Extensive research has been done on the analytical and empirical examination of financial data in annual reports to detect fraud; however, there is scant research on the analysis of text in annual reports to detect fraud. The basic premise of this research is that there are clues hidden in the text that can be detected to determine the likelihood of fraud. In this research, we examine both the verbal content and the presentation style of the qualitative portion of the annual reports using natural language processing tools and explore linguistic features that distinguish fraudulent annual reports from nonfraudulent annual reports. Our results indicate that employment of linguistic features is an effective means for detecting fraud. We were able to improve the prediction accuracy of our fraud detection model from initial baseline results of 56.75 percent accuracy, using a “bag of words” approach, to 89.51 percent accuracy when we incorporated linguistically motivated features inspired by our informed reasoning and domain knowledge.

SUMMARY Unlike previous fraud detection research, a vast majority of which has focused primarily on the use of quantitative financial information to predict fraud, in this study we examine qualitative textual content in annual reports to predict fraud and see whether there are discernible differences in the writing and presentation style between companies that committed fraud and those that did not. We believe that while numeric financial information in the annual reports can hide details of fraud, textual information relating to writing and presentation styles in such reports provides valuable clues pertaining to the existence of fraud. In this study we use the chi‐square test to analyse our data and test hypotheses about predictors of fraud that may explain linguistic feature variations in fraudulent and nonfraudulent annual reports. We provide new results on the usefulness of the qualitative content of annual reports in detecting fraud. Copyright © 2012 John Wiley & Sons, Ltd.

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Many governmental agencies and businesses organizations use networked systems to provide a number of services. Such a service-oriented network can be implemented as an overlay on top of the physical network. It is well recognized that the performance of many of the networked computer systems is severely degraded under node and edge failures. The focus of our work is on the resilience of service-oriented networks. We develop a graph theoretic model for service-oriented networks. Using this model, we propose metrics that quantify the resilience of such networks under node and edge failures. These metrics are based on the topological structure of the network and the manner in which services are distributed over the network. Based on this framework, we address two types of problems. The first type involves the analysis of a given network to determine its resilience parameters. The second type involves the design of networks with a given degree of resilience. We present efficient algorithms for both types of problems. Our approach for solving analysis problems relies on known algorithms for computing minimum cuts in graphs. Our algorithms for the design problem are based on a careful analysis of the decomposition of the given graph into appropriate types of connected components.

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