DIGITAL FINANCE AS A TOOL FOR FINANCIAL INCLUSION IN NEPAL MADHESH PROVINCE

Abstract

In order to reduce poverty, promote economic growth, and assist the secondary sectors, financial inclusion is emerging as a critical, essential facilitator. The emphasis now is on increasing financial inclusion through digital finance because of how well it works to increase financial inclusion in developing nations. As a result, in the wake of digital finance, digital financial inclusion has become increasingly important. It is crucial to pinpoint the components that encourage digital financial inclusion as a result. This study attempts to examine societal behaviour regarding digital financial inclusion and assists in identifying the factors that support it. With the help of this study, the banking industry and policymakers will be better able to comprehend how perception and perceived risk can be used to advance digital financial inclusion in Nepal's Madhesh Province. This research reveals that digital banking access, usage, and quality significantly influence digital financial inclusion achieved by digital banking. Among the three factors, 'Quality,' 'Usage,' and 'Access' have the most impact on digital financial inclusion. The current study found that there was a very strong positive link between ADB, UDB and QDA. It this also discovered in the present study, that the rate of voluntary exclusion was quite high in the regions of in Nepal Madhesh Province. while prospects of shifting to the use of QDA was also very high.

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DIGITAL FINANCE AS A TOOL FOR FINANCIAL INCLUSION IN NEPAL MADHESH
PROVINCE
Dr. Abdul Rahman
Professor, Department of Management at Birgunj Public College Panitanki, Birgunj-10, Nepal.
E-mail: iam21rahman@gmail.com
Abstract
In order to reduce poverty, promote economic growth, and assist the secondary sectors, financial inclusion is
emerging as a critical, essential facilitator. The emphasis now is on increasing financial inclusion through
digital finance because of how well it works to increase financial inclusion in developing nations. As a result,
in the wake of digital finance, digital financial inclusion has become increasingly important. It is crucial to
pinpoint the components that encourage digital financial inclusion as a result. This study attempts to examine
societal behaviour regarding digital financial inclusion and assists in identifying the factors that support it.
With the help of this study, the banking industry and policymakers will be better able to comprehend how
perception and perceived risk can be used to advance digital financial inclusion in Nepal's Madhesh Province.
This research reveals that digital banking access, usage, and quality significantly influence digital financial
inclusion achieved by digital banking. Among the three factors, 'Quality,' 'Usage,' and 'Access' have the most
impact on digital financial inclusion. The current study found that there was a very strong positive link
between ADB, UDB and QDA. It this also discovered in the present study, that the rate of voluntary exclusion
was quite high in the regions of in Nepal Madhesh Province. while prospects of shifting to the use of QDA
was also very high.
Keywords: Financial Inclusion, Digital financial inclusion, Digital Finance in Nepal, Digital banking in
Madhesh Province.
I. Introduction
When all economic activity and societal segments have simple, affordable access to financial services,
financial inclusion is increased. In Nepal, a significant segment of the population is still not a part of the
formal financial system, despite recurrent emphasis on the need for financial inclusion by bringing more and
more of the excluded population into the formal financial system in the nation's policy framework. Financial
inclusion is developing as a crucial critical enabler of reducing poverty, ending hunger, fostering economic
growth, and helping the secondary sectors, among other things. ADB, UDB, and QDA were found to have a
very strong positive association in the current study (digital financial inclusion achieved through digital
banking). This study also reveals a wide variation in how users in Nepal evaluate the usage, usability, and
benefits of different DFS for digital financial inclusion. This can be accomplished by raising public
knowledge of the use of DB and informing them of its resulting advantages. Reducing complexities in the
technicalities of using DB & making it more consumer-friendly can also help in reducing such differences.
A slightly strong negative relationship existed between digital financial inclusion achieved through digital
banking. Such a dilemma in the mind of the people can hamper the growth of the use of digital finance in
Nepal. Thus, it becomes necessary to understand the perception of people before targeting them with various
DB, this can be done at unit levels of the banks through one-to-one contact. This study also found no
significant difference in terms of privacy risk, but substantial disparities in terms of performance risk and
financial risk. To mitigate the impact of perceived risk on digital financial inclusion, steps must be
implemented to lessen the large disparity between performance risk and financial risk. This can be done by
building a robust & secured digital financial transactional platform, where the fear of conducting a financial
transaction online is the least. It is because people have concerns over the current security systems used for
conducting financial transactions online. Usage of the DB was found satisfactory among the users of the DB.

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Thus, measures must be taken to bring the unbanked & non-DBS users with a bank account under the ambit
of the digital financial services, this will further enhance the status of digital financial inclusion in Nepal.
II. Literature Review
The relevant literature has been reviewed to explore the theoretical foundation behind digital finance,
financial inclusion and digital financial inclusion. CGAP (2011), defined financial inclusion as "A state in
which all working-age adults, including those currently excluded by the financial system, have effective
access to the following financial services provided by formal institutions: credit, savings (defined broadly to
include current accounts), payments, and insurance”. According to Usha Thorat (2007), the use of IT
solutions for. Mandira Sarma (2008) followed a multidimensional approach to develop an Index of Financial
Inclusion (IFI). The approach is similar to the computation of development indexes such as HDI, the HPI, and
the GDI and so on. Firstly, the dimension index for each dimension of financial inclusion is calculated, and this
dimension index has a direct relationship with the country’s achievement in that dimension. Laxmi Mehar
(2014) it was observed that the use of mobile banking had accelerated financial inclusion in India.
Nonetheless, it is far from sufficient, covering only 2.5% of the overall population. In India, poor people have
mobile phones as well, but many are unaware of mobile banking. As a result, steps must be taken to raise
awareness of mobile banking. According to Roy & Sahoo (2016), the electronic payment system of any
country faces several risks like bank failures, frauds, counter-party failures, etc. These risks can trigger
disruptions in the electronic payment system. RTGS is a widely accepted mean of electronic payment system
amongst the banks and business firms but it requires a push on the retail side. Report of McKinsey &
Company (September, 2016) defined digital finance as "as financial services delivered over digital
infrastructure—including mobile and internet—with low use of cash and traditional bank branches". Their
definition of digital finance is used by them in a wider sense. According to Rajiv Anand (2017), mobile
phones especially smartphones have created many opportunities; mobile banking and mobile wallets are the
two fastest-growing segments in the economy. It is an enabler for faster and secure banking transactions for
the clients while it is cost-efficient for the banks as well. Prasanna Lohar (2017) identified three driving
factors of Digital Banking. First is, Adoption. Second is, Agility, And the last factor is, Arrival of Players,
new players have entered into the market like payment banks and Fintech, these players are giving completion
to the traditional banks. Kosta Peric (2015), stated that if emerging digital payment technologies are combined
with mobile phone technology than it will enable the re-engineering of financial services which can bring
down 90% cut in the cost of the transaction. The author called this as Digital Financial Inclusion. If this thing
happens then it can immensely help in including poor and the rural people under the umbrella of financial
inclusion. According to Soren Heitmann (2018), Sub-Saharan Africa exemplifies mobile money's potential
to drive financial inclusion. Evidence of Digital Financial Inclusion from neighbors developing countries of
Nepal, India is also in the list of technology-driven financial inclusion. In 2006, RBI called on banks to
provide basic financial services in all the financially excluded villages, by adopting a technology-driven
banking correspondent model. This was done in two phases, in the first phase villages with a population of
more than 2000 were covered while in the second phase villages with a population of less than 2000 were
covered. To fulfil this goal, banks have deployed a combination of new branches, fixed site business
correspondent outlets, and mobile technology-based banking correspondents. As of March 2012, the initiative
had established 96,828 new customer service stations. In 2011, only approximately 35% of India's adult
population had a bank account, according to Findex data. and China established one of the largest agent
banking networks in the world, to provide basic financial services in remote and rural areas in a cost-effective
manner. China provided subsidies and social transfers through bank cards, by using the agent banking
networks. By the end of the year 2016, 983,400 agent-based service points were established covering more
than 90% of administrative villages in China. The regulatory space provided for innovations in digital finance
is one of the key factors behind the success of digital finance in China. (World Bank, 2018).

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III. Research Methodology
This section outlines the aim and objectives and specifies the hypothesis for the research. It also provides the
steps involved in the designing of the questionnaire. Objectives of the study are as follows:
1. To study the relationship between demography and use of Digital Financial.
2. To identify the critical factors responsible for promoting financial inclusion through Digital Financial.
Research Hypothesis: Based on the review; three indicators; namely; Access, Usage, and Quality were
considered & tested, as an indicator of digital financial inclusion, and the following hypothesis were
formulated:
Hypothesis H1: Access significantly predicts the Digital Financial Inclusion.
Hypothesis H2: Usage significantly predicts the Digital Financial Inclusion.
Hypothesis H3: Quality significantly predicts the Digital Financial Inclusion.
In this study Analysis of Normality of the Data Checking Normality through Q-Q PLOT.
Figure 1.1: Q-Q Plot Diagram of Digital Banking & Digital Financial Services
Source: Survey
Q-Q Plot (Quantile-Quantile plot) helps in determining the normality of the data. In the Q- Q plot if the data
is normally distributed then the data point follows the diagonal straight line and if is not normally distributed
then data falls away from the diagonal straight line (Field, 2009). The normality of the data using the Q-Q
plot can be observed from figures 1.1
Sampling Technique and Sample Size: This study uses convenience sampling as a sampling technique. While
sample size required for the study was found to be 381, taking the confidence level at 95% with a 5% of
margin of error. Pre-testing of the Questionnaire, results show α > 0.70 in 11 constructs, while it was less than
0.70 in only one cases that is Usage DB. Though values of α > 0.60 or 0.70 are also acceptable (Griethuijsen,
et al., 2014).
IV. Discussion and Results
Multicollinearity Analysis- To check the multicollinearity between the latent constructs, two collinearity
diagnostics are used: tolerance and variance inflation factor (VIF). critical values for tolerance and VIF are
>0.10 and <10 respectively. It can be observed, that the value of tolerance for debit card varies between 0.494
to 0.788 and VIF varies between 1.268 to 2.023, thus multicollinearity is not a problem in the case of debit
card. Tolerance and VIF for credit card vary between 0.290 to 0.683 and 1.464 to 3.454. For digital banking
they vary between 0.340 to 0.756 and 1.323 to 2.944. For digital financial services they vary between 0.359
to 0.745 and 1.342 to 2.787 respectively. Thus, based on the values of tolerance and VIF, it can be said that
multicollinearity is not an issue for the debit card, credit card, digital banking, and digital financial services.
Confirmatory Factor Analysis (CFA)- In total there are 10 latent constructs, namely: Access DB, Usage DB,
Quality DB. Access, Usage, and Quality are used as an indicator of Digital Financial Inclusion (DFI).

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Table 1.1 Descriptives & SRW of the Items of Digital Banking
Variables
x

S. D
Sk.
Kur.
SRW
Access DB (ADB)
3.718
.7262
-.487
-.025
B1: Information provided by bank regarding
the digital banking is relevant for me.
3.86
.789
- 1.186
2.511
0.571
B2: Bank provides me timely information
regarding the digital banking.
3.79
.894
-.838
.409
0.777
B3: Bank provides me complete information
regarding the digital banking.
3.65
.955
-.725
.023
0.913
B4: Bank provided me the complete guide to
use digital banking.
3.58
.920
-.529
-.328
0.713
Usage DB (UDB)
4.211
.5301
-.532
.879
B5: I frequently use digital banking.
4.10
.743
-.860
1.116
0.548
B6: I find digital banking as a better option
against cash.
4.15
.767
- 1.019
1.695
0.758
B7: Digital banking provides me the
convenience to use it 24x7.
4.31
.663
-.925
1.675
0.721
B8: I think that I can enjoy the service of digital
banking 24 hours.
4.29
.649
-.670
.829
0.567
Quality DB (QDB)
3.861
.6900
-.688
1.384
B9: I believe that I am completely aware of
digital banking.
4.00
.790
- 1.103
2.347
0.620
B10: I am satisfied with the process of digital
banking.
3.98
.738
- 1.104
2.643
0.845
B11: I am fully satisfied with digital banking.
3.91
.799
-.720
.715
0.910
B12: I believe that digital banking will assure
me of an error free transaction.
3.55
.995
-.619
.146
0.707
Source: Survey. Note: x
: Mean; S.D: Standard Deviation; Sk.: Skewness; Kur.: Kurtosis; SRW: Standardized Regression
Weights
CFA model for Access Digital Banking
CFA (Figure 1.2) is conducted on the latent construct i.e., Access DB, which consisted of four items: B1:
Information provided by bank regarding the DB is relevant for me; B2: Bank provides me timely information
regarding the DB; B3: Bank provides me complete information regarding the DB; B4: Bank provided me the
complete guide to use DB.
Figure 1.2: CFA Model for Access-Digital Banking
Source: Survey. Note: DB: Digital Banking; B1: Information provided by bank regarding the DB is relevant
for me; B2: Bank provides me timely information regarding the DB; B3: Bank provides me complete
information regarding the DB; B4: Bank provided me the complete guide to use DB.
of these items were 0.571, 0.777, 0.913 & 0.713 respectively, which are greater than 0.50. This helps in
establishing the convergent validity of the latent construct. While Composite/Construct Reliability (CR)
(=0.837) & Cronbach’s alpha (=0.831) were above 0.70 (table 1.1), which helps in establishing the reliability
of the construct. Thus, this construct is both reliable and valid.

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CFA model for Usage DB
CFA (Figure 1.3) is conducted on the latent construct i.e., Usage DB, which consisted of four items: B5: I
frequently use DB; B6: I find DB as a better option against cash; B7: DB provides me the convenience to use
it 24x7; B8:
Figure 1.3: CFA Model for Usage-Digital Banking
Source: Survey. Note: DB: Digital Banking; B5: I frequently use DB; B6: I find DB as a better option against
cash; B7: DB provides me the convenience to use it 24x7; B8: I think that I can enjoy the service of DB 24
hours. It can enjoy the service of DB 24 hrs. items were 0.548, 0.758, 0.721 & 0.567 respectively, which are
greater than 0.50. This helps in establishing the convergent validity of the latent construct. While
Composite/Construct Reliability (CR) (=0.747) & Cronbach’s alpha (=0.740) were above 0.70 (table 1.1),
which helps in establishing the reliability of the construct. Thus, this construct is both reliable and valid.
CFA model for Quality DB
CFA (Figure 1.4) is conducted on the latent construct i.e., Quality DB, which consisted of four items: B9: I
believe that I am completely aware of DB; B10: I am satisfied with the process of DB; B11: I am fully satisfied
with DB; B12: I believe that DB will assure me of an error free transaction. items were 0.620, 0.845, 0.910 &
0.707 respectively, which are greater than 0.50. This helps in establishing the convergent validity of the latent
construct. While Composite/Construct Reliability (CR) (=0.858) & Cronbach’s alpha (=0.844) were above
0.70 (table 1.1), which helps in establishing the reliability of the construct. Thus, this construct is both reliable
and valid.
Figure 1.4: CFA Model for Quality-Digital Banking
Source: Survey. Note: DB: Digital Banking; B9: I believe that I am completely aware of DB; B10: I am satisfied with the process of DB; B11: I am
fully satisfied with DB; B12: I believe that DB will assure me of an error free transaction.
Table 1.2: Regression Model Summary
Independent
Variable
Dependent
Variable
R
R2
Adjusted
R2
Standard Error
of Estimate
ADB
ADFS
0.790
0.624
0.610
0.31794
URD
UDFS
0.583
0.340
0.321
0.41928
QDB
QDFS
0.593
0.351
0.336
0.35867
Source: Survey

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Table 1.3: ANOVA Results in Case of Regression
Independent Variable
Dependent Variable
F
Sig. Value
ADB
ADFS
42.605
0.000
UDB
UDFS
17.810
0.000
QDB
QDFS
62.486
0.000
Source: Survey.
Table 1.4: One-Way ANOVA Output of Age-Digital Banking
Variable
Nature
Sum of
Squares
df
Mean
Square
F
Sig.
Remarks
ADB
Between Group
3.705
2
1.853
3.598
.029
Significant
Within Group
108.644
211
.515
Total
112.350
213
UDB
Between Group
.403
2
.201
.714
.491
Insignificant
Within Group
59.467
211
.282
Total
59.869
213
QDB
Between Group
.198
2
.099
.207
.813
Insignificant
Within Group
101.228
211
.480
Total
101.427
213
Source: Survey. Note: Grouping Variable (Age): 18-25, 26-49 and 50 & above
Table 1.5: One-Way ANOVA Output of Religion-DB
Variable
Nature
Sum of
Squares
df
Mean
Square
F
Sig.
Remarks
ADB
Between Group
1.079
2
.540
1.023
.361
Insignificant
Within Group
111.270
211
.527
Total
112.350
213
UDB
Between Group
2.269
2
1.135
4.156
.017
Significant
Within Group
57.600
211
.273
Total
59.869
213
QDB
Between Group
1.924
2
.962
2.040
.133
Insignificant
Within Group
99.503
211
.472
Total
101.427
213
*Source: Survey. Note: Grouping Variable (Religion): Hinduism, Muslims & Other
Table 1.6: One-Way ANOVA Output of Education- DB
Variable
Nature
Sum of
Squares
df
Mean
Square
F
Sig.
Remarks
ADB
Between Group
3.440
4
.860
1.650
.163
Insignificant
Within Group
108.910
209
.521
Total
112.350
213
UDB
Between Group
1.798
4
.450
1.618
.171
Insignificant
Within Group
58.071
209
.278
Total
59.869
213
QDB
Between Group
.368
4
.092
.191
.943
Insignificant
Within Group
101.058
209
.484
Total
101.427
213
Source: Survey. Note: Grouping Variable (Education): Up to Higher Secondary (Class 12th), Graduate,
Post- Graduate, Diploma/Certificate/Professional Course and Higher Studies (M.Phil., Ph.D. etc.).
Table 1.7: One-Way ANOVA Output of Occupation- DB
Variable
Nature
Sum of
Squares
df
Mean
Square
F
Sig.
Remarks
ADB
Between Group
3.305
3
1.102
2.121
.099
Insignificant

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Within Group
109.045
210
.519
Total
112.350
213
UDB
Between Group
1.306
3
.435
1.561
.200
Insignificant
Within Group
58.563
210
.279
Total
59.869
213
QDB
Between Group
.839
3
.280
.584
.626
Insignificant
Within Group
100.588
210
.479
Total
101.427
213
Source: Survey. Note: Grouping Variable (Occupation): Employee, Self-Employed, Business and Not
Employed/Dependent/Student.
Table 1.8: One-Way ANOVA Output of Income- Digital Banking
Variable
Nature
Sum of
Squares
df
Mean
Square
F
Sig.
Remarks
ADB
Between Group
3.396
6
.566
1.075
.378
Insignificant
Within Group
108.953
207
.526
Total
112.350
213
UDB
Between Group
2.028
6
.338
1.209
.303
Insignificant
Within Group
57.842
207
.279
Total
59.869
213
QDB
Between Group
3.699
6
.616
1.306
.256
Insignificant
Within Group
97.728
207
.472
Total
101.427
213
Source: Survey. Note: Grouping Variable (Income INR): 0-16000, 16000-32000, 32000-48000,
48000-64000, 64000-80000, 80000-96000, 96000 & above.
Table 1.9: T-Test Output of Gender- Digital Banking
Variable
Grouping
Variable
Mean
Std.
Deviation
t
df
Sig.
Remarks
ADB
Male
3.7302
.76263
.281
212
.779
Insignificant
Female
3.7017
.67473
.288
200.447
.774
Insignificant
UDB
Male
4.2381
.55123
.879
212
.380
Insignificant
Female
4.1733
.49908
.895
198.244
.372
Insignificant
QDB
Male
3.8889
.75137
.707
212
.480
Insignificant
Female
3.8210
.59324
.737
208.764
.462
Insignificant
*Source: Survey.
Table 1.10: T-Test Output of Marital Status- Digital Banking
Variable
Grouping
Variable
Mean
Std.
Deviation
t
df
Sig.
Remarks
ADB
Married
3.7042
.79878
-.202
212
.840
Insignificant
Unmarried
3.7255
.69030
-.192
123.187
.848
Insignificant
UDB
Married
4.1620
.53134
-.962
212
.337
Insignificant
Unmarried
4.2360
.52973
-.961
139.402
.338
Insignificant
QDB
Married
3.8099
.56135
-.763
212
.446
Insignificant
Unmarried
3.8864
.74631
-.838
178.866
.403
Insignificant
Source: Survey.
V. Conclusion
This finding suggests that digital finance access, usage, and quality are major predictors of digital financial
inclusion. Thus, it can be claimed that digital financial inclusion is affected by three factors: 'Access', 'Usage'

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& 'Quality'. It should also be observed that, of the three factors, 'Quality' has the greatest influence on digital
financial inclusion, followed by 'Usage' and 'Access'. This suggests that if a bank provides high-quality
services to its consumers, individuals would use digital finance more and more. As a result, steps must be
done to increase the quality of digital financial services (DFS), as higher quality will improve the access and
application dimensions of digital financial inclusion. This will assist the economy in achieving greater digital
financial inclusion.
This study further concludes that the situation of digital financial inclusion in the regions of Nepal Madhesh
Province is quite satisfactory. Also, the inclusiveness in the regions of Nepal Madhesh Province is partially
significant. To further improve the status of digital financial inclusion, measures must be taken to change the
perceived image of DFS in the eyes of the public. This study suggests that the in Nepal Madhesh Province,
DFS are associated with higher risk because all the items of perceived risk, which implied that most of the
people weren’t able to make their stance clear in case of perceived risk. Such a dilemma in the mind of the
people can hamper the growth of the use of digital finance in Nepal Madhesh Province. Also, the respondent
of people towards DFS directly impacts digital financial inclusion, thus a negative and it will impact digital
financial inclusion negatively, while positive result will impact digital financial inclusion positively.
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