Proposal Literature Review 2022

ADVANCE RESEARCH ON PREDICTION OF FINANCIAL FRAUD USING DATA
MINING TECHNIQUES

PROPOSAL 1
Literature review
Data mining is an effective technology which is control and evaluates the
gathered data in an appropriate manner. It is very important to find fraud
cases from the systems and data mining is the best approach to determine
the financial fraud data from the networks and provide effective services to
the companies. The aim of the literature review is to examine the importance
of data mining in the prediction of fraud financial data from the systems and
demonstrate skills in data mining techniques.
Albashrawi and Lowell, (2016) identified that financial fraud is a common
problem faced by the companies and business communities where the
attackers transfer the unwanted signals to the networks and produce
security threats in the systems [1]. In this generation, many companies are
facing data breach and fraud-related issues where the management teams
are not able to identify the financial fraud data from the systems. With the
help of the data mining technique, the companies can easily handle and
predict fraud signals from the computing devices and improve the
effectiveness of the financial data. It is identified that there are major three
key elements due to which the companies suffer from fraud cases including
lack of privacy, the involvement of third parties and unauthorized access
present in the computer systems.
Recent literature provided by Albashrawi, and Lowell, (2016) observed that
banking sectors are mostly suffering from fraud cases where the hackers
produce unwanted information and fake details of the bank account
including credit cards [2]. Bank fraud and financial statements both are
major issues linked with the user’s data and many consumers were lost their
personal information because of fraud cases and information [14]. Therefore,
the business communities can use the concept of data mining technique as it
detects and predicts the fraud signals related to the financial information
and personal data of the users. In this generation, the use of an information

PROPOSAL 2
system is increasing day by day which also enhances the rate of hacking due
to which the consumers are not able to protect data against a data breach.
Chen, (2016) identified the risk factors linked with the computer systems and
recommended that the developers should use the data mining technique in
order to predict the fraud signals from the systems and handle the unwanted
cases that occurred in the networks [3]. From previous literature, it is
observed that the use of computer networks requires proper security in order
to reduce the unwanted access that occurred in the systems and fraud cases
of financial data can be handled by developing and implementing effective
strategies in the workplace. Chitra and Subashini, (2013) examined that
financial fraud information can be managed to utilize logistic regression
models and advance computing systems along with the data mining
methods [4].
By using such kind of approaches the companies can easily manage and
reduce the rate of fraud information and manage the privacy of sensitive
data from hackers. It is observed that data mining has the potential to
identify unauthorized access from the systems and financial frauds from the
data centers and more than 67% of banking communities are using the data
mining approach as a security technique [12]. It is true that the involvement
of the data mining technique may help the companies for predicting and
addressing financial frauds in an appropriate manner. Using automated fraud
detection systems the companies can automatically identify the unwanted
signals and fraud information transmitted by the criminals.
The automated fraud detection uses various kinds of sensors and networks in
order to detect the fraud signals from the computing devices and help the
companies for managing the security of the data collected from the
networks. Durairaj and Ranjani, (2013) supported this point and identified
that automated fraud detection systems are capable to provide better
services to the companies and it is mainly divided into several models