Predicting Financial Inclusion and Access to Credit in Kenya  

 
 

 

 

 

 

 

By  

Chebet Tanui 

051687 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Masters of Science in Data Science and Analytics 

2024 



ii 
 

 

Predicting Financial Inclusion and Access to Credit in Kenya  

 

 

 

 

By  

Chebet Tanui 

051687 

 

 

 

 

 

Submitted in Partial fulfilment of the Requirements for the Degree of 

Master of Science in Data Science and Analytics at Strathmore University 

 

  

 

 

 

 

 

Institute of Mathematical Sciences 

Strathmore University 

Nairobi, Kenya 

 

 

 

JUNE 2024 
 

This thesis is available for Library use on the understanding that it is copyright 

material and that no quotation from the thesis may be published without proper 

acknowledgement.  



iii 
 

Declaration and Approval 
Declaration  

I declare that this work has not been previously submitted and approved for the award of a 

degree by this or any other University. To the best of my knowledge and belief, the thesis 

contains no material previously published or written by another person except where due 

reference is made in the thesis itself. 

 

© No part of this thesis may be reproduced without the permission of the author and 

Strathmore University   

 

Student’s Name: Chebet Tanui 

 

Sign: .          .                      Date:        25/03/2024                .      

 

 

Approval 

The thesis of Chebet Tanui was reviewed and approved for examination by the following: 

 

             Date:      25/03/2024                   . 

Dr. John Olukuru  

Institute of Mathematical Sciences, 

Strathmore University 

 

  



iv 
 

Dedication 
This dissertation is dedicated to my beloved parents, Daniel Tanui and Stella Tanui, whose 

unwavering support has been invaluable throughout this journey. To my siblings, Chirry, 

Chemu, and Junior, and to my niece, Nataana Tai, your presence and encouragement have 

lightened the burden and enriched every step of the way. To my dad, Daniel Tanui, your 

guidance and wisdom have been a source of strength and inspiration. Thank you, from the 

depths of my heart, for believing in me.  



v 
 

Acknowledgements 
My sincere gratitude goes to everyone that has supported me during the dissertation and the 

entire Masters Programme. I thank God for His mercies and grace which have guided me 

throughout. 

My Friends, Classmates and Colleagues who motivated and supported me throughout the 

process. My Supervisor Dr John Olukuru, the faculty at Institute of Mathematical Sciences 

and Strathmore University, whose invaluable guidance steered me to completion. 

 

 

  



vi 
 

Abstract 
Financial inclusion, particularly access to credit, is a crucial aspect of economic development 

in Kenya. This study aims to investigate the determinants of financial inclusion and access to 

credit in Kenya, employing logistic regression modeling to predict financial inclusion 

patterns; and construct a forecast model that can support policymakers and financial 

organizations in boosting financial inclusion. The study analyzed several factors including 

demographics, technology adoption, financial services usage and barriers to assess their 

impact on financial inclusion and access to credit. 

The results revealed that the use of mobile phones and the internet as technological indicators 

of financial inclusion were the most effective predictors. Contrary to previous studies, gender 

was not found to significantly affect financial inclusion in this context. The development of a 

machine-learning model achieved an overall prediction accuracy of 90.9%. An interactive 

user dashboard was also developed using flexdashboard in R and hosted in the web, with 

visualizations and regression models to provide insights into the key factors driving financial 

access in Kenya 

The results showed that demographics, technology adoption, financial services usage and 

barriers to financial inclusion were the most significant factors that impacted financial 

inclusion; however, there were no significant correlations between these factors and financial 

inclusion as a whole. 

This research study will offer insights into the causes of financial exclusion in the country 

and how to overcome them. 

 

 

 

 

 

 

 

  



vii 
 

Table of Contents 

Declaration and Approval ................................................................................................................... iii 

Dedication ............................................................................................................................................. iv 

Acknowledgements ............................................................................................................................... v 

Abstract ................................................................................................................................................. vi 

List of Figures ........................................................................................................................................ x 

List of Tables ........................................................................................................................................ xi 

List of Abbreviations .......................................................................................................................... xii 

CHAPTER ONE: INTRODUCTION ................................................................................................. 1 

1.1 Study Background ...................................................................................................................... 1 

1.2 Problem Statement ...................................................................................................................... 3 

1.3 Research Aim .............................................................................................................................. 3 

1.4 Research Objective ..................................................................................................................... 4 

1.5 Research Questions ..................................................................................................................... 4 

1.6 Significance of Study ................................................................................................................... 4 

1.7 Scope of Study ............................................................................................................................. 4 

CHAPTER TWO: LITERATURE REVIEW .................................................................................... 6 

2.1 Overview ...................................................................................................................................... 6 

2.2 Theoretical Review ...................................................................................................................... 6 

2.2.1 Capability Approach Theory ............................................................................. 6 

2.2.2 Technology Acceptance Model (TAM) ............................................................. 7 
2.3 Empirical Review ........................................................................................................................ 7 

2.3.1 Factors Influencing Financial Inclusion ........................................................... 8 

2.3.2 The Influence of Technology Adoption and Financial Inclusion ................... 9 

2.3.3 The Influence of Informal Financial Services in Promoting Financial 

Inclusion ...................................................................................................................... 11 
2.3.4 Potential Barriers to Achieving Financial Inclusion...................................... 12 
2.3.5 Access to Credit ................................................................................................. 13 

2.4 Research Gaps ........................................................................................................................... 14 

2.5 Conceptual Framework ............................................................................................................ 15 

CHAPTER THREE: METHODOLOGY ......................................................................................... 17 

3.1 Research Design ........................................................................................................................ 17 

3.2 Data Understanding .................................................................................................................. 17 

3.3 Data Preparation ....................................................................................................................... 17 

3.4 Modelling ................................................................................................................................... 18 

3.5 Evaluation/Performance Metrics............................................................................................. 19 

3.6 Dashboard Development Using R ............................................................................................ 19 

3.6.1 Tool Selection .................................................................................................... 19 



viii 
 

3.6.2 Dashboard Design ............................................................................................. 20 

3.6.3 Integration of Logistic Regression Results ..................................................... 20 
3.7. Deployment and Accessibility ................................................................................................. 20 

3.7.1 Deployment Tools.............................................................................................. 20 
3.7.2 Deployment Platform........................................................................................ 20 

CHAPTER FOUR: RESULTS .......................................................................................................... 21 

4.1 Demographic characteristics of respondents .......................................................................... 21 

4.2 Factors influencing financial inclusion and access to credit ................................................. 23 

4.3 Association between the factors and financial inclusion and credit access .......................... 24 

4.4 Influence of technology adoption on financial inclusion ....................................................... 26 

4.5 Association between technology adoption and financial inclusion ....................................... 27 

4.6 Informal financial services contribution to advancing financial inclusion and access to 

credit ................................................................................................................................................ 28 

4.7 Association between informal financial services and advancement of financial inclusion 

and access to credit ......................................................................................................................... 30 

4.8 Potential barriers to financial inclusion and access to credit ................................................ 31 

4.9   Data Product - Dashboard...................................................................................................... 32 

4.9.1 The Overview ................................................................................................ 32 
4.9.2 Demographic Statistics Page ....................................................................... 33 
4.9.3 Analytics Page ............................................................................................... 33 

4.9.4 Logistics Regression Page ............................................................................ 34 
4.9.5 User-Centric Design: .................................................................................... 34 

CHAPTER FIVE: DISCUSSION ...................................................................................................... 36 

5.1 Factors influencing financial inclusion and access to credit ................................................. 36 

5.2 Influence of technology adoption on financial inclusion ....................................................... 36 

5.3 Informal financial services contribution to advancing financial inclusion and access to 

credit ................................................................................................................................................ 37 

5.4 Potential barriers to financial inclusion and access to credit ................................................ 37 

5.5 Machine Learning Model ......................................................................................................... 38 

CHAPTER SIX: CONCLUSION AND RECOMMENDATION ............................................... 39 

6.1 Conclusion ................................................................................................................................. 39 

6.2 Recommendation ....................................................................................................................... 41 

6.3 Future Work .............................................................................................................................. 42 

References ............................................................................................................................................ 44 

Appendix .............................................................................................................................................. 50 

Appendix A: Dataset used .............................................................................................................. 50 

Appendix B: Dashboard Screenshots ............................................................................................ 51 

Demographics ............................................................................................................. 51 
Analytics...................................................................................................................... 53 
Factors influencing financial inclusion access to credit.......................................... 54 



ix 
 

Appendix C: R Dashboard Codes.................................................................................................. 56 

Appendix D: Python Code for Modelling ..................................................................................... 65 

Appendix E: Ethical Clearance Exemption .................................................................................. 76 

Appendix F: Turnitin Report ........................................................................................................ 77 

 

 

 

 

 

  



x 
 

List of Figures 

Figure 2-1: Conceptual framework ....................................................................................................... 16 

Figure 4-1: Pie chart of gender distribution .......................................................................................... 22 

Figure 4-2: Pie chart of age group distribution ..................................................................................... 23 

Figure 4-3: Bar graph of financial inclusion against access to mobile money services ........................ 24 

Figure 4-4: Bar graph of financial inclusion against use of electronic payment ................................... 27 

Figure 4-5: Bar graph of financial inclusion against mobile phone access ........................................... 27 

Figure 4-6: Line graph of financial inclusion against shylock moneylenders ...................................... 29 

Figure 4-7: Bar graph of financial inclusion against use of informal credit ......................................... 29 

Figure 4-8: Line graph of financial inclusion against informal savings account .................................. 30 

Figure 4-9: Bar graph of financial Inclusion against financial illiteracy .............................................. 32 

Figure 4-10: Bar graph of financial Inclusion against Limited Access to Credit ................................. 32 

Figure 4-11: Demographic Statistics Page ............................................................................................ 33 

Figure 4-12: Analytics Page .................................................................................................................. 33 

Figure 4-13: Logistics Regression Page ................................................................................................ 34 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



xi 
 

List of Tables 

Table 1: Demographic characteristics of respondents ......................................................................... 21 

Table 2: Factors influencing financial inclusion and access to credit ................................................... 23 

Table 3: Association between the factors, financial inclusion, and credit access ................................ 25 

Table 4: Influence of technology adoption on financial inclusion ........................................................ 26 

Table 5: Association between technology adoption and financial inclusion ........................................ 28 

Table 6: Informal financial services contribution to advancing financial inclusion and access to credit

 .............................................................................................................................................................. 28 

Table 7: Association between informal financial services and advancement of financial inclusion and 

access to credit ..................................................................................................................................... 30 

Table 8: Potential barriers to financial inclusion and access to credit ................................................. 31 

 

 

 

 

 



xii 
 

List of Abbreviations 

ATM                                            Automated Teller Machine 

AUC                                            Area under Curve 

CHFS                                           China Household Finance Survey 

KNBS                                          Kenya National Bureau of Statistics 

TAM                                            Technology Acceptance Model 

 

 



1 
 

CHAPTER ONE: INTRODUCTION 

1.1 Study Background  

Financial inclusion is an act of ensuring that people who have been conventionally shut out of 

the conventional financial system, have financial services access including banking, credit, and 

insurance (The World Bank, 2011). A growing body of research evidence indicates that 

inclusive financial markets assist in the reduction of poverty and the wealth divide. Providing 

people and families with the means of managing their finances, pay their bills, obtain bank 

loans, and obtain insurance coverage assists in attaining this goal (Pomeroy, 2022; Zeqiraj et 

al., 2022; Cicchiello et al., 2021 & Mader, 2018). The World Bank (2021) reports that close to 

2.5 billion individuals worldwide are financially excluded, mainly due to reasons such as high 

fees, distrust, or inadequate financial products. However, since the World Bank Group began 

monitoring global financial service accessibility through the Global Findex database in 2011, 

there has been a significant increase in financial inclusion (Hall, 2022). Adults who have a 

bank account have increased in number from 51% in 2011 to 69% in 2017, an addition of 515 

million people (The World Bank, 2021). 

In the United Kingdom, statistics indicate that 14.1% of the adult population (7.1 million 

people) are financially marginalized (Elliot, 2022). This includes 5.8 million people without 

operational bank accounts. A study by Cambridge University linked ethnicity to financial 

exclusion in the UK (Adami, 2022). Additionally, a report by the UK parliament revealed that 

a third of the population (about 17 million people) lacked financial education and capability, 

which resulted in financial exclusion (UK Parliament, 2017). The Financial Inclusion 

Commission's goal in the UK is to foster a financially inclusive community where people can 

readily access financial services that are user-friendly, convenient, and can be tailored to their 

evolving needs over time. Financial inclusion index as measured by India’s Reserve Bank stood 

at 53.9% as at March 2022 (Reserve Bank of India, 2022). According to the 2017 Global Findex 

Report, 80% of the Indian population owned a bank account which was approximately double 

the figure when compared to 42% of the adult population who owned bank accounts in 2014 

(The World Bank, 2023). According to Department of Financial Services (2022), the Indian 

Government has implemented several initiatives promoting financial inclusivity and granting 

financial access for individuals who currently lack them. Access to money is changing because 

to the India Stack, which provides a digital ID card and eliminates barriers to entry for financial 

services (International Monetary Fund, 2021). 



2 
 

Financial inclusivity has received increasing attention in policy discussions in several African 

nations. As stated by Beck et al. (2014), access to finance has gotten better in African nations, 

mainly due to emerging market foreign banks aiding in this process. The usage of mobile 

money transactions has been widely adopted in the continent, leading to significant 

advancements in financial inclusion in the region. The financial sector in South Africa is very 

advanced, and financial inclusion is assessed based on accessibility. In 2017, over 67 percent 

of adults held accounts with official financial institutions, whereas the average for Sub-Saharan 

Africa was 39 percent. (Demirguc-Kunt, et al., 2018). Through the introduction of the Msanzi 

account concept, attempts have also been made to provide banking services to rural 

communities (National Treasury, 2020; Jameson, 2014). 

According to CARE International's report in 2014, more than half of Uganda's adult population, 

around 54%, were able to use formal financial services in 2013. However, a considerable 

number of 2.6 million adults, approximately 15%, were still unable to access such services. A 

study conducted by Finscope in 2018 on financial inclusion in Uganda noted that 76% of adults 

resided in the rural areas (Finscope, 2018. In Tanzania, a report on the status of financial 

inclusion revealed that 16.7% of the adult population used bank services, 48.6% did not operate 

bank accounts but used other formal services, 6.7% did not operate bank accounts but used 

informal services while 28% were financial excluded (National Financial Inclusion Strategy, 

2018). 

The 2021 Global Findex Database of the World Bank shows that Kenya has achieved the 

highest financial inclusion rate in Africa, scoring 82.9%. The high score has been linked to M-

PESA success that has resulted in a tremendous financial inclusion (Ventures Africa, 2016). 

Among the East African countries, it leads in financial inclusion through the advancing of 

mobile transactions platforms and the widespread usage (Johnson, 2016). Further, Johnson 

(2016) observes that at least 73% of Kenya’s adult population are financially included of which 

98% of that population operate mobile money accounts. Consequently, over 66% of rural 

residents have access to banking services and utilize mobile transactions to make payments and 

send and receive money domestically. 

The overarching objective of financial access is to bring in the segment of the society that lack 

bank accounts or integration of financial services into the formal financial system. This would 

get them to have unrestricted access to affordable and appropriate financial instruments. Kenya 

has experienced remarkable progress in financial inclusion since 2006, when only a small 



3 
 

group of adults, approximately 27%, took advantage of formal financial services and products 

that were available to them. This is a great contrast to 2021 where 84% had access to the basic 

financial services (Makunda & Heyer, 2022). 

1.2 Problem Statement  

Although there have been efforts to enhance the availability of formal financial services, a 

considerable proportion of people still do not have access to such services. This is a worldwide 

issue, with about 2 billion people lacking access to such services, and a large portion of these 

individuals reside in rural places in developing countries (Demirguc-Kunt et al., 2015). There 

is a widespread agreement that granting the majority of individuals access to fundamental 

financial services can bring about beneficial effects on household livelihoods (Mujeri 2015). 

However, the current financial infrastructure in developing countries is underdeveloped, 

inefficient, and offers limited options for individuals. In contrast to developed nations where 

there is a wide-ranging choice of financial services available, including debit and credit cards, 

among others internet banking services, and an efficient payment system, most developing 

countries, particularly those in sub-Saharan Africa, do not have as many readily available 

financial services. Although there has been substantial growth in the financial industry in sub-

Saharan Africa, financial services accessibility is still limited for a considerable number of 

people. For instance, in Kenya, only 55.2 % of adult people have access to formal financial 

accounts, while 43.8 % do not, and a large portion of these individuals reside in rural regions, 

according to Demirguc-Kunt et al. (2015).  The actual number of individuals who use these 

financial services may be even lower, but there is no data available on voluntary exclusion. 

Individuals who lack access to financial services encounter challenges in acquiring credit and 

saving, which hinders their ability to make substantial contributions to the development and 

growth of their respective economies. Financial inclusivity is a vital aspect of addressing 

poverty, achieving financial stability, and fostering economic growth. In order to tackle 

financial exclusion among a section of the population, it is critical to identify the elements that 

impact financial inclusion, analyse the function of informal financial services and technology, 

and recognize the obstacles to financial inclusion in Kenya. 

1.3 Research Aim  

The general aim goal of this research is to identify the crucial elements that impact financial 

inclusion in Kenya, and construct a forecast model that can support policymakers and financial 

organizations in boosting financial inclusion in the nation 



4 
 

1.4 Research Objective 

a) To identify the key factors that influence financial inclusion and access to credit in 

Kenya. 

b) To examine the relationship between technology adoption and financial inclusion in 

Kenya. 

c) To examine how informal financial services contribute to advancing financial inclusion 

and access to credit in Kenya. 

d) To identify potential barriers to financial inclusion in Kenya. 

 

1.5 Research Questions 

a) What are the key factors that influence financial inclusion in Kenya? 

b) What is the influence of technological adoption on financial inclusion in Kenya? 

c) How do informal financial services contribute to advancing financial inclusion in 

Kenya? 

d) What are the potential barriers to financial inclusion in Kenya? 

 

1.6 Significance of Study 

First, Kenya like many other nations, has a sizable population that is socially and economically 

disenfranchised. Therefore, this research study will offer insights into the causes of financial 

exclusion in Kenya and how to overcome them. Furthermore, the study's results will assist 

policymakers in determining the appropriate policies and measures required to improve 

financial inclusion in Kenya. The study may highlight certain regions or demographic groups 

that are especially susceptible to financial exclusion and recommend focused interventions. 

Additionally, financial service providers can gain insights into the demands and preferences of 

prospective clients through the study, enabling the creation of financial instruments that are 

better adapted to the requirements of population segments that don't have access to the banking 

system. 

1.7 Scope of Study 

This study aims to examine the pertinent variables linked to financial inclusion in Kenya 

utilizing the dataset from Central Bank of Kenya on FinAccess Household Survey and to 

explore the connections between these variables to address the research queries. The study will 

concentrate on the financial sector in Kenya, taking into account the context of the Kenyan 



5 
 

financial market and the social, economic, and political aspects that impact financial access in 

the nation. The study will offer valuable insights into the aspects that foster financial inclusion 

in Kenya, as well as identifying and promoting financial access in the country, it is necessary 

to identify and tackle any potential barriers that may hinder its progress.  



6 
 

CHAPTER TWO: LITERATURE REVIEW 

 

2.1 Overview 

The term "financial inclusion," also referred to as outreach in the banking sector, has a broad 

definition. Sarma and Pais (2011) define financial inclusion as offering essential financial 

services to all citizens at a reasonable cost, in a convenient form, and without discrimination in 

terms of time and place. The main aim is providing formal financial services to those living in 

poverty who lack access to them, addressing market inefficiencies that prevent less privileged 

persons from participating in the financial system. Financial inclusion creates new 

opportunities to combat poverty, promote inclusive development, and achieve the Millennium 

Development Goals by enabling people to engage with the formal financial system. According 

to Thankom and Rajalaxmi (2015), for countries to achieve economic growth, it is necessary 

to prioritize the implementation of financial inclusion policies that promote small-scale 

businesses. Mutegi and Phelister (2013) found that many adults struggle to sustain their daily 

livelihood due to financial policies that limit their access to financial services. The World Bank 

(2017) advocates that financial inclusion for both individuals and enterprises are crucial for 

countries worldwide to achieve economic stability and advancement. Several studies, including 

DemirgüçKunt and Klapper (2012) and Allan et al. (2012), suggest that making financial 

services available to low-income workers, unbanked populations, and other disadvantaged 

individuals can boost a country's economic growth. 

2.2 Theoretical Review 

The study will be fixed on two theories namely: Capability Approach Theory and Technology 

Acceptance Model. 

2.2.1 Capability Approach Theory 

The capability (capabilities) approach theory was conceived by Amartya Sen and Martha 

Nussbaum in the 1980s. The theory emphasises how important it is for people to have the 

freedom to pursue happiness and get just compensation. By giving priority to resources, 

usefulness, and thorough assessment, the Capability Approach seeks to address a variety of 

concerns with contemporary well-being measurement techniques. Additionally, it considers 

various life circumstances, making it more inclusive when assessing people's financial 

capacity. 



7 
 

The capability approach directs attention away from the resources and public goods that 

individuals possess and towards what they can accomplish and attain through the use of those 

resources and goods. This change is justified because merely having resources and goods does 

not guarantee that individuals can transform them into actual doings and beings. Even if two 

people have similar sets of resources and goods, they may still attain different outcomes 

depending on their situation. 

The capacity approach can be used to evaluate how financial inclusion affects disadvantaged 

groups' ability to acquire appropriate credit and develop the required capabilities and 

functioning (Kuriakose & Kylasam Iyer, 2015). This strategy enables a thorough review of 

many positive aspects, as well as the evaluation of current social norms or actual data on 

financial inclusion (Robeyns & Byskov, 2021). (Kimhur, 2020). Additionally, it can be used 

to assess the impact of housing regulations or programmes on people's capacities (Kimhur, 

2020). The current study can benefit from this theory as it offers a beneficial perspective on 

comprehending the idea of financial inclusion and the possible benefits it could bring.  

2.2.2 Technology Acceptance Model (TAM) 

The TAM theory, proposed by Davies in 1989, was created to anticipate and explain the 

adoption and acceptance of information technology by an organization and its users. Its aim is 

to use specific technology or services to describe attitudes. The TAM theory is a strong and 

valid model that has been widely applied in the field of technology acceptance. External 

variables such as ease of use and utility are considered, along with facilitating situations, 

subjective norms, and self-efficacy. Personal capacities are presented as variables that vary 

among investigations. The concept of perceived ease of use is a term used to describe to 

describe a person's perception of the amount of effort required to utilize technology. The 

behaviour intention predicts use in different versions of the Technology Acceptance Model, 

but the relationship between them is not deterministic. This model examines how people use 

technology based on their perceptions of its usefulness, ease of use, and level of acceptance. 

The TAM theory will be used in this study to explain an individual’s willingness to make use 

of fintech e-payments and other technological systems. 

2.3 Empirical Review 

Several prior research studies have investigated financial inclusion, each with specific research 

objectives. This section will provide a brief overview of these studies.  



8 
 

2.3.1 Factors Influencing Financial Inclusion 

Gakii (2012) examined the factors affecting Kenyans' usage of mobile financial services, 

particularly those for money transfers in Nairobi. According to the research, the utilization of 

advanced financial instruments such as mobile banking and payments, is influenced by 

variables such as gender, age, education, wealth, and service fees. To address this issue, the 

research proposed improving financial education in both urban areas and rural creating more 

inclusive financial products and services. 

Kimutai's (2015) research looked at the agent outlets of the Kenya Commercial Bank in the 

Marakwet West Sub-County in order to determine what factors affect the financial inclusion in 

rural parts of Kenya. Researchers employed a survey research strategy to gather data from all 

KCB Mtaani agents in the study's sub-county. They used questionnaires and interview guides 

to gather information from 113 KCB Mtaa agents. Financial inclusion in rural areas of 

Marakwet West Sub-County was found to be significantly influenced by variables like 

infrastructure, agent quality, financial education, and network connectivity. The study provides 

a foundation for the current study on predicting financial inclusion by identifying the key 

factors that need to be considered in the predictive model. 

Nanda (2018) conducted an evaluation of the determinants of financial inclusion in India using 

existing literature and empirical studies. The study utilized secondary data, including surveys 

and empirical evidence, to investigate the association between various factors and financial 

inclusion. The research found that in India, financial integration is influenced by several 

factors, including income levels, educational attainment, gender, social status, financial 

services access, and institutional factors. The research highlighted that encouraging financial 

inclusion requires such elements as the accessibility of financial facilities and financial literacy. 

The research outcome could offer useful perspectives on the aspects that might impact financial 

inclusion in Kenya. 

Mhlanga and Denhere (2020) carried out research to evaluate the primary factors behind the 

promotion of financial access in South Africa. Data collection was achieved using a structured 

questionnaire. The study used the logit model whereby it was established that the education 

level, gender, age, race, total salary (income proxy) and marital status influenced financial 

inclusion. The study further established that gender unlike the other factors negatively 

influenced financial inclusion. By identifying the factors that drive financial inclusion in 



9 
 

specific countries, researchers can better understand how to promote financial inclusion across 

the continent, particularly in areas with similar economic, social, and political contexts. 

Nyasani (2021) conducted a study carried out research to assess the factors that impacted 

financial inclusion among SME owners in Nairobi County. Data collection was achieved 

through questionnaires. Stratified random sampling was employed to select 384 SME owners. 

Structured questionnaires were utilized in collecting original data, which was then analysed 

using various statistical techniques. The research outcomes indicated that the demographic 

aspects were important in determining the financial inclusion level among SME proprietors in 

the county. 

2.3.2 The Influence of Technology Adoption and Financial Inclusion 

To better appreciate the impact of wireless communication in financial inclusion, Mulwa 

(2012) specifically focused at mobile banking services in Makueni County. The research 

utilized a qualitative approach to explore and comprehend the idea of mobile banking. The 

study gathered data through interviews, focus group discussions, and questionnaires. The 

study's findings showed that adapting products to fit the local context was essential for 

successful implementation, and that the stability of the products relied on the interplay between 

all relevant actors, both human and nonhuman. Moreover, the study identified three crucial 

factors to consider in mobile banking, which are avoiding network control by a single actor, 

ensuring an adequate number of users and access points for long-term viability, and 

determining the most suitable mobile banking model before implementation. The research 

provided various recommendations, including revising regulations to promote market growth, 

enhancing awareness programs and offering financial literacy workshops for rural populations, 

and providing training and financial assistance to agents. This study is relevant to the current 

research as it will provide valuable insights relating to the aspects of mobile technology on 

financial access. 

Prasad et al. (2013) investigated how Information Technology (IT) impacted financial 

inclusion. The research found that IT played a crucial role in cutting down the costs associated 

with providing banking services, especially in rural areas and for financially marginalized 

populations. The significance of IT was highlighted due to its ability to serve remote locations 

at lower costs and its greater population penetration, which is a vital requirement for achieving 

financial inclusion. 



10 
 

In 2018, Mwania conducted research to investigate the factors influencing the adoption of 

technology on financial inclusion in Machakos County, Kenya. Descriptive research 

methodology was employed to determine a connection between the variables tested. 

Technology adoption, transaction costs, collateral, the study's regression analysis revealed a 

positive correlation among perceived value, financial inclusion, and other variables. This study 

elucidates the elements that influence small business adoption of technology and financial 

services in a given area. 

Fanta and Makina conducted a cross-sectional study in 2019, utilizing quantitative methods 

and secondary data sourced from the World Bank's Global Findex database. The study aimed 

to examine the correlation between financial access and technology in Africa, specifically 

focusing on the usage of mobile phones and the internet as technological indicators of financial 

inclusion. By utilizing a logistic regression model, the researchers analysed publicly accessible 

datasets from the database. Based on the results, it was evident that ATM and internet 

technologies had a notable and favourable effect on financial accessibility and usage. Despite 

the positive correlation between having mobile and telephone subscriptions and using financial 

services, the connection was not deemed statistically significant. The study's conclusion 

emphasized the importance of technology in increasing access and utilization of financial 

services, thus supporting the idea that technology and financial inclusion are connected. This 

research is significant as it provides valuable insights into the potential impact of mobile 

technology on financial inclusion. 

Alabi and Olaoye conducted a study in 2022 to evaluate the influence of the usage of 

technology on financial inclusion in China and Nigeria, using a cross-country panel analysis 

methodology. The research analysed World Bank Global Findex data and the International 

Telecommunication Union (ITU) for the period 2011-2017, using publicly available datasets 

gathered through surveys conducted by the World Bank and ITU. The focus was on mobile 

phones and internet utilization as indicators of the use of technology related to financial 

inclusion. However, the findings indicated that the impact on financial inclusion was not 

significant despite the use of internet, mobile phone subscriptions, and ATMs. In contrast, the 

technology factors had a significant positive influence on financial inclusion in other non-

specific nations included in the panel. The study provides insights into how technology 

adoption can be an effective tool for promoting financial inclusion, which is valuable in 

forecasting financial inclusion. 



11 
 

2.3.3 The Influence of Informal Financial Services in Promoting Financial Inclusion 

A study by FinScope (2018) in Uganda revealed that 56% of the adult population (10.3 million) 

used informal financial services of which 6.7 million additionally used formal financial 

services. The report which examined the financial inclusion status in Uganda utilized data from 

the FinScope 2018 Survey. The research gathered information from a nationwide survey 

comprising of more than 5,000 participants, alongside focus group conversations and 

interviews with key informants in the financial industry to ensure representation. The report 

evaluated different metrics of financial inclusivity, such as the availability and utilization of 

formal financial services such as bank accounts and mobile money. It was also discovered that 

76% of the adult population lived in rural areas and preferred to use informal financial services. 

Therefore, the informal financial services contributed to increased financial inclusion among 

the rural based population. 

Alhassan et al. (2019) utilized a quantitative research approach to investigate the influence of 

formal financial access on monetary preferences and informal financing. The study analysed 

the association between the utilization of formal and informal financial instruments and cash 

preferences as indices of financial inclusion utilizing the 2014 World Bank Global Findex panel 

data and Afrobarometer surveys. The methods of data collection involved accessing and 

analysing publicly available datasets, which were gathered through surveys conducted by the 

World Bank and Afrobarometer. The study applied a fixed-effects regression model to 

investigate the relationship between mainstream banking inclusion and informal financial 

facilitation. The results revealed that informal financial services played a significant role in 

mobilizing resources through channels such as family members, friends, and self-help groups. 

Furthermore, the financially excluded individuals can make use of informal intermediations to 

meet their financial needs. 

Agabalinda and Steel (2021) analysed the impact of training and informal financial services in 

enhancing financial inclusion and literacy in Uganda. The study collected data through a 

randomized controlled trial involving more than 1,000 participants, focus group discussions, 

and interviews with financial sector stakeholders. The research assessed various indicators of 

financial literacy and inclusion, such as financial knowledge, usage of formal financial 

services, and saving behaviour. The findings revealed that financial training was significantly 

related to financial literacy and this in turn resulted in increased usage of both informal and 



12 
 

formal financial services. The study concluded that the promotion of the use of informal 

financial services compared to training was more efficient in promoting financial inclusion.  

2.3.4 Potential Barriers to Achieving Financial Inclusion  

Fungacova and Weill (2014) conducted a study that sought to highlight various aspects of 

financial inclusion in China. The research employed a quantitative research approach by 

utilizing China Household Finance Survey (CHFS) data and conducting regression analysis to 

examine the variables that influenced financial inclusion. Data from the CHFS, a nationally 

representative survey that collected information on household finance and economic behaviour 

was used. The research examined various metrics of financial inclusion, such as the access and 

utilization of mainstream financial services such as credit and bank accounts. The study 

identified several hurdles that study participants identified as impeding their ability to use 

formal bank accounts, including lack of funds, especially among the impoverished, inadequate 

documentation, elevated expenses associated with operating bank accounts and related 

services, and distance to banking institutions, particularly among older adults. 

Ahmed and Jianguo (2014) employed qualitative research techniques to investigate financial 

inclusion in Tanzania and the associated challenges. Semi-structured interviews were 

conducted to gather the data from policymakers, financial institutions, and other stakeholders 

in the financial sector, as well as focus group discussions with members of the public. The 

study examined various financial inclusion indicators, including mainstream financial services. 

The findings suggested that although financial inclusion had increased in the country, a big 

segment of the population still required access to formal banking. The study identified 

obstacles such as high costs, regulatory requirements, lack of advanced technology, and limited 

awareness that were hindering financial inclusion in Tanzania.  

Varghese and Viswanathan (2018) conducted a study on financial inclusion in India, focusing 

on its opportunities, issues, and challenges. The research employed a literature review 

methodology, using existing research and publications to gather data. The methods of data 

collection involved reviewing existing literature on financial inclusion, including academic 

research, policy documents, and reports from international organizations. The research 

identified supply and demand issues that acted as obstacles to financial inclusion. The demand 

challenges identified included a lack of technological and financial literacy, a large unbanked 

population spreading across a vast area and limited access to credit facilities. Among the supply 

challenges identified is the reluctance of banks to provide credit facilities to people with small 



13 
 

and irregular incomes, high transaction costs and documentations such as birth and income 

certificates, which the rural poor could not easily produce. 

2.3.5 Access to Credit 

Bhanot et al. (2012) conducted a study with the objective of determining the significant 

elements that impact financial inclusion in remote geographical locations and recommending 

expansion strategies for banks into underserved markets. Using a standardized questionnaire, 

primary data was gathered from 411 families in the states of Meghalaya and Assam in northeast 

India. Using a logistic regression model, the important contributors to financial inclusion were 

identified. The research revealed that financial inclusion in northeast India was exceedingly 

low. Income, having access to financial information through several channels, familiarity with 

self-help groups (SHGs), and education were identified as crucial factors in promoting 

inclusion. In addition, the proximity to postal office banks increased inclusion probability. 

Location and receipt of government assistance did not help integration on their own, though 

recipients of government assistance in plain regions indicated a higher degree of integration. 

Óskarsdóttir et al. (2019) investigated the potential of utilizing phone networks as a novel 

source of data to improve credit scoring models. The study employed sophisticated social 

network analysis techniques to estimate influence ratings, and combined call-detail data with 

credit and debit account information to provide credit card applicants with scorecards. The 

study's findings indicated that integrating call-detail records into credit scoring models 

enhances their performance, especially in terms of AUC, with calling behaviour features 

proving to be the most effective predictors. The study also raised concerns regarding the ethical 

use of call-detail information, regulatory compliance, and data privacy. Additionally, the 

characteristics of calling behaviour were found to be the most effective statistical and economic 

predictors in other models. These findings have important ramifications for the ethical usage 

phone logs, regulatory rules, financial inclusion, information exchange, and privacy concerns. 

Gomber et al. (2018) examined the recent technological advancements and process disruptions 

in the financial services industry, including the rise of fintech startups seeking to improve 

business models, customer experiences, and services transformation. The authors argued that 

experts viewed these developments as a revolution, with the industry requiring significant 

improvements in efficiency, customer focus, and knowledge. The authors further suggested a 

new fintech innovation mapping methodology to study developments and adaptations in four 

financial services domains: operational management, technology advances, and 



14 
 

transformations in the financial regulatory environment, lending and deposit services, and 

investment and financial markets, which are influenced by various innovations, such as P2P 

lending, social media use, cryptocurrencies, blockchain, and robo-advisory services. 

2.4 Research Gaps 

Various research studies have investigated financial inclusion. However, Kimutai's (2015) 

study specifically concentrated on the factors that impacted financial inclusion in the rural 

regions of Kenya, with a specific focus on Kenya Commercial Bank agent outlets located 

within Marakwet West Sub-County, which may limit the generalizability of the findings to 

other rural areas in Kenya or other countries. Also, the research only identified the factors that 

significantly impacted financial inclusion in the rural regions of Marakwet West Sub-County 

however, no causal association was established between these characteristics and financial 

inclusion. Additionally, Nanda's (2018) study, which examined financial inclusion in India, 

may not be relevant to Kenya or other nations due to unique cultural, economic, and political 

factors. Mhlanga and Denhere's (2020) research on the determinants of financial access in 

South Africa was based solely on structured questionnaires, which might not offer an in-depth 

comprehension of the variables influencing financial inclusion in the country. Qualitative data, 

such as interviews or focus groups, may provide deeper insights into the experiences and 

perceptions of individuals regarding financial inclusion. 

The study by Mulwa (2012) specifically focused on mobile banking services in Makueni 

County. The study presents several research gaps, which include a limited geographic scope 

focusing only on Makueni County, lack of quantitative data using only a qualitative research 

approach; this might not offer a thorough comprehension of how wireless communication 

affects financial inclusion. A limited scope focusing only on mobile banking services, and did 

not explore other forms of wireless communication, such as internet-based banking or digital 

wallets. An examination of these alternative types of wireless communication may offer a more 

comprehensive illustration of how wireless communication affects financial inclusion. 

Alabi and Olaoye's (2022) research evaluated how the adoption of technology affects financial 

inclusion, and it uncovered several areas that require further investigation. These include a 

narrow emphasis on particular technological metrics such as mobile phone usage, the internet, 

and ATMs as indicators of technology adoption for financial inclusion. Secondly, the study 

had a limited sample size examining data only from two countries, China and Nigeria, which 

may not be representative of other countries or regions. To obtain a more in-depth 



15 
 

understanding of the influence of technology adoption on financial inclusion, future studies 

could scrutinize a broader range of nations with different economic, social, and political 

settings. Additionally, contextual elements like regulatory frameworks, infrastructure, and 

cultural norms that might impact the connection between technology adoption and financial 

inclusion were not explored in the present study. The upcoming research will aim to address 

these gaps. 

2.5 Conceptual Framework 

This is an illustration that depicts the variables being studied in research (Mugenda & 

Mugenda, 2003). The diagram helps the researcher understand and connect with the concepts 

being analysed, and it displays how the variables are related to each other. The study’s 

independent variables are factors influencing financial inclusion, technology, informal 

financial services and barriers to financial inclusion. The dependent variable is financial 

inclusion. Factors influencing financial inclusion will be measured by sociocultural, economic, 

infrastructural, regulatory and policy indicators. The indicators of technology are mobile phone 

penetration, internet access, ATMs and Electronic Payment Systems. The informal financial 

services are measured by savings/rotating groups, mobile money, informal remittance services 

and money lenders. The lack of physical financial services access is an indicator of obstacles 

to financial inclusion, limited financial literacy and high costs of financial services. Financial 

inclusion is indicated by access, usage and affordability. 

 

 

 

 

 

 

 

 

 

 



16 
 

Independent Variable                                                                            Dependent Variable 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 2-1: Conceptual framework 

 

 

 

  

Factors 
 Sociocultural 
 Economic 
 Infrastructural 
 Regulatory and 

Policy 
 

Challenges to Financial 

Inclusion 

 Lack of physical 

financial services 

access 
 Limited financial 

literacy 
 High costs of 

financial services 

Informal Financial 

Services 
 Savings/rotating 

groups 
 Mobile money 
 Informal remittance 

services 
 Money lenders 

Financial Inclusion 

 Access 

 Usage 

 Affordability 

Technology 

 Mobile phone 

penetration 
 Internet access 
 ATMs 
 Electronic Payment 

Systems 

 



17 
 

CHAPTER THREE: METHODOLOGY 

The research design, data understanding, data preparation, modelling, evaluation/performance 

metrics and deployment tools is presented in this section. 

3.1 Research Design  

For this study, a descriptive research design will be implemented, which is fitting as it will 

enable the identification of the variables in their current state without attempting to manipulate 

them, as explained by Mugenda and Mugenda (2013). The design is well-suited as it focuses 

on describing and summarizing the data to identify trends, patterns, and relationships between 

the variables of interest. This design is especially useful when working with large datasets, 

such as the 2021 FinAccess Household Survey dataset, which will be analysed in this study. 

3.2 Data Understanding 

The 2021 FinAccess Household Survey dataset found in Kenya National Bureau of Statistics 

(KNBS) website (https://finaccess.knbs.or.ke/reports-and-datasets) contains data here linked 

Anonymised and weighted dataset - FinAccess Household Survey 2021 - (EXCEL FORMAT). 

The dataset comprises of 22,024 respondents and has 2,326 variables from rural and urban 

Kenya. The dataset comprises a broad spectrum of topics encompassing financial behaviour, 

access to financial services, demographics, and socio-economic status. It contains categorical, 

binary, and continuous variables. To better understand the variables in the dataset, the data will 

be grouped into the following categories: financial behaviour, financial services access, 

demographics and socio-economic status. The study’s independent variables are factors 

influencing financial inclusion, technology, informal financial services and barriers to financial 

inclusion. The dependent variable is financial inclusion. Indicators of financial inclusion 

include sociocultural, economic, infrastructural, regulatory and policy aspects. The indicators 

of technology are mobile phone penetration, internet access, ATMs and Electronic Payment 

Systems. Indicators for the informal financial services are savings/rotating groups, mobile 

money, informal remittance services and money lenders. Indicators of barriers to financial 

inclusion include financial exclusion, limited financial literacy and high costs of financial 

services. Financial inclusion is measured by access, usage and affordability. 

3.3 Data Preparation  

To allow effective processing and evaluation during data analysis, data preparation entails 

gathering, collecting, structuring, and arranging the data (Manikandan, 2010). The data will be 

https://finaccess.knbs.or.ke/reports-and-datasets
https://docs.google.com/spreadsheets/d/1IZ-6HCDzeypoa4IQNr-nH0Y26Qc5EwSE/edit?usp=sharing&ouid=115471613002921533452&rtpof=true&sd=true


18 
 

examined for missing entries, which could result from skipped questions or non-responses, 

during data preparation. The missing values will be replaced by the mode and descriptive 

analysis done for each research objective by use of frequency. Some variable categories were 

recoded to attain the required sample size required for inferential analysis. 

3.4 Modelling  

To create a financial inclusion prediction model, logistic regression was utilized in this study. 

This technique is employed to create a statistical model that shows the relationship between a 

binary outcome variable and one or more predictor factors (Zhang, 2016). Inferential analysis 

by use of generalized linear model (GLM) for the binomial family and logit link was utilized. 

Bivariate logistic regression was carried out between the predictor and outcome variables. 

Using significant variables at p-value = 0.05 from the bivariate binary logistic regression 

models, a multivariable binary logistic regression model was fitted to identify the independent 

predictors of financial inclusion. The strength of association was measured by odds ratio with 

95% confidence intervals. Variables with p<0.05 in the multivariable logistic regression model 

were considered as significant factors associated with financial inclusion. The logistic 

regression model was established by using maximum likelihood estimation, and the Hosmer-

Lemeshow test was employed to evaluate the model's goodness of fit. The odds ratio (OR) was 

employed to gauge the magnitude of the predictor variables' effect in the model. A significance 

level of p < 0.05 was utilized to determine the statistical significance of both the model and 

predictor variables. The logistic model for predicting financial inclusion and access to credit 

can be represented by the equation: 

P(y=1) = 1 / (1 + e^(-z)) 

where P(y=1) is the probability of financial inclusion, z is the linear combination of predictor 

variables, and e is the exponential function. 

z = bo + b1x1 + b2x2 + b3x3 + b4x4  

And; 

x1 = Factors 

x2 = Adoption of technology 

x3 = Use of informal financial services 

x4 = Potential barriers to financial inclusion 



19 
 

 

3.5 Evaluation/Performance Metrics 

In evaluating the effectiveness of predictive models, evaluation measures are essential (Hossin 

& Sulaiman, 2015).  In this study, the logistic regression's performance was evaluated using a 

variety of metrics that assess the model's accuracy, precision, recall, and F1 score. Accuracy 

refers to the proportion of correct predictions made by the model (Li, 2017).  

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃 + 𝑇𝑁

𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
 

According to Steyerberg et al. (2010), precision gauges the ratio of accurate positive 

predictions to the overall number of positive predictions. In this research, precision will gauge 

the ratio of correctly predicted individuals with financial inclusion to the total number of 

individuals predicted to have financial inclusion. A high precision means that the model is 

generating fewer false-positive predictions.  

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑇𝑃

𝑇𝑃 + 𝐹𝑃
 

Conversely, recall measures the ratio of correctly predicted individuals with financial inclusion 

to the total number of individuals who have financial inclusion. A high recall value signifies 

that the model is making fewer false-negative predictions. 

𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇𝑃

𝑇𝑃 + 𝐹𝑁
 

The harmonic mean of Recall and Precision is the F-score. 

𝐹 − 𝑠𝑐𝑜𝑟𝑒 =
𝑅𝑒𝑐𝑎𝑙𝑙 ∗ 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛

𝑅𝑒𝑐𝑎𝑙𝑙 + 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛
 

 

3.6 Dashboard Development Using R 

3.6.1 Tool Selection 

R was used as the primary tool for dashboard development due to its versatility in statistical 

analysis and visualization. The Flexdashboard and plotly libraries were employed to create an 

interactive and user-friendly dashboard.  



20 
 

3.6.2 Dashboard Design 

The dashboard was designed to present key findings from the logistic regression analysis in an 

intuitive and visually appealing manner. Graphs, charts, and tables were used to communicate 

the relationships between different variables and their impact on financial inclusion. 

3.6.3 Integration of Logistic Regression Results 

The dashboard will include the logistic regression analysis's findings, such as coefficients, odds 

ratios, and statistical significance. Users have the ability to interactively assess the variables 

impacting financial inclusion and obtain supplementary information regarding the model. 

3.7. Deployment and Accessibility 

3.7.1 Deployment Tools 

Data analysis was done using Python whereby the python libraries were used to clean and pre-

process the data, explore the FinAccess Household Survey dataset and identify patterns, and 

generate descriptive statistics. 

3.7.2 Deployment Platform 

To make the dashboard accessible to a broad spectrum of users, it was implemented on a web 

platform. Accessibility factors such as ease of manoeuvring from one web page to another or 

accessing various components of the dashboard were considered in order to support users with 

varying levels of technological proficiency. 

3.7.3 Documentation 

To help users interpret the data, comprehend the methodology, and make the most of the 

dashboard, extensive documentation was supplied alongside it. 

 

 

  



21 
 

CHAPTER FOUR: RESULTS 

This chapter presents the study results, which have been analyzed in line with the study 

objectives. The first section presents the demographic profile of respondents, and the 

subsequent sections present the analysis, presentation, and interpretation of the factors under 

investigation in the current study. Descriptive analysis was first done for each research 

objective by use of frequency. It was then followed by inferential analysis by use of generalized 

linear model (GLM) for the binomial family and logit link. Bivariate logistic regression was 

carried out between the predictor and outcome variables. Using significant variables at p-value 

= 0.05 from the bivariate binary logistic regression models, a multivariable binary logistic 

regression model was fitted to identify the independent predictors of financial inclusion. The 

strength of association was measured by odds ratio with 95% confidence intervals. Variables 

with p<0.05 in the multivariable logistic regression model were considered as significant 

factors associated with financial inclusion. 

4.1 Demographic characteristics of respondents 

The participants’ mean age was 38.9 years (SD=17.2), most of the respondents were between 

26-35 years (27.3%), male (59%), married or living with their partners (54.5%), had primary 

school as their highest level of education (40.2%), were from the poorest wealth quintile 

(26.1%) and were residing in the rural areas (65.6%). The summary of the demographic 

characteristics is shown in the table below. 

Table 1: Demographic characteristics of respondents 
 Total 

Variables n % 

Age(mean and SD)* 38.9                        17.2 

Age   

16-17 1115 5.1 

18-25 4382 19.9 

26-35 6022 27.3 

36-45 4065 18.5 

46-55 2404 10.9 

>55 4036 18.3 

Gender   

Female 9026 41.0 

Male 12998 59.0 

Marital status   

Single/never married 5801 26.3 

Married/living with partner 12004 54.5 



22 
 

Divorced/separated 1612 7.3 

Widowed 2582 11.7 

Don’t know 13 0.1 

Refused to answer 12 0.1 

Highest Education Level   

None 3987 18.1 

Other 25 0.1 

Primary school 8846 40.2 

Secondary school 6631 30.1 

Tertiary education 2535 11.5 

Wealth quintile   

Middle 4600 20.9 

Poorest 5742 26.1 

Second poorest 4609 20.9 

Second wealthiest 3804 17.3 

Wealthiest 3269 14.8 

Residence    

Rural 14455 65.6 

Urban 7569 34.4 

 

The table 4-1 above is a summary of the demographic characteristics of the respondents, 

classified according to age, gender, marital status, education level, wealth quantile and 

residence.  

 

 

Figure 4-1: Pie chart of gender distribution 

The male respondents accounted for 59% (12,998) compared to females who were 41% 

(9,026). 



23 
 

 

Figure 4-2: Pie chart of age group distribution 

The highest proportion of respondents, 27.3% (6,022) were within the age group of 26-35 years 

while the lowest proportion, 5.1% (1,115) was recorded within the age group of 16-17 years. 

4.2 Factors influencing financial inclusion and access to credit 

Majority of the respondents who reported to have access to financial services; used any credit 

institutions (35.2%), did not use formal digital credit services (63.7%), had access to mobile 

money services (56.2%), no mobile money savings account (34.3%), no mobile banking 

savings account (61.8%), and currently have formal savings account (32.5%).  

Table 2: Factors influencing financial inclusion and access to credit 

  Financial Inclusion 
 Yes No 

Variables n % n % 

Uses any Credit institution     

No 7209 32.7 3078 14.0 

Yes 7742 35.2 3995 18.1 

Formal digital credit users     

No 14029 63.7 6167 28.0 

Yes 922 4.2 906 4.1 

Access to mobile money services    

No 2582 11.7 378 1.7 

Yes 12369 56.2 6695 30.4 

Mobile money savings     

No 7564 34.3 2078 9.4 

Yes 7387 33.5 4995 22.7 

Mobile banking savings     



24 
 

No 13607 61.8 5302 24.1 

Yes 1344 6.1 1771 8.0 

Formal savings account     

Currently have 7162 32.5 5250 23.8 

Never had 6302 28.6 1278 5.8 

Used to have 1487 6.8 545 2.5 

 

 

Figure 4-3: Bar graph of financial inclusion against access to mobile money services 

 

4.3 Association between the factors and financial inclusion and credit access 

Respondents within the age group of above 55 years were 1.35 times more likely to have access 

to financial services and credit compared to those aged between 16 and 17 years (AOR =1.35, 

95% CI=1.082-1.686, P-value=0.008). Additionally, those who resided in urban areas were 

more than two folds more likely to have access to financial services and credit compared to 

those in the rural areas (AOR =2.88, 95% CI=2.675-3.102, P-value=<0.001). Participants 

whose highest level of education were primary, secondary and tertiary were more than four 

times more likely to  have access to financial services and credit compare to those who had no 

education (AOR =4.10, 95% CI=3.531-4.753, P-value=<0.001; AOR =5.51, 95% CI=4.723-

6.425, P-value=<0.001; AOR =18.14, 95% CI=15.160-21.703, P-value=<0.001). 



25 
 

Respondents who used formal digital credit services were approximately two times more likely 

to have access to financial services and credit compared to those who do not (AOR =2.01, 95% 

CI=1.360-2.967, P-value=<0.001). Participants who currently have and used to have formal 

savings account had higher odds of accessing financial services compared to those who never 

had (AOR =2.63, 95% CI=2.193-3.148, P-value=<0.001; AOR =1.47, 95% CI=1.172-1.849, 

P-value=0.001). Respondents who had mobile banking savings account had higher odds of 

accessing financial services and credit compared to those who did not have (AOR =1.29, 95% 

CI=1.163-1.426, P-value=<0.001). Those who subscribe to any credit institution were 0.49 

folds less likely to have access to financial services and credit compared to those who do not 

(AOR =0.49, 95% CI=0.333-0.710, P-value=<0.001). 

 
Table 3: Association between the factors, financial inclusion, and credit access 

  Financial Inclusion 

Variable COR(95%CI) P-value AOR(95%CI) 
P-

value 

Age     

16-17 REF REF REF REF 

18-25 1.81(1.549 -2.112) <0.001 1.00(0.829-1.212) 0.981 

26-35 2.17(1.862-2.518) <0.001 1.17(0.957-1.441) 0.124 

36-45 1.86(1.589-2.171) <0.001 1.16(0.935-1.434) 0.178 

46-55 1.5(1.265-1.765) <0.001 1.08(0.861-1.353) 0.509 

>55 1.11(0.945-1.300) 0.206 1.35(1.082-1.686) 0.008 

Gender     

Female REF REF REF REF 

Male 1.14(1.075-1.207) <0.001 1.07(0.986-1.155) 0.106 

Residence     

Rural REF REF REF REF 

Urban 3.66(3.449-3.887) <0.001 2.88(2.675-3.102) <0.001 

Marital status     

Single/never married REF REF REF REF 

Married/living with partner 0.92(0.858-0.979) 0.009 0.91(0.816-1.007) 0.067 

Divorced/separated 0.76(0.674-0.857) <0.001 0.98(0.833-1.154) 0.814 

Widowed 0.45(0.402-0.502) <0.001 1.03(0.869-1.216) 0.748 

Highest Education Level     

None REF REF REF REF 

Primary school 5.21(4.51-5.964) <0.001 4.10(3.531-4.753) <0.001 

Secondary school 9.65(8.425-11.054) <0.001 5.51(4.723-6.4250 <0.001 

Tertiary education 41.09(35.211-47.951) <0.001 18.14(15.160-21.703) <0.001 

Formal digital credit users     

No REF REF REF REF 

Yes 2.24(2.030-2.462) <0.001 2.01(1.360-2.967) <0.001 

Uses any credit institution     



26 
 

No REF REF REF REF 

Yes 1.21(1.142-1.280) <0.001 0.49(0.333-0.710) <0.001 

Formal savings account     

Never had REF REF REF REF 

Currently have 3.61(3.371-3.876) <0.001 2.63(2.193-3.148) <0.001 

Used to have 1.81(1.611-2.028) <0.001 1.47(1.172-1.849) 0.001 

Mobile money savings     

No REF REF REF REF 

Yes 2.46(2.317-2.615) <0.001 1.08(0.852-1.372) 0.521 

Mobile banking savings     

No REF REF REF REF 

Yes 3.38(3.129-3.655) <0.001 1.29(1.163-1.426) <0.001 

 

4.4 Influence of technology adoption on financial inclusion 

The table below shows responses on adoption of technology on financial inclusion. Among the 

respondents who reported to have access to financial services 39.6% had access to internet, 

59.5% used ATM, 56.2% used electronic payment systems, and 58.8% had access to a mobile 

phone.  

Table 4: Influence of technology adoption on financial inclusion 

  Financial Inclusion 
 Yes No 

Variables N % n % 

Internet access     

No 6209 28.3 319 1.5 

Yes 8700 39.6 6709 30.6 

Don't know 9 0.0 2 0.0 

Refused to answer 3 0.0 2 0.0 

Uses ATM     

No  1850 8.4 1867 8.5 

Yes 13095 59.5 5195 23.6 

Uses electronic payment     

No 2582 11.7 378 1.7 

Yes 12369 56.2 6695 30.4 

Mobile phone access     

No 2008 9.1 255 1.2 

Yes 12943 58.8 6818 31.0 



27 
 

 
Figure 4-4: Bar graph of financial inclusion against use of electronic payment 

 

 

Figure 4-5: Bar graph of financial inclusion against mobile phone access 

 

4.5 Association between technology adoption and financial inclusion 

Participants who reported to have access to internet were more than ten folds more likely to 

have access to financial services compared to those who do not have access (AOR =12.84, 95% 

CI=11.325-14.566, P-value=<0.001). Conversely, participants who used ATM were 0.75 less 

likely to access to financial services compared to those who do not (AOR =0.75, 95% 

CI=0.683-0.820, P-value=<0.001).  



28 
 

Table 5: Association between technology adoption and financial inclusion 

  Financial Inclusion 

Variable COR(95%CI) P-value AOR(95%CI) P-value 

Internet access     

No REF REF REF REF 

Yes 14.84(13.216-16.671) <0.001 12.84(11.325-14.566) <0.001 

Uses ATM     

No  REF REF REF REF 

Yes 0.39(0.366-0.423) <0.001 0.75(0.683-0.820) <0.001 

Uses electronic payment     

No REF REF REF REF 

Yes 3.70(3.346-4.135) <0.001 0.97(0.818-1.161) 0.771 

Mobile phone access     

No REF REF REF REF 

Yes 4.15(3.629-4.741) <0.001 1.18(0.981-1.424) 0.079 

 

4.6 Informal financial services contribution to advancing financial inclusion and access 

to credit 

The table below shows responses on informal financial services and the magnitude of their 

contribution to advancing financial inclusion and access to credit. The largest proportion of 

respondents who reported  to have access to financial services had never used Shylock money 

lenders (67.3%), were not informal credit users (45.9%), never had informal savings account 

(48.6%), never used informal remittance services (31.2%), did not use informal digital credit 

(37.5%).  

Table 6: Informal financial services contribution to advancing financial inclusion and access to credit 

  Financial Inclusion 
 Yes No 

Variables n % n % 

Informal savings account     

Currently have 3353 15.2 2605 11.8 

Never had 10698 48.6 3996 18.1 

Used to have 900 4.1 472 2.1 

Informal remittance services     

Currently use 6654 30.2 4696 21.0 

Never use 6877 31.2 1864 8.5 

Used to use 1419 6.4 573 2.6 

Informal digital credit users     

No 8258 37.5 4040 18.3 

Yes 6693 30.4 3033 13.8 

Shylock money lenders     

Currently use 41 0.2 59 0.3 

Never used 1485 67.3 6930 31.5 



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Used to use 84 0.4 81 0.4 

Informal credit users     

No 10099 45.9 5780 26.2 

Yes 4852 22.0 1293 5.9 

 

 

 
Figure 4-6: Line graph of financial inclusion against shylock moneylenders 

 

 

Figure 4-7: Bar graph of financial inclusion against use of informal credit 

 



30 
 

 

Figure 4-8: Line graph of financial inclusion against informal savings account 

 

4.7 Association between informal financial services and advancement of financial 

inclusion and access to credit 

Participants who currently have and used to have informal savings account had higher odds of 

accessing financial services compared to those who never had (AOR =1.64, 95% CI=1.511-

1.785, P-value=<0.001; AOR =1.18, 95% CI=1.021-1.369, P-value=0.025). Those who 

currently use informal remittance services were 0.65 less likely to access financial services and 

credit compared to individuals who do not (AOR =0.65, 95% CI=0.486-0.857, P-value=0.002). 

Moreover, respondents who currently use shylock money lending services were 2.01 times 

more likely to have access to financial services and credit compared to those who had never 

used the services (AOR =2.01, 95% CI=1.223-3.313, P-value=0.006). Those who used 

informal credit services were 0.70 times less likely to have access to financial services and 

credit compared to those who do not (AOR =0.70, 95% CI=0.627-0.784, P-value=<0.001). 

Individuals who used informal digital credit services were 1.68 folds more likely to have access 

to financial services and credit compared to those who do not (AOR =1.68, 95% CI=1.144-

2.458, P-value=<0.001) 

 
Table 7: Association between informal financial services and advancement of financial inclusion and access to credit 

  Financial Inclusion 

Variable COR(95%CI) P-value AOR(95%CI) P-value 

Informal savings account     

Never had REF REF REF REF 

Currently have 2.08(1.953-2.215) <0.001 1.64(1.511-1.785) <0.001 



31 
 

Used to have 1.40(1.249-1.579) <0.001 1.18(1.021-1.369) 0.025 

Informal remittance 

services 
    

Never had REF REF REF REF 

Currently use 2.57(2.413-2.739) <0.001 0.65(0.486-0.857) 0.002 

Used to use 1.49(1.335-1.662) <0.001 0.81(0.623-1.061) 0.128 

Informal digital credit users     

No REF REF REF REF 

Yes 0.93(0.875-0.981) <0.001 1.68(1.144-2.458) 0.008 

Shylock money lenders     

Never used REF REF REF REF 

Currently use 3.08(2.064-4.589) <0.001 2.01(1.223-3.313) 0.006 

Used to use 2.06(1.518-2.802) <0.001 1.18(0.806-1.737) 0.391 

Informal credit users     

No REF REF REF REF 

Yes 0.47(0.434-0.499) <0.001 0.70(0.627-0.784) <0.001 

 

4.8 Potential barriers to financial inclusion and access to credit 

The table below shows the responses on the potential barriers to financial inclusion and access 

to credit. Majority of respondents who had a monthly income of between 3001 and 7500 

Kenyan shillings (18.6%), were financially literate (62.1%) and had no limited access to credit 

(43%) reported to be financially included and able to access credit. Conversely, those who 

earned between 7501 and 15000 Kenyan shillings monthly (8.4%), were financially literate 

(21.4%) and had no limited access to credit (21.5%) reported to be financially excluded and 

unable to access credit. 

 
Table 8: Potential barriers to financial inclusion and access to credit 

  Financial Inclusion 
 Yes No 

Variables n % n % 

Monthly income     

0-100 52 0.2 7 0.0 

101-1500 2547 11.6 506 2.3 

1501-3000 3541 16.1 881 4.0 

3001-7500 4093 18.6 1677 7.6 

7501-15000 2169 9.8 1845 8.4 

15001-30000 639 2.9 1008 4.6 

30001-70000 103 0.5 443 2.0 

70001-200000 13 0.1 89 0.4 

200001-400000 1 0.0 3 0.0 

Don't know 1746 7.9 528 2.4 



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Refused to answer 46 0.2 85 0.4 

Financially illiterate     

No 13684 62.1 4719 21.4 

Yes 1267 5.8 2354 10.7 

Limited access to credit     

No 9467 43.0 4737 21.5 

Yes 5484 24.9 2336 10.6 

 

 

Figure 4-9: Bar graph of financial Inclusion against financial illiteracy 

 

Figure 4-10: Bar graph of financial Inclusion against Limited Access to Credit 

4.9   Data Product - Dashboard 

4.9.1 The Overview 

The data product is an interactive dashboard that carefully breaks down all financial inclusion 

issues in Kenya for practical use. This potent tool is systematically separated into different 

pages, which focus on various areas of the research. 



33 
 

Therefore, it is a strong means that links complicated output of logistic regression with 

meaningful information for banking practitioners, policy makers and academia. 

It summarizes important performance measures depicting at a glance the level of accuracy, 

precision, recall and F1-score of the logistic regression model under study. 

4.9.2 Demographic Statistics Page 

The Demographic page has various tabs that illuminates demographic information revealing 

intricacies surrounding Gender, marital status, Residence , wealth quartile, informal savings, 

monthly income, education, urbanization, and financial incorporation for the population of 

interest.  

 

Figure 4-11: Demographic Statistics Page 

 

4.9.3 Analytics Page 

This page has five tabs that contains distribution of factors affecting financial inclusion. 

 

Figure 4-12: Analytics Page 



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4.9.4 Logistics Regression Page 

The logistics regression page communicates the logistic regression model's findings through 

visual representations, emphasizing the significance of predictor variables and their impact on 

financial inclusion. 

 

Figure 4-13: Logistics Regression Page 

4.9.5 User-Centric Design: 

Simplicity and Accessibility: 

User-centric design ensures clarity and simplicity, making statistical insights accessible to both 

experts and non-experts alike. 

Web Deployment: 

Accessible on a web server, the dashboard accommodates universal access from various 

devices and locations. 

Dissemination and Impact: 

Stakeholder Engagement: 

The dashboard will be shared with policymakers, financial institutions, and researchers, 

offering a comprehensive view of financial inclusion dynamics. 

Documentation and Support: 

Accompanied by thorough documentation, the dashboard provides guidance on interpretation 

and exploration.  

 



35 
 

Future Development: 

For iterative enhancement; planned continuous updates and refinements will be based on 

emerging research, policy changes, and user feedback, ensuring the dashboard remains a 

relevant and valuable resource. 

This analysis looks at phase 1 of the work. Preparation of a data lake/ data warehousing can be 

done where data is automatically fed to a dashboard   

Summary of Results 

The financial inclusion dashboard reveals that overall access to formal financial services in 

Kenya is low at just 44% and there are significant disparities across demographic segments - 

with higher education, urban, younger, and male populations having much higher inclusion. 

Key barriers are lack of income and financial literacy. While informal financial mechanisms 

partially fill the gap, over-reliance on them limits inclusion. Digital financial services like 

mobile money and electronic payments are strongly linked to higher financial inclusion, 

especially in enabling access to credit and savings. The dashboard analysis provides critical 

insights on the target groups, barriers, and opportunities to guide policy and product decisions 

to enhance financial inclusion. The recommendations outline how to build on this static view 

and develop a full dynamic data architecture and machine-learning pipeline to create an end-

to-end solution. 

The dashboard can be accessed through https://rpubs.com/Chebet/1146800  

 

  

https://rpubs.com/Chebet/1146800


36 
 

CHAPTER FIVE: DISCUSSION 

 

5.1 Factors influencing financial inclusion and access to credit 

Consistent with Gakii (2012) and Nanda (2018), the study shows that respondents who had 

higher levels of education were more likely to have access to financial services and credit. The 

results indicate that individuals with primary, secondary, and tertiary education were more than 

four times more likely to have access compared to those with no education. 

Similarly, the study findings align with Kimutai (2015), as it shows that participants residing 

in urban areas were more likely to have access to financial services and credit compared to 

those in rural areas. The odds of access were more than two times higher for urban residents, 

consistent with the higher infrastructure, connectivity, and agent quality found in urban 

settings. 

Contrasting with Mhlanga and Denhere (2020), the study found that gender did not 

significantly influence financial inclusion. The literature review indicated that gender had a 

negative impact on financial inclusion, whereas the current findings do not support this 

relationship. 

The study also highlights the importance of digital financial services. Consistent with Gakii 

(2012), the results show that individuals who used formal digital credit services were more 

likely to have access to financial services and credit. Additionally, the findings align with 

Nyasani (2021), as respondents who had mobile banking savings accounts were more likely to 

access financial services and credit compared to those who did not. 

In contrast to the literature review, the study found that participants with subscriptions to any 

credit institution were less likely to have access to financial services and credit. This result 

contradicts the findings of Gakii (2012), which suggested that utilizing advanced financial 

instruments, such as mobile banking and payments, would increase financial inclusion. 

5.2 Influence of technology adoption on financial inclusion 

Fanta and Makina (2019) found that ATM technologies had a notable and favorable effect on 

financial accessibility and usage. However, the study's results suggest a contrasting finding, 

indicating that participants who used ATMs were 0.75 times less likely to have access to 

financial services compared to those who did not use ATMs. This contrasting result suggests 

that the impact of ATM usage on financial inclusion might be more complex and context-

dependent. 



37 
 

On the other hand, the finding that participants who reported having access to the internet were 

more than ten times more likely to have access to financial services aligns with the literature. 

Prasad et al. (2013) highlighted the crucial role of Information Technology (IT), including 

internet access, in reducing costs and improving access to financial services, particularly in 

remote areas and for marginalized populations. This finding reinforces the significance of 

internet technology as a facilitator of financial inclusion. 

5.3 Informal financial services contribution to advancing financial inclusion and access 

to credit 

The findings indicate that a significant proportion of respondents did not use informal financial 

services, which contrasts with previous studies that have shown the widespread usage of such 

services in Uganda (FinScope, 2018). However, the study found that individuals who currently 

or previously had informal savings accounts had higher odds of accessing financial services 

compared to those who never had such accounts, which is consistent with the findings of 

Alhassan et al. (2019). On the other hand, those who currently use informal remittance services 

were less likely to access financial services and credit, which is inconsistent with the study by 

FinScope (2018) that found informal financial services to contribute to increased financial 

inclusion among the rural population.  

Additionally, the results showed that individuals who currently use shylock money lending 

services were more likely to have access to financial services and credit, which is consistent 

with the study by Agabalinda and Steel (2021) that found the promotion of the use of informal 

financial services to be more efficient in promoting financial inclusion. Furthermore, 

individuals who used informal credit services were less likely to have access to financial 

services and credit, which is consistent with the findings of Alhassan et al. (2019). The study 

found that individuals who used informal digital credit services were more likely to have access 

to financial services and credit, which is consistent with the results of Agabalinda and Steel 

(2021) that found financial training to be significantly related to financial literacy, resulting in 

increased usage of informal and formal financial services. 

5.4 Potential barriers to financial inclusion and access to credit 

In the study conducted by Fungacova and Weill (2014) in China, they identified several barriers 

to financial inclusion, including lack of funds, inadequate documentation, high expenses, and 

distance to banking institutions. These barriers were particularly significant for the 

impoverished and older adults. The finding that lack of funds was a barrier to financial 



38 
 

inclusion aligns with the result mentioned in the findings. It states that respondents with a 

monthly income between 3,001 and 7,500 Kenyan shillings, who were financially literate and 

had no limited access to credit, reported being financially included and able to access credit. 

This suggests that having a higher income can positively impact financial inclusion by 

providing individuals with the necessary funds to access financial services. 

On the other hand, the study by Varghese and Viswanathan (2018) in India identified limited 

access to credit facilities as a supply-side challenge to financial inclusion. They mentioned that 

banks were often reluctant to provide credit to individuals with small and irregular incomes. 

This finding aligns with the findings that respondents earning between 7,501 and 15,000 

Kenyan shillings monthly, who were financially illiterate, reported being financially excluded 

and unable to access credit. It suggests that despite having a higher income than the previous 

group, these individuals may still face challenges in accessing credit due to factors such as 

limited financial literacy. 

5.5 Machine Learning Model 

The developed machine learning model achieved an overall prediction accuracy of 90.9%. This 

means that the model correctly classified individuals as financially included or not in Kenya 

with an accuracy of nearly 91%. Moreover, the precision score of 86.9% indicates the 

proportion of true positive predictions out of all positive predictions made by the model. In 

other words, when the model predicted an individual to be financially included, it was correct 

around 86.9% of the time. The recall score of 84.4% indicates that the model's ability to 

correctly identify financially included individuals among the total population is approximately 

84%. The F1 score, which combines both precision and recall, was calculated to be 85.6%. 

This score provides a balanced measure of the model's performance, considering both its ability 

to correctly classify positive instances and its overall accuracy. 

 

 

 

 

 

 

 



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CHAPTER SIX: CONCLUSION AND RECOMMENDATION 

6.1 Conclusion 

In conclusion, the findings highlight several factors that influence financial inclusion and 

access to credit. Higher levels of education were found to be positively associated with access 

to financial services and credit, emphasizing the importance of education in improving 

financial inclusion. Urban residence is also a significant factor, with urban areas offering better 

infrastructure and connectivity that facilitate access to financial services. Gender, contrary to 

some previous studies, was not found to have a significant influence on financial inclusion in 

this study. Digital financial services, such as formal digital credit services and mobile banking 

savings accounts, were found to be associated with increased access to financial services and 

credit. However, the study also revealed a contradictory finding that participants with 

subscriptions to any credit institution were less likely to have access to financial services and 

credit, challenging the notion that advanced financial instruments automatically enhance 

financial inclusion. The impact of ATM usage on financial inclusion appears to be more 

complex and context-dependent. While some studies have shown positive effects, this study 

suggests that ATM usage may be associated with reduced access to financial services. 

Conversely, access to the internet is found to be a significant facilitator of financial inclusion, 

aligning with previous research highlighting the role of information technology in improving 

access to financial services. The contribution of informal financial services to financial 

inclusion and access to credit varied. While the usage of informal savings accounts was 

associated with higher odds of accessing financial services, current users of informal remittance 

services were less likely to have access. The promotion of informal financial services, 

particularly shylock money lending services, was found to be efficient in promoting financial 

inclusion. However, the usage of informal credit services was associated with reduced access 

to financial services and credit. The study also identified potential barriers to financial inclusion 

and access to credit. Lack of funds and limited access to credit facilities were significant 

challenges. Higher income levels were found to positively impact financial inclusion, while 

limited financial literacy posed obstacles to accessing credit. These findings underscore the 

importance of addressing supply-side challenges and improving financial literacy programs to 

enhance financial inclusion. 

For the dashboard, it provides insightful visualizations and analysis into the key factors driving 

financial access in Kenya, based on survey data.  



40 
 

 

The interactive charts allow users to explore financial inclusion across different demographics 

like gender, age, education level, and residence. The regression models quantify the direction 

and magnitude of influence of various factors.  

Key takeaways are: 

i. Formal financial account ownership is still low, highlighting the need for greater 

financial inclusion 

ii. Digital financial services and technology adoption are strong enablers for improving 

access 

iii. Younger, educated, urban populations have higher financial inclusion   

iv. Women and rural groups need tailored products to address their lower access 

v. Barriers like low income, lack of credit, and financial illiteracy need to be addressed 

 

Below are responses to the Research Objectives: 

Research Objective 1) To identify the key factors that influence financial inclusion and 

access to credit in Kenya. 

This research objective has been successfully achieved. The study analyzed several factors 

including demographics, technology adoption, financial services usage and barriers to assess 

their impact on financial inclusion in Kenya. Key factors that were found to have a significant 

influence include - education level, location type, use of digital financial services and access 

to credit. The results align with multiple past studies such as Nanda (2018) and Gakii (2012) 

that also identified socioeconomic indicators as drivers of financial inclusion. Thus, the 

objective of recognizing major determinants of financial access has been adequately 

accomplished based on the analytical outcomes. 

 

Research Objective 2) To examine the relationship between technology adoption and 

financial inclusion in Kenya 

The objective to examine the technology-financial inclusion relationship has been partially 

fulfilled and allows scope for further investigation. Measures for technology adoption mainly 

comprised mobile phone ownership, internet access and digital payments systems. Significant 

positive association was discovered between internet access and higher likelihood of financial 



41 
 

inclusion. However, usage of ATMs was linked to lower access contrary to some prior 

research. While indicative of the role of technology, studying more intricate metrics like 

frequency and purpose of technology use could provide greater insight. Follow up work 

should explore the contextual and demographic nuances around technology usage.   

 

Research Objective 3) To examine how informal financial services contribute to 

advancing financial inclusion and access to credit in Kenya 

The impact assessment of informal financial services achieved substantial coverage through 

metrics like savings groups, lending services and remittances but remains slightly incomplete. 

Current usage of some informal mechanisms like lending services showed increased financial 

inclusion probability. However, directions were mixed across different services warranting 

additional focus into service-specific effects. Comparisons with other developing country 

contexts could also highlight the unique financial culture influencing preference for formal or 

informal access channels in Kenya. Thus, while informative patterns have emerged, more 

targeted probing is required into distinct traditional financial instruments. 

 

Research Objective 4) To identify potential barriers to financial inclusion in Kenya 

The identification of barriers restricting financial access has been fulfilled based on the 

study's findings. Major obstacles recognized include lack of income, high costs of financial 

services, remote locations with accessibility challenges, and inadequate financial awareness. 

The results align with barriers uncovered in similar developing country contexts by studies 

like Varghese & Viswanathan (2018) and Fungacova & Weill (2014). Significant gaps in 

financial inclusion exist across income levels, geographies, literacy status and access to credit 

channels. Addressing these pain points through policy reforms, infrastructure improvement 

and tailored financial products is pivotal to enhance usage and affordability of formal 

financial services among the unbanked population. Thus, the fourth objective to highlight 

impediments to universal financial inclusion provides critical recommendations for targeting 

reforms. 

 

6.2 Recommendation 

Given the positive association between education and financial inclusion, efforts should be 

made to strengthen financial literacy programs. Providing accessible and comprehensive 

financial education initiatives will empower individuals with the knowledge and skills 

necessary to make informed financial decisions, access financial services, and effectively 



42 
 

manage credit. Urban areas have shown better infrastructure and connectivity, enabling easier 

access to financial services. To bridge the urban-rural divide, it is crucial to invest in developing 

infrastructure in rural areas. This includes establishing physical branches or agents, expanding 

digital connectivity, and ensuring adequate banking infrastructure to extend financial services 

to underserved regions. The study highlights the positive impact of digital financial services on 

financial inclusion. Policymakers and financial organizations should continue to promote the 

adoption of formal digital credit services and mobile banking savings accounts. This can be 

done by creating an enabling regulatory environment, encouraging partnerships between 

financial institutions and technology providers, and raising awareness about the benefits and 

accessibility of digital financial services. The study identifies several barriers to financial 

inclusion, such as lack of funds, limited access to credit facilities, and low financial literacy. 

To overcome these challenges, policymakers should focus on addressing