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Financial fraud investigations are becoming increasingly complex. The volume of data continues to increase along with the volume and complexity of underlying source code logic. As the volume and complexity increase, so too does the importance of identifying techniques for reducing the data to manageable sizes and identifying fraudulent activity as quickly as possible. This paper presents how to ensure that all data was properly collected and a methodology for reducing the complexity of such investigations by identifying similarities and differences between the source code and structured data.
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