A framework for detecting financial statement fraud through multiple data sources

This project deals with how to detect fraud and non-compliance in financial statements in the present day in one of the biggest economies in the world, the U.S. Since it is mainly public companies that release detailed financial information, they are the focus. This project focuses on the top five market sectors where fraud is most common. It focuses on a variety of fraud types, but not on cases of deception that do not constitute fraud. A framework will be proposed which accounts for both structured data (the numbers in the balance sheet, income statement and cash flow statement) and unstructured data (the footnotes in these financial statements). It uses ontology-driven data mining techniques to do so.