The World Economic Forum estimates that the cost of corruption amounts to more than 5% of global GDP ($2.6 trillion), with more than $1 trillion paid in bribes each year.  Creative data mining is one of the most effective tools in identifying transactions connected to this illicit behavior.  It is commonplace in most every fraud and corruption investigation nowadays to pull raw data from ERP systems, identify relevant pools of data and design queries to find anomalies.  What happens, though, when an organization is faced with a situation where such raw data is unreliable, incomplete, or not available at all?  More often than not, the robust data sets that one would likely have access to in corporate investigations are not available in the case of development projects financed by institutions such as the United Nations and the World Bank.  Such projects are often plagued by inadequate accounting systems, archaic banking practices and a general lack of management and fiduciary controls.  In a guest article based on dozens of global corruption investigations, Jean-Michel Ferat, Managing Director at The Claro Group, describes the primary corruption risks inherent in development projects and – using slides taken directly from his investigative experience – details five workable methods for mitigating those risks.  See also “How Forensic Accountants Help Identify Corruption Risk and Delve into the Details of Books and Records” (Jun. 12, 2013).