INTRODUCTION
Consumer protection has expanded in the worldwide
financial sector. Consumer protection began with business practises including
fraud, counterfeiting, and selling low-quality items (Febriandika et al., 2022). Laws and regulations were initially prioritised in
consumer protection (Ip & Marshall, 2015).
Consumer protection is essential for banking security,
confidence, and stability. Consumer protection in the financial business has
protected clients against fraud and low-quality items (Lusardi
& Mitchell, 2014). The Indonesian Financial Services Authority (FSA/OJK)
regulates banks and other financial services (OJK, 2013).
Indonesia's financial sector gained 5.5 percent annually
between 2000 and 2021, according to World Bank data. Consumer protection is
important when banking goods and services become more complicated. Consumer
protection includes correct information, protection from unfair financial
practises, and protection from new hazards (OJK, 2013).
OJK Regulation Number 1/POJK.07/2013 on Financial
Services Consumer Protection (OJK, 2013) governs
consumer protection. Rofikoh (2017) found that consumer protection in the banking business
is still plagued by public misunderstanding, opaque procedures, and credit
risk. Consumer protection measures can help banks provide better credit and
reduce bad loans (Beck et al., 2013). According to Orevi and Orevi (2015), consumer protection regulations in Serbia reduced credit
risk over time.
Consumer protection may lower bank earnings, raise
operating expenses, and increase reputational risk (Choudhury et al., 2019). These implications require strong bank policy and risk
management. They must also routinely evaluate their systems and processes (BCBS, 2015).
Consumer protection keeps the banking industry safe,
reliable, and stable. Even though consumer protection can complicate regulation
execution and good banking practises, they can minimise credit risk and
strengthen banks (Beck et al., 2013).
The
study's obvious goal is to determine how introducing consumer protection will
affect bank financial performance and credit risk. The study's findings can offer
banks advice on how to better implement effective consumer protection in order
to improve their financial health and stability through the analysis of
specified variables.
The
findings of this study also add to the body of knowledge on organizational
behavior as it relates to consumer protection and bank soundness, which will be
useful to academics, regulators, practitioners, and other researchers who are
interested in this subject.
The
study's policy proposals could benefit banking regulators like OJK by enhancing
consumer protection and bank financial performance, and they might also help
the Indonesian banking sector grow in a favorable way.
The
findings of this study can be used by banks to optimize the application of
consumer protection laws, thereby enhancing bank financial performance and
lowering credit risk. Research can also assist banks in coming up with
profitable and long-lasting business plans.
Proper and
efficient implementation can result in better consumer protection since it can
deter unethical business activities, safeguard consumers' interests, and boost
public confidence in the banking sector.
As a
result, the findings of this thesis research not only benefit a number of
parties but also make a significant contribution to the improvement and
sustainability of the Indonesian banking sector. To better understand consumer
protection and bank financial performance, it is crucial that this research be
sustained and enhanced. Moreover, the
study aims to examine how the OJK regulation's implementation on financial
services consumer protection affected national commercial banks' financial
performance from 2017 to 2021.
RESEARCH
METHOD
Independent
variable
The Value of
Consumer Protection Implementation submitted to OJK with the following details:
1) Implementation
of Education Implementation in the Framework of Increasing Financial Literacy
of Consumers and/or Society
2) Service
Implementation and Complaints at PUJK
3) Implementation
of Submission of Information in the context of product marketing and/ or
financial services
4) Implementation
of Standard Agreements
5) Implementation
of Confidentiality and Security of Consumer Data and/or Personal Information
Dependent
variables
NPL, DPK, CAR,
ROA, and ROE
Control
variable
Total Asset
Data
collection method
Report on the
implementation of consumer protection carried out by the National Commercial
Bank for the 2017-2021 model year
Data
analysis method
multiple linear
regression analysis to find out the effect of independent variables on
dependent variables. Apart from that, a descriptive analysis was also carried
out to see an overview of the data obtained.
Research
hypothesis
A
bank's financial performance is an important concept in measuring the success
of a bank in managing its portfolio. One indicator of a good bank's financial
performance is the bank's ability to manage credit risk so that the risk of bad
credit or Non-Performing Loans (NPL) can be minimized, which in turn affects
the bank's overall financial health. Implementation of OJK regulations
regarding consumer protection in the banking sector can affect financial
performance by affecting the quality of a bank's credit portfolio.
Several
studies regarding financial performance have been carried out. Dehghani (2018) in "Bank Risk Management: Theory"
states that credit risk is the main risk faced by banks and greatly affects the
financial health of banks. Therefore, credit risk management is very important
to maintain financial performance. Furthermore, Fitriyah
and Huda (2020) in
"Banking Regulations and Bank Performance: Evidence from Indonesia"
found that strict banking regulations affect bank performance through credit
quality. In this context, OJK regulations regarding consumer protection in the
banking sector can be a factor affecting credit quality, which in turn affects
financial performance.
Other
studies that support the relationship between credit risk and financial
performance, such as Tripathi (2019) which found a significant effect of credit risk
on financial performance in India, Hasan
et al. (2018) concluded
that the significant effect of credit risk on bank profitability in Bangladesh.
The
effect of implementing consumer protection on financial performance is an
important aspect to understand in this study. In the theory of consumer
protection and financial performance, there are several arguments showing a
positive relationship between the two variables. First, effective consumer
protection can increase consumer confidence in banking or financial
institutions. The Consumer Trust Theory explains that high trust can influence
consumer decisions to use banking products or services, which in turn can
improve financial performance. For example, when consumers feel that their
personal information is safe and confidential, they will be more likely to use
banking products or services and have the potential to increase banking
revenue. Second, good consumer protection can also increase consumer
satisfaction. Customer Satisfaction Theory shows that customer satisfaction has
a positive correlation with customer loyalty and customer retention. In the
context of financial performance, high customer satisfaction can have a
positive impact on ROA and ROE, because satisfied consumers tend to use banking
products or services for a longer time.
In
addition, implementing good consumer protection can also help reduce credit
risk and NPLs. This is supported by Risk Management Theory, which emphasizes
the importance of risk management in achieving good financial performance. By
minimizing credit risk, banks can maintain adequate CAR and avoid losses caused
by bad loans.
Finally,
effective consumer protection can have a positive impact on credit growth and
Third-Party Funds (DPK). Customer Value Theory explains that good consumer
protection can increase perception. This encourages the growth of Third-Party
Funds because consumers will be more inclined to use customer value and
increase consumer confidence in banking, the financial services offered by
banks. In addition, consumers who feel protected will also be more likely to
save and invest their funds in banking, which can increase DPK.
In
the context of this research, the effect of implementing consumer protection
will be tested on financial performance indicators such as ROA, ROE, CAR, NPL,
and DPK. The research hypothesis is:
1)
H1_1: There is a significant negative
effect between each variable in the implementation of consumer protection
submitted to OJK on NPL at National Commercial Banks for the 2017-2021 period.
2)
H1_2: There is a significant positive
effect between each variable in the implementation of consumer protection
submitted to the OJK on DPK at National Commercial Banks for the 2017-2021
model period.
3)
H1_3: There is a significant positive
effect between each of the implementation of consumer protection variables
submitted to OJK on CAR at National Commercial Banks for the 2017-2021 period.
4)
H1_4: There is a significant positive
effect between each variable of the Implementation of Consumer Protection
submitted to OJK on ROA at National Commercial Banks for the 2017-2021 period.
5)
H1_5: There is a significant positive
effect between each variable of the Implementation of Consumer Protection
submitted to OJK on ROE at National Commercial Banks for the 2017-2021 model
period.
The following is a
regression model for each independent factor:
ü
NPL = SA1 + SA2 + SA3 + SA4 + SA5 +
Control
ü
CAR = SA1 + SA2 + SA3 + SA4 + SA5 +
Control
ü
DPK = SA1 + SA2 + SA3 + SA4 + SA5 +
Control
ü
ROA = SA1 + SA2 + SA3 + SA4 + SA5 +
Control
ü
ROE = SA1 + SA2 + SA3 + SA4 + SA5 +
Control
Clarity:
NPLs : Non-performing loans ratio
DPK : Total of Third-Party Funds
CAR : Capital Adequacy Ratio
ROA : Return on Asselts
ROE : Return on Equity
control:
Total Asset
SA1 : Implementation of Data Confidentiality
and Security
SA2:
Educational implementation
SA3:
Implementation of services and settlement of complaints
SA4:
Implementation of Information Submission (Product Marketing)
SA5 : Implementation of BakU Agreement
To do the
estimation of parameter coefficients, doubled linear regression estimation is
used.
RESULT
AND DISCUSSION
Table 1. Summary of
Descriptive Statistics Unit of Analysis (n=460)
|
Maximum |
Minimum |
Observations |
|||
|
NPL_NElT |
1.34 |
14,26 |
9,92 |
0 |
460 |
|
CAR |
32.03 |
23.56 |
820.9 |
9.01 |
460 |
|
ROA |
1.15 |
61.99 |
6,52 |
-50.4 |
460 |
|
ROEl |
19.33 |
164,21 |
97.85 |
-141.41 |
459 |
|
Total_DPK |
63577936 |
13594384 |
1127834771 |
528 |
460 |
|
control |
87827732 |
21504952 |
1575049662 |
664673 |
460 |
|
SA1 |
93.67 |
98.48 |
100 |
0 |
410 |
|
SA2 |
72,27 |
76,79 |
100 |
0 |
409 |
|
SA3 |
77,94 |
81.01 |
100 |
0 |
410 |
|
SA4 |
82.75 |
85,76 |
100 |
0 |
410 |
|
SA5 |
83,44 |
90 |
100 |
0 |
410 |
|
SA1 |
Implementation of Data Confidentiality and Security |
|
SA2 |
Implementation of Education |
|
SA3 |
Implementation of Services and Complaint Resolution |
|
SA4 |
Implementation of Information Submission (Product
Marketing) |
|
SA5 |
Standard Agreement Implementation |
|
control |
Total Assets |
Based on the summary of all the data in this study,
this research shows descriptive data for some indicators of consumer protection
and protection from 460 observations.
NPL, or Non-Performing Loans, is a term used in the banking
industry to describe loans that are unable to generate the expected returns or
loans whose payments have been delayed for a certain time. In Indonesia, the
financial services sector regulator (OJK) sets a minimum NPL limit to ensure
that banks have good credit quality, which is around 5%. Based on the NPL_NET
or Net Non-Performing Loans observation data, the average value is 1.34 and the
median value is 14.26 which indicates that the data has a distribution that is
skewed to the right. This means that most banks have a low NPL_NET ratio, but
there are many banks with much higher ratios. A maximum value of 9.92, owned by
PT. PT. Bank Nelo
Commelrcel Tbk, shows that this
bank has a high level of problem loans. This could be a sign that this bank has
problems in credit assessment or risk management. While a minimum score of 0
indicates that there is a bank with very good credit performance. This could be
an indication that this bank has a strong and effective credit assessment process
for managing risk.
Capital Adequacy Ratio (CAR)
is a measure used to determine the extent to which banks can bear the risk of
losses that may occur. This is usually expressed as a part of the bank's
capital with total risk-balanced assets. In Indonesia, financial services
regulators (OJK) set a minimum CAR limit to ensure that banks have enough
capital to cover 9% risk. The CAR or Capital Adequacy Ratio shows an average
value of CAR of 32.03 and a score of 23.56 shows that in general, national
commercial banks have sufficient capital to bear risk. Average and median that
are far from each other can show a wide variation in capital between banks. The
maximum value of 820.9 owned by PT. Bank Digital BCA shows that this bank has
very large capital compared to its risk-sensitive insurance. This can be a sign
that this bank is able to bear very heavy risks, or it can also show that this
bank is not utilizing its capital by efficient. The minimum value of 9.01,
owned by PT Bank BPD Pelmbangunan
Banteln, Tbk, almost
reaches the minimum limit set by the regulator. This could be a sign that the
bank is at the threshold of its ability to carry risk and may need to increase
capital to ensure its sustainability.
In the context of financial management, this data
shows the importance of having sufficient capital to bear risks, but also the
importance of utilizing this capital efficiently. Banks with high CARs may need
to find ways to use their capital more efficiently, while banks with low CARs
may need to find ways to increase their capital
Return on Assets (ROA) is a measure used to determine how
efficiently management uses assets to generate profits. ROA is calculated by
dividing net profit by total assets. The higher the ROA, the more efficient the
bank is in using its assets to generate profits. Based on the test results, it
shows that the average value of ROA is greater than 1.15 and the median is
61.99 indicating a large variation in bank performance in using their free
assets to generate profits. A median value that is higher than the average can
indicate that several banks with very high ROA have increased the median value.
The maximum value of 6.52 indicates that there are banks that are very
efficient in using their assets to generate profits, and the minimum value is
-50.4, owned by PT. Bank KB Bukopin, Tbk, which indicates that this bank experienced a
significant loss. This could be a sign that this bank has serious problems in
managing assets and/or income.
Return on Equity (ROE) is a measure used to determine how
efficiently management is using its capital (equity) to generate profits. ROE
is calculated by dividing net profit by total equity. The higher the ROE, the
more efficient the bank is in using its equity to generate profits. Return on
Equity shows an average value of 19.33 and a median of 164.21 indicating a
large variation in bank performance in using their equity to generate profits.
A median value that is higher than the average can indicate that several banks
with very high ROE increase the median value. The maximum value of 97.85
indicates that there are banks that are very efficient in using their equity to
generate profits, and the minimum value is -141.41, owned by PT. Bank KB
Bukopin, Tbk, Tbk,
indicating that this bank experienced a significant loss. This could be a sign
that this bank has serious problems in managing equity and/or income.
Total_DPK or Total Third-Party Funds shows the mean and median values
of 63,577,936 and 13,594,384 respectively, indicating a very skewed
distribution. The maximum value of 1,127,834,771 far exceeds the average and
median, indicating that there are banks capable of attracting large amounts of
third-party funds. The minimum value of 528 indicates that there is a bank with
a very small DPK, namely PT Bangkok Bank Comp Ltd, which from its business
process is not focused on collecting retail consumer funds. Based on the
average and median TPF values, the mean and median values are far enough to
indicate that the distribution of TPF is not evenly distributed among these
banks. This could mean that certain banks can attract much larger amounts of
third-party funds than other banks. A very high maximum value indicates that
there are banks that are very successful in attracting third-party funds. These
banks may have a good reputation, superior customer service, or attractive
deposit products. On the other hand, a very low minimum value indicates that
there are banks that may face difficulties in attracting third-party funds.
This can be caused by various reasons, such as a lack of trust from customers,
unattractive products or services, or other financial performance problems. a
very low minimum value indicates that there are banks that may face
difficulties in attracting third-party funds. This can be caused by various
reasons, such as a lack of trust from customers, unattractive products or
services, or other financial performance problems. a very low minimum value
indicates that there are banks that may face difficulties in attracting
third-party funds. This can be caused by various reasons, such as a lack of
trust from customers, unattractive products or services, or other financial
performance problems.
Total assets are the total value of all assets
or ownership owned by a company or bank, including cash, investments, property,
and others. This is an important measure of the size and financial strength of
a bank. Based on the results of the analysis showing that the average and
median values are quite far apart, it can be seen that the distribution of
total assets among these banks is not evenly distributed. This could mean that
there are large banks that have far more assets than other banks. A very high
maximum value indicates a bank with very large assets. Banks with large total
assets usually can handle greater financial risks and have advantages on an
economic scale, and the minimum value indicates that there are banks with
relatively small assets.
Implementation of various aspects of consumer
protection (SA 1 to SA5) consisting of implementation of Confidentiality and
Data Security, Education, Service and Complaint Resolution, Information
Submission, and Standard Agreements, the results of which all aspects have a
minimum value of 0 (indicating a bank that does not carry out this
implementation at all) and a maximum of 100 (receipts). show that there are
banks that carry out this implementation perfectly). However, considering that
the estimation is done using panel data, this does not become a problem. In
addition, unbalanced panel data construction will be used, as there are many
incomplete data (Gujarati, 2004).
The average and median values indicate that most banks carry out
consumer protection well, although there is still room for improvement. From
the reporting data to regulators, the level of banking compliance with
reporting on the implementation of consumer protection is around 84%, so there
is room for regulators to increase compliance through enforcement actions
regarding the obligation to report on the application of consumer protection so
that regulators obtain comprehensive information on the application of consumer
protection, especially in the banking sector.
Multicollinearity Test
Table 2. Multicollinearity Test Results
Based on the pair wise correlation test, no pair of
variables has a partial correlation of more than 0.85. The highest correlation
value we found was 0.63, between SA3 and SA4. This is far below the limit of
0.85 determined by Gujarati, indicating that there is no strong indication of
multicollinearity in this model. In addition, based on the test results, it was
found that bank size (as expressed by Total Assets or CONTROL) generally has a
weak correlation with other operational variables. This shows that bank size
does not significantly affect performance in this operational aspect. Thus,
based on this multicollinearity test, we decided to retain all variables in our
model.
Heteroscedasticity Test
Table 3. Heteroscedasticity Test Results
|
Heltelroskeldasticity Telst: Brelusch-Pagan-Godfrely |
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F-statistic |
1.02 |
Prob.
F(6,402) |
0.41 |
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Obs*R-squareld |
6.14 |
Prob.
Chi-Squarel(6) |
0.40 |
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Scaleld elxplaineld SS |
21.87 |
Prob.
Chi-Squarel(6) |
0.0013 |
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Based on this test, it can be seen that the
significance value of the test results is greater than 10 tests. That is, based
on the test results, the null hypothesis of the test cannot be rejected. Thus,
it can be concluded that the estimation model has a residual which is constant
(homoscedasticity).
Furthermore, according to the results of the Brelusch-Pagan-Godfrely test, the probability value
(p-value) produced is around 0.41 which is greater than the 10% or 0.1
significance level. This shows that there is not enough evidence to reject the
null hypothesis which states that the residual of the estimation model is homoscedastic
(constant) at a significance level of 10%. With that in mind, it can be
concluded that the self-estimation model does not experience heteroskedasticity
problems. Based on the results of the Brelusch-Pagan-Godfrely test above, it shows that the probability value
(p-value) produced is greater than 0.41. This means that there is not enough
evidence to reject the null hypothesis which states that the residual of the
estimation model is homoscedastic (constant) at a significance level of 10%.
Top of Form
As
previously explained, at this stage a feasibility test of the model is carried
out to see what kind of model properties are appropriate to be applied to the
regression model of this research panel. The test will be carried out in two
stages, namely the Chow test to compare the Fixed Effect Model and the Common
Effect Model as well as the Hausman test to compare between the Fixed Effect
Model and the Random Effect Model. Considering that there are many models used in
this study, for simplification, the Chow and Hausman tests will be carried out
on one model which will then be applied to the entire model.
Chow test
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Reldundant Fixeld Elffelcts Telsts |
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Equation: ElQ1 |
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Test cross-selection fixed
effects |
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Effects Test |
Statistics |
df |
Prob. |
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Cross-selection F |
5.96 |
(91311) |
0.00 |
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Cross-selection Chi-square |
413,19 |
91 |
0.00 |
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The Chow test was carried out to see
between the common effect and fixed effect models. If the probability
significance value is greater than 0.05, then the common effect model is used.
Meanwhile, if the significance value of the probability is less than 0.05, then
the fixed effect model is used. Based on the results of the Chou test, a
significance value of 0.000 was obtained, which means that the fixed effect
model was used.
1) Cross-section F: The F-statistic is tested with the
null hypothesis that all individual elves are zero. The value of the
F-statistic is 5.96 with degrees of freedom (91.311). The probability
associated with this test is 0.00, which is less than the 0.05 significance
level. Therefore, we reject the null hypothesis and conclude that individual
elves are significantly different from zero.
2) Chi-square cross-sections: This Chi-square test also
evaluates the null hypothesis that all individual elves are zero. The
Chi-square value is 413.19 with a freedom of 91. The probability associated
with this test is also 0.0000, which is also less than the 0.05 significance level.
Therefore, we also reject the null hypothesis in this test and conclude that
individual elves are significantly different from zero.
Overall, the results of the
Redundant Fixed Elbows Test show that the model with fixed attitudes is better
than the model without fixed elves. In other words, individual fixed elf makes
a significant contribution to the explanation of variation in the data.
Hausman test
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Correllateld Random Elffelcts - Hausman Telst |
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Equation: ElQ1 |
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Test cross-selection random
effects |
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Test Summary |
Chi-Sq. Statistics |
Chi-Sq. df |
Prob. |
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Cross-selection random |
58,42 |
6 |
0.00 |
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Cross-selection random effects
test comparisons: |
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Variable |
Fixed |
Random |
Var(Diff.) |
Prob. |
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SA1 |
-0.0040 |
-0.0044 |
0.0000 |
0.71 |
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SA2 |
0.0009 |
0.0000 |
0.0000 |
0.05 |
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SA3 |
-0.0036 |
-0.0028 |
0.0000 |
0.33 |
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SA4 |
0.0050 |
0.0041 |
0.0000 |
0.42 |
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SA5 |
0.0038 |
0.0038 |
0.0000 |
0.95 |
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LOG(CONTROL) |
0.2637 |
-0.0410 |
0.0016 |
0.00 |
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The Hausman test was carried out to
see between random effect and fixed effect models. If the probability
significance value is greater than 0.05, then the fixed effect model is used.
Meanwhile, if the significance value of the probability is less than 0.05, then
the random effect model is used. Based on the results of the Hausman test, a
significance value of greater than 0.05 was obtained, which means that the
model with a fixed effect was still used.
With that in mind, the estimation
model used in this research will all use the fixed effect model.
Table 6. Output Model
Control Variables (Total Assets)
Based on the results of the
correlation test, it can be explained as follows:
The implementation of consumer
protection through the implementation of confidentiality and data security has
an efficiency coefficient of 0.0004430 for NPL, -0.005624 for CAR (significant
at the 1% level), 0.0003460 for ROA, 0.0009340 for ROE, and -0.0018650 for DPK.
Implementation of confidentiality
and security of data (SA1), shows a significant and negative impact on CAR.
According to agency theory, companies with a more dispersed ownership structure
require better internal control mechanisms, such as data security (Jelnseln and Melckling, 1976). At the same time, CAR is a
measure used to evaluate a bank's ability to absorb potential losses. A study
by Sullivan (2006) shows that better data security practices can strengthen
data integrity, which can positively affect CAR. However, this effect is not
significant on NPL, ROA, ROE, and DPK.
Implementation of education (SA2),
shows coefficients of -0.0002090 for NPL, -0.0008050 for CAR, 0.0007430 for
ROA, 0.0011120 for ROE, and 0.0037690 for DPK. In this context, the effect of
the implementation of education on NPL, CAR, ROA, ROE, and DPK is not
significant. Implementation of education does not show a significant effect on
learning variables. Nevertheless, the financial literature views education as a
tool to strengthen financial literacy. According to Lusardi and Mitchell (2014), financial education can increase
customer understanding of financial management and has the potential to affect
financial performance in the long term. In this context, it may take longer to
see the significant effect of education on the deletion variables. Nonetheless,
financial education can help increase customer understanding of financial
management, which can have a positive impact on other financial indicators. The
study by Hastings et al. (2013)supports this, finding that good
financial education can improve financial risk management.
Implementation of services and
settlement of complaints (SA3), shows an efficiency coefficient of 0.0003200
for NPL, -0.0019920 for CAR, -0.0008850 for ROA, -0.0009360 for ROE, and
0.0073500 for DPK. Implementation of services and settlement of complaints did
not show a significant impact on NPL, CAR, ROA, ROE, and DPK. However, in this
context, performance may not be direct or significant to financial performance
indicators but has the potential to provide long-term performance in terms of
customer retention and growth. A study by Gellbrich and Roschk (2011) found that effective
complaint resolution can have a positive impact on customer satisfaction, but
in this context, the effectiveness is not significant enough to affect the
leisure indicators mentioned above.
Implementation of information
delivery - product marketing (SA4), has an efficiency coefficient of 0.0003610
for NPL, 0.003478 for CAR (significant at 10% level), -0.000555 for ROA
(significant at 5% level), -0.002088 for ROE (significant at 1% level), and
-0.0027 480 for DPK. The implementation of information dissemination and
product marketing has a significant positive effect on CAR and a significant
negative effect on ROA and ROEL. Disclosure theory (verifiability theory) by
Felltham and Xie (1992) suggests that the delivery of honest and accurate
information can help customers make better decisions. A study by Thakor and
Merton (2018) shows that transparency in the delivery of product information
can have an impact on financial indicators such as CAR, ROA, and ROE.
Furthermore, the implementation of the standard agreement (SA5), has an
efficiency coefficient of -0.000629 for NPL (significant at 5% level), 0.003497
for CAR (significant at 5% level), 0.0000236 for ROA, 0.001543 for ROE
(significant at 5% level), and -0.005041 for DPK (significant at the 10%
level). The significant negative effect on NPL and DPK shows that increasing
the implementation of standard agreements can reduce the level of NPL and DPK.
Meanwhile, the significant positive effect on CAR and ROEL shows that the
implementation of standard agreements can increase CAR and ROEL. This is in
line with a study by Shalev (2004) which found that clear and fair contracts
can increase trust between banks and customers, which has an impact on
financial performance.
CONCLUSION
Conclusions
obtained Based on
research results, (1) Consumer Protection at National Commercial Banks in
Indonesia (2017-2021) has experienced a significant increase. In general, banks
have made considerable efforts to ensure consumer protection, marked by an
increase in consumer protection variables, such as data confidentiality and
security, financial education, complaint handling and resolution, information
transparency, and standard agreements. However, there are variations in the
level of improvement between these variables, with the smallest increase
occurring in the handling and resolution of complaints, indicating that there
is still room for improvement in this aspect. and (2) the implementation of
consumer protection has a significant impact on the financial performance of
national commercial banks in Indonesia during the 2017-2021 period. Consumer
protection has a significant impact on financial performance indicators such as
Capital Adequacy Ratio (CAR), ROA and ROE, Non-Performing Loans (NPL), and
Total Third-Party Funds (DPK). In this way, it can be concluded that the
implementation of consumer protection, through the various aspects that have
been described, as a whole has had a significant impact on increasing the
financial performance of national commercial banks in Indonesia. Non-Performing
Loan (NPL) and Total Third-Party Funds (DPK). In this way, it can be concluded
that the implementation of consumer protection, through the various aspects
that have been described, as a whole has had a significant impact on increasing
the financial performance of national commercial banks in Indonesia.
Non-Performing Loan (NPL) and Total Third-Party Funds (DPK). In this way, it
can be concluded that the implementation of consumer protection, through the
various aspects that have been described, as a whole has had a significant
impact on increasing the financial performance of national commercial banks in
Indonesia.
Recommendations and suggestions for further research are; (1) bearing in mind the significant
positive relationship between several aspects of consumer protection and bank
financial performance, national commercial banks in Indonesia must focus on
increasing the implementation of consumer protection. Banking management needs
to consider how to improve policies, and procedures, and implement consumer
protection, including maintaining confidentiality and security of data,
handling complaints, and resolving disputes, transparency of information, and
fair standard agreements implemented under the provisions and consistency to be
able to improve consumer satisfaction and ultimately has a positive impact on
financial performance, (2) from research results, it appears that the
implementation of consumer protection has a significant impact on bank
financial performance. Therefore, regulators need to be more active in
encouraging and ensuring that banks implement consumer protection in an
elective manner. This could involve increasing oversight and imposing sanctions
if necessary, (3) further research is also needed to dig deeper into the
specific aspects of consumer protection and how this might impact the financial
performance of banks in Indonesia.
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