The problem of fraud detection and risk management is first and foremost a statistical one where, in the face of overwhelming amounts of data, the investigator is well advised to impose structure. After defining three types of risk commonly encountered in the financial serives industry. a generic modeling framework is presented in terms of conditional expectations. Now we can impose structure on: the functional form of the conditioning relationship, the stochastic distribution as well as variable (or feature) selection. As an example we present what is rapidly becoming an industry standard for measuring and modeling market risk, characterizing the possibility of losses resulting from unfavorable market movements. Finally we tackle the difficult issue of going from risk measurement to risk management.