Analysis of Online Fraud and Cyber Security Measures at Bank A, Sydney

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Business analysis
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Table of Contents
Executive summary.........................................................................................................................3
Introduction......................................................................................................................................4
Research Design..............................................................................................................................5
Hypothesis development..................................................................................................................6
Statically tools and Techniques.......................................................................................................8
Results, and Statistical and non-statistical interpretation..............................................................11
Analysis and summary report of the statistical data......................................................................15
Recommendations..........................................................................................................................16
Reference.......................................................................................................................................17
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Executive summary
Bank A needs to identify the cyber-security for the issues of the company and further they need
to recognize their security arrangement in the relevance of the cyber and online fraud for Bank.
To identified Online frauds and there, reason Bank conducted a Market survey in which they fill
up some of the questionnaires from the customer and also evaluated those results to identify the
issues of cyber-crime in the banking activities for the company. The company faced a high
amount for cyber issues in the context of online frauds. The company collected information from
a population in which they identified major resources from online fraud conducted and also
identified the quality of the security issue for Bank.
Bank collected information and further they need to identify the future possibility for online
frauds in the business activities of the bank and further various statistical tools are implemented
by the bank management on the data collected from the population. In which various finding are
identified and recommended various solutions which are based on the research finding for the
purpose of the bank. Bank collected data from 200 sample size in which they achieved approx.
492 samples for their research perspective for the company.
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Introduction
Sydney is the placed which is highly developed in the world. In Sydney, a bank is Bank A is a
financial institution work in Sydney, Australia where they faced many of the problems related to
their online transaction as online fraud is taking placed in business activities of the bank. Bank
A is a financial institution which is faced with a various issue related to online fraud activities in
Sydney. Bank conducted a market survey in which they identified various issues and try to
overcome that issue to improve the security framework for the company. In this study, Bank
evaluates information collected from the survey and also interpreted that data in appropriate
finding which helps management in better decision making for the purpose of improvement in
the quality of security. Banks can analysis data by using T-test, Descriptive analysis and further
statistical tool for an adequate recommendation for the information.
Bank conducted research and market analysis on the sample size of 2000 customer in which 492
customers provide information which is further analysis by the use of the parametric and non-
parametric test for proper finding and recommendations to overcome online fraud activities and
improve their framework for security. Company's main objective to analyze the information
which helps them to increased their brand value and customer experience in the market.
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Research Methodology
Research methodology is the principle which includes all tools and techniques which helps the
researcher in collection and interpretations of the data which helps the researcher in better
decision-making (Kumar, 2019). Research Methodology guides the researcher from the
collection of the data to the interpretation of the data. It provides a guideline for the research to
implement their research schedule to achieve a certain objective.
Primary Data
Bank A collected information and raw data through questionnaire from the population. For the
purpose of collection of data Bank selected a random sample from the population and collected
information by filling a questionnaire on their experience with the bank.
Sample Selection - Randomly from the population
Sample Size - 2000 customer
Sample Collected - 492 Questionnaire
Statistical Test - Parametric and Non-Parametric test
Secondary Data
For the purpose of reference and analyses of the tools secondary data is also collected by the
bank for market analysis Secondary data is defined as the data collected from the analysis or
work of another researcher (McCusker, &Gunaydin, 2015). Secondary data is defined as the
work done by someone else experts and researcher for the reference of the study. Secondary data
provide helps for the experts to understand the need for the objective. Bank collected secondary
data from various sources which are as follows
 Newspaper
 Research Article
 Research Paper
 Books and reference guide
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Hypothesis development
Bank A drafted some of the hypothesis for the point of view of the research and further, they
recognized various point on which hypothesis is constructed by Bank. The hypothesis is the
statement which defines the objective of the researcher which they need to find from the
analysis. Here are some of the hypothesis is drafted by Bank to fulfill their objective of market
analysis which is as followed.
Management wants to know about the similarity of online frauds in Genders for the
company.
Null Hypothesis (H0) = Similarity exist among the gender and online fraud for bank
Alternative Hypothesis (H1) = No similarity among the gender and online fraud for bank
The difference between the different age group for online fraud
Null Hypothesis (H0) = There is No relevance between age of customer for online fraud
Alternative Hypothesis (H1) = There is relevance between age of customer for online fraud
The targeted Time period for resolution for Card fraud is reasonable.
Null Hypothesis (H0) = Bank is not identified a reasonable time of solution of online frauds.
Alternative Hypothesis (H1) = Bank identified a reasonable time of solution of online frauds.
Is a Benchmark of ‘Zero' Incident can be attainable for the company.
Null Hypothesis (H0) =benchmark for the company is not attainable from activities.
Alternative Hypothesis (H1) = benchmark for the company is attainable from activities.
Is personal Data is Used for card fraud?
Null Hypothesis (H0) = There is no relation among personal data or card fraud.
Alternative Hypothesis (H1) = There is relation among personal data or card fraud.
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The bank defines a series of hypothesis for the study which provides a broad area for discussion
and identification of the issue and also helps management in better decision-making which helps
them in improvement for their security framework and also identified major issue for online
fraud for Bank in the market of Sydney.
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Statically tools and Techniques
Bank A need to analyze the information for the purpose of identifications of their objective in
which they work on overcoming the online fraud activities for the bank and also increased their
security framework for better management of business issues. The company used various
statistical and non-statically tools which provide some of the relevant information which guides
them in better decision making to overcome the company's vulnerability(Frost, 2019).There are
some of the parametric and non-parametric tests are listed below which is used to analysis of the
data collected from the market.
Correlation analysis
Correlation analysis is a statistical tool which analyses the relation among the variable for the
research. It is the tool which identified relation among the two variables which is independent in
nature and further, it helps management in identified any affected of the variable from each
other. Bank A uses this tool for the better analyses of the data and analysis for the information
for the company which can majorly influence the decision making for the management
(Statisticssolutions, 2019). Online fraud is dependent on the various activities and factors in the
environment Bank wants to know about the similarity level among the variable which is online
fraud and Genders.
Bank Want to know about the significance relation among the genders and online fraud. For this
purpose, correlation is a tool which is the right fit for management and also helps management to
identify any significant relationships among the occurrence of online fraud and gender of the
customer.
Regression Analysis
Regression analysis is a significant tool which identified relation among the two variables for the
study. It interpretsrelation among the variable and also guides management for analyses the
influence factor on which variable can impactanother variable in the marketConducted to
identify the relationships among the two variables. Regression analysis provides information to
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management about the relevance of two factors and guides management to take corrective action
for improvement in business activities.
From this study, management wants to know about the significance different among age of the
group and the occurrence of online fraud. The bank here needed to recognize in which age
customer are mostly occur online fraud for company Regression can help management to
identify differences among both variables (age of the customer and online fraud).
Descriptive analysis
Descriptive analysis is a tool which is used to understand the descriptive details for the data
analyses from the information collected from the population. Further Management can use this
statistical tool which is identified various result like mean, mode and median from the data
further significant and coefficient also identified from the research (Narkhede S., 2018). Detailed
analysis is conducted by management by use of descriptive tools so that management can know
the Center response of the population and also standard error for the finding which need for
decision making for management.
In this scenario, Bank wants to know about the details related to the targeted time period which is
required by the company to resolve the issue for online fraud and also majorly detailed analysis
for center responds from a population which is justified better decision making of the company.
Bank can identify details related to the targeted time period in which they can resolve the issue
related to card fraud for the company.
T-Test
The t-test is a non-parametric test which is implemented on the research information for the
company and further, it defines a relation among the mean of the population and also the mean of
the sample size (Lakens,2017). This test identified any significant difference among the sample
mean and population means which interpret the relationship between the sample and population.
T-test helps management in identification of the representation of the sample on total population
size which related the finding which is applicable to the overall population by Bank.
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In this scenario, a bank needs to recognize about the probability of attainability of the benchmark
for that purpose the company can use T-Test for evaluation of chances on which company can
achieve their benchmark of Zero Online fraud.
As the fraud activities are implemented on the information collected from the customer in which
a number of occurrences and also management response to the online fraud can influence the
attainability of the benchmark. So management can use T-test to analysis the factor which can
influence the benchmark to the bank.
One way ANOVA Test
Annova test is a parametric test which is used by management for evaluation and identifications
of significance relation among the variables further it identified about helps to know about the
difference between two independent variables. One way ANOVA test guide managementtowards
one side of the relation between the variable and provide effective information which helps them
to take better decision for further growth and achievement of the sustainable goal of the
company.
The Bank needs to identify the role of personal sources in online fraud for the Bank and analyses
their impact on the occurrence of online fraud from personal sources of the customer. Annova
test can be the best fit for better analysis of information which helps them In identifications for
better analyses of the data and guides proper strategy which overcomes the online frauds in
banking activities.
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Results, and Statistical and non-statistical interpretation
1. Management wants to know about the similarity of online frauds in Genders for the
company.
Correlation Analysis
Fraud Gender
Fraud 1 -0.01729
gender -0.01729 1
Bank analysis relation among the gender and online fraud for which correlation is conducted by
management to identified significance relation among the variables. From the collected data of
the survey, the result is -0.01729 which defines a significant low relation among the two
variables (Heeringa, et. al, 2017). It means the Gender of the customer has not affected the
occurrence of online fraud for the bank. Hence the Null hypothesis is rejected and the alternative
hypothesis is accepted which indicates there is low relation among the variable (Online Fraud
and Gender of the customers)
2. The difference between the different age group for online fraud.
SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.08091
5484
R Square 0.00654
7316
Adjusted R
Square
0.00417
0635
Standard
Error
1.23866
6523
Observations 420
ANOVA
df SS MS F Significa
nce F
Regression 1 4.226698 4.226 2.7548 0.09771
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698 1455 2
Residual 418 641.3352 1.534
295
Total 419 645.5619
Coeffici
ents
Standard
Error
t Stat P-value Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 2.68773
042
0.084181 31.92
789
3.6141
E-114
2.52225
9
2.8532
02
2.52225
9
2.85320
2
X Variable 1 -
0.02870
4692
0.017294 -
1.659
76
0.0977
12047
-0.0627 0.0052
9
-0.0627 0.00529
Bank need to identify any significant difference among the age group in the context of online
fraud. Bank conducted Regression analysis to analysis for the relation among the
(Statisticshowto, 2019).They identified a significance level of 0.097712 and F factor is 4.226698
which defines that there is significance diffidence among the age of the customer for the purpose
of online fraud. From this study, H0 is rejected which means there is a significant difference for
the age in the context of online fraud.
3. The targeted Time period for resolution for Card fraud is reasonable.
Targeted Time Period
Mean 1.419047619
Standard Error 0.108357637
Median 0
Mode 0
Standard Deviation 2.220670747
Sample Variance 4.931378566
Kurtosis -1.038890131
Skewness 0.966040413
Range 5
Minimum 0
Maximum 5
Sum 596
Count 420
Due to multiple responses for the objective of the study from the population descriptive analysis
is conducted by Bank. In the descriptive analysis, various information is collected from
management from which they found Skewness and kurtosis which is 0.96 and 4.9. Further, in
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