With rapidly growing interest in the development of knowledge-based computer consulting systems for various problem domains, the difficulties associated with knowledge acquisition have special importance. This paper reports on the results of experiments designed to assess the effectiveness of an inductive algorithm in discovering predictive knowledge structures in financial data. The quality of the results are evaluated by comparing them to results generated by discriminant analysis, individual judgments, and group judgments. A partial intersection of predictive attributes occurs. More importantly, for all cases tested, the inductively produced knowledge structures perform better than the competing models.artificial intelligence, expert systems, machine intelligence, judgment modeling

Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators.

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Electronic data interchange (EDI) is the movement of information electronically between a buyer and seller for purposes of facilitating a business transaction. EDI represents a powerful application of computer-communications technology. Its value includes such benefits as reduced paperwork, elimination of data entry overheads, improved accuracy, timely information receipt, accelerated cash flow, and reduced inventories. EDI brings with it, however, new and important control considerations. This article discusses, in a non-technical fashion, the control architectures and concerns associated with EDI. Audit considerations in the EDI environment, as well as related audit tools, are also outlined.