Identification of Enterprise Financial Risk Transfer Path Based on Data Mining

Author(s):  
Yan Zhang ◽  
Kaixi Ji ◽  
Yong An
Author(s):  
Ali Serhan Koyuncugil

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.


2021 ◽  
Vol 21 (1) ◽  
pp. 99-113
Author(s):  
Delioma Oramas-Dorta ◽  
Giulio Tirabassi ◽  
Guillermo Franco ◽  
Christina Magill

Abstract. Volcanic eruptions are rare but potentially catastrophic phenomena, affecting societies and economies through different pathways. The 2010 Eyjafjallajökull eruption in Iceland, a medium-sized ash-fall-producing eruption, caused losses in the range of billions of dollars, mainly to the aviation and tourism industries. Financial risk transfer mechanisms such as insurance are used by individuals, companies, governments, etc., to protect themselves from losses associated with natural catastrophes. In this work, we conceptualize and design a parametric risk transfer mechanism to offset losses to building structures arising from large, ash-fall-producing volcanic eruptions. Such a transfer mechanism relies on the objective measurement of physical characteristics of volcanic eruptions that are correlated with the size of resulting losses (in this case, height of the eruptive column and predominant direction of ash dispersal) in order to pre-determine payments to the risk cedent concerned. We apply this risk transfer mechanism to the case of Mount Fuji in Japan by considering a potential risk cedent such as a regional government interested in offsetting losses to dwellings in the heavily populated prefectures of Tokyo and Kanagawa. The simplicity in determining eruptive column height and ash fall dispersal direction makes this design suitable for extrapolation to other volcanic settings worldwide where significant ash-fall-producing eruptions may occur, provided these parameters are reported by an official, reputable agency and a suitable loss model is available for the volcanoes of interest.


Author(s):  
Paul Raschky ◽  
Sommarat Chantarat

ASEAN countries are frequently hit by a variety of natural disasters, and a large fraction of economic activity in ASEAN countries is located in areas exposed to these natural perils. Increasing disaster damages require ASEA countries to manage the financial losses in a more efficient and proactive manner. Currently, most risk-transfer mechanisms in this region rely on ad-hoc government relief, which is not sustainable. Multilateral cooperation in the areas of risk-modeling and mapping as well as joint efforts to establish financial risk-transfer solutions could help to overcome existing challenges in this area.


2014 ◽  
Vol 513-517 ◽  
pp. 1940-1943
Author(s):  
Li Hong Yu ◽  
Ya Li Xu ◽  
Lin Dai

The computer data mining technology plays an important role in the financial risk management. It can extract the implicit data that people don't know in advance, in the mean time, and potentially useful information and knowledge for managers to provide decision-making reference. This paper introduces the concept of data mining, the process and main technology first, and then introduces the typical application of data mining in the financial risk management, such as customer relationship management, credit risk assessment and financial crisis early warning analysis. At last, it has a summary to provide the risk management for the financial industry.


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