Principles of Risk Selection and Classification

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.

Few issues in business ethics are as polarizing as the practice of risk classification and underwriting in the insurance industry. Theorists who approach the issue from a background in economics often start from the assumption that policy-holders should be charged a rate that reflects the expected loss that they bring to the insurance scheme. Yet theorists who approach the question from a background in philosophy or civil rights law often begin with a presumption against so-called “actuarially fair” premiums and in favor of “community rating,” in which everyone is charged the same price. This paper begins by examining and rejecting the three primary arguments that have been given to show that actuarially fair premiums are unjust. It then considers the two primary arguments that have been offered by those who wish to defend the practice of risk classification. These arguments overshoot their target, by requiring a “freedom to underwrite” that is much greater than the level of freedom enjoyed in most other commercial transactions. The paper concludes by presenting a defense of a more limited right to underwrite, one that grants the legitimacy of the central principle of risk classification, but permits specific deviations from that ideal when other important social goods are at stake.

SummaryIn cellular networks, global channel state information (CSI) shall be shared among neighboring cells to mitigate various inter‐cell interference (ICI). In this paper, we propose that comparable ICI is mitigated while weak ICI is treated as noise. The amount of shared CSI is highly reduced, because of that only the CSI corresponding to comparable ICI is shared among neighboring cells. In the case of single user equipment in each cell, a joint singular value decomposition and interference alignment method is proposed along with the analysis of its approximate sum rate. Some nodes select singular value decomposition method, while others choose interference alignment method. The method at each node is determined by global selection principle or user classification principle. In the case of multiple UEs in each cell, all nodes utilize minimum mean square error method, but ICI is neglected at cell center users. Copyright © 2014 John Wiley & Sons, Ltd.