Implementation of Basel and Solvency Risk Assessment Standards in Banks and Insurance Companies of Southeastern Europe Countries

Introduction. Employees of Emercom of Russia often participate in extreme tourism during their vacations, which helps to maintain their professional physical fitness. They are also attracted to help tourist groups that find themselves in a difficult situation in nature in a particular destination. Participation in extreme tourism is associated with the risk of injury. A general approach to the assessment of the impact of the management of the safety of tourist services in the instances associated with extreme tourism on insurance risks is presented. The purpose of the article is to create models for assessing the risks of extreme recreation. To do this, it is necessary to analyze the routes of extreme recreation in destinations from a mathematical point of view, and, based on this, to propose a number of management measures related, among other things, to insurance. Research methods are based on the use of the theory of discrete mathematics in the construction of a weighted graph describing the risks on the routes of tourists. The model allows us to quantify the risks on individual routes and, therefore, to build a target criterion for supporting the management of the safety of services in extreme tourism in a particular destination. Results and discussion. Building a graph of the route of a tourist group allows you to identify the least dangerous routes of tourist groups. This approach allows insurance companies to solve the problem of calculating the optimal and adequate amount of payments in the event of an insured event, as well as to reduce uncertainty in the actions of rescue units of Emercom of Russia. Conclusions. The methods of managing the safety of providing tourist services presented in the article reduce the probability of an insured event. Key words: insurance risk assessment, management of tourist services, tourist safety, security methods.

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Guide for Development of Fire-Risk-Assessment Standards

10.1520/e1776-07 ◽

2007 ◽

Author(s):

Keyword(s):

Risk Assessment ◽

Fire Risk ◽

Fire Risk Assessment ◽

Assessment Standards

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Risk assessment of building damage related to extreme winter windstorms in the canton of Zurich, Switzerland

10.5194/egusphere-egu2020-17644 ◽

2020 ◽

Author(s):

Thomas Röösli ◽

Christoph Welker ◽

David Bresch

Keyword(s):

Risk Assessment ◽

Claims Data ◽

Rare Events ◽

Damage Model ◽

Building Damage ◽

Insurance Companies ◽

Wind Gust ◽

Storm Damage ◽

Insurance Claims ◽

Insurance Claims Data

We compare the risk assessment for storm related building damage based on three different foundations: (1) insurance claims data, (2) modelled building damages based on a historic event set of wind gust data, and (3) modelled building damages based on a probabilistic extension of the historic event set. Windstorms cause large socio-economic damages in Europe. In the canton of Zurich (Switzerland) they are responsible for one third of the building damages caused by natural hazards.The Wind Storm Information Service (WISC) of the Copernicus Climate Change Service provides open wind gust datasets for the insurance sector to understand and assess the risk of windstorms in Europe. This is the first open climatological data set covering a longer time range than the insurance claims data of most small insurance companies. Our science-practice collaboration is a case study to illustrate how climatological data can be used in risk assessments in the insurance sector and how this approach compares to risk assessments based on proprietary claims data. We describe and use a storm damage model that combines wind gust data with exposure and vulnerability information to compute an event set of modelled building damages. These modelled damages are used to calculate relevant risk metrics for the insurance industry like the annual expected damage (AED) as well as the damage of rare events, with a return period of up to 250 years.The AED calculated based on the insurance claims data (i.e. the mean damage over the observation period of 35 years) is 2.34 million Swiss Francs (CHF). This is almost double the value of the AED computed based on the storm damage model and historic event set (CHF 1.36 million). The storm Lothar/Martin in December 1999 is the most damaging event in the insurance claims data (CHF 62.4 million) as well as the historic event set (modelled building damage of CHF 62.7 million).Both the insurance claims data and the modelled building damages based on historic events are not well suited to derive information about rare events with return periods considerably exceeding the observation period. To provide some information about rare events, we propose a new probabilistic event set, by introducing various perturbations, resulting in 4&#8217;200 events. This probabilistic event set results in an AED of CHF 1.45 million and a damage amount of CHF 75 million for a return period of 250 years. The probabilistic event set allows for testing the sensitivity of the risk to e.g. portfolio changes and changes in the insurance condition for events of a higher intensity than the historic events.Our analysis is implemented in the GVZ&#8217;s proprietary storm damage model as well as the open-source risk assessment platform CLIMADA (https://github.com/CLIMADA-project/climada_python). This guarantees scientific reproducibility and offers insurance companies the opportunity to apply this methodology to their own portfolio with a low entry threshold.

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Analysis on Credit Risk Assessment for Accounts Receivable Supply Chain Financing Based on Credit Insurance

E3S Web of Conferences ◽

10.1051/e3sconf/202127501065 ◽

2021 ◽

Vol 275 ◽

pp. 01065

Author(s):

Keran Bi ◽

Zheng Hua ◽

Qinwen Shi ◽

Yu Zhu

Keyword(s):

Risk Assessment ◽

Supply Chain ◽

Credit Risk ◽

Insurance Companies ◽

Accounts Receivable ◽

Credit Risk Assessment ◽

Credit Insurance ◽

Credit Risks ◽

Financing Mode ◽

Supply Chain Financing

This paper studies the model of accounts receivable supply chain financing based on credit insurance from the perspective of banks. First of all, the paper analyzes two different financing modes of the innovative model - the pledge financing mode and the factoring financing mode. Secondly, the paper explains the sources of credit risks for accounts receivable supply chain financing under credit insurance, and the necessity of using credit insurance. The sources of credit risks mainly include: the enterprises’ comprehensive strength under systemic and non-systemic risks, status of accounts receivable, supply chain operation, performance of insurance companies, and so on. In addition, based on the credit risks explained in this paper, the risk assessment system and the credit risk assessment model are built. At the end, the paper offers three suggestions for the banks’ financing risk control: bank should carefully check the policy’s exclusions clauses; bank must carefully check the authenticity of accounts receivable; bank can use dynamic monitoring on qualification checking for financing enterprises, core enterprises and insurance companies.

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A globally consistent probabilistic wildfire risk model to assess economic damages

10.5194/egusphere-egu21-2241 ◽

2021 ◽

Author(s):

Samuel Lüthi ◽

David Bresch

Keyword(s):

Risk Assessment ◽

Random Walk ◽

Climate Adaptation ◽

Risk Model ◽

Grid Point ◽

Probabilistic Risk Assessment ◽

Insurance Companies ◽

Wildfire Risk ◽

Economic Damages ◽

Probabilistic Risk

Wildfire risk around the world is rapidly increasing, leading to dramatic impacts on ecosystems and society. Economic damages of the past seasons threaten individual households, insurance companies, brokers and governmental authorities alike. Here, we present a probabilistic wildfire risk model to assess fire and economic risk. The model creates synthetic fire seasons through probabilistic ignition and dynamic random-walk spreading of fires.The risk of natural catastrophes is commonly modeled using the three components hazard, exposure and vulnerability. This approach is used in the well-established open-source platform CLIMADA (CLIMate ADAptation). Here we show its extension for a globally consistent wildfire risk model. The model allows for the evaluation of economic damages of past and current wildfire events as well as a probabilistic risk assessment for any exposure on a seasonal basis. It is built on open and global data to ensure consistent modelling, including in data-sparse regions.The hazard component uses Fire Information for Resource Management System (FIRMS) data acquired by the MODIS and VIIRS satellite missions and provided by Earthdata. We aggregate point information of fire activity using clustering algorithms over space and time to identify separate events while allowing for different resolutions (minimum of 375 m). For the exposure component, CLIMADA&#8217;s LitPop model is used, which geographically distributes assets using data on night-light intensity and population density. To assess the vulnerability, the model has been calibrated using reported damage data. Although uncertainties remain large, error scores after calibration resemble those of well-established hazards, such as tropical cyclones. To allow for probabilistic risk assessment, synthetic fire seasons are generated using a random-walk-type stochastic fire generator, which hinges on grid-point specific fire spread probabilities combined with an overall fire propagation probability. The framework further allows for a simple integration of additional data in order to reflect climate trends.

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