9 Pages Posted: 18 Nov 2013
Date Written: November 15, 2013
Assessing exposure to potential risk events and initiating proactive risk mitigation actions is currently a clear priority of businesses, organizations and governments world-wide. Managing risks with data is a growing discipline that involves data acquisition and data merging, risk analytics and risk management decisions support systems. The paper will provide an overview of modern integrated risk management, including examples of how qualitative unstructured data, like text and voice recordings, can be combined with quantitative data like balance sheets and technical performance, to generate integrated risk scores. We suggest that data based risk analysis is an essential competency complementing and reinforcing the more traditional subjective scoring methods used in classical risk management. The examples we will use consist of applications of risk scoring models, Bayesian Networks to map cause and effect, Ontologies to interpret automated text annotation, ETL to merge various data bases and a follow up integrated risk management approach. The main theme of the paper is that risk management can be intuitive, based on qualitative assessments and expert opinions, quantitative in scope, exploit semantic unstructured information or integrated. We aim to show the advantages of integrated data based risk management over the more basic intuitive approach practiced in many organizations.
Keywords: Risk Management, Integrated Risk Management, Risk Scores, Semantic Analysis, Bayesian Networks, Social Networks, Risk Maps.
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