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know the data assets and protect them, and these assets are the collateral for which the enterprise receives revenue or bene ts Executives must know the data on which the enterprise performance is dependent They must know about the sources of data and understand how they may in uence the sources of data such that the data received is optimally engineered for their use Likewise, top executives must know where their data outputs are headed and they must be concerned about how well their outputs satisfy the needs of the user community The integration of smart data, application of arti cial intelligence techniques, and adoption of a smart data SOE paradigm can be used to counter accounting fraud ascos, such as Enron and World Com, and are set forth in the case Utilization of Data Mining Techniques to Detect and Predict Accounting Fraud: A Comparison of Neural Networks and Discriminant Analysis [20] Accounting information systems enable the process of internal control and external auditing to provide a rst-line defense in detecting fraud There are few valid indicators at either the individual or the organizational level which are reliable indicators of fraud prevention Recent studies have shown that it is nearly impossible to predict fraud In fact, many of the characteristics associated with white-collar criminals are precisely the traits that organizations look for when hiring employees This case proposes the use of information systems and smart data and utilization of the SOE model to deal with fraud through proactive information collection, data mining, and decision support activities Here is an example of citizens being recipients of services provided by government enterprise All participants in detecting accounting fraud have a symbiotic relationship Success in detecting and deterring accounting fraud is measured in an aggregation that is best for citizens best for government, best for government trading partners, and best for allies Results show that while traditional methods, such as discriminant analysis, yielded 504% of original grouped cases correctly classi ed, no signi cant relationship was found (0149) between attitude, morale, internal controls, increases in expenditures, and whether or not fraud was actually committed Cronbach s alpha of reliability was 06626 and offered somewhat reliable results in this exploratory research Neural networks did a much better job of predicting fraud (759%) than discriminant analysis (504%) Neural networks were able to nd patterns in the training set and then correctly identify more than three-fourths of similar patterns in the testing set Therefore it can be concluded that neural networks outperform discriminant analysis by 255% in this data set It is not until a comparison of the various linear and nonlinear techniques are made that actual knowledge or information in context has been supplied to move the decision maker toward the smart data wisdom paradigm shift The knowledge experience context is to recognize that the application of arti cial intelligence mechanisms for predicting fraud involves not only the context of a symbiotic relationship between the stakeholders, but also a realization that, in a smart data sense, we do not necessarily live in a parametric, bellshaped world Nonlinear relationships must be taken into account as well if we are to nd true wisdom . birt ean 13 BIRT Barcode Plugin for eclipse BIRT versions 2.x, 3.x and 4.x
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