Using Artificial Intelligence to Create Value in
Insurance Sector
Chapter 1
1.0 Introduction
In this chapter, the researcher will explain the background of the study, the problem statement,
research questions, the objective of the research, significance of the study, scope of the study
limitations of the study, and the definitions of the study relates with the research topic, using
Artificial Intelligence to create value in insurance sector specifically in fraud detection and
prevention.
1.1 Background of Study
The world that we are living today is full of uncertainties and risks such as accident, health
danger, business crisis and many more. Losses of money will happen once these uncertainties
happen in our life. Thus, people created a protection in managing these risks which we called as
insurance today. Insurance is a contract, represented by a policy, in which an individual or an
organization receives financial protection to prevent or reduce costs of loss from an insurance
company (Investopedia, 2020).
Over the past several years, human advanced technologies have been improved significantly.
One of the most valued technologies would be Artificial Intelligence (AI). AI is a highly
advanced technology which allows machines or computer systems to have similar thinking and
intelligence behavior as humans. This includes gathering information, analyzing data, learning
new things and making decisions.
The improvement in accessible data, computing abilities and even predicting the future has led to
a high acceleration of AI development. Humans are now using AI throughout the landscape of
our lives often without realizing it. World leading companies such as IBM, Apple, Google and
Amazon are using AI platforms and solutions for customers, partners and employees. It is
disrupting and improving companies and organizations across all industries including insurance
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industry. In the insurance industry, AI has proved itself in a successful way in areas such as
underwriting, customers service and also fraud detection (Content.naic.org, 2020).
The purpose of this study is to evaluate how AI brings advantages in insurance industry, more
specifically in detecting fraud claiming. Many people try to take advantage of the insurance
system and make quick profit by committing fraud claiming. Insurance fraud happens when the
claimant makes up information or exaggerates information in order to get the money guaranteed
by their insurance policy. Some of the most common types of insurance fraud claimed are
accident insurance fraud, contractor insurance fraud, break-in insurance fraud and also disaster
insurance fraud (TransUnion, 2020). Thus, this study is important as we can understand how AI
can encounter or reduce the cases of fraud claiming in worldwide.
1.2 Problem Statement
The issue of fraudulent activities had risen for a long time ago. Insurers have historically relied
on mathematics and manual work to measure the risk to detect fraud. Thus, it is time consuming
and complex. They mainly depend expert scrutiny, adjusters and special investigations team
(Dhieb, Ghazzai, Besbes and Massoud, 2020). Based on the Federal Bureau Investigation (FBI),
there is a total cost of US$40 billion caused by insurance fraud a year in United States which is
the largest insurance market in the world. According to the Association of British Insurers,
United Kingdom has detected around 125000 insurance fraud cases amounting to 1.3 billion
pounds in 2016 (The Edge Markets, 2020). According to businesswire, in 2018, the total global
insurance fraud detected was US$3.29 billion and is expected to grow further, mounting at a
compound annual growth rate of 15.2% from 2019 to 2027 (WIRE, B, 2020). Therefore, from
the statistic gathered from all around the world, relying on mathematicians and manual work to
detect fraudulent activities in insurance sector did not work well.
Thus, people start to use advanced machine such as AI to accomplish what they cannot in a high
efficient ways. Studies show that companies such as Anadolu Sigorta, the first and one of the
largest insurance company in Turkey, they used an AI predictive software from Friss, they had
successfully saved US$5.7 million in fraud detection and prevention costs. This is because
before they apply the AI predictive software, they have to investigate 25000 to 30000 claims
2
within two weeks manual process. Another example we can see from AXA, one of France’s top
insurance companies, they also purchased an AI fraud detection software. The software they
used is monitoring their entire network and the ability to contain emerging threats before they
become a main issue (Mejia, N, 2020).
However, although most respondents’ companies that deployed AI software claims that it helps
achieving what manual work can’t, only 21% of those respondents really apply it into more
detail usage. This indicates that AI haven’t be the first choice of problem solver in their
consideration. Many organizations still do not require the fundamental skills to use AI in
detecting fraud like having clear strategies in collecting data required by AI (mcKinsey, 2020).
Besides that, there are studies that shows that the adoption of AI in insurance sector are having a
significant different between Asia and other western countries. According to the Insurance AI &
Analytics Survey 2018, more than 70% of all North American insurance carriers have already
implementing and investing in AI projects (Bharadwaj, R, 2020). On the other hand, according to
Srikanth Venkatesan, the Asia Pacific head of insurance, only around 40% of all large insurance
organizations reported AI deployment in some of their projects (Olano, G, 2020).
Thus, the purpose of this research is to provide the insurance sector a better understanding about
the importance and value that an AI can provide to every companies. It can increase the
efficiency of the company by significantly outperform the ability of a human and also decrease
fraudulent claiming insurance sector to avoid unnecessary monetary losses.
1.3 Research Questions
Central Questions: How does artificial intelligence relates in insurance industry?
Sub Questions: How artificial intelligence create values in insurance industry?
1.4 Significance of Study
The purpose of this study is to investigate the value of Artificial Intelligence in insurance
industry specifically in detecting fraudulent activities. This allows insurers to understand the
seriousness of the fraudulent claim which caused billions of financial losses in the insurance
3
sector. Besides that, the researcher believes that this study will also provide the insurers on the
benefits of Artificial Intelligence in detecting and preventing fraud claiming.
1.5 Scope of Study
The scope of this research is the insurance sector in general. There are so many insurance
companies in the world from different field such as life insurance, general insurance, health
insurance and many more. In the United States, according to the National Association of
Insurance Commissioners, there are a total of 5965 insurance companies in 2018 (Iii.org, 2020).
In Malaysia, there are a total of 22 licensed insurance companies and takaful operators based on
the Central Bank of Malaysia (Bnm.gov.my, 2020). This focal point of this study is on insurance
companies that implement artificial intelligence specifically in detecting and preventing
fraudulent claims.
1.6 Limitations of Study
Limitations of study are inevitable. In data collection process, there might be some limitations
such as the data or information needed may be confidential, participants may be unwilling to
provide them due to corporate restrictions. Furthermore, the usage or technology of artificial
intelligence are overall still in developing or research process, the tech still has very huge
improvement conditions. In other words, artificial intelligence still isn’t very common and
acceptable by people. On top of that, time, cost and access to this study information are also
considered limitations of study as this research must be completed within the limited time given.
However, a flexible and suitable research methodology will be proposed in order to deal with
these challenges.
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1.7 Definitions of Terms
The conceptual and operational definitions of key terms in this study are as follow:
Term
Machine Learning
Supervised Learning
Conceptual Definition
Operational Definition
Machine learning is the
Machine learning operations
concept that a computer
is the use of machine learning
program can learn and adapt
models by
to new data without human
development/operations
intervention. Machine
teams. It seeks to add
learning is a field of artificial
discipline to the development
intelligence that keeps a
and deployment of machine
computer’s built-in
learning models by defining
algorithms current regardless
processes to make ML
of changes in the worldwide
development more reliable
economy (Investopedia,
and productive (WhatIs.com,
2020).
2020).
Supervised learning is the
Supervised learning is where
machine learning task of
you have input variables (x)
learning a function that maps
and an output variable (Y)
an input to an output based on and you use an algorithm to
example input-output pairs. It
learn the mapping function
is the process of an algorithm
from the input to the output.
learning from the training
Y = f(X)
dataset can be thought of as a
The goal is to approximate
teacher supervising the
the mapping function so well
learning process (Brownlee,
that when you have new input
J., 2020).
data (x) that you can predict
the output variables (Y) for
that data (Brownlee, J.,
2020).
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Unsupervised Learning
Unsupervised learning is a
Unsupervised learning is
type of machine learning that
where you only have input
looks for previously
data (X) and no
undetected patterns in a data
corresponding output
set with no pre-existing labels
variables. The goal for
and with a minimum of
unsupervised learning is to
human supervision. It is
model the underlying
unlike supervised learning
structure or distribution in the
above there is no correct
data in order to learn more
answers and there is no
about the data (Brownlee, J.,
teacher. Algorithms are left to 2020).
their own devises to discover
and present the interesting
structure in the data
(Brownlee, J., 2020).
Data Mining
Data mining is a process of
Data mining involves
discovering patterns in large
exploring and analyzing large
data sets involving methods
blocks of information to
at the intersection of machine
glean meaningful patterns and
learning, statistics, and
trends. It can be used in a
database systems. It depends
variety of ways, such as
on effective data collection,
database marketing, credit
warehousing, and computer
risk management, fraud
processing (Investopedia.
detection, spam Email
2020).
filtering, or even to discern
the sentiment or opinion of
users (Investopedia. 2020).
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Chapter 2: Literature Review
2.0 Introduction
This chapter reviews the extant literature on fraudulent claiming in insurance sector, the
importance of artificial intelligence in insurance sector specifically in detecting and preventing
fraud follow by how artificial intelligence works in fraudulent claiming. Besides that, the
researcher will be discussing in the challenges and issues faced by artificial intelligence while
detecting and preventing fraud.
2.1 Fraudulent Claiming in Insurance Sector
There are many definitions of fraud and fraudulent activities. The Association of Certified Fraud
Examiners (ACFE) defines fraud as the use of one's occupation for personal enrichment through
the deliberate misuse or misapplication of the employing organization's resources or assets. In
recent years, insurance fraud detection has garnered large amounts of attention because a range
of fraudulent methods have brought great loss to insurance companies and society as a whole.
Insurance fraud detection is a branch of financial fraud detection (Abdallah, A. and Zainal, A,
2016). Nevertheless, the amounts involved in fraud have certainly increased as insurance made
its transition into modern consumer society. The industry has been facing a problem of
increasing prevalence and of sizeable proportions. Insurance fraud and, more generally, abuse of
insurance not only put the profitability of the insurer at risk, but also negatively affect its value
chain, the insurance industry, and may be extremely detrimental to established social and
economic structures. They are believed to materially escalate the cost of certain types of
insurance such as automobile, fire and health insurance. Eventually, they form a threat to the
very principle of solidarity that keeps the insurance concept alive (The Geneva Papers on Risk
and Insurance, 2004). Thus, based on previous studies, it shows that insurance fraudulent
activities have been occur ever since the beginning and claims that almost all technological
system that involves money and services can be compromised by fraudulent acts (Almeida,
2009).
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2.2 Artificial Intelligence in Insurance Industry
According to Wang Y (2018), in order to conquer the problem of insurance fraud, researchers
have invested great effort into finding effective fraud indicators and methods. Fraud indicators
play a critical role in insurance fraud detection. Appropriate indicators definitively make it
possible for detection methods and algorithms to maximize the effectiveness of detection.
Previous studies show that in the past, insurers have historically relied on mathematicians to
measure risk and formulate premium rates for policy underwriting that would ensure rational
levels of payouts without endangering the company’s financial health. Traditional insurance
fraud detection methods are complex and time-consuming. They mainly depend on expert
scrutiny, adjusters, and special investigation services. Added to that, manual detection results in
additional costs and inaccurate results. Moreover, late decisions might lead to extra losses for the
insurance companies (Dhieb, N., Ghazzai, H., Besbes, H. and Massoud, Y, 2020).
Artificial intelligence methods include data mining, statistical, mathematical, and machine
learning techniques to extract and identify useful information and subsequent knowledge from
large databases. These systems have several main advantages such as fraud patterns are obtained
automatically from data, specification of “fraud likelihood” for each case, consequently that
efforts in investigating suspicious cases can be prioritized and revelation of new fraud types that
were not defined or seen before (Li et al., 2008). Thus, with the advantages mentioned, it can
increase the efficiency in detecting and preventing fraud claiming as compare to manual process.
2.3 How artificial intelligence detect and prevent fraud
Fraud is increasing dramatically with the progression of modern technology and global
communication. As a result, fighting fraud has become an important issue to be explored. The
detection and prevention mechanisms are used mostly to combat fraud. Fraud protection systems
can be divided into two categories, fraud prevention system and fraud detection system.
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2.3.1 Fraud Prevention System
Fraud prevention system is the first layer of protection to secure the technological systems
against fraud. The purpose of this phase to stop fraud from occurring in the first place.
Mechanisms in this phase restrict, suppress, destruct, destroy, control, remove, or prevent the
occurrence of cyber-attacks, in computer systems (hardware and software systems), networks, or
data. Example of such mechanism includes using encryption algorithm that is applied to
scramble data. Another mechanism is firewall where it forms a blockade between the internal
privately owned network and external networks. It does not only help to secure systems from
unauthorized access but also to allow an organization to enforce a network security policy on
traffic flowing between its network and the Internet (Asherry, M, 2013). However, according to
Belo and Vieira (2011), this layer of the system is not always efficient and strong. There are, in
some occasions, where prevention layer could be breached by fraudsters.
2.3.2 Fraud Detection System
Fraud detection system is the next layer of protection which is also the concern of this paper.
Fraud detection tries to discover and identify fraudulent activities as they enter the systems and
report them to a system administrator (Behdad et al., 2012). In previous years, manual fraud audit
techniques such as discovery sampling have been used to detect fraud, such as in Tennyson and
Forn (2002). These complicated and time-consuming techniques transact with various areas of
knowledge like economics, finance, law and business practices. Therefore, to raise the
effectiveness of detection, computerized and automated fraud detection system was invented.
However, fraud detection system capabilities were limited because the detection fundamentally
depends on predefined rules that are stated by experts (Li et al., 2008).
2.4 Machine Learning
Machine learning is an application of artificial intelligence that have the ability to learn by itself
automatically and improve over time from experience independently. Machine learning focuses
on the development of computer programs that can access data and use it learn for themselves.
According to Michalski and M. Kubat (1998), To go beyond, a data analysis system has to be
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equipped with a substantial amount of background knowledge and be able to perform reasoning
tasks involving that knowledge and the data provided. In effort to meet this goal, researchers
have turned to ideas from the machine learning field. This is a natural source of ideas, since the
machine learning task can be described as turning background knowledge and examples into
knowledge. The machine learning and artificial intelligence solutions can be categorized into
two, supervised and unsupervised learning. These methods seek for data such as accounts,
customers, suppliers and many more that behave unusually (Bolton, R. & Hand, D, 2002).
2.4.1 Supervised Learning
When comes to supervised learning, all the records taken can be automatically classified as
fraudulent or non-fraudulent. This can help insurer fraud examiners to save their time to monitor
each and every claim. In the care of fraudulent activities, it requires huge sample size to obtain
the results. These records are then used to train a supervised machine learning algorithm and
eventually have the ability to automatically classify new records as either fraudulent or nonfraudulent (Dal Pozzolo et al., 2014). However, supervised learning has several limitations. The
first one is caused by the difficulty of collecting supervision or labels. When there is a huge
volume of input data, it is prohibitively expensive, if not impossible, to label all of them. Second,
sometime, it is extremely hard to find distinctive label, there are uncertainties and ambiguities in
the supervision or labels. These limitations may obstruct the implementations of the supervised
learning approaches in some cases. Therefore, unsupervised learning and semi-supervised
learning is used to overcome these disadvantages (Liu, Xu and Yu, 2011).
2.4.2 Unsupervised Learning
Unsupervised learning techniques detect fraudulent in an unlabeled test data set under the
assumption that majority of the instances in the data set is nonfraud. Unlike supervised
technique, unsupervised means there is no class label for model construction. The main benefit
of using unsupervised approach is that it does not rely on accurate identification for label data
which is often in short supply or non-existent (Bolton, R. & Hand, D, 2002).
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2.4.3 Semi-supervised Learning
Semi-supervised learning lies between supervised and unsupervised learning since it involves a
small number of labeled samples and a large number of unlabeled samples. The main goal of
semi-supervised approach is to train a classifier from both labeled and unlabeled data (Zhu et al.,
2011). Semi-supervised learning has more advantage compared to supervised learning because it
achieves better performance by utilizing both labeled and unlabeled data, but with fewer labeled
instances. Furthermore, semi-supervised learning also provides a computational model to
understand human category learning, where most of the input is self-evidently unlabeled (Xiaojin
Zhu and Goldberg, 2009).
2.5 Challenges and Issues
Fraud detection is a complex domain; we may find that a fraud detection system is prone to fail,
has a low accuracy rate, or gives many false alarms. It is extremely difficult for electronic
commerce systems to handle fraud problem forcing them to incur heavy losses. This happens
because fraud detection systems need to deal with multiple challenges to be taken into
considerations.
2.5.1 Concept Drift
Fraud detection systems work in dynamic environment where behavior of legitimate user or
fraudster is continuously changing is called the drift phenomenon concept. Concept drift
primarily refers to an online supervised learning scenario when the relation between the input
data and the target variable changes over time. Whereas, in supervised learning, the aim is to
predict a target variable y given a set of input features X. In the training instance that are used for
model building, both X and y correspond to input data and target variable, respectively. In the
new instance on which the predictive model is applied, X is known, but y is not known at the
time of prediction, and the relation between the input data and the target variable may change
(Gama et al., 2013).
2.5.2 Skewed Class Distribution
Skewed distribution (imbalanced class) is considered as one of the most critical issues faced by
fraud detection system. Generally, the imbalanced class issue is the situation where there are
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much fewer samples of fraudulent instance than normal instance. In a supervised learning
approach, the class imbalance problem happens when the minority class is very small, leading to
numerous problems such as disability of learners to discover patterns in the minority class data.
Furthermore, imbalance class has a serious impact on the performance of classifiers that tend to
be overwhelmed by the majority class and ignore the minority class (Liu et al., 2012).
2.6 Theory
2.6.1 Benford’s Law
Benford’s Law specifies the distribution of the digits for naturally occurring phenomena. For a
long time, this technique, commonly used in areas of taxation and accounting, was considered
mostly a mathematical curiosity as it described the frequency with which individual and sets of
digits for naturally growing phenomena such as population measures should appear. Such
naturally growing phenomena, however, has been shown to include practical areas such as
spending records and stock market values. One of the limits to the use of classic Benford’s Law
in fraud detection has been its requirement that analyzed records have no artificial cutoffs. In
other words, records must be complete (Lu and Boritz, 2005).
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Chapter 3: Methodology
3.1 Research Philosophy
According to (Saunders, Thornhill and Lewis, 2009), the term research philosophy defined as a
system of beliefs and assumptions about the development of knowledge. In other words, a
research philosophy is a belief of knowledge and data be gathered, analyzed and used. Positivism
relates to the philosophical stance of the natural scientist and entails working with an observable
social reality to produce law-like generalizations. Interpretivism developed as a critique of
positivism but from a subjectivist perspective. Interpretivism emphasizes that humans are
different from physical phenomena because they create meanings (Saunders, Thornhill and Lewis,
2009).
This research relies on Interpretivism. Interpretive approaches encompass social theories and
perspectives that embrace a view of reality as socially constructed or made meaningful through
actors' understanding of events. This emphasizes the difference between conducting research
among people instead of objects such as trucks and computers (Saunders, Thornhill and Lewis,
2009).
3.2 Research Approach
According to (Saunders, Thornhill and Lewis, 2009), research approaches are mainly based on
the research philosophies, whereby the deductive approach is commonly used by researchers
with traditional natural scientific views (positivism), while inductive approach is usually based
on phenomenology (interpretivism). With deduction a theory and hypothesis are develop and a
research strategy designed to test the hypothesis. With induction, data are collected, and a theory
developed as a result of the data analysis.
This research is based on inductive approach so as to get a better understanding on the nature of
the problem. Data will be collected through interview and the result of this analysis would be the
formulation of a theory. Research using an inductive approach is likely to be particularly
concerned with the context in which such events were taking place. Therefore, the study of a
13
small sample of subjects might be more appropriate than a large number as with the deductive
approach (Saunders, Thornhill and Lewis, 2009)
3.3 Research Strategy
According to (Saunders, Thornhill and Lewis, 2009), your choice of research strategy will be
guided by your research question and objectives, the extent of existing knowledge, the amount of
time and other resources you have available, as well as your own philosophical underpinnings.
According to Saunders, there are seven research strategies, namely: experiment, survey, case
study, action research, grounded theory, ethnography and archival studies (Saunders, Thornhill
and Lewis, 2009).
3.4 Research Choices
According to Vibha, Bijayini and Sanjay (2020), Qualitative research focuses in understanding a
research query as a humanistic or idealistic approach. Though quantitative approach is a more
reliable method as it is based upon numeric and methods that can be made objectively and
propagated by other researchers. Qualitative method is used to understand people’s beliefs,
experiences, attitudes, behavior, and interactions. Thus, due to the nature of this research, I will
be choosing qualitative research approach. A small sample size of in-depth interview will be
conducted.
3.5 Data
According to Hox and Boeijie (2020), primary data are data collected for specific research
problem, using procedures that fit the problem best. On every situation that primary data are
collected, new data are added to the existing store of social knowledge. On the other hand, the
material created by other researchers is made available for reuse by the general research
community, it is then called secondary data. For this research, primary data will be collected
using questionnaires. Questionnaires tend to be used for descriptive or explanatory research.
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3.6 Instrumentation/Questionnaire
In this qualitative research, the researcher will conduct a face-to-face in-depth interview with
participants. Interviews are useful to explore experiences, views, opinions, or beliefs on specific
matters. It is to develop an understanding of the underlying structures of beliefs. This interview
will involve semi-structured and generally open-ended questions that are few in number and
intended to elicit views and opinions from the participants. There will be around 4 to 5 questions
prepared prior the interview and follow by sub questions accordingly to ask the participants in
more details manner so as to obtain more useful information.
3.7 Sources of data collection
According to DiCicco‐Bloom and F Crabtree (2020), interviews are among the most familiar
strategies for collecting qualitative data. The different qualitative interviewing strategies in
common use emerged from diverse disciplinary perspectives resulting in a wide variation among
interviewing approaches. In this research, unlike the highly structured survey interviews and
questionnaires used in epidemiology and most health services research, the researcher examines
less structured interview strategies in which the person interviewed is more a participant in
meaning making than a conduit from which information is retrieved.
Semi-structured interviews are often the sole data sources for a qualitative research project and
are usually scheduled in advance at a designated time and location outside of everyday events.
They are generally organized around a set of predetermined open-ended questions, with other
questions emerging from the dialogue between interviewer and interviewees. The author will
have the ability to maintain the flow of interviews by adjusting the pace which suited both the
interviewer and interviewees. Thus, interviewees will have greater freedom to express and
discuss their ideas and thoughts compared to other research method like questionnaires. This
research method will allow the author to have direct interaction with interviewees, develop
deeper personal relationships and obtain a more in-depth understanding.
3.8 Sampling
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For this research, purposeful sampling will be used, This technique is widely used in qualitative
research for the identification and selection of information-rich cases for the most effective use
of limited resources (Patton, 2002). This will involve identifying and selecting individuals or
groups of individuals that are especially knowledgeable about or experienced with a
phenomenon of interest (Cresswell & Plano Clark, 2011). In addition to knowledge and
experience, the importance of availability and willingness to participate, and the ability to
communicate experiences and opinions in an articulate, expressive, and reflective manner is
important (Bernard, 2002).
There are no specific rules when determining an appropriate sample size in qualitative research.
Research suggested that sample size is influenced by many considerations, among them time and
cost (Bryman, 2008). Qualitative sample size may best be determined by the time allotted,
resources available, and study objectives (Patton, 1990). For phenomenological studies, Creswell
(1998) recommends 5 to 25 and Morse (1994) suggests at least six. Qualitative methods place
primary emphasis on saturation (i.e., obtaining a comprehensive understanding by continuing to
sample until no new substantive information is acquired) (Miles & Huberman, 1994. Therefore
this study will comprise between 5 to 20 interviews with respondents until the ‘saturation’ point
is reached. Invitations will be sent to qualified respondents. The objective was gathering ‘deep’
information and perceptions through interviews and representing it from the perspective of the
research participant.
3.9 Data Analysis
Qualitative research studies involve a continuous interplay between data collection and data
analysis. This research study followed the Creswell’s (2016) six steps during the data analysis
process. The steps to look at qualitative data analysis as following steps from the specific to the
general and as involving multiple levels of analysis. Step l is to organize and prepare the data for
analysis. This involves transcribing interviews, typing up field notes, or sorting and arranging the
data into different types depending on the sources of information. Step 2 is read through all the
data to obtain a general sense of the information and to reflect on its overall meaning. Step 3 is
beginning detailed analysis with a coding process. Coding is the process of organizing the
material into chunks or segments of text before bringing meaning to information. A qualitative
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code book was developed, and it is a record that contains a list of predetermined codes that
researchers use for coding the data. Manual hand coding was done. Step 4 is to use the coding
process to generate a description of the setting or people as well as categories or themes for
analysis. Step 5. is to advance how the description and themes will be represented in the
qualitative narrative. The most popular approach, a narrative passage to convey the findings of
the analysis. Step 6 is a final step in data analysis involves making an interpretation or meaning
of the data (Creswell, J., 2016).
3.10 Ethical Consideration
Ethical considerations relate to all phases of the research process. Research ethics refer to the
appropriateness of your behavior in relation to the rights of those who become the subject of
your work or are affected by the work. They also relate to yourself and ensuring no harm comes
to you and other researchers (Saunders, Thornhill and Lewis, 2009). As stated by (Saunders,
Thornhill and Lewis, 2009), potential ethical issues should be recognized and considered from
the outset of your research and are one of the criteria against which your research is judged. As
researchers anticipate data collection, they need to respect the participants and the sites for
research. Many ethical issues will arise during this stage of the research.
In this proposal, the researcher develops an informed consent form for participants to sign before
they engage in the research. This form acknowledges that participants' rights will be protected
during data collection. Another issue to comply about confidentiality is that some participants
may not want to have their identity remain confidential. In the interpretation of data, researchers
need to provide an accurate account of the information. This accuracy may require debriefing
between the researcher and participants in quantitative research. In order to protect personal
information of the interviewees, they will remain anonymous throughout the whole research
from data collection to data analysis. Their identifications will be hidden in the transcriptions.
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