6. Analysed consumer behaviour and relationship using Financial literacy dataset. Identified patterns and predictor variables using logistic regression. Methodologies & Tools: IBM SPSS, RapidMiner, PowerBI

1. Analyzing Financial Well-being of a person based
on the Financial Behaviour
Shantanu Deshpande
x18125514
MSc. Data Analytics Jan 19
Analytical CRM - Cohort B
Abstract—In order to improve the economics of a certain
demography, it is important to understand the level of financial
knowledge possessed by individuals and the current financial
situation they are dealing with. This study interprets the distri-
bution of financial knowledge and situation based on different
demographic factors like the age group, income slab, gender etc.
which could be further utilized by policymakers, practitioners
and financial advisors to strategize and improve their business
prospects. Various techniques like grouping, lookups, graphical
representations among related variables have been carried out
using Tableau and R to find out clusters of people for specific
targeting of products and services. The results show that people
with high income and young age group have their financial goals
defined and also have understanding of financial knowledge. This
cluster could be targeted to promote financial products whereas
additional efforts in certain areas like distribution of financial
knowledge to the lower income group by the policymakers can
make them potential customers in the future.
Index Terms—Keywords: Financial literacy, Financial well-
being, Community survey, Analysis
I. INTRODUCTION
The importance of financial literacy is mostly understated
throughout ones education career. Various studies have ob-
served that financial knowledge is one of the key determinants
of wealth inequality. The right amount of financial knowledge
enables a person in better allocation of resources and helps
keep one secure from the world of uncertainty and financial
crisis. Without a fair understanding of fundamental financial
concepts, people normally fail to make sound decisions related
to financial management. Adequate financial literacy can im-
prove a persons ability to make appropriate financial choices
with regards to saving, borrowing, investing and more. This
study focuses on the key aspects of financial literacy of a
person and the impact that it can have on their financial
freedom and goals. A community survey that assessed the
financial knowledge of individuals in a community has been
taken as the source for this study.
Community surveys are one of the best methods to under-
stand the demographics of a region as it involves the direct
responses from the citizens. A fair analysis would help the
citizens and practitioners and policymakers to provide better
financial life to more families and thereby help them in leading
better lives.
II. HYPOTHESIS
The null hypothesis for this study is People who have a good
level of income also have a good level of financial freedom
determined through their financial situation.
III. GOALS OF THE PROJECT
The primary goal of this study has been to determine
whether there exists any relationship between the financial
situation of an individual with the persons gender, age, income
and various other demographics. With the analysis we can then
figure out and understand the extent of a persons financial
behaviour based financial knowledge possesed. From the de-
mographics data, it can also be possible to understand which
age group of people are better in recognizing a sound financial
investment quickly. Moreover, which type of gender is more
strong in taking complex financial decisions. All these study
and analysis would thereby be very helpful for policymakers
and professionals to specifically target the clusters based on
their different product and service portfolio.
IV. LITERATURE REVIEW
In latest years, attention to financial literacy has increased
by many folds, and it has gained attention across a wide range
of banking companies, also among the government agencies
and community interest groups. It is equally important for an
individual to understand the importance of financial literacy
as it’s deficiency will inadvertently affect a family’s well
being. Such deficiencies in financial literacy may influence
the routine management of funds by an individual or family
and the capacity to save for long-term objectives such as
purchasing a home, higher education or pension funding.
According to a previous study [1], individuals who do not
fairly understand the concept of compound interest generally
spend higher amounts on transaction fees, move into bigger
debts and attract high interest rates on the loans. Sound
investment decisions can accrue the wealth of an individual
and improve the persons living conditions however such
decisions are only possible if the person is well acquainted
with the financial jargons and key investment areas. Else these
individuals end up borrowing more money and saving less.
The importance of financial literacy is also mentioned in [2],
it emphasizes the fact that individuals having strong financial

2. skills are betters in terms of job planning and better in savings
for retirement.
In the Global SP FinLit Survey, the literacy of an individual
was assessed based on four fundamental concepts that mea-
sured the financial decision-making capability of the person.
They were interest compounding, risk diversification, inflation
and numeracy. The person with atleast three correct answers
out of four was considered literate. Through this study, it was
observed that only 33% percent of adults have knowledge of
basic financial concepts.
In another interesting study[3], multiple regression has been
performed by the author in order to understand whether
objective and subjective knowledge about finance influence the
financial behaviour of an individual. The statistical technic of
liner regression was used while best practice behaviours has
been used as the dependent variable.
Education plays a key role in the lack of financial knowledge
with a direct relation between education and the level of
financial knowledge. Improved knowledge will also result in
the better decision-making capability of an individual [4].
Although few studies have shown that improved knowledge
does not necessarily mean that the person has a good level of
financial knowledge and the studies till date have shown that
financial knowledge is no doubt important however the exact
nature of the acquired knowledges impact is of big question
on the overll financial well being[5].
V. DATASET
The survey dataset that has been used for this analysis is
titled as Financial well-being survey data and is published on
data.world. The survey is mainly focussed on understanding
the factors that would assist in supporting consumer well-being
by helping practitioners and policymakers to target specific
individuals based on the financial knowledge they possess.
This data contains individual survey data wherein numerous
financial information from a user has been collected and
maintained in an csv format. The initial data contained 6,395
rows and 217 columns. For each person who undertook the
survey, an ID has been assigned and the columns consist of
the users rating surrounding the current financial situation of
the individual. In the following section we will see the data-
preprocessing and analytical techniques.
VI. METHODS AND TECHNIQUES
A. Data Preprocessing
This part plays an important role in any type of analysis.
It is crucial to know the fact that the data we extract is never
in the exact form that we need for analysis however it needs
to be cleaned and trimmed to perform further activities. For
the following study, R has been used extensively to clean and
process the data and thus prepared data for analysis. The steps
that have been used to clean the data are
1. As mentioned above, the number of columns were 217.
Only the columns that were required for analysis i.e. 20
columns were kept, and the rest were removed from the
dataset.
Fig. 1. Distribution of individuals based on age group
2. There were few missing values in the dataset which were
then taken care of by using the is.na function.
3. Due to the nature of data being survey data, it contained
categorical values which were in the numeric form and a
separate pdf file was maintained that had the corresponding
response variable. It was then replaced using the look up
function.
4. The column names were in a different format and were
then given appropriate names and wherever required replaced
using names() function.
B. Methodology
The strategy and purpose behind this study is to figure out
the group of individuals who have a better level of financial
knowledge and to what extent it is an important factor to
have a good amount of money in their savings account. This
analysis would be useful in improving the financial condition
of individuals by specifically targeting the ones who lack in
certain aspects of financial planning and this would thus help
them in achieving their financial goals.
The specific individuals can be targeted based on their
income, age, gender and their responses to certain determinant
questions regarding financial literacy. While working with a
demographic survey data, it is important to first understand
how the demographics are categorized and distributed in order
to properly assess the analytical outcome of the survey and
accordingly strategize to target the audience. In this case,
we plotted a bar graph (Fig. 1) of age group to analyse
the population distribution and found out that the maximum
number of individuals questioned in the survey lie in the age
group of 25-34 with count of 1,116. Let us also have a look at
the correlation and weights between the savings variable and
the related variables. For figuring this factor, we have made
use of RapidMiner. It is evident from the figure that paying off
credit card bills in full each month is highly correlated among
other variables with the savings variable.

3. Fig. 2. Weights and correlation among variables
Fig. 3. Distribution of individuals based on income slab
C. Analysis
The dataset contains the income slab of the individuals and
the groups are assigned a numeric value. This numeric value
had to be replaced with the specific income slab by using
the look up function. Through this the proportion of people
based on their income slab could be understood and the highest
proportion can be noted. Figure 3 clearly portrays that majority
of people considered for this survey are within the income slab
of $100,000 to $150,000 with count 1,115 and this is also
the second largest slab in the dataset.
After understanding the demographic distribution of the
dataset, we can now move on to figure out the level of
financial knowledge and the current financial situation of these
individuals. To do so, first lets see how many individuals have
a current or recent financial goal in mind. From fig 4. It can
be noted that majority of people have a financial goal in their
mind however from the chart it is interpretable that the largest
Fig. 4. Financial goals of individuals based on income slab
proportion of individuals with income lying between $100,000
- $149,999 have a current or recent financial goal in mind.
Second largest proportion of individuals who have figured out
their recent financial goals lie in the income slab of $75,000 -
$99,999. From figure 5. it can be noted that within the former
income slab, individuals between age group 45-54 have their
financial goals figured out followed by individuals between
age group 25-34.
Lets drill down further and understand whether these indi-
viduals have enough money in their bank accounts at the end of
every month so as to divert a part of it to achieve their financial
goals. From figure 6. it can be well noted that more than fifty
percent of people within age group of 45-54 often or always
have money leftover at the end of every month. Therefore,
policymakers and financial advisors can target these specific
cluster of people to pitch their investment portfolio/schemes.
For further analysis on the individuals having a financial
goal in mind, gender-based bifurcation has been done on the
level of struggle people face to understand the financial infor-
mation. From figure 7. it is evident that females normally face
more difficulty in understanding financial information than
males who rarely find it difficult to figure out an investment
option. Fig. 8 corresponds to the proportion of females and
males who have a significant amount of money in their savings.
Among the respondents, around thirty percent of females have
a savings of more than $20,000 whereas there are more males,
thirty five percent among the respondents who have a savings
more than $20,000. Although we are aware of the fact that

4. Fig. 5. Financial goals of individuals within income slab $100,000 - $149,999
Fig. 6. Whether money left in account at month end
females normally have a tendency of maintaining a savings
cushion however based on our data, it is the males who are
better at saving hence the financial advisors or policymakers
need to target more males than females.
Figure no. 9 is useful for credit card companies to particu-
larly target the age group who sometimes or seldom pay their
credit card bills on time. This type of targeting is helpful for
the companies as the interest rates are high if bills not paid on
time and thus they can strategize and target accordingly. The
figure clearly depicts that males females within the age group
25-34 have highest proportion claiming that they sometimes
pay credit card bills on time. Also from this graph they can
avoid targeting the age group below 24 years and above 70
years.
In figure 10 it is significant that the individuals in the age
group 25-34 are the ones with the largest proportion who live
Fig. 7. Understanding of financial information
Fig. 8. Gender based monthly savings
on rent whereas the trend decreases with increase in age. In
the previous graph we observed that the individuals in this age
group have their recent or current financial goals figured out.
Based on both the graphs, we can say that most of them plan
on purchasing a home for themselves. This information would
be particularly useful for property consultants in identifying
the cluster of people who are more likely to purchase a
property and limit their time in marketing their properties to
individuals beyond the age slab of 54 years. The value of all
the above analysis is discussed in detail in the next section.

5. Fig. 9. Regularity in credit card bill payments
VII. VALUE
The primary goal of this project is to note the persisting
financial situation of an individual based in a certain demog-
raphy. These individuals may then be grouped under certain
clusters to further derive a pattern and help the policymakers
and financial advisors to particularly target the ones that best
suits their business requirements. Although it is evident from
the study that the individuals with a good amount of income
have their financial goals sorted, we have further drilled down
based on age group and gender. Following are some proposed
schemes that are able to support with the help of this study
1. As noted from the initial graphs, it is of foremost
importance that an individual must have a financial goal in
mind. People with high income and low age group of 25-34
are the ones with maximum count of individuals who figured
out their goals. Most financial goals are either purchasing
property or buying a vehicle. Financial advisors and property
consultants can strategize and target the individuals within
this clusters to improve their sales and thereby contribute in
helping individuals achieve their financial goals.
2. Secondly, credit card companies make their earnings
through the high rate of interest applicable if a person exceeds
the interest free period and fails to pay bills on time. Through
one of the visualization, we note that for both genders, people
within age group 25-34 are most lenient regarding the payment
of credit card bills on time. Credit card agents could thereby
introduce schemes that would promote sales for this age group
and thereby the revenue.
3. The housing situation of individuals is well portrayed
through the area graph which highlights the fact that with
increase in age, people move into their own homes. Thus
property consultants must spend little efforts in targeting the
aged people into purchasing a house and really focus on the
cluster of young individuals who would most likely have their
financial goal as purchasing a property.
4. Through the pie chart, the significant percent of women
having large amount of savings can be noted although it can be
noted from the vertical bar graph that as compared to males,
females find it somewhat difficult to understand financial
information. If further drilled down, financial advisors can
Fig. 10. Housing status of individuals
leverage this cluster of individuals and promote their various
investment schemes.
VIII. CONCLUSION
In this study, we have analyzed the financial well being
survey data of a particular demography using various data an-
alytical methods like grouping, look ups, graphical representa-
tions of the related variables using different tools like Tableau
and R. The visalizations that have been derived from this data
are well explanatory and appealing which thereby makes it
easily interpretable for business purposes. The primary thing
to note from the data is that the people who have high
income have a more thorough financial understanding and have
their financial goals figured out. Although our visualizations
could immediately help the policymakers and consultants to
specifically target the cluster of people who could readily
chose their investment recommendations however they can
also take additional efforts to expand the financial knowledge
of individuals who lack a direction regarding their financial
goals and have limited money in the savings. Through the
inferences that have been drawn from the study, there has been
an attempt to provide a certain level of business value that can
help certain sectors to expand their business presence.
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