Electronic Banking Fraud Detection Using Data Mining Techniques Research Proposal 2022

Running head: ELECTRONIC BANKING FRAUD DETECTION USING DATA MINING TECHNIQUE
ELECTROONIC BANKING FRAUD DETECTION TECHNIQUE USING DATA MINING
TECHNIQUES
Name of the Student
Name of the University
Author Note

ELECTRONIC BANKING FRAUD DETECTION USING DATA MINING TECHNIQUES 1
Proposal:
In spite of the challenging economy, the utilization of e channel related platforms
such as ATM, Internet Banking, Web, POS has continued for experiencing the significant
growth. The major problems for that are, the communication that has been done over internet
in not secure as well as it is often congested, the finance related institution should also have
to content with the challenges related to internet including the quality of services, insecurity
as well as some aberrations in the electronic banking. The research will deal with the
processes for the computing to the presence of outliers by utilizing difference measures of
distance as well as natural performance related to detection for the machine learning that are
unsupervised like principle component analysis and K-mean Clustering Analysis. For a
comprehensive evaluation of the techniques related to data mining, predictive modelling and
machine learning for the algorithms of Anomaly Detection on the transaction dataset of
electronic banking. It can be concluded that the integrated technique systems will be able to
provide better performance efficiency than the systems that are singular. Along with that the
clustering based is required for the classification model.
Synthesis matrix:
Article Main idea 1 Main idea 2 Main idea 3
Effective detection
of sophisticated
online banking
fraud on extremely
imbalanced data
An online banking
fraud detection
system will be a
typical use case of
the Web of things
methodology that
In the paper the
authors have
summarise the
primary
characteristics of the
frauds related to
The authors have
implemented a
system of risk
management of
online banking. The
system integrates

ELECTRONIC BANKING FRAUD DETECTION USING DATA MINING TECHNIQUES 2
are broad based. The
online banking fraud
detection requires to
be instant as this is
so much difficult to
recover the loss if
during the period of
detection, a fraud
will remain as
undetected.
sector of banking.
As most of the
works that are
published are related
to the computer
intrusion, domain of
fraud related to
credit card. The
authors have
discussed each of the
limitations for better
detection of online
fraud in the banking
sector.
several features as
the models that are
related to data
mining as well as
aims for
consolidating
various sources of
the resources for
solving the
problems.
Data Mining
Techniques and its
Applications in
Banking Sector
The authors have
used decision tree
model for solving
the prediction
problems and
classification where
the examples are
classified in the
classes. Usually the
decision trees are
becoming so large as
The authors have
discussed about the
value prediction
methods instead of
classification of loan
application that are
new. This attempts
for predicting the
default amount that
are expected for the
new loan
Most of the common
methods for the data
mining that are used
for the customer
profiling are,
clustering,
regression and
classification and
lastly the association
rule discovery.