profit margins [H2: The NIM is increasing after bank digitalization], and also reveal the shift of services to digital also affects operating costs down from a proportional increase in revenue in the ten years after the adoption of technological innovation [H3: The ROA is increasing after bank digitalization]. This is in line with Kagan et al. (2005), which show that Internet banking increases banks' performance and directly affects ROE performance [H4: The ROE is increasing after bank digitalization].
Dependent Variables : This study uses a total of four models with four dependents. The efficiency group use a measurement model: the Cost Efficiency Ratio (CER). The profitability uses three measurement models: the Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM) models.
Independent Variables : The independent variables in this study use dummy variables.Variable on the bank digitalizing (DIGIT), 1 if the bank has adopted digital banking; otherwise, it takes a value of 0. Variable in period (YDIGIT), 1 if data is in 2018-2021; 0 if in 2012-2015
Covariates Variables : Experiments in this study could not be completely randomized. This causes the researcher also to review the existence of covariate variables. Covariate variables are variables used to eliminate or reduce noise in data analysis caused by variables other than the variables studied so that the effects of the variables studied can be seen more clearly (Linden et al., 2006). The covariate variables in this study are Capital Adequacy Ratio (CAR) as a determinant of capital, Non-Performing Loan (NPL) as a determinant of business risk, and Loan to Deposit Ratio (LDR) as a determinant of liquidity (Sawir, 2005).
3.2 Measurement
Analysis of the impact of bank digitalization strategic policies on bank performance in Indonesia in this study was analyzed by the Difference in Differences (DiD) method. The DiD
method requires recording the situation in two periods, before and after treatment. DID is an interaction term between time and treatment group dummy variables (Ryan et al., 2019). The measurement of the DiD method is indicated by:
𝑌 = 𝛽0 + 𝛽1 ∗ [𝑇𝑖𝑚𝑒] + 𝛽2 ∗ [𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡] + 𝛽3 ∗ [𝑇𝑖𝑚𝑒 ∗ 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡] + 𝛽4 ∗ [𝐶𝑜𝑣𝑎𝑟𝑖𝑎𝑡𝑒𝑠] + 𝜀
β0: constant term
β1: treatment group-specific effect
β2: time trend shared by both the control and treatment groups β3: the true effect of treatment (difference in changes over time) β4: parameter of covariates variable
ε: error
3.3 Data Analysis
In this study, to analyze the collected data and statistical activities, from testing the model assumptions to running the Difference-in-Differences model, STATA 17 MP software was used.
3.3.1 Parallel Trend Assumption Test
The DiD method requires basic assumptions so that the results obtained are robust. An assumption is that the two treatment and control groups have the same linear trend if there is no difference in the form of treatment. The assumption is a Parallel Trend Assumption (Ryan et al., 2019). With this assumption, the DID method can precisely analyze how digitalization impacts bank financial performance.
Figure 1: CER Trend
Based on visual observations in the CER model, it can be seen that there are similarities in CER in the both control and treatment groups before the treatment. This can indicate that the parallel trend assumption in this model is met.
Figure 2: ROA Trend
Based on visual observations in the ROA model, it can be seen that there are similarities in ROA in the both control and treatment groups before the treatment. This can indicate that the parallel trend assumption in this model is met.
Figure 3: ROE Trend
Based on visual observations in the ROE model, it can be seen that there are similarities in ROE in the both control and treatment groups before the treatment. This can indicate that the parallel trend assumption in this model is met.
Figure 4: NIM Trend
Based on visual observations in the NIM model, it can be seen that there are similarities in NIM in the both control and treatment groups before the treatment. This can indicate that the parallel trend assumption in this model is met.
4. Results and Discussion
Table 1: DiD Result on Cost Efficiency Ratio Outcome var. CER S. Err. | t | P> | t | Before
Control 58.604 Treated 59.790
Diff (T-C) 1.186 2.697 0.44 0.660*
After
Control 46.826 Treated 58.265
Diff (T-C) 11.439 2.763 4.14 0.000***
Diff-in-Diff 10.253 3.863 2.65 0.008***
These estimators are accompanied by their standard errors, t-statistics, and p-values. The last row is the DiD treatment-effects estimation implying an increase in the Cost Efficiency Ratio by 10.253. A p-value accompanies the degree of significance with a star, which reflects the statistical inference at various significance levels, as shown in the table below. The model significance with alpha is 1% in this model.
Table 2: DiD Result on Return on Asset Outcome var. ROA S. Err. | t | P> | t | Before
Control 1.592 Treated 2.119
Diff (T-C) 0.527 0.177 2.98 0.003*
After
Control 1.454 Treated 0.704
Diff (T-C) -0.750 0.153 4.90 0.000***
Diff-in-Diff -1.277 0.235 5.43 0.000***
These estimators are accompanied by their standard errors, t-statistics, and p-values. The last row is the DiD treatment-effects estimation implying a decrease in the Return on Asset by 1.277. A p-value accompanies the degree of significance with a star, which reflects the statistical inference at various significance levels, as shown in the table below. The model significance with alpha is 1% in this model.
Table 3: DiD Result on Return on Equity Outcome var. ROE S. Err. | t | P> | t | Before
Control 18.053 Treated 22.292
Diff (T-C) 4.239 1.171 3.62 0.000***
After
Control 14.846 Treated 11.051
Diff (T-C) -3.795 1.346 2.82 0.005***
Diff-in-Diff -8.034 1.792 4.48 0.000***
These estimators are accompanied by their standard errors, t-statistics, and p-values. The last row is the DiD treatment-effects estimation implying a decrease in the Return on Equity by 8.034. A p-value accompanies the degree of significance with a star, which reflects the statistical inference at various significance levels, as shown in the table below. The model significance with alpha is 1% in this model.
Table 4: DiD Result on Net Interest Margin Outcome var. ROA S. Err. | t | P> | t | Before
Control 4.943 Treated 6.849
Diff (T-C) 1.907 0.536 3.56 0.000***
After
Control 3.971 Treated 4.417
Diff (T-C) 0.446 0.262 1.28 0.200 Diff-in-Diff -1.461 0.626 2.33 0.020**
These estimators are accompanied by their standard errors, t-statistics, and p-values. The last row is the DiD treatment-effects estimation implying a decrease in the Return on Equity by 1.461. A p-value accompanies the degree of significance with a star, which reflects the
statistical inference at various significance levels, as shown in the table below. The model significance with alpha is 1% in this model.
4. Conclusion
Based on the analysis of the data in the previous chapter, the output results of the CER model in Table 1, show that digital banking has a significant and positive effect on bank efficiency by measuring CER, with a probability level of 0.008 or less than 0.01 so that it is statistically significant with a significance value of 1%. From the results of this influence, the adjusted r- squared value shows that the digital banking variable can explain 34% of the CER variable.
With the results of this study, it can be concluded that statistically, there is an increase in CER after being given digitalization treatment to banks in Indonesia. This is contrary to H1, which predicts that digitalization will increase efficiency by reducing the CER. This reinforces the findings by Al-Smadi & Al-Wabel (2011), which explain that e-banking has not improved the performance of these banks.
The results of the profitability model show that digital banking has a significant and negative effect on ROA, ROE, and NIM. In the ROA model in Table 2, the probability level of 0.000 or less than 0.01 is statistically significant with a significance value of 1% with an adjusted r- squared value indicating digital banking variables can explain 31% of the ROA variable. In the ROE model in Table 3, the probability level is 0.000 or less than 0.01, so it is statistically significant with a significance value of 1% with adjusted r-squared value indicating the digital banking variables can explain 32% of the variable ROE. In the NIM model in Table 4, the probability level is 0.020 or less than 0.05, so it is statistically significant with a significance value of 5% with an adjusted r-squared value indicating that the variables of digital banking can explain 19% of the NIM variable. With the results of this study, it can be concluded that statistically, there was a decrease in ROA, ROE, and NIM after being given digitalization treatment to banks in Indonesia. This is contrary to H2, H3, and H4, which predicts that digitalization will increase profitability by ROA, ROE, and NIM respectively. This reinforces the findings by Stegmeier & Verburg (2022) that only 2 out of 25 advanced-stage digital-only banks have reached operational breakeven and forecasted that less than 5% of the world's 400 digital-only banks have turned profitable so far. This suggests that even after adjusting for the high acquisition costs, most digital-only banks still haven't found a way to profit.
A conclusion can be drawn as the result of the study as an answer to the research question that is the basis for the research and becomes a summary of the overall content of the research.
From the results of this study, it can be concluded that statistically, there is a significant effect of digitalization treatment on banking performance in Indonesia throughout 2018-2021.
Nevertheless, the digitalization strategy are not proven to improve banks' financial performance in Indonesia throughout 2018-2021, in term of efficiency and profitability. This can be caused by various factors, apart from the dynamics of the bank's strategy and the time of the study looking at the short term, according to previous studies the amount of investment that is not proportional to the increase in customers affects the bank's return on profit ratio, besides that the culture and culture of a region has a role, such as customer trust only in traditional services and/or customers are not so aware of the urgency of using digital in banking services.
Compared to the results of previous research on a similar topic (Sadr, 2013; Wadesango, 2020), which explained the increase in performance after banks adopted digital innovation, there are some factors also taking part in the effects of digital transformation on banks. According to certain studies, its high infrastructure costs may result in negative profitability due to a lack of
e-banking consumers (Gutu, 2014). However, a surge in online consumers is not just due to the bank's offerings; nation culture plays an important part in promoting e-banking and internet access in many nations (Takieddine & Sun, 2015). Customers worldwide encounter distinct e- banking models due to cultural differences (Yuen et al., 2015). This means that the impact of the digitalization program on the growth of bank financial performance in Indonesia is indicated to be caused by differentiating factors that customer behavior in response to the presence of digital banks varies. This is also in line with the findings in Jordan in Al-Smadi &
Al-Wabel (2011), which show that banks' customers in Jordan depend on traditional channels to carry out their banking operations. As a result, costs associated with adopting electronic banking are still higher than revenues.
4.1 Recommendation
Based on the discussion, research analysis, and conclusions of this study, the following suggestions are given:
For Banking Companies, Banks must review the readiness of the digitalization strategy before entering and establishing themselves as a digital bank, especially since the change in business model towards digitalization is a big investment for banks. In implementing digital banking innovation, banks are expected to be able to detect deeper risks and face various risks with the existence of digital banking innovations in Indonesia. The thing that needs to be prepared is that banks need to pay attention to the characteristics and needs of the Indonesian people for the emergence of digital banks. In other words, banks are expected to be able to recognize the potential target characters of their digital service users. This research is able to capture short- term performance trends during the early 4 years of implementing the digitalization strategy.
The turning point of digital infrastructure investment has not yet been reached, apart from the high expenditure on infrastructure development, one of the reasons is due to the lack of customers who still feel that traditional channels have more benefits than digital channels.
Therefore, to get the optimal results of this strategy in the long term, banks can provide socialization and increase target market awareness of the bank's digital presence by ensuring that customers feel that they get higher benefits from using digital services compared to traditional services.
For Further Research, The data in this study from digital banking variables are very limited.
The presence of digital banks, which is still very new in Indonesia, makes the posttreatment of this research can only be carried out for the 2018-2021 period. The results of this study can be developed as further research material for subsequent researchers to enrich the sample with the presence of a new digital bank.
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