Perception of environmental insurance risk vs. implementation of control measures in organizations managed according to ISO 14001:2015 in Poland

The numerous studies of risk perception to date have been focused on three different areas of the subject. Firstly, researchers have aimed at the identification of risk perception determinants, secondly, they have created recommendations on how to communicate the risk to society, and finally they have analysed the relationships between risk perception measures and the means of risk control implemented in organizations. The last indicated research area is complemented by the following paper. On the basis of data derived from online interviews it was concluded that the higher the assessment of the measures of environmental insurance risk perception, the more frequently organizations implemented selected means of risk control in the environmental management system according to ISO 14001 (EMS ISO 14001). The largest number of statistically significant dependencies were observed in the area of variables which describe the informative potential of EMS ISO 14001 (informative potential variables) and those which directly determine the volume of environmental insurance risk (the volume of risk variables). Simultaneously, not all categories of risk perception measures are equally related to the frequency of risk control means implementation. A particularly large number of statistically significant correlations were identified for the following variable: the evaluation of the likelihood of the occurrence of environmental damage.

Optimization Model of Cross-Border E-commerce Payment Security by Blockchain Finance

Cross-border trade is also changing and innovating. Electronic payment has become the core application of modern cross-border e-commerce. However, although electronic payment has brought convenience and efficiency to enterprises and social life, there are also many problems in transaction information security. These problems not only hinder the development of electronic payment but also bring hidden dangers to people’s property security. Therefore, an encryption algorithm of e-commerce was proposed, and its algorithm security and risk control mode was further studied by means of case analysis, empirical analysis, and comparative analysis. Experimental data show that blockchain technology is a breakthrough. The aim of the study is to explore its application in specific circumstances and strive to make the new e-payment mode in line with international standards.

Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms

On the basis of traditional credit risk control, this paper proposes the demand and direction of a new credit risk control strategy based on machine learning and relying on big data. First, on the basis of introducing the basic algorithmic principles of machine learning, we give reasons for choosing machine learning models and build a machine learning-based Internet consumer finance credit risk control strategy model to provide theoretical support for the empirical analysis later. Second, we take the test data of Internet consumer finance S company as the research sample and carry out empirical analysis according to the machine learning-based Internet consumer finance credit risk control strategy model. The comparison of the training results is based on the comprehensive consideration of training time, validation set accuracy, TPR evaluation indicators, and interpretability of the results; it verifies the advantages of the machine learning model in screening the key influencing factors that cause the overdue performance of credit customers. According to the optimized credit risk control strategy, corresponding strategy suggestions are provided for the credit risk control of S company. The research results show that the prediction effect of the classification model based on traditional linear regression is generally lower than that of the model based on the classification algorithm based on machine learning, and there is a complex nonlinear relationship between platform default and its related influencing factors. The accuracy of classification and early warning results of the random forest algorithm is relatively high, and the detection rate of the decision tree model is relatively high, but the cost is also the highest. In addition, the accuracy of the four types of early warning models is relatively stable, reaching an average of 80%. This paper proposes a machine learning-based Internet consumer finance credit risk control strategy model. Its system, timeliness, and risk prediction capabilities provide new ideas and suggestions for Internet consumer finance companies to design risk control strategies.

RISK MANAGEMENT OF TERMINAL ON-SITE OPERATIONS FOR SPECIAL BULK CARGOS IN TAIWAN

The main purpose of this article was to study the risk management of terminal on-site operations for special bulk cargos in Taiwan. This study applied the concept of Formal Safety Assessment approach as the foundation of risk management assessment. At first, a total of four risk aspects with eighteen preliminary risk factors were generated from literature and experts interviews. Three methods – namely analytical hierarchy process (AHP), risk matrix model (RMM), and costs and benefit analysis (CBA) methods – were employed to perform an empirical study in Taiwan. The empirical results showed: (1) The most severe risk factor found using the AHP method was ‘failure to perform periodic machinery maintenance and examination.’ (2) Ten risk factors placed in the highest-risk area via the RMM method. (3) All risk control strategies were evaluated for applicability by using the CBA method. This study recommended that improvement and reinforcement of the staff aspect and the related risk factors for the on-site operation of special bulk cargos. Through implementation of risk control strategies, the risks of accidents can be controlled.