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Risk Management Based on Intelligent Information Processing
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Risk Management Based on Intelligent Information Processing

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (30 June 2017) | Viewed by 27237

Special Issue Editor

Prof. Dr. Xiao-Guang Yue

E-Mail Website
Guest Editor
1. CIICESI, ESTG, Politécnico do Porto, 4610-156 Felgueiras, Portugal;
2. European University Cyprus, Nicosia 1516, Cyprus
Interests: sustainable risk management; intelligent information processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Risk Management is an important issue for economics and management researchers. Intelligent information processing (IIP) methods can be useful tools for managing and controlling risks. The main topics of IIP are multi-agent systems, automatic reasoning, big data mining, cloud computing, cognitive modeling, computational intelligence, deep learning, evolutionary computation, information retrieval, knowledge-based systems, knowledge engineering, machine learning, natural language processing, neural computing and web intelligence. Risk management based on IIP theory, system, modeling and simulation are hot topics for future research. We encourage researchers, teachers, students, engineers, academicians as well as industrial professionals from all over the world to present their current insights in this special issue.

Dr. Xiao-Guang Yue
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • risk management
  • intelligent information processing (IIT)
  • big data mining
  • deep learning

Published Papers (3 papers)

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16621 KiB  
Article
Intelligent Decision Support in Proportional–Stop-Loss Reinsurance Using Multiple Attribute Decision-Making (MADM)
by Shirley Jie Xuan Wang and Kim Leng Poh
J. Risk Financial Manag. 2017, 10(4), 22; https://doi.org/10.3390/jrfm10040022 - 28 Nov 2017
Cited by 3 | Viewed by 6736
Abstract
This article addresses the possibility of incorporating intelligent decision support systems into reinsurance decision-making. This involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. [...] Read more.
This article addresses the possibility of incorporating intelligent decision support systems into reinsurance decision-making. This involves the insurance company and the reinsurance company, and is negotiated through reinsurance intermediaries. The article proposes a decision flow to model the reinsurance design and selection process. This article focuses on adopting more than one optimality criteria under a more generic combinational design of commonly used reinsurance products, i.e., proportional reinsurance and stop-loss reinsurance. In terms of methodology, the significant contribution of the study the incorporation of the well-established decision analysis tool multiple-attribute decision-making (MADM) into the modelling of reinsurance selection. To illustrate the feasibility of incorporating intelligent decision supporting systems in the reinsurance market, the study includes a numerical case study using the simulation software @Risk in modeling insurance claims, as well as programming in MATLAB to realize MADM. A list of managerial implications could be drawn from the case study results. Most importantly, when choosing the most appropriate type of reinsurance, insurance companies should base their decisions on multiple measurements instead of single-criteria decision-making models so that their decisions may be more robust. Full article
(This article belongs to the Special Issue Risk Management Based on Intelligent Information Processing)
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11171 KiB  
Article
Safety Evaluation of Evacuation Routes in Central Tokyo Assuming a Large-Scale Evacuation in Case of Earthquake Disasters
by Kayoko Yamamoto and Ximing Li
J. Risk Financial Manag. 2017, 10(3), 14; https://doi.org/10.3390/jrfm10030014 - 27 Jun 2017
Cited by 8 | Viewed by 6365
Abstract
The present study aims to conduct a quantitative evaluation of evacuation route safety using the Ant Colony Optimization (ACO) algorithm for risk management in central Tokyo. Firstly, the similarity in safety was focused on while taking into consideration road blockage probability. Then, by [...] Read more.
The present study aims to conduct a quantitative evaluation of evacuation route safety using the Ant Colony Optimization (ACO) algorithm for risk management in central Tokyo. Firstly, the similarity in safety was focused on while taking into consideration road blockage probability. Then, by classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of Geographic Information Systems (GIS), and their safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites for cases when the possibility of large-scale evacuation after an earthquake disaster is high is made possible. As the evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas, regardless of whether the information is from the past or future. Therefore, in addition to spatial reproducibility, the evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, the selected highly safe evacuation routes have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes. Full article
(This article belongs to the Special Issue Risk Management Based on Intelligent Information Processing)
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1432 KiB  
Article
A Risk Management Framework for Cloud Migration Decision Support
by Shareeful Islam, Stefan Fenz, Edgar Weippl and Haralambos Mouratidis
J. Risk Financial Manag. 2017, 10(2), 10; https://doi.org/10.3390/jrfm10020010 - 22 Apr 2017
Cited by 21 | Viewed by 13527
Abstract
Keywords: risk management framework; risk assessment; cloud migration; security; analytic hierarchy process (AHP); business value Full article
(This article belongs to the Special Issue Risk Management Based on Intelligent Information Processing)
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