| Information Sciences - Special Issue- Impact Factor: 3.095 |
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Also see the link at the journal web: http://www.elsevier.com/wps/find/journaldescription.cws_home/505730/description#description
Special Issue Business Intelligence in Risk Management Guesteditors： Desheng Dash Wu， Shu-Heng Chen and David L. Olson
Introduction Risks exist in every aspect of our lives, and can mean different things to different people, while negatively in general they always cause a great deal of potential damage and inconvenience for the stakeholders. For example, recent disaster risks include terrorism leading to the gassing of the Japanese subway system, to 9/11/2001, to bombings of Spanish and British transportation systems, and the SARS virus disrupting public and business activities, particularly in Asia. More recently, the H1N1 virus has sharpened the awareness of the response system world-wide; the financial crisis has resulted in recession in all aspects of the economy. Risk management has become a vital topic in both academia and practice during the past several decades. Integrated approaches are required to manage risks facing an organization; sometimes effective risk-taking strategies may involve new business philosophies such as enterprise risk management. Most business intelligence tools have been used for enhancing risk management, and the risk management tools benefit from business intelligence approaches. For example, artificial intelligence models such as neural networks and support vector machines have been widely used for establishing the early warning system for monitoring a company’s financial status (e.g., Martens et al. 2007; Alfaro et al. 2008; Lin and Chen 2007). Agent-based theories are employed in supply chain risk management (e.g., Julka et al. 2002; Liang and Huang, 2006). Business intelligence models are also useful in hedging financial risks by incorporating market risks, credit risks, and operational risks (Wu and Olson 2009). Investigation of business intelligence tools in risk management is beneficial to both practitioners and academic researchers. This special issue of Information Sciences is intended to present the recent advances in using business intelligence for enterprise risk management. Authors are encouraged to submit both theoretical and applied articles addressing this theme in this special issue. TopicsPotential topics include, but are not limited to:· Artificial intelligence in enterprise risk management· Agent-based supply chain risk management· Portfolio selection of various financial instruments· Credit scoring using data mining· Data mining in managing market risks· Intelligence multi-criteria decision making in financial services· Agent-based simulation in operational risk management· Game agents in risk management· Artificial intelligence for natural disasters risk management· Many other uses of business intelligence for enterprise risk management Manuscript Preparation and SubmissionTo prepare their manuscripts, authors are asked to closely follow the “Instructions to Authors,” which can be found on the Information Sciences Journal Home page: http://www.ees.elsevier.com/ins/Manuscripts will be refereed according to the standards of Information Sciences. All papers will be rigorously refereed by 2 or 3 peer reviewers of the Journal. Authors are also expected to refer to the INS J.'s papers in order to show the relevance of their work to the INS scope. To submit your article online, go to: http://www.ees.elsevier.com/ins/. All papers will be handled through the EES. Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Important dates Paper submission: 15-11-2010 Acceptance notification: 15-08-2011 Final papers: 01-10-2011
E. Alfaro, N. García, M. Gámez, D. Elizondo. Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, Volume 45, Issue 1, April 2008, Pages 110-122 N. Julka, R. Srinivasan, I. Karimi. Agent-based supply chain management—1: framework, Computers & Chemical Engineering, Volume 26, Issue 12, 15 December 2002, Pages 1755-1769
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| Last Updated ( Thursday, 04 February 2010 ) |
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