Conceptual Mapping of Risk Management to Data Mining

Risk Management is a logical and systematic method of identifying, analyzing, treating and monitoring the risks involved in any activity or process. The key to successful risk management lies in the ability to tailor a formal risk management process that addresses the complementary needs of the business and its customers. A formal risk management process is a continuous process for systematically addressing risk throughout the product/project life-cycle. Risks can be introduced (or latently reside) at the very earliest stages of the project life-cycle. The ability to identify risks earlier translates into earlier risk removal, at less cost, which promotes higher project success probability. Data mining refers to discovery or “mining” of knowledge from large amounts of data. Data Mining has been described as a confluence of different disciplines primarily database systems, statistics, machine learning and information science. This paper aims to study the conceptual mapping of Risk Management to Data Mining. A new paradigm has been suggested for Risk Management using the main attributes and key aspects of Data Mining.