Impact of Digital Financial Services on Financial Performance of
Commercial Banks in Nigeria
AISHA ISA-OLATINWO, UCHE UWALEKE, UMAR ABBAS IBRAHIM
Department of Business Administration, Faculty of Management Sciences,
Nile University of Nigeria
NIGERIA
Abstract: - Since the introduction of information communication technology and the widespread use of the internet,
the banking business has undergone considerable changes. Information and communication technology (ICT) is at
the heart of Nigeria's current worldwide shift curve in the electronic banking system. In light of this, this study
looked into the influence of digital financial services (DFS) on the financial performance of Nigeria's publicly
traded commercial banks. The study aims to see if there is a link between the dependent variable, which is financial
performance as assessed by banks' earnings-per-share (EPS), and the main independent variables, which are the
volume of ATM and POS transactions as a proxy for digital financial services (DFS). Secondary data was
employed in the study. The data was collected from the annual report of target banks and the Central Bank of
Nigeria from 2012 to 2020. The study used both descriptive and inferential statistics in analysing the data. In
general, the study revealed that digital financial services (DFS) have substantial and significant marginal effects on
earnings per share in Nigeria’s banking sector. Thus, there exists a positive relationship between digital financial
services (DFS) and bank financial performance. In conclusion, electronic banking has made banking transactions to
be more accessible by bringing services closer to its customers hence improving banking industry performance.
Thus, the study recommends that bank management should enhance digital banking to improve financial
performance in commercial banks.
Key-words: - Digital Financial Services, Financial Performance, Commercial Banks
Received: August 15, 2021. Revised: March 14, 2022. Accepted: April 17, 2022. Published: May 6, 2022.
1 Introduction
In the last decade, the banking industry has seen
significant changes as technological advancements,
and the inexorable forces of globalisation have
created both opportunities for expansion and
challenges for banking executives seeking to remain
profitable in an increasingly competitive
environment. Banking industries around the world
are increasingly becoming more connected to
information technology infrastructure, making
banking operations, services, and commercial
activities more accessible, faster, more efficient, and
effective for individuals and businesses alike [1]. [2]
describes Digital Financial Services (DFS) to include
a broad range of financial services accessed and
delivered through digital channels, including
payments, credit, savings, remittances and insurance.
Digital channels refer to the internet, mobile phones,
ATMs, and POS device. According to [3], Digital
financial services can be described as a method of
banking that uses digital means to conduct
transactions.
The concept of digital financial services (DFS) has
been beneficial in improving the banking industry.
DFS must be made through a sound analysis of risks
and costs associated with avoiding harm to banks'
performance. Bank performance is directly
dependent on the efficiency and effectiveness of DFS
and, on the other hand, tight controls in standards to
prevent losses associated with internet banking
frauds. This is only possible if the impacts of DFS on
the financial performance of banks and their
customers are well analysed and understood.
Digital financial services have contributed
enormously to banks' profits [4]. Hence, the impact
of Digital Financial Services in the banking sector is
very evident like products and services through
various delivery channels. The introduction of direct
banking and Internet-based financial services over
the past decade has spurred profound changes in
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customer behaviour and service expectations. In
today's financial services, customers demand
immediate fulfilment. In short, they want to conduct
real-time transactions from any location, through any
device, for products and services, all at their
convenience. At the same time, they demand
consistent levels of service across all delivery
channels [5]. Several recent studies have also
explored in great detail the resultant impact of
technology on the Nigerian banking sector. A causal
study by [6] revealed bank performance is enhanced
by digital financial services adoption. In the
happening, a recommendation emphasized that banks
must focus on their needs to achieve their goals by
using the appropriate technology. Electronic banking
service provides convenience and promptness to
customers along with cost savings; banks are also
interested in expanding their market through digital
financial services [3], [7] [8], [9], [10], [9], [11],
[12], [13], [14], [15] studies report that digital
financial services and innovation in banking is one of
the key profitability drivers of banks and, in the 21st
century, it is becoming increasingly decisive in
performance and competitiveness. While the
deployment of digital financial services (DFS) has
proliferated, there is not enough evidence of its
impact on the performance of banks, particularly
within the Nigerian banking industry. A gap still
exists on exactly whether Digital financial services
(DFS) have resulted in the growth of commercial
banks' profitability. Therefore, it would be
fascinating to know which aspect of the Digital
financial services (DFS): Automated Teller Machine
(ATM) services, Point of Sale (POS) services,
Mobile Banking services and internet banking (web)
services is more impactful to the financial
performance of the commercial banks in Nigeria. A
Survey of existing literature worldwide revealed
conflicting results: [4], [16]–[21]. Moreover, despite
the increasing rate of deployment of e-channels by
Nigerian banks, there is a scarcity of empirical
research that provides quantitative information on the
influence of digital financial services on bank
financial performance in Nigeria. Furthermore,
technology is projected to improve service delivery
efficiency, which would help a company improve its
performance. The most recent research in operations
improvement has assumed that technological
innovation has a direct impact on improving
performance and profitability [22]. The concept is the
same in the banking sector, with banks incorporating
it with the expectation of great returns. However, the
adoption of technology requires high initial capital,
which affects the profits of the firm in the short run.
This is caused by the heavy capital expenditure and
/or debts with attendant interest expense in acquiring
the technology. This affects the dividends payout to
shareholders and casts doubts on the worthiness of
the investment. Digital financial services can only be
supported with technology, and banks' performance
in the current competitive environment is tied to how
banks can deliver banking products and services
efficiently through digital financial services.
Therefore, this paper seeks to reduce the conflict by
investigating the impact of digital financial services
using the mainly used e-channels (ATM and POS) as
a proxy for Digital Financial Services on the
financial performance of quoted commercial banks in
Nigeria.
1.1 Objectives of the Study
The main aim of this research is to assess the impact
of digital financial services on the financial
performance of quoted commercial banks in Nigeria.
The study pursues the following specific objectives
and thus described in Fig.1 below:
i. Examine the impact of Automated Teller
Machine (ATM) transaction volume on the
earnings per share of the quoted commercial
banks in Nigeria.
ii. Determine the influence of Point of Sale
(POS) adoption on the earnings per share of
the quoted commercial banks in Nigeria.
1.2 Research Hypotheses
The following hypothesis is formulated to address the
problem of the study:
Ho1: There is no significant impact of the
Automated Teller Machine (ATM) volume
on the earnings per share of quoted
commercial banks in Nigeria.
Ho2: There is no significant effect of Point of Sale
(POS) service adoption on earnings per share
of the commercial banks in Nigeria.
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Fig. 1: Conceptual Framework
Source: Researcher, 2021
2.2 Theoretical Framework
Theoretical review is a foundation or basis that which
every research or study underpins its variables and
hypothesis; this is to ascertain whether the findings
agree with a particular selected theory or not. Adopted
theories for this study are:
Innovation Diffusion Theory (IDT), this theory is
used to underpin this study because it emphasizes the
introduction of new financial innovation by improving
the traditional ways of rendering banking services.
This study considers this theory because innovation in
the banking system, more especially the Digital
financial services (DFS), will lead to lower transaction
costs and thus have some impact on the general
organizations’ performance as described in the study
of [23]–[25].
Technology Acceptance Model (TAM): In like
manner the technology acceptance model is also used
to underpin this study because it has advocated for the
establishment of a relationship between individuals'
behaviour and the use of Information and
Communication Technology (ICT) as in the digital
finance services of the banks [26], [27]. This model
brings in the behavioural aspect of individuals to the
acceptance and use of technological innovations.
3 Methodology
Research Design: The study adopts an ex-post facto
research design. The methodology is preferred
because the study analyses quantitative statistical
secondary data and makes inferences on the effect of
digital financial services on the financial performance
of commercial banks in Nigeria. Emphasis is on the
four most popularly used digital payment channels:
Automated Teller Machine (ATM) and Point of Sale
(POS), which are taken as the independent variables to
explain the effect on the banks Earning Per Share
(EPS) as a proxy for the financial performance of the
bank.
Population and Sample size of the study: The
population of the study is all the thirteen (13) licensed
commercial banks that are listed on the Nigeria Stock
Exchange (NSE) and are operating in Nigeria as of
December 31, 2020. The sample size is thus the
thirteen (13) quoted banks operating in Nigeria as of
December 2020, which is termed a universe study.
Method of Data Analysis: The statistical instrument
employed is the Pearson correlations, and variance
inflation factor to check for the presence of
multicollinearity and the variables attain stationarity at
either level or first difference.
Then if there is no presence of collinearity among
variables and the variables are stationary at either
level or first difference, the ARDL model will be used
to fit the long and short-run relationship between the
digital financial services variables and the banks'
performance. Otherwise, panel data Ridge and
Weighted Regression is used to fit the relationship
between digital financial services and the banks'
performance. Since the principle of ridge-regression is
correct for multicollinearity in a linear model. It is
worthy of note that other modelling procedures like
the difference and system GMM will not be robust if
the independent variables are highly collinear. Thus,
violating the basic assumption of multicollinearity in
fitting any linear model.
Collected data were scrutinised for errors of omission
before being inputted into the statistical data analysis
software (E-views 9 and STATA 15). Descriptive
statistics, which includes mean and standard deviation
and all the pre-modelling diagnostics were performed
in Eviews. At the same time, the modelling proper
was done in STATA due to its solid and detailed
computational capabilities over e-views for any panel
data related analysis.
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Model Specification: The model specification is for
this study is therefore adapted from studies of [4],
[28], [29] to describe the relationship between the
variables in the study in a mathematical functional
form:
𝑙𝑛 𝐸𝑃𝑆𝑡= 𝛼0+ 𝛼1𝑙𝑛 𝐴𝑇𝑀𝑡+ 𝛼2𝑙𝑛 𝑃𝑂𝑆𝑡+ 𝜀𝑡 (1)
Where: α0 is the intercept, α1 and α2 are the slope or
coefficient to capture the nature and effect of the
relationship between the variables, and µ is the error
term.
4 Data Analysis and Interpretation
Descriptive Statistics: This sub-section presents the
descriptive statistics of the bank-specific digital
financial service indicators that determine the
financial performance of deposit money banks in
Nigeria. It shows their respective mean, median,
maximum/minimum value, standard deviation and the
Jarque-Bera normality test, which is a goodness-of-fit
test to ascertain if the sample data have the skewness
and kurtosis that show normal distribution. This is a
precondition for fitting the panel regression model.
Table 1 below shows the descriptive statistics of all
the variables in the study.
Table 1. Descriptive Statistics and Test of Normality
for the two (2) digital financial services Indicators and
the Financial Performance of Quoted Deposit Money
Banks in Nigeria
LNEPS
LNATM
Mean
4.52
16.912
Median
4.585
16.713
Maximum
6.599
19.161
Minimum
1.386
14.496
Std. Dev.
1.324
1.218
Skewness
-0.36
0.206
Kurtosis
2.333
1.952
Jarque-Bera
4.704
6.183
Probability
0.095
0.045
Sum
528.841
1978.687
Sum Sq.
Dev.
203.258
172.112
Observations
117
117
Source: Researcher’s computation
Descriptive statistics in table 1 explains and
summarises the variables with the fundamental
statistic like mean, standard deviation and maximum
and minimum value of each series. The Jarque-Bera
test of normality also shows that all of the variables in
the models are normally distributed as expected and
are a prerequisite for the application of any OLS based
model.
4.1 Pre-model Diagnostic Test
This checks for probabilities of the presence of conditions
and biases that may occur so as not to undermine the
accuracy of outcomes. The tests are carried out to ensure
that the data meets the basic assumptions of a panel
regression model.
4.1.1 Correlational Analysis
Pearson Correlation as a statistical method is employed to
evaluate the strength of the relationship among various
variables and the extent of linearity. The outcome is as
shown in Table 2.
Table 2. Correlational Matrix of Study Variables
lnEPS
lnATM
lnPOS
lnEPS
1
lnATM
.665**
1
lnPOS
.551**
.745**
1
n
117
117
117
**. Correlation is significant at the 0.01 level (2-
tailed).
*. Correlation is significant at the 0.05 level (2-
tailed).
Key:
EPS: Earnings per share
ATM: Automated Teller Machine
POS: Point of Sale
Source: Researcher’s computation
The correlation matrix table 2 presents the correlation
coefficient for the variables on the effect of the
complete set of two (2) prudential indicators and the
financial performance of quoted deposit money banks
in Nigeria as considered in this study. Correlation
values ranged from -1 to +1; where 0.75-0.99 signifies
a "very strong" relationship between the intersecting
variables, 0.5-0.74 implies a "strong" relationship
within the intersecting variables, and 0.35-0.49
implies a "weak" relationship among variables as
presented in table 2. As observed, there exists a strong
relationship between all the digital financial services
indicators and EPS. Also, a very strong but negative
relationship was observed between the digital
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financial services indicators and the EPS. And so on,
as seen in the correlation matrix table above.
4.1.2 Cointegration and Unit-Root Test for
Stationarity of Data
This is a test for stationarity in time series data.
Stationarity is present in a time series if a shift in time
does not cause a change in the shape of the
distribution, and on the other hand, there is no
stationarity if a shift in time causes a change in the
shape of the distribution. The unit root is a cause of
non-stationarity. The test result and interpretation are
contained in the Cointegration test and Unit-Root
Table 3 a & b.
Table 3a. Kao Residual Cointegration Test
Series: LNEPS LNATM LNPOS
Sample: 2012 2020
Included observations: 117
Null Hypothesis: No cointegration
Trend assumption: No deterministic trend
User-specified lag length: 1
Newey-West automatic bandwidth selection and
Bartlett kernel
t-Statistic
Prob.
ADF
-1.970848
0.0244
Residual variance
0.595168
HAC variance
0.490048
The cointegration test result shows that panel is
cointegrated since the ADF p-value of 0.0244 is less
than the 0.05 (5%) level of significance.
Table 3b. Unit-Root
Newey-West automatic bandwidth selection and Bartlett kernel
Balanced observations for each test
Series
Test
Unit root
p-value
Panel
LNEPS
Levin, Lin & Chu t*
-1.59905
0.055
13
LNPOS
Levin, Lin & Chu t*
-2.11032
0.012
13
LNATM
Levin, Lin & Chu t*
-8.62523
0.000
13
Source: Researcher’s compilation
As a precondition for modelling panel data variables,
the need to ensure that the variables are stationary
requires unit root tests of each of the variables in the
model. The outcome of our unit root tests using the
Levin-Lin-Chu unit-root test for panel data shows all
variables are stationary in the natural logarithm form;
all other variables are stationary, as seen in the unit
root test table above.
Table 4. Test of Multicollinearity
Independent variable
Collinearity Statistics
Tolerance
VIF
1
lnATM
0.38
2.633
lnPOS
0.001
747.628
a. Dependent Variable: lnEPS
Source: Researcher’s computation
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From the test multicollinearity shown in section table
4, it was not noticed that POS exceeded the minimum
condition (>5) for no collinearity stated by the
Variance Inflation Factor (VIF). As such, it is seen
that POS exceed the minimum condition, thus
implying that POS is highly collinear, and hence we
cannot apply ARDL, GMM modelling technique as
the case may be. The panel data Ridge and Weighted
Regression will be used to fit the relationship between
digital financial services and the banks' performance
since the principle of the ridge is proposed to correct
for multicollinearity in any linear model.
4.2 Test of Hypothesis
Assessment of the plausibility of the hypotheses was
carried out on the available data, using the Ridge and
Weighted Regression. Ridge and Weighted
Regression Table 5 are generated to explain the
various relations between the digital financial service
variables and the banks' performance.
Ridge and Weighted Regression model is a statistical
model containing model parameters of random
variables. The assumption is that the data being
analysed are drawn from a hierarchy of diverse
populations, and differences are related to the
hierarchies. This is depicted in Table 5.
Table 5. The Fixed-Effects Panel Data: Ridge and Weighted Regression
4.3 Discussion of Findings
The first variable with a null hypothesis - Ho1: There is
no significant impact of the Automated Teller Machine
(ATM) volume on the earnings per share of quoted
commercial banks in Nigeria”. The variable automated
teller machine volume (ATM) has a panel regression
coefficient of 1.90e-06. This implies that the ATM has
a positive impact on the bank's earnings per share (eps)
as a measure of financial performance; thus, suggesting
that, with a unit increase in the ATM, the banks will
see about the 1.90e-06-unit increase in financial
performance as explained by their earnings per share.
Furthermore, the automated teller machine volume
(ATM) has a p-value of 0.000, which is less than the
0.05 (5%) level of significance. Hence, the null
hypothesis “There is no significant impact of the
Automated Teller Machine (ATM) volume on the
earnings per share of quoted commercial banks in
Nigeria” is then rejected. We, therefore, conclude that
the relationship observed between the automated teller
machine volume (ATM) and the earnings per share is
significant and generalizable.
Similarly, the second variable with a null hypothesis -
Ho2: There is no significant effect of Point of Sale
(POS) service adoption on earnings per share of the
commercial banks in Nigeria”. The variable point of
sale (POS) has a panel regression coefficient of 1.19e-
06 which implies that the point of sale (POS) has a
positive impact on the bank's earnings per share (eps)
as a measure of performance. This, suggests that with a
unit increase in the point of sale (POS), the banks will
see about a 1.19e-06-unit increase in their performance,
as explained by their earnings per share. Furthermore,
the point of sale (POS) has a p-value of 0.004, which is
less than the 0.05 (5%) level of significance. Hence, the
null hypothesis that states that “There is no significant
effect of Point of Sale (POS) service adoption on
earnings per share of the commercial banks in
Nigeria” is rejected. Hence, a conclusion that though
there is an observed relationship between the Point of
_cons 2.909206 2.239219 1.30 0.197 -1.532275 7.350686
lnPOS 1.19e-06 1.37e-07 8.68 0.000 9.16e-07 1.46e-06
lnATM 1.90e-06 1.41e-07 13.47 0.000 1.62e-06 2.18e-06
lnEPS2 Coef. Std. Err. t P>|t| [95% Conf. Interval]
- R2v= 0.8064 R2v Adj= 0.7798 F-Test = 237.43 P-Value > F(2 , 102) 0.0000
- R2h= 0.9522 R2h Adj= 0.9457 F-Test = 1136.13 P-Value > F(2 , 102) 0.0000
------------------------------------------------------------------------------
Root MSE (Sigma) = 24.2209 | Log Likelihood Function = -530.8936
(Buse 1973) R2 Adj = 0.9711 | Raw Moments R2 Adj = 0.9711
(Buse 1973) R2 = 0.9746 | Raw Moments R2 = 0.9746
F-Test = 455.8688 | P-Value > F(2 , 102) = 0.0000
Wald Test = 911.7375 | P-Value > Chi2(2) = 0.0000
Sample Size = 117 | Cross Sections Number = 13
------------------------------------------------------------------------------
Ridge k Value = 0.00130 | Generalized Ridge Regression
lnEPS2 = lnATM + lnPOS
==============================================================================
* Fixed-Effects Panel Data: Ridge and Weighted Regression
==============================================================================
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Sale (POS) service adoption and the earnings per share
of the banks and therefore we conclude that the
relationship observed can be generalized in the long
run.
5 Conclusion
The impact of the two (2) key digital financial services
of the banks: ATM and POS on financial performance
(using the earnings per share (EPS) as the proxy) of the
quoted deposit money banks (DMBs) in Nigeria's
banking sector jointly is tested with Wald statistics.
The Wald statistics were obtained to be 911.7375 with
a p-value of 0.000, which is greater than the 0.05 (5%)
level of significance for the EPS model, therefore
implying that there is a significant joint impact of the
digital financial services indicators on the quoted
deposit money banks' performance as observed in EPS
model. Also, the models return an r-squared adjusted
value of 97.46%, which is the extent to which the
digital financial services have impacted the banks'
performance in terms of EPS, leaving less than a 5%
effect on other extraneous variables not captured in the
model.
These findings thus corroborate the studies of [8], [9],
[10], [11], [12], [13], [14], [15] who researched the
impact of digital banking services on banks' financial
performance variables experience and financial
performance but negates the findings of [30] whose
study evaluates the relationship between financial
innovation mostly in aspect of digital financial services
and bank efficiency as well as the impact of financial
innovation on efficiency ratio of deposit money banks
in Nigeria from 2006 to 2014. The findings also
disagree with the study of [6], who investigated
internet banking in Northern Cyprus for some time
2004-2009, in a panel data of 22 retail banking and
found that despite the internet banking increases the
performance in different sectors, the authors entail that
in case of these two ratios they were not used wisely or
adequately.
5.1 Recommendations
The study recommends that bank management should
improve digital banking services to boost the banks'
financial performance. The necessity for commercial
banks to implement internet banking is obvious, as it
has brought the benefit of constant access to certain key
services, eliminating the need for many individuals to
engage with bank staff and improving banks' earnings
per share (EPS). The government, through the financial
sector regulatory bodies, particularly the CBN, should
encourage banks to strengthen digital banking while
also tightly regulating such development expenditures
to ensure the integrity of payment systems in particular.
According to the findings of the study, digital banking
is the engine of increasing EPS in banks. Financial
assistance that is both faster and more comprehensive
financial service delivery spurs the development of
businesses and economic growth in all other sectors in
addition to facilitating financial deepening.
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Aisha Isa-Olatinwo, Uche Uwaleke, Umar Abbas Ibrahim
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