12,157 research outputs found
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
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An Overview of the Use of Neural Networks for Data Mining Tasks
In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
Critical success factors for preventing E-banking fraud
E-Banking fraud is an issue being experienced globally and is continuing to prove costly to both banks and customers. Frauds in e-banking services occur as a result of various compromises in security ranging from weak authentication systems to insufficient internal controls. Lack of research in this area is problematic for practitioners so there is need to conduct research to help improve security and prevent stakeholders from losing confidence in the system. The purpose of this paper is to understand factors that could be critical in strengthening fraud prevention systems in electronic banking. The paper reviews relevant literatures to help identify potential critical success factors of frauds prevention in e-banking. Our findings show that beyond technology, there are other factors that need to be considered such as internal controls, customer education and staff education etc. These findings will help assist banks and regulators with information on specific areas that should be addressed to build on their existing fraud prevention systems
Fake View Analytics in Online Video Services
Online video-on-demand(VoD) services invariably maintain a view count for
each video they serve, and it has become an important currency for various
stakeholders, from viewers, to content owners, advertizers, and the online
service providers themselves. There is often significant financial incentive to
use a robot (or a botnet) to artificially create fake views. How can we detect
the fake views? Can we detect them (and stop them) using online algorithms as
they occur? What is the extent of fake views with current VoD service
providers? These are the questions we study in the paper. We develop some
algorithms and show that they are quite effective for this problem.Comment: 25 pages, 15 figure
Financial statements fraud identifiers
Contemporary research among fraud professionals indicates that
organizations lose 5% of revenues from fraud every year which
makes the research in this area and the derivation of fraud detection
models very important. The purpose of the article is to
develop a new accounting tool that will help companies and
investors in prompt fraud detection and prevention which can
finally result in the preservation of financial stability as well as
more efficient capital allocation. In this context the main objective
of the research is to test the significance of some financial statements
positions’ relations that has not been used in the previous
research using the dataset from SEC AAERs presented and
included in Bao et al.’s research as well as to combine them with
existing ones and consequently develop new financial statement
fraud detection model. Another objective consists of presenting
some of the most significant and contemporary research in the
field of financial statement fraud detection models and comparing
their quality using the ROC analysis. Research results were
generated by using the SMOTE algorithm and logistic regression
analysis on the dataset of 146,045 cases for a period from 1982
to 2014 and point out five independent variables used by Bao
et al. The financial statement fraud detection model comprised of
change in free cash flow, percentage of soft assets, sale of common
and preferred stock, change in cash sales, and change in
receivables shows a sufficient level of discriminant power with
67% area under ROC curve. The model derived could be used as
a starting point for fraud detection preventing the significant
losses the company and stakeholders could face
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