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

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