Reinsurance and Solvency Capital: Mitigating Insurance Companies’ Ruin Probability

ABSTRACT Context: insurance companies are important to society, since they guarantee financial protection to individuals from property losses, in addition to fostering the capital market through the allocation of guarantee assets. Thus, it is essential to evaluate the instruments that guarantee their long-term financial solvency. Among them are the adoption of reinsurance treaties, the sizing of the solvency capital, and the actuarial modeling of risk processes, which allow the measurement of the ruin probability. Objective: estimate the ruin probability in risk processes with the adoption of reinsurance contracts (quota share and excess of loss), compared to scenarios without such treaties. Methods: the Cramér-Lundberg process was simulated using the Monte Carlo method, adjusting several probabilistic distributions to the severity of the compound Poisson process, which is calibrated with a set of 3,917,863 real microdata, from 30 insurance lines of business. Results: it was found that, although each branch presents particularities in the claim severity, the correct choice of reinsurance (proportional or not) implies the reduction of the ruin probability for a fixed solvency capital. Conclusion: the appropriate choice of the reinsurance contract, especially when there is evidence of high kurtosis in the claim values, intensifies the exponential decline in the relationship between the solvency capital and the ruin probability.

Resseguro e Capital de Solvência: Atenuantes da Probabilidade de Ruína de Seguradoras

ABSTRACT Context: insurance companies are important to society, since they guarantee financial protection to individuals from property losses, in addition to fostering the capital market through the allocation of guarantee assets. Thus, it is essential to evaluate the instruments that guarantee their long-term financial solvency. Among them are the adoption of reinsurance treaties, the sizing of the solvency capital, and the actuarial modeling of risk processes, which allow the measurement of the ruin probability. Objective: estimate the ruin probability in risk processes with the adoption of reinsurance contracts (quota share and excess of loss), compared to scenarios without such treaties. Methods: the Cramér-Lundberg process was simulated using the Monte Carlo method, adjusting several probabilistic distributions to the severity of the compound Poisson process, which is calibrated with a set of 3,917,863 real microdata, from 30 insurance lines of business. Results: it was found that, although each branch presents particularities in the claim severity, the correct choice of reinsurance (proportional or not) implies the reduction of the ruin probability for a fixed solvency capital. Conclusion: the appropriate choice of the reinsurance contract, especially when there is evidence of high kurtosis in the claim values, intensifies the exponential decline in the relationship between the solvency capital and the ruin probability.

Data-driven model for ship encounter intention inference in intersection waters based on AIS data

Frequent collision accidents of ships in intersection waters have caused huge casualties and property losses. Unclear encounter intention, poor communication, or inaccurate judgment of the encounter intention are often the major causes of ships falling into dangerous and urgent situations, leading to collision accidents. There are few methods and models for automatically inferring ship encounter intention. In this study, an intelligent model driven by AIS data is proposed to infer the ship encounter intention in intersection waters. The Hidden Markov Model (HMM) is adopted to formulate the encounter process and perform intention inference. The encounter intentions, including crossing, overtaking and head-on, are modeled as unobservable states of the formulated HMM. The observable measures of HMM extracted from AIS data, include the relative distance, relative speed, and course difference between two ships. Subsequently, the Forward-Backward algorithm is employed to obtain the model parameters and the Viterbi algorithm is exploited to estimate the hidden state with the highest probability, resulting in the inferred intention. The main advantage of the proposed model is its ability to capture the spatial-temporal characteristics of the encounter process, that is, the spatial interaction between ships and the dynamic evolution of states of the encounter process. The AIS data collected from the Lantau Strait intersection waters are adopted to verify the effectiveness of the proposed model. The experimental results reveal that the model can achieve an inference accuracy of 95%, 91.33%, and 92.67% for crossing, overtaking, and head-on, respectively. Moreover, it has real-time performance that ensures the encounter intentions can be recognized at an early stage, which is very critical for the safe navigation of any ships encountered. Our results show that our model can infer the encounter intentions in a timely manner and with high accuracy.

CoT-Enabled Robust Surveillance System using Fog Machine Learning

Surveillance system is a method of securing resources and loss of lives against fire, gas leakage, intruder, earthquake, and weather. In today’s time, people own home, farm, factory, office etc. It has become more crucial to monitor everything for securing resources and loss of lives against fire, gas leakage, intruder, earthquake. As a part of surveillance, monitoring weather is also essential. Climate change and agriculture are interrelated processes, Today's sophisticated commercial farming like weather monitoring, suffers from a lack of precision, which results huge loss in farm. Monitoring residential and commercial arenas throughout is an efficient technique to decrease personal and property losses due to fire, gas leakage, earthquake catastrophes. Internet of Things make it possible and can be implemented separately for each thing or site. But it is very difficult to monitor each site and have centralized access of it across the world. This arises the need of heterogenous system which will monitor all IoTs and perform decision making accordingly. IoT itself a large-scale thing. For single IoT application, sensors used are more in number. These sensors generate thousands of records for an instance of time, some of those are valuable and some requires just analysis. This huge amount of data on servers requires better data processing and analytics. Maintenance is also a critical task. Cloud extends these functionalities but storing all the data on cloud entail users to pay tremendous cost to the cloud service providers. This problem is catered by “CoTsurF” framework. This paper presents novel and cost effective “CoTsurF” framework, CoT-enabled robust Surveillance system using fog machine learning, a Proof-Of-Concept implementation of heterogenous and robust surveillance system based on internet of things and cloud computing by leveraging a groundbreaking concept of Fog machine learning that is Fog Computing and machine learning in Cloud of Things.

How Is the Risk of Major Sudden Infectious Epidemic Transmitted? A Grounded Theory Analysis Based on COVID-19 in China

The outbreak of a sudden infectious epidemic often causes serious casualties and property losses to the whole society. The COVID-19 epidemic that broke out in China at the end of December 2019, spread rapidly, resulting in large groups of confirmed diagnoses, and causing severe damage to China's society. This epidemic even now encompasses the globe. This paper takes the COVID-19 epidemic that has occurred in China as an example, the original data of this paper is derived from 20 Chinese media reports on COVID-19, and the grounded theory is used to analyze the original data to find the risk transmission rules of a sudden infectious epidemic. The results show that in the risk transmission of a sudden infectious epidemic, there are six basic elements: the risk source, the risk early warning, the risk transmission path, the risk transmission victims, the risk transmission inflection point, and the end of risk transmission. After a sudden infectious epidemic breaks out, there are three risk transmission paths, namely, a medical system risk transmission path, a social system risk transmission path, and a psychological risk transmission path, and these three paths present a coupling structure. These findings in this paper suggest that people should strengthen the emergency management of a sudden infectious epidemic by controlling of the risk source, establishing an efficient and scientific risk early warning mechanism and blocking of the risk transmission paths. The results of this study can provide corresponding policy implications for the emergency management of sudden public health events.

Property Risk Assessment of Sinkhole Hazard in Louisiana, U.S.A

Sinkholes (or dolines) are an often-overlooked environmental hazard. The processes that lead to their formation are slow and insidious, which encourage a lack of awareness or concern for the potential danger, until the sudden, climactic formation leads to unexpected property damage and possibly human casualties. This research identifies the risk to residential properties to the sinkhole hazard, using Louisiana, United States as a case study. Risk is defined as the product of the hazard intensity and the loss to structure and contents within the building resulting from the hazard-related disaster. Results suggest that risk is highly scale-dependent. Although the risk due to sinkholes is small on a per capita basis statewide, especially when compared to the per capita risk of other natural hazards, the property risk for census tracts or census blocks partially or completely overlying a salt dome is substantial. At finer scales, Terrebonne Parish, in coastal southeastern Louisiana, has the greatest concentration of salt domes, while Madison Parish, which is east of Monroe, has the highest percentage of area at risk for sinkhole formation, and St. Mary Parish—immediately west of Terrebonne—has the greatest risk of property loss. An Acadia Parish census tract has the maximum annual property losses in 2050 projected at $40,047 (2010$), and the highest projected annual per building ($43) and per capita ($18) property loss are in the same St. Mary Parish census tract. At the census block level, maximum annual property loss ($7,040) is projected for a census block within Cameron Parish, with maximum annual per building loss ($85 within West Baton Rouge Parish), and maximum per capita annual property loss ($120 within Plaquemines Parish). The method presented in this paper is developed generally, allowing application for risk assessment in other locations. The results generated by the methodology are important to local, state, and national emergency management efforts. Further, the general public of Louisiana, and other areas where the developed method is applied, may benefit by considering sinkhole risk when purchasing, remodeling, and insuring a property, including as a basis of comparison to the risk from other types of hazard.

Realization of GPS Dynamic Hijacking Based on Software Radio

GPS positioning technology has a wide range of use scenarios, but due to the known characteristics of GPS signal open reception and modulation methods, many solutions can easily deceive GPS signals. Once this technology is abused in violation of regulations, it may cause major safety and property losses. In order to better study the anti-hijacking technology of PGS signals, this article starts with the generative dynamic hijacking of PGS, gives the implementation method based on software radio, and further learns the code phase and dopper between asynchronous hijacking and synchronous hijacking through software radio. The compensation calculation method of Leer frequency shift parameters, and verified the effect of the software radio’s GPS dynamic hijacking implementation method through examples, and provide theoretical support for the design and improvement of future anti-hijacking systems.

Construction Safety during Pandemics: Learning from the Xinjia Express Hotel Collapse during COVID-19 in China

Many construction accidents occur in China each year, leading to a large number of deaths, injures, and property losses. Due to the outbreak of COVID-19, little attention is paid to construction safety, resulting in severe accidents. To prevent construction accidents and learn to how address safety issues in future pandemics, this study proposed an improved STAMP (Systems Theoretic Accident Modeling and Processes) model to analyze the collapse accident of the Xinjia Express Hotel used for COVID-19 quarantine in China. Through the application of the STAMP approach, the causes of the construction accident and the relationship between various causal factors are analyzed from a systematic perspective. The identified causes are divided into five categories: contractors, management of organizations, technical methods, participants, and interactive feedback. Finally, safety recommendations are drawn from this study to improve construction safety and safety management in pandemics.

Properties of debris flow deposits in Masamba, South Sulawesi, Indonesia

On 13 July 2020, a huge landslide-induced debris flow struck Masamba city and its vicinity, providing a massive amount of sedimentation materials. The landslide-induced debris flow was triggered by heavy rainfall, causing damages such as life and property losses, traffic disruption. Landslide-induced debris flow is a sudden natural hazard in mountain regions characterized by fast velocity, huge impact area, large scale, and often causes disastrous accidents. This study aims to investigate the properties of the debris flow materials including the soil size distribution, density, shear strength and bearing capacity. The results showed that the sedimentation material at landslide sites containing a large amount of sand which is classified as a poorly graded sand (SP). The engineering properties such as density, cohesion, internal friction angle and the value California Bearing Ratio (CBR) were also discussed.

Application of Well Control Technology in Drilling Engineering

Drilling operation is the leader of the oil exploration and development industry. The complexity of the process determines the characteristics of high investment and high risk. The particularity of the operating conditions determines the characteristics of labor intensity, gravity, and three-dimensional intersection, which all make the drilling operation process have various risks. These risks affect the efficiency and progress of operations, and even cause major accidents in serious cases, leading to casualties and property losses. Therefore, it is necessary to further improve the well control safety technology, so that the drilling work will gradually move towards scientific, safe and refined technology development direction. Through field investigation and literature reading, the risk of drilling operation is analyzed, and the causes, classification and characteristics of drilling operation risk are summarized. This paper summarizes the complexity of drilling accidents and the importance of risk control research. Since the risk control of drilling operations involves many fields, this paper only makes preliminary analysis and exploration, and further research and exploration are needed to improve the risk control of drilling operations.