Achieving the Target of Complete Financial Inclusion in ... 215-236(1).pdf · Application of...

Prajnan, Vol. XLVIII, No. 3, 2019-20 © 2019-20, NIBM, Pune

Received: 20/06/2019

Accepted: 03/09/2019

Achieving the Target of CompleteFinancial Inclusion in India through

Financial Technologies

Arindam SarkarOnkar Shivraj Swami

Financial Inclusion (FI) is yet to be completely achieved in India assubstantial numbers of people are still out of formal financial system.Progress in Financial Technologies (FinTech) gives an opportunity tofinancially include these segments of Indian economy where traditionalmodels of banking and finance could not reach. The study aims toidentify these segments of financially excluded population ortransactions of Indian economy, which can be catered by formalfinancial institutions through the use of financial technologies. It appliesLogistic Regression (Logit) Model on micro-unit level Global Findex datacollected for India by World Bank, to measure the significance ofdifferent variables used in the study. It identifies different segments ofpopulation and transactions in India through various demographic,exclusion, cash and non-cash transaction variables, where accountscan be opened and used regularly with the help of financial technologiesto make the financial inclusion initiative, a sustainable one.

Keywords: Financial Inclusion, Financial Exclusion, FinTech, Banking, LogitModel

JEL Classification: G18, G21, G28, O38, R51

Section IIntroduction

Financial Inclusion (FI) being an important tool for development of economy,formally began in India with the nationalisation of banks in late 1960s. However,it progressed in a structured manner since 2005, when its significance was

Arindam Sarkar ([email protected]) Ph. D (Economics), Asst. General Manager, DirectivesDivision, Department of Banking Regulation, Reserve Bank of India, Mumbai.

Onkar Shivraj Swami ([email protected]), Assistant General Manager, Department of BankingRegulation, Reserve Bank of India, Mumbai.

Disclaimer: The views expressed in this paper are solely of the authors and does not representviews of the Reserve Bank of India.

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recognized in the Annual Policy Statement of Reserve Bank of India(RBI) andthe regulator advised every bank to include FI in their business objective. Sincethen, it was monitored by the regulator through implementation of individualbanks' Board approved Financial Inclusion Plan (FIP) in terms of predeterminedparameters. Over decades, Indian banking system has relied upon thetraditional method of brick-mortar branches and also alternative bankingoutlets at a later stage to expand the reach of formal financial services. However,all these efforts over many decades could not bring the entire population underthe coverage of formal financial institutions.

The complete FI is still a target to be achieved, as substantial number of peopleare still out of the purview of formal financial system, inspite of substantialnumber of people being financially included over the last few years. Accordingto World Bank's Global Findex Data for the year 2017, India is the secondlargest country in the world, after China, in terms of financial exclusion wherenearly 190 million people do not have a bank account. Amongst the peoplewho have bank accounts, half of them remain inoperative implying that thesepeople are still using mostly cash as a mode of their daily transactions. Thus,Financial Inclusion is still an unfinished task, which calls for exploringalternative measures to complete the target of financial inclusion, and FinancialTechnologies (FinTech) is one such alternative.

Bank for International Settlement (BIS) has defined FinTech as "technologicallyenabled financial innovation that could result in new business models,applications, processes, or products with an associated material effect onfinancial markets and institutions and the provision of financial services",BCBS (2018). In simple terms, FinTech uses different software, technologiesand digital platforms to deliver different financial services to the customers.Thus, FinTech while making few changes in the traditional business model ofbanks and financial institutions can provide financial products and servicesto the financially excluded population in a more accountable and efficientmanner within least possible time.

Application of technology in Indian banks, however, dates back to theintroduction of Magnetic Ink Character Recognition (MICR) technology in thebanks in 1984. Later, computerization in banks took place and othertechnologies like settlement of accounts, inter-connecting of branches,Electronic Fund Transfer (EFT) system, BANKNET communications, etc. werealso introduced in banks. Financial technology helped the cause of FI withwidespread installation and use of Automated Teller Machines (ATM) throughoutthe country. Over the last decade, FI got a new push with further utilisation ofinformation, technology and communication in banking in the form of mobilebanking, internet banking, biometric identification, Cheque Truncation System(CTS), Prime Minister Jan Dhan Yojana (PMJDY), Business Correspondencethrough the use of Information and Communication Technology(BC-ICT), Ru-Pay Card Payment Scheme, online KYC verification, etc.

Sarkar & Swami: Achieving the Target of Complete Financial Inclusion in India ... 217

Currently in India, financial technologies are being applied in some form orthe other for a substantial number of financial transactions of banks. However,application of technology in financial system has relatively served more to thatsegment of population who are already financially included, so much so thatsuch services offered by the formal financial institutions are now available tothem at their doorsteps or fingertips, which was earlier available at the branchesor banking outlets. It is believed that FinTech has the potential to providefinancial services to the excluded population due to its high convenience,effectiveness, speed, lower cost, penetration of internet and mobile phones,computer technology, etc. Moreover, use of big data analysis and machine-learning techniques can boost the expansion of financial services in thosesegments of economy where the financial services are yet to reach. If that is thecase, then why not harness the potential of FinTech firms in achieving thetarget of complete Financial Inclusion.

This paper contributes to the literature, through identifying potential segmentsof Indian population and various types of transaction in the economy, whereFinTech can be used to expand the reach of formal institutions of finance andthus achieve the target of complete Financial Inclusion. This paper providespolicymakers a set of such segments of Indian population and economy whereFinTech can be used to open accounts and also to ensure that such accountsare used for financial transactions. Post introduction, the paper has beenpresented in the following manner: Section II gives review of literature; SectionIII presents the methodology and data used for the study; Section IV presentsthe findings of the study in terms of application of FinTech to achieve thetarget of complete Financial Inclusion and policy recommendations, and lastlySection V concludes the paper.

Section IIReview of Literature

Reserve Bank of India (RBI) has defined Financial Inclusion as "the process ofensuring access to appropriate financial products and services needed by allsections of society in general and vulnerable groups such as weaker sectionsand low income groups in particular at an affordable cost in a fair andtransparent manner by mainstream institutional players", Chakrabarty (2013).An individual can be considered as included in economy's financial system ifhe/she has an account with one of the formal financial institutions and theaccount is being used by the individual for various financial transactions.

Hogarth & Donnell (2000) stated that the ownership of an account by anindividual is the most important factor in financial inclusion. It's the ownershipof account which determines the accessibility and usage of other financialservices by the individual. Having ensured the availability of the account, thenext important thing is to ensure that the person regularly use it. To ensure

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this, regular flow of funds in the account is also very important. This pointwas insisted by Caskey (1997) when he stated that availability of sufficientfunds in the account is very important for the individual to avail and use otherfinancial services available in the economy. Apart from funds, there are otherfactors which ensure that the person uses the account regularly to remainfinancially included.

Hogarth & Donnell (1997) found that for the US economy, factors likeemployment, income, education level, social class, race, religion, gender, age,family size etc. of an individual determines the types of financial services beingowned by the person. Hogarth & Donnell (2000) also shown that factors likeage, gender, net worth, social class, level of education, total number of membersin the household, etc. were important factors in determining the level of financialexclusion in the economy of UK. Jacobson (1995) shown the importance oflevel of income in determining the total number of transaction in bank accountsof economy of USA. He found a positive relationship between the level ofhousehold income and total number of transactions in bank accounts. Reportof the FSA (2000a) has shown the crucial role of socio-economic and cultural-demographic variables in determining the level of financial exclusion in theeconomy of UK. Segments of society that were mostly financially excluded werethe underclass, low-income groups, lone parents, older people, homeless, etc.

Lee (2002) proved for US economy that use of various financial services andownership of credit cards is very less amongst the lower-income segments ofthe society. It was also shown that even after introduction of various policyinitiatives there was no change in use of various financial services and ownershipof credit cards in these segments of society. Report of FSA (2003a) has shownthat employment level, income, ethnicity, region, race and marital status playsimportant role in determining whether a person has access to financial servicesor not. James (2005) has shown that in the economy of UK major factorswhich influenced the non-achievement of complete financial inclusion werestatus of employment, income of household, tenure of housing, marital status,education level and age. Apart from these, gender, ethnicity, regional features& social class also played important role.

Kempson & Whyley (1999) stated that people from the low-income groups justneed very basic products to get financially included and contribute to thefinancial sector over a longer period. However, such an argument may not beconsidered beneficial by the financial institutions in the long run, as the marginand profit generated from such products are very less. Hence, there will be atendency on part of the financial institutions to neglect those segment ofpopulation. This, in turn, will help the financial exclusion to continue in theeconomy. Thus, regulatory intervention becomes important at this stage interms of opening accounts for the financially excluded ones and ensuring regularuse of these accounts.


According to Da-Silva (2018), FinTech has huge potential for the EmergingMarket Economies (EMEs). In comparison to Advanced Economies (AEs), theEMEs have a relatively less developed financial system, low level of FI,impressive growth in mobile phone-based banking and payment services, wideexpansion of internet, etc. However, there is gradual adoption of variousFinancial Technologies (FinTech) in their banking and financial sector. Moreover,Distributed Ledger Technology (DLT), Machine Learning and Big Data analysisare also playing their roles in getting Financial Technologies adopted in thebanking and financial services. Guo, Kong et al, (2016) stated that, with moreuse of big data, information available with alternative sources and use of newfinancial technologies in banking, expansion of financial services to the excludedpopulation is possible. Carney (2016) stated that FinTech can promote growthin the finance sector through improvements in various core banking activitieslike retail banking in the hinterlands, payments and settlement, risk assessmentof borrowers, allocation of credit, monitoring of recovery etc. Buchak, Matvos,Piskorski and Seru (2017) stated that when FinTech is applied to bankingthey have the potential to financially include lower strata of society, minoritygroups, unemployed people, rural people, etc. Havrylchyk, Mariotto, Rahimand Verdier (2017) proved that FinTech has the potential to take bankingservices to those areas which has low density of branch network. Xun, Zongyueand Jiajia (2018) stated that in Chinese economy it was observed that use ofFinTech has reduced the barriers faced by households who were earlier excludedfrom various financial services and thus promoted their borrowing capacity,entrepreneurial activities, etc., which in turn decreased income inequality andfinancial exclusion.

Gangopadhyay (2009) stated that for India one of the main reason for slowprogress in Financial Inclusion is lesser or almost no operation in large shareof the newly opened accounts. Worldwide various models have been tried toincrease the transactions in these dormant accounts, however, they had mixedresults in terms of achieving this target. It is now being believed that promotingmobile banking has the maximum potential in terms of fulfilment of completeFinancial Inclusion as it has the advantage of last-mile connectivity.

RBI's Report of the Working Group on FinTech and Digital Banking (2017)stated that FinTech companies in India are making use of various initiativeslike PMJDY, Aadhaar, online KYC authentication, RuPay, etc. to further expandFinancial Inclusion in India. Its recommendation on introduction of a"Regulatory Sandbox" as an innovation hub to live test different products andservices in a simulated environment and providing regulatory guidance to thestakeholders are crucial to use FinTech in ensuring Financial Inclusion. RBI'sReport on Trend and Progress of Banking in India (2017) also mentioned thatFinTech can be applied in financial services like payments, clearing, settlement,deposit mobilization, lending, capital raising, insurance, investmentmanagement, market support, etc. It has acknowledged the role of initiativeslike PMJDY, Aadhaar-enabled e-KYC verification, linking of Aadhaar with bank

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accounts, UPI App, etc. in helping the integration of FinTech with the existingfinancial institutions to ensure last-mile connectivity and financially includethe excluded segment.

Financial Technologies (FinTech) and financial institutions are complementaryin nature. As financial institutions need FinTech to constantly implement andupdate the latest technology to smoothen and expand banking/ financial servicesto those segments of population where traditional banking could not reach,FinTech also needs financial institutions to leverage upon the already existinghuge customer base. FinTech can offer economical and customized productsor services to the financially excluded population. Thus, FinTech has thepotential to cater to needs of the financially excluded population and fill upthat gap which the traditional banking could not achieve over the years.

Section IIIData and Methodology

The review of literature shows that there could be many reasons for peopleremaining financially excluded. Various socio-economic factors are responsiblefor such exclusion in different countries. Prevalence of financial exclusion indifferent countries for many decades has forced these countries to opt foralternative measures to end financial exclusion in these countries. One of thevery popular alternative measures, which is being adopted by many countries,is use of financial technologies in the financial services.

The study aims to identify those segments of financially excluded populationor transactions of Indian economy, which can be catered by formal financialinstitutions through the use of financial technologies. To fulfil this objectivethe study has been formulated on the basis of principle thought mentioned inthe World Bank's Report of Global Findex Database-2017 on 'MeasuringFinancial Inclusion and the FinTech Revolution'. It states that, in order tofinancially include an individual, the first and foremost requirement is thatthe person should have an account, and thereafter, it needs to be ensured thatthis account is being regularly used by the individual for various financialtransactions. This study is also based upon this principle thought. The studyhas used micro unit-level data pertaining to India captured in The Global FindexDatabase prepared by World Bank for the year 2017.

The data used in this study has been collected by Gallup Inc., on behalf ofWorld Bank, which conducts annual surveys in more than 160 countries. Therespondents were randomly selected from the national representative samples.In the first stage, primary sampling units were identified. Later, these identifiedunits were stratified on the basis of size of population and geography. Clusteringwas also done in some stages of sampling. To select household in a locality, therandom route procedure was adopted. Thereafter, up to a maximum, three


attempts were made to zero in on a household. Within the household,respondent was randomly selected to conduct the interview. This data iscompiled at the national level from different countries by surveying more than1,50,000 people above the age of 15 years mostly on variables regarding theuse of financial and non-financial services. It has a wide range of data onavailability of accounts, transactions in the account, use of technologies, mobilephones, internet, cash and non-cash transactions in financial services.

Global Findex Data captures 101 data fields for an individual in India tomeasure his/her level of Financial Inclusion. Of these, data captured in 42fields for an individual were used in this study. Amongst these, five variablescapture the demographic features of an individual like gender, age, education,income and employment; nine variables capture reasons related to individualnot having an account; twenty seven variables map the level of operation in anaccount; and ownership of mobile is another variable which has been used inthe study.

Logistic Regression has been used in the study to model the dependent variable,which is binary in nature, with more than one independent variables, whichare also binary in nature. The study has referred the literature on LogisticRegression model used by Swami et al (2011). In comparison to linearregression, the logistic regression is believed to be superior. In linear regressionmodels, there is a linear relationship between dependent and independentvariables and the model is based on the assumption of normal distribution ofindependent variables. Whereas, the relationship between independent anddependent variables is log-linear in Logistic Regression model. This impliesthat for the independent variables the normal distribution assumption willnot have any restriction. This statistical technique is generally used to estimatethe likelihood of occurrence of some event (dependent variable) on the basis ofvarious prognostic factors (independent variables). In the paper, the logisticregression model will predict the conditional probability of non-opening ofbank accounts and non-operation of the bank accounts in India given the setof independent variables considered in the study.

Use of Logit model was considered to be appropriate for the analysis due tobinary nature of the sample and logistic regression helped in quantifying thestrength of relationship between various independent variables with thedependent variables to understand the reason for people not having an accountand not operating the accounts if they have one. Both the Univariate andMultivariate Logistic Regression (Logit) Models have been used in the study, asthe data captured in the Global Findex is binary(1 meaning 'Yes' and 2 meaning'No') in nature. Wald test was done in both Univariate and Multivariate LogisticRegression (Logit) Models to check the significance of the independent variablesin the model. Cross Tabulation was done at appropriate places when therewere more than two variables and it was felt necessary to club it within onecategory. This research paper used the above statistical technique to identify

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the financially excluded segments of Indian economy, which can be broughtunder the ambit of formal financial institutions through the help of appropriatefinancial technologies and make financial inclusion a sustainable one.

Indian banking system has relied upon the traditional method of brick-mortarbranches/alternative banking outlets to expand the reach of formal financialservices to achieve financial inclusion. However, there is substantial numberof people still out of the purview of formal financial institutions. So, there is aneed to identify those segments of financially excluded population ortransactions of Indian economy, which can be catered to by formal financialinstitutions through the use of financial technologies. To be financially included,an individual should first have an account, and thereafter he/she shouldregularly use this account for his/her various financial transactions, preferablynon-cash transactions. A huge segment of Indian population scattered all overthe country still do not have an account. There is another segment of populationthat have accounts but they do not regularly transact through their accounts.If at all they do such transactions, often they use cash deposit or withdrawalin/from these accounts. This unfinished task calls for exploring alternativemeasures to identify such segment of population and bring them under thepurview of formal financial institutions by opening accounts and/or to ensureregular non-cash transactions in them to complete the target of financialinclusion, and Financial Technologies (FinTech) is one such alternative. Hence,keeping in mind various demographic, exclusion, transactions variables andall other aspects, following two hypothesis were formulated:

Null Hypothesis I: Demographic and exclusion variables used in the Logitmodel can significantly explain the non-opening of accounts by segments ofIndian population or economy so that accounts can be opened for themthrough the application of FinTech to achieve the target of complete FI.

Null Hypothesis II: Demographic and transaction variables used in the Logitmodel can significantly explain the insufficient number of transactions inthe accounts so that non-cash transactions can be increased in these accountsthrough the application of FinTech to achieve the target of complete FI.

Section IVComplete Financial Inclusion through

Financial Technologies

India opted for the traditional brick and mortar branch expansion model fordecades to provide financial services to its financially excluded population.However, this traditional method of providing financial services, and at a laterstage through banking outlets, could not bring the entire population under thecoverage of formal financial institutions. With the development of variousfinancial technologies, there is an option to achieve this target. Initially,


appropriate FinTech can be utilized to open accounts for the excludedpopulation. Thereafter, some other FinTech can be used to ensure that there isregular transaction in all the accounts. Keeping the above argument in mind,the study has been carried out in following two segments:

A. Individuals with 'No Accounts'Global Findex Data-2017 of the World Bank provides micro unit-level data forIndia. For the 'No Accounts' segment of the study, 2852 micro unit-level datawere considered. These are nothing but binary responses of 2852 individualsagainst 15 identified variables. General characteristics of these selected sampleshows that out of 2852 individuals, nearly 82 per cent of the people had anaccount and remaining 18 per cent did not have an account. In total 49 percent of the sample size was male and remaining 51 per cent was female. Withinthe genders, 84 per cent male and 80 per cent female had an account. In termsof age, 11 per cent of the population comprised of individuals less than 20years, 30 per cent between 20-30 years, 24 per cent between 30-40 years, 16per cent between 40-50 years, 11 per cent between 50-60 years and 8 per centof more than 60 years. In terms of education, 65 per cent of the populationhad studied till primary or lower, 28 per cent had completed secondary leveland only 7 per cent had completed tertiary or more. In terms of income, 18per cent of the total population belonged to the poorest 20 per cent, whilesecond, middle and fourth level of income group had around 20 per cent ofthese individuals each. Remaining 22 per cent of the population belonged torichest 20 per cent group. Around 57 per cent of the people belonged toworkforce and the remaining 43 per cent were out of workforce. Mobileownership was considered to be an important aspect for financial inclusionand it was noted that around 70 per cent of the population owned a mobilephone. The General characteristics of selected sample for individuals havingno accounts are given in Table 1 mentioned below.

For the 'No Accounts' segment of the study, amongst various reasons cited bythe sample population for not having an account, reasons like living far awayfrom institution, cost of having an account, lack of trust, lack of money andother family member already having an account, were found to be the primeones. Three other responses related to individuals not having an account viz.lack of documents, religious factors and no need felt, were subsequently ignoredin the study for this analysis as the total number of such responses were veryless and were considered insignificant. There could be more than one reasonfor an individual not having an account. The 'Yes' or 'No' response of anindividual for a specific reason describes his/her views or response againstthat reason for not having an account.

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Table 1General characteristics of selected sample for individuals having 'No Accounts'

(Figures in bracket are in per cent)

Variable With Account No Account Total

Total Individual 2348 (82) 504 (18) 2852

Gender Male (49) 1182 (84) 218 (16) 1,400Female (51) 1166 (80) 286 (20) 1,452

Age Less than 20 years (11) 215 (69) 96 (31) 31120-30 years (30) 698 (81) 159 (19) 85730-40 years (24) 565 (83) 117 (17) 68240-50 years (16) 404 (88) 55 (12) 45950-60 years (11) 264 (87) 41 (13) 305

More than 60 years (8) 202 (85) 36 (15) 238

Education Primary or less (65) 1481 (79) 386 (21) 1,867Secondary (28) 691 (86) 111 (14) 802

Tertiary or more (7) 176 (96) 7 (4) 183

Income Poorest 20 (18) 408 (79) 111 (21) 519Second 20 (20) 458 (80) 111 (20) 569Middle 20 (20) 470 (83) 99 (17) 569Fourth 20 (20) 463 (82) 100 (18) 563

Richest 20 (22) 549 (87) 83 (13) 632

Employment In workforce (57) 1387 (85) 237 (15) 1,624Out of workforce (43) 961 (78) 267 (22) 1,228

Mobile owner Yes (70) 1709 (86) 275 (14) 1,984No (30) 639 (74) 229 (26) 868

Reasons for not having an account

Far away from institution Yes (4) 2(2) 116(98) 118No (14) 19(5) 386(95) 405

Too expensive to have an account Yes (5) 5(4) 136(96) 141No (15) 16(4) 368(96) 384

Lack of trust Yes (5) 4(3) 117(97) 121No (14) 17(4) 387(96) 404

Lack of money Yes (12) 8(3) 289(97) 297No (8) 13(6) 215(94) 228

Family already has an account Yes (9) 14(5) 243(95) 257No (10) 7(3) 261(97) 268

Univariate Logit Model (ULM) was formulated to identify independent variablesthat are significantly associated with the dependent variable i.e. people whodid not have an account. This identifies the significant reasons for people nothaving any account. Details of the ULM are given in Table 2. Wald test wasdone to check the significance of the independent variables in the model.


Table 2Univariate Logit Model for 'No Accounts'

Variable Coefficient Std. Wald SignificanceError Test (P Value)

Gender (Reference-Male)

Female 0.2851256 0.0989309 2.88 0.004*

Age (Reference-30-40 years)

Less than 20 years 0.7683620 0.1593259 4.82 0.000*20-30 years 0.0953369 0.1343089 0.71 0.47840-50 years -0.4194299 0.1759943 -2.38 0.017#50-60 years -0.2877252 0.1962015 -1.47 0.143

More than 60 years -0.1500970 0.2074734 -0.72 0.469

Education (Reference-Tertiary or more)Primary or less 1.879934 0.3896207 4.83 0.000*

Secondary 1.395960 0.3987413 3.5 0.000*

Income (Reference-Richest 20 per cent)Poorest 20 per cent 0.5875208 0.159153 3.69 0.000*Second 20 per cent 0.4719187 0.1583101 2.98 0.003*Middle 20 per cent 0.3316449 0.1615499 2.05 0.040#Fourth 20 per cent 0.3567009 0.1613366 2.21 0.027#

Out of workforce 0.4861125 0.098622 4.93 0.000*

Far away from institution 1.0490450 0.7508948 1.4 0.162

Too expensive to have an account 0.1677228 0.5220827 0.32 0.748

Lack of trust 0.2506682 0.5656452 0.44 0.658

Lack of money 0.7812965 0.4582952 1.7 0.088^

Family already has an account 0.7646066 0.4714153 1.62 0.105

Does not have a mobile 0.8007101 0.1007634 7.95 0.000*

Note: * Significant at 1 per cent; # Significant at 5 per cent; ^ Significant at 10 per cent

It was found that variables like being a female, belonging to the lower agegroup, having low level of education, belonging to the poorest segment of thesociety, being out of workforce, were highly significant in terms of impactingthe dependent variable. Whereas, not having sufficient money and not owninga mobile phone were moderately significant in impacting the dependent variable.This implies that above factors are the primary reasons for people not havingan account. These are those segments of population, which will not attractmany formal financial institutions on further expansion through traditionalmodel of brick-mortar branches and banking outlets. Hence, financial exclusionwas still found to exist in these segments. ULM, therefore, identified a set ofdemographic and exclusion variables which had significant influence over nonopening of accounts by the individuals.

Taking the above findings into consideration, significant variables identifiedin the ULM were further examined for enhanced significance. Two Multivariate

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Logit Models (MLMs) e.g. Model I-'MLM: Demographic' and Model II-'MLM:Demographic and Exclusion factors' were formulated to further examine thesignificance of the above independent variables in terms of their associationwith the dependent variable of people not having an account. Model I-'MLM:Demographic' as detailed in Table 3, comprises only of demographic variablesin isolation, in determining their significance in terms of people not having anaccount. Wald test was done to check the significance of the independentvariables in the model.

Table 3Model I-MLM: Demographic for 'No Accounts'

Variable Name Coefficient Std. Wald p-Value P>zError. Test z

Gender (Reference-Male)Female 0.0381882 0.1111622 0.34 0.731

Age (Reference-30-40 years)Less than 20 years 0.9184271 0.1716082 5.35 0.000*

20-30 years 0.2411164 0.1386394 1.74 0.082^40-50 years -0.4832687 0.1784163 -2.71 0.007*50-60 years -0.4599956 0.2001578 -2.3 0.022#

More than 60 years -0.4243801 0.2148399 -1.98 0.048#

Education (Reference-Tertiary or more)Primary or less 2.058389 0.3967232 5.19 0.000*

Secondary 1.26881 0.4020868 3.16 0.002*

Income (Reference-Richest 20 per cent)Poorest 20 per cent 0.1951589 0.1677263 1.16 0.245Second 20 per cent 0.057295 0.167104 0.34 0.732Middle 20 per cent 0.0110561 0.1683279 0.07 0.948Fourth 20 per cent 0.2201855 0.1658891 1.33 0.184

Employment(Reference-In workforce)

Out of workforce 0.4191342 0.1118489 3.75 0.000*

Constant -3.684662 0.4129112 -8.92 0.000

Log likelihood -1254.2012

LR chi2(16) 151.83

Prob > chi2 0.00


From 'MLM: Demographic' it was found that independent variables likebelonging to the lower age group, having less education and being out ofworkforce, were highly significant in terms of impacting the dependent variable.This implies that above demographic variables are primary reasons behindpeople not having an account. These are such variables and segment ofpopulation, which needs focused attention of the formal financial institutions.Appropriate financial technologies can be utilized in a manner such that


accounts get opened in the above-mentioned segments of population and usedregularly for transaction.

Taking above findings into consideration, another Multivariate Logit Model(MLM) i.e. 'MLM: Demographic and Exclusion' as detailed in Table 4, wasformulated by incorporating both demographic variables and other exclusionvariables to find their significance in terms of association with the dependentvariable of people not having an account. Wald test was done to check thesignificance of the independent variables in the model.

Table 4Model II-MLM: Demographic & Exclusion for 'No Accounts'

Variable Name Coefficient Std. Wald P-ValueError. Test z

Gender (Reference-Male)Female -0.3008779 0.5101194 -0.59 0.555

Age (Reference-30-40 years)Less than 20 years -0.891386 0.7107635 -1.25 0.210

20-30 years 1.148313 0.8991518 1.28 0.20240-50 years -1.3205 0.7550509 -1.75 0.080^50-60 years -0.9419005 0.9604324 -0.98 0.327

More than 60 years -1.616038 1.000312 -1.62 0.106

Education (Reference-Tertiary or more)Primary or less 1.332019 1.201114 1.11 0.267

Secondary 0.8324027 1.279759 0.65 0.515

Income (Reference-Richest 20 per cent)Poorest 20 per cent 0.3290864 0.7395099 0.45 0.656Second 20 per cent 0.054731 0.7371217 0.07 0.941Middle 20 per cent -0.2848276 0.666817 -0.43 0.669Fourth 20 per cent 1.002106 0.8803418 1.14 0.255

Employment (Reference-in workforce)Out of workforce 1.561874 0.5806698 2.69 0.007*

Lack of money 0.9221274 0.4929206 1.87 0.061^Does not own a Mobile 0.9606342 0.5251677 1.83 0.067^

Constant 1.034213 1.359643 0.76 0.447

Log likelihood -74.720613

LR chi2(5) 26.9

Prob > chi2 0.0296


From 'MLM: Demographic and Exclusion' it was found that most of thedemographic variables are not closely and significantly associated with financialexclusion. Which means they are not the primary reason behind people nothaving an account. Instead, being out of workforce is the most dominant variablebehind people not having an account. Amongst other variables, people not

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having sufficient money and not owning a mobile phone are other reasons ofpeople not having an account. Thus, apart from opening of accounts it shouldbe ensured that people get included in the workforce and there is sufficientflow of funds with them. Even though incorporating an individual in theworkforce is not a direct mandate of the banking and financial sector, but flowof funds through the banking channel can be ensured by the banks. Similarly,mobile phones can be considered as alternative mode for opening an account.Thus, use of other financial technologies to open accounts in these financiallyexcluded segments can be explored. Innovative financial technologies in theform of data analytics, artificial intelligence, blockchain technologies, datamining, algorithms, machine learning, etc., can be used to identify more suchexcluded segment of population or economy where accounts are yet to beopened. Hence, appropriate financial technologies can be encouraged andadopted in such a manner that more accounts get opened in the excludedsegments of the population. Hence, the Null Hypothesis-I formulated in thestudy stands proven and it can be stated that demographic and exclusionvariables used in the Logit model can significantly explain the non-opening ofaccounts by segments of Indian population or economy, so that accounts canbe opened for them through the application of FinTech to achieve the target ofcomplete FI.

B. Accounts with 'No Transaction'Global Findex Data-2017 of the World Bank provides micro unit level data forIndia. In total 2183 micro unit level data were considered for the 'NoTransaction' segment of the study. By 'No transaction' in the account it meansthat there is no inflow or outflow of funds in/from the account in last one year.This comprises of binary responses of 2183 people, who already have accounts,against 32 identified variables. General characteristics of selected sample forindividuals who do not transact in their accounts are given in Table 5. Datashows that of this selected sample around 49 per cent of both males and femalesdid not transact regularly in their accounts. Amongst people of various agegroups, 51 per cent of the people between 30-49 years did not transact in theiraccounts. In terms of education, 65 per cent of the people who have completedtertiary or more did not transact in their accounts. In terms of income, 51 percent of the people belonging to both second 20 per cent and middle 20 percent of income did not transact in their accounts. Around 49 per cent of thepeople belonging from both in and out of the workforce did not transact intheir accounts. Apart from these, there were different types of transaction inthe economy where substantial number of people did transactions in theiraccounts, but that was mostly in cash. These areas were domestic remittancessent or received in cash or through an MTO, payment of utility bills in cashand receipt of wages/ government transfers/ agricultural payments/ self-employment payments in cash. Moreover, there were accounts where non-cashtransactions were done, but within these accounts, there were many accountsin which number of such non-cash transactions were insufficient.


Table 5Summary of Statistics for 'No Transaction'

(Figures in Bracket are in per cent)

Variable Transaction No Transaction Total

Total Accounts 1119 (51) 1064 (49) 2183

Demographic Variables

Gender Male (50) 564 (51) 532 (49) 1,096Female (50) 555 (51) 532 (49) 1,087

Age Less than 20 years (13) 140 (51) 134 (49) 27420-30 years (26) 287 (51) 281 (49) 56830-40 years (24) 257 (49) 269 (51) 52640-50 years (17) 204 (54) 172 (46) 37650-60 years (11) 135 (54) 114 (46) 249

More than 60 years (9) 96 (51) 94 (49) 190

Education Primary or less (62) 707 (52) 654 (48) 1,361Secondary (30) 329 (50) 325 (50) 654

Tertiary or more (8) 83 (49) 85 (51) 168

Income Poorest 20 (17) 201 (53) 178 (47) 379Second 20 (19) 208 (49) 213 (51) 421Middle 20 (20) 213 (49) 221 (51) 434Fourth 20 (20) 226 (52) 208 (48) 434

Richest 20 (24) 271 (53) 244 (47) 515

Employment In workforce (58) 651 (51) 620 (49) 1,271Out of workforce (42) 468 (51) 444 (49) 912

Cash Transactions

If sent domestic remittances:in cash/through an MTO 63 (58) 45 (42) 108

If received domestic remittances:in cash/through an MTO 119 (54) 102 (46) 221

If paid utility bills: in cash 446 (54) 384 (46) 830

If received wage payments: in cash 180(50) 183 (50) 363

If received government transfers: in cash 67 (52) 63 (48) 130

If received agricultural payments: in cash 131 (52) 121 (48) 252

If received self-employment payments: in cash 30 (43) 39 (57) 69

Non-Cash Transactions

If sent domestic remittances: through a FI/a mobile phone 145 (55) 118 (45) 263

If received domestic remittances: through a FI/ 188 (53) 168 (47) 356a mobile phone

If paid utility bills: using an account/a mobile phone 528 (53) 474 (47) 1,002

If received wage payments: into an account/a mobile phone 267 (52) 246 (48) 513

If received wage payments: to a card 180 (50) 183 (50) 363

If received government transfers: into an 184 (56) 146 (44) 330account/a mobile phone

(Contd.)

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Table 5 (Contd.)Summary of Statistics for 'No Transaction'

(Figures in Bracket are in per cent)

Variable Transaction No Transaction Total

If received government transfers: to a card 67 (52) 6348 () 130

If received agricultural payments: into an 167 (52) 154 (48) 321account/a mobile phone

If received agricultural payments: to a card 131 (52) 121 (48) 252

If received self-employment payments: into an 36 (42) 50 (58) 86account/ through a mobile phone

If received self-employment payments: to a card 30 (43) 39 (57) 69

Univariate Logit Model (ULM) was formulated to identify independent variablesthat are significantly associated with the dependent variable i.e. people whodo not transact in their accounts. This identifies the primary reasons behindpeople not transacting in their accounts. Details of the ULM are given in Table6. Wald test was done to check the significance of the independent variables inthe model.

Table 6Univariate Logit Model for 'No Transactions'

Variable Name Coefficient Std. Wald SignificanceError. Test (P Value)

Demographic Variables

Gender (Reference-Male)Female 0.0160861 0.08564 0.19 0.851

Age (Reference-30-40 years)Less than 20 years -0.0894379 0.14904 -0.6 0.548

20-30 years -0.0667628 0.12104 -0.55 0.58140-50 years -0.2162608 0.13537 -1.6 0.110#50-60 years -0.2147116 0.15423 -1.39 0.164#

More than 60 years -0.0666887 0.1693 -0.39 0.694

Education(Reference-Tertiary or more)Primary or less -0.101734 0.16357 -0.62 0.534

Secondary -0.0360432 0.173 -0.21 0.835

Income(Reference-Richest 20 per cent)Poorest 20 per cent -0.0165708 0.13558 -0.12 0.903Second 20 per cent 0.1287047 0.1315 0.98 0.328Middle 20 per cent 0.1418211 0.13042 1.09 0.277Fourth 20 per cent 0.0219537 0.13046 0.17 0.866

Employment (Reference-In workforce)Out of workforce -0.0038536 0.08682 -0.04 0.965

(Contd.)


Table 6 (Contd.)Univariate Logit Model for 'No Transactions'

Variable Name Coefficient Std. Wald SignificanceError. Test (P Value)

Cash Transactions((Reference-No)

If sent domestic remittances: in cash -0.0391638 0.3957 -0.1 0.921

If sent domestic remittances: through an MTO -0.366463 1.24061 -0.3 0.768

If received domestic remittances: in cash 0.086568 0.33591 0.26 0.797

If received domestic remittances: through an MTO -0.8664189 0.69097 -1.25 0.210

If paid utility bills: in cash -0.1654953 0.18198 -0.91 0.363

If received wage payments: in cash 0.1608235 0.30771 0.52 0.601

If received government transfers: in cash 0.4328641 0.35305 1.23 0.220

If received agricultural payments: in cash 0.5717005 0.30532 1.87 0.061#

If received self-employment payments: in cash -0.5753641 0.57333 -1 0.316

Non-Cash Transactions(Reference-Yes)

If sent domestic remittances: through a -0.2124586 0.25219 -0.84 0.400financial institution

If sent domestic remittances: through -0.7515284 0.4677 -1.61 0.108#a mobile phone

If received domestic remittances: through a -0.1076423 0.21939 -0.49 0.624financial institution

If received domestic remittances: through a -0.6931472 0.63601 -1.09 0.276mobile phone

If paid utility bills: using an account -0.1434061 0.17211 -0.83 0.405

If paid utility bills: through a mobile phone -0.7632093 0.41719 -1.83 0.067#

If received wage payments: into an account 0.3393027 0.19593 1.73 0.083#

If received wage payments: through a mobile phone 0.8561523 0.59835 1.43 0.152#

If received wage payments: to a card -0.142722 0.56653 -0.25 0.801

If received government transfers: into an account 0.2629894 0.22585 1.16 0.244

If received government transfers: through a 0.886642 0.82415 1.08 0.282mobile phone

If received government transfers: to a card 0.6458943 1.23754 0.52 0.602

If received agricultural payments: into an account 0.0076046 0.27199 0.03 0.978

If received agricultural payments: through a -0.082065 1.0063 -0.08 0.935mobile phone

If received agricultural payments: to a card 1.560642 1.10284 1.42 0.157#

If received self-employment payments: into an -0.3437715 0.56263 -0.61 0.541account

If received self-employment payments: to a card 0.2702903 1.43555 0.19 0.851


232 Prajnan

It was found that variables like people with age group 40-50 years and 50-60years; people who received agricultural payments in cash; people who sentdomestic remittances through a mobile phone; people who paid utility billsthrough a mobile phone; who received wage payments into an account orthrough a mobile phone; and those who received agricultural payments in acard, were the ones who were not using their accounts regularly for transactions.If at all they operated their accounts, they did it mostly through cash. Hence,they were highly significant in terms of impacting the dependent variable. Thisimplies these segments of population were not doing sufficient number of non-cash transactions in their accounts, hence, appropriate financial technologieshave potential for further expansion in the above-mentioned segments ofeconomy. In this case also innovative financial technologies in the form of dataanalytics, artificial intelligence, blockchain technologies, data mining,algorithms, machine learning etc. can be used to identify more such accountswhere there is potential to increase total number of non-cash transactions.Hence, the Null Hypothesis-II formulated in the study stands proven and it canbe stated that demographic and transaction variables used in the Logit modelcan significantly explain the insufficient number of transactions in the accounts,so that non-cash transactions can be increased in these accounts through theapplication of FinTech to achieve the target of complete FI.

C. Policy RecommendationsFrom the study it was found that major reasons for people not having accountsor not opening such accounts were that they were living far away from formalfinancial institutions, high cost of having an account, lack of trust oninstitutional sources of finance, people having lack of money and other familymembers already having an account. Amongst demographic factors, the majorreasons for people not having accounts or not opening such accounts werethey being female, belonging to the lower age group of the population, havinglow level of education, belonging to poorest segment of the society and beingout of workforce. Amongst other important variables, the major reasons forpeople not having accounts or not opening such accounts were people nothaving sufficient money, being out of workforce and not owning a mobile phone.

Moreover, it was also found that people who were not using their accountsregularly for transactions are people with age group 40-50 years and 50-60years, who received agricultural payments in cash, who sent domesticremittances through a mobile phone, who paid utility bills through a mobilephone, received wage payments into an account or through a mobile phoneand those who received agricultural payments in a card. If at all these peopleoperated their accounts, they did it mostly through cash. Thus, abovementioned segments of Indian population and economy are the ones whichhave maximum potential in term of account opening and/or in whichtransactions can take place through appropriate use of financial technologies.These accounts will also have sufficient scope for increasing total number of


non-cash transactions. Innovative financial technologies in the form of dataanalytics, artificial intelligence, blockchain technologies, data mining,algorithms, machine learning, etc., can be used to identify more such excludedsegment of Indian population or economy where accounts are yet to be openedand/or to identify more such accounts where there is potential to increasetotal number of non-cash transactions. However, with the application ofFinTech, business model of existing financial institutions will get alteredproportionately, depending upon the level of adoption of new technologies.Smooth operation and continuous availability of these vital FinTech serviceswill become crucial in terms of delivery of financial services and benefit ofconsumers. Moreover, economies of scale of these firms may create a situationwhere they may give competition to the existing financial institutions. Systemicrisk thus generated from these entities needs to be addressed by the regulator.As FinTech firms are intermediaries between the financial institutions andconsumers, this will expose the banking system to various risks. Therefore,the level of risk which these firms and the financial institutions take in financialtransactions has to be taken into consideration by all the stakeholders.Accordingly, the risk management practices in these firms and financialinstitutions needs to be ensured. Moreover, a major portion of consumer andother sensitive data will be accessible to the FinTech firms, the issue of cybersecurity, data protection and general system control should be given importancein policy formulation and actual operation. Banking regulator, therefore, needsto make suitable modification in its rules and regulations to suit marketrequirements in a best possible sustainable manner.

As there will be changes in the existing business model of financial institutionswith the introduction of FinTech, smooth operation of various products andservices needs to be ensured. This can be done by testing these products andservices in a flexible yet controlled set up. Regulatory Sandbox (RS) is onesuch option, which has been adopted by majority of the nations which hasmoved ahead in application of FinTech in their financial sector. Need for sucha regulatory milestone was long overdue in India to examine the applicabilityof FinTech in different activities of the financial sector and especially to achievethe target of complete Financial Inclusion. With the finalization of 'EnablingFramework for Regulatory Sandbox' of RBI on August 13, 2019, the bankingregulator has given the necessary regulatory support to the market. It has alsoacknowledged that FinTech can provide significant solution to further FinancialInclusion.

Areas highlighted in the study in terms of maximum potential of opening newaccounts and/or accounts in which total number of non-cash transactions canbe increased, also gets easily fit under the new regulatory environment ofRegulatory Sandbox (RS) with the following indicative list of innovative productsand services which has been permitted to be included under RS i.e. FinancialInclusion products, Retail payments, Money transfer services, Financialadvisory services, Digital KYC, Digital identification services, Marketplace

234 Prajnan

lending, etc. As mentioned in the study, there is a need to identify more suchexcluded segments of Indian population or economy. The new regulatoryenvironment under RS allows an indicative list of innovative technologies, whichcan be utilized to identify such segments of Indian population or economywhere accounts can be opened and/or accounts in which total number of non-cash transactions can be increased viz. mobile technology applications,application program interface services, data analytics, artificial intelligence,applications under blockchain technologies, machine learning applications,etc.

Existing banks and financial institutions may be capable to adopt newtechnologies into their financial services, but FinTech companies includingstartups and other companies may need some support from the governmentto establish and operationalize themselves. This will require some hand-holdingat the initial stage. Necessary support in this regard needs to be provided bythe government which include capital infusion in the lending institutions ownedby the government for onward lending to FinTech companies, interestsubvention schemes to formal financial institutions for lending to Fintechstartups, subsidy for establishing FinTech companies, tax exemptions for initialyears to these companies, permission for foreign investments in the FinTechsector through FII or FDI, etc.

Awareness programmes and Financial Literacy campaigns for the general publicand concerned stakeholders needs to be conducted on a regular basis to givenecessary push for implementation and adoption of financial technologies inthe financial services. Moreover, various incentives in form of cashback,discounts, subsidies, etc., can be considered to be given if an individual movesfrom cash to non-cash transaction. Role of both government and regulator willbe very crucial in this regard, as it will help to reinforce the market forcesfrom both demand and supply side of the economy to achieve the target ofcomplete financial inclusion.

Segments of Indian economy identified in the paper gives an indicative list ofpotential areas for application of appropriate financial technologies. Innovativefinancial technologies can help in identifying more such excluded segment ofpopulation or economy where accounts are yet to be opened and more suchaccounts where there is potential to increase total number of non-cashtransactions. Resources available with different FinTech firms also needs tobe focused in such a manner so that it ensures that not only accounts getopened in the financially excluded segments of economy but also non-cashtransaction takes place in them. Close coordination amongst stakeholders viz.banks, financial institutions, FinTech firms, telecom operators, internet serviceproviders, regulators, government agencies, etc., will be crucial in this regardand necessary policy environment may be provided to the economy by allconcerned, which in the long run will help to achieve the target of completeFinancial Inclusion.


Section VConclusion

Inspite of continuous efforts by the stakeholders over decades to providefinancial services to the entire population through traditional model of brick-mortar branches and banking outlets, there still exists a substantial numberof people who are out of the formal financial system. Availability of differentfinancial technologies provides an option to the policymakers to expand thereach of financial services to these people through opening of bank accountsand ensuring non-cash transactions through them. Use of Logistic Regression(Logit) Model at the micro unit-level data thus helped to identify those segmentsof population which do not have an account and those accounts where sufficientnumber of non-cash transactions are not taking place. These are the segmentsin the economy where appropriate FinTech can be applied to achieve the targetof complete financial inclusion. Channelizing the resources in these identifiedsegments of Indian economy and bringing these under the ambit of formalfinancial institutions through the help of appropriate financial technologiescan make the initiative of financial inclusion, a sustainable one.

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