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

Identification of financial statement fraud in Greece by using computational intelligence techniques

The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements

Aberrant epigenetic changes and gene expression in cloned cattle dying around birth

<p>Abstract</p> <p>Background</p> <p>Aberrant reprogramming of donor somatic cell nuclei may result in many severe problems in animal cloning. To assess the extent of abnormal epigenetic modifications and gene expression in clones, we simultaneously examined DNA methylation, histone H4 acetylation and expression of six genes (<it>β-actin</it>, <it>VEGF</it>, <it>oct4</it>, <it>TERT</it>, <it>H19 </it>and <it>Igf2</it>) and a repetitive sequence (<it>art2</it>) in five organs (heart, liver, spleen, lung and kidney) from two cloned cattle groups that had died at different stages. In the ED group (early death, n = 3), the cloned cattle died in the perinatal period. The cattle in the LD group (late death, n = 3) died after the perinatal period. Normally reproduced cattle served as a control group (n = 3).</p> <p>Results</p> <p>Aberrant DNA methylation, histone H4 acetylation and gene expression were observed in both cloned groups. The ED group showed relatively fewer severe DNA methylation abnormalities (p < 0.05) but more abnormal histone H4 acetylations (p < 0.05) and more abnormal expression (p < 0.05) of the selected genes compared to the LD group. However, our data also suggest no widespread gene expression abnormalities in the organs of the dead clones.</p> <p>Conclusion</p> <p>Deaths of clones may be ascribed to abnormal expression of a very limited number of genes.</p

Identification of Inappropriately Reprogrammed Genes by Large-Scale Transcriptome Analysis of Individual Cloned Mouse Blastocysts

Although cloned embryos generated by somatic/embryonic stem cell nuclear transfer (SECNT) certainly give rise to viable individuals, they can often undergo embryonic arrest at any stage of embryogenesis, leading to diverse morphological abnormalities. In an effort to gain further insights into reprogramming and the properties of SECNT embryos, we performed a large-scale gene expression profiling of 87 single blastocysts using GeneChip microarrays. Sertoli cells, cumulus cells, and embryonic stem cells were used as donor cells. The gene expression profiles of 87 blastocysts were subjected to microarray analysis. Using principal component analysis and hierarchical clustering, the gene expression profiles were clearly classified into 3 clusters corresponding to the type of donor cell. The results revealed that each type of SECNT embryo had a unique gene expression profile that was strictly dependent upon the type of donor cells, although there was considerable variation among the individual profiles within each group. This suggests that the reprogramming process is distinct for embryos cloned from different types of donor cells. Furthermore, on the basis of the results of comparison analysis, we identified 35 genes that were inappropriately reprogrammed in most of the SECNT embryos; our findings demonstrated that some of these genes, such as Asz1, Xlr3a and App, were appropriately reprogrammed only in the embryos with a transcriptional profile that was the closest to that of the controls. Our findings provide a framework to further understand the reprogramming in SECNT embryos