| Issue |
MATEC Web Conf.
Volume 193, 2018
International Scientific Conference Environmental Science for Construction Industry – ESCI 2018
|
|
|---|---|---|
| Article Number | 05072 | |
| Number of page(s) | 8 | |
| Section | Economics and Management in the Concept of Sustainable Living Environment | |
| DOI | https://doi.org/10.1051/matecconf/201819305072 | |
| Published online | 20 August 2018 | |
Classification of operational risks in construction companies on the basis of big data
Saint Petersburg State University of Architecture and Civil Engineering, Vtoraya Krasnoarmeiskaya str. 4. St. Petersburg, 190005, Russia
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Corresponding author : tnktn@yandex.ru
Nowadays, Big Data is commonly used in many business sectors. Its use is also relevant for the construction industry. One of the most promising areas of Big Data technologies application is their use for risk analysis and assessment. Big Data represents an efficient way to manage modern risks by analyzing the unlimited amount of structured and unstructured information. The study examines principles of operational risks classification in construction companies on the basis of Big Data technologies. The final goal of such classification is the creation of a solution pattern for subsequent use of Big Data. As an example, a solution pattern for such business problem as "Construction: Detection of Insurance Fraud" is created. Application of the Big Data analytics for fraud detection has a series of advantages as compared to traditional approaches. Insurance companies can build systems that include all relevant data sources. An analysis of operational risks by means of self-organizing Kohonen maps on the basis of the Deductor analytical platform is performed.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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