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Abstract:
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Most businesses have to adapt strategies and operations to deal with various agents of change, such as customer requirements, technology evolution, etc. Business decisions under such constraints require taking some risks. Effective decision making requires extensive use of adaptive models which exploit various data sources for risk identification, assessment, & mgmt. In this paper we discuss some key statistical models for Operational risk mgmt and focus especially on IT projects risk analysis. We present an adaptive system for project portfolio risk assessment using a combination of statistical & machine learning techniques for estimation, simulation, & portfolio optimization. The system consists of a series of processes which can be summarized in three steps: risk factor identification and scoring; probabilistic estimation of project health; & project portfolio optimization.
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