Microsoft Word - 50-FBEM012522排板.docx Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 11, No. 3, 2023 267 Risk Management Strategy Based on Data Inference of Separation Metric Space Junhan Zhang Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China Abstract: In this paper, the risk management strategy based on data inference of separation metric space is studied, aiming at improving the accuracy and efficiency of risk management. Firstly, this paper introduces the related theories of data inference and risk management of separation metric space, and the advantages of combining data inference and risk management of separation metric space. Then, the advantages and disadvantages of risk management strategy based on data inference of separation metric space are expounded in detail, and the application of data inference of separation metric space in risk management strategy is discussed. Finally, the potential and challenges of separating metric spatial data inference in risk management are analyzed. The research results of this paper not only help us to understand the application of spatial data inference in risk management, but also provide useful reference and enlightenment for researchers and practitioners in related fields. It is hoped that the research in this paper can promote the progress and development in the field of risk management and improve the organization's ability to cope with various risks. Keywords: Big data, Separate metric space data inference, Data processing, Risk management. 1. Introduction In today's highly complex and changeable social and economic development environment, the importance of risk management is increasingly prominent [1]. All kinds of organizations, whether government agencies, enterprises or non-profit organizations, are trying to find effective methods and strategies to identify, evaluate and manage the risks they face [2-3]. Although the traditional risk management strategy has played an important role in many cases, with the progress of the times and the diversification of risks, it is necessary to find new methods and perspectives to improve the effectiveness of risk management [4]. In the past decades, with the development of big data technology, data-driven decision-making has been widely used in various fields [5]. The field of risk management is no exception. The emergence of big data technology has brought new opportunities and challenges to risk management. However, how to effectively use these data has become an urgent problem [6]. As a new data analysis method, data inference from separated metric space can extract valuable information from a large number of complex data and provide a new perspective and method for risk management [7]. The purpose of this paper is to explore a risk management strategy based on data inference of separation metric space, and provide scientific basis for risk management decision-making by extracting valuable information from a large number of data. 2. Separation of Metric Spatial Data Inference and Risk Management Related Theories 2.1. Separation metric spatial data inference theory Inference of separated metric space data is a data-driven method, which provides scientific basis for decision-making by extracting valuable information from a large number of complex data. It mainly involves data preprocessing, feature extraction, model building and evaluation [8]. The main purpose of this theory is to transform high-dimensional and complex data into low-dimensional and easy-to-understand forms through data analysis and mining, so as to better understand and explain phenomena. In the inference of separating metric space data, data preprocessing is very important. Because the original data often have problems such as missing, noise and abnormal values, it is necessary to clean, fill in and normalize the data for subsequent analysis. Feature extraction is the core step of separating metric space data inference, which transforms high-dimensional data into low-dimensional representation by extracting useful information from data. Commonly used feature extraction methods include principal component analysis, linear discriminant analysis, multi-dimensional scale analysis and so on. Model building and evaluation is to use the extracted features to build a classification, regression or clustering model, and to evaluate and optimize the performance of the model. 2.2. Risk management theory Risk management refers to the process of reducing or avoiding adverse consequences by identifying, evaluating and controlling potential risk factors. It mainly includes risk identification, risk assessment, risk control and risk monitoring [9]. Risk identification is the basis of risk management, which requires organizations to find and confirm internal and external factors that may adversely affect themselves in time. Risk assessment is a quantitative and qualitative analysis of the identified risks, so as to understand the occurrence probability, influence degree and relationship of each risk factor. Risk control is to take corresponding measures to reduce or eliminate the adverse effects of risks on the basis of evaluation, so as to reach the acceptable level of the organization. Risk monitoring is to find and deal with new risk factors in time through continuous tracking and monitoring of risk factors. In traditional risk management methods, risk identification and evaluation mainly rely on 268 expert experience and qualitative analysis. However, with the increasing complexity and diversification of risks faced by organizations, this single method can no longer meet the needs. Therefore, the application of data inference from separated metric space to risk management can make full use of its powerful data processing and analysis capabilities and improve the accuracy and efficiency of risk identification and evaluation. 2.3. Combination of data inference and risk management in separation metric space The application of data inference from separated metric space to risk management can give full play to its data-driven advantages and improve the scientificity and effectiveness of risk management. First of all, through data preprocessing, risk data can be cleaned and standardized, and the influence of noise and outliers on the analysis results can be reduced. Secondly, through feature extraction, high-dimensional risk data can be transformed into low-dimensional representation, which is convenient for further analysis and interpretation. In addition, the inference of separated metric space data can also be applied to the construction and optimization of risk assessment model to improve the accuracy and stability of risk prediction. Finally, through the method of inferring from separated metric space data, we can monitor and analyze the risk data in real time, and find and deal with new risk factors in time. The risk information management system architecture is shown in Figure 1. Figure 1. Risk information management system architecture 3. Advantages and Disadvantages of Risk Management Strategy Based on Data Inference of Separation Metric Space 3.1. Advantages of risk management strategy based on data inference of separation metric space Data-driven: Separating metric space data inference can make full use of a large number of data, extract valuable information from the data, and help enterprises better identify and evaluate risks. Compared with traditional qualitative risk management methods, the method based on spatial data inference of separation metric is more objective and accurate. High-dimensional data analysis: separating metric space data inference can process high-dimensional data, extract risk characteristics from multiple dimensions, and improve the accuracy of risk identification and evaluation. Traditional risk management methods often only focus on a few dimensions, and it is difficult to describe risks comprehensively. Real-time monitoring: The method of data inference based on separation metric space can monitor and analyze risk data in real time, and discover and deal with new risk factors in time. This is of great significance for enterprises to avoid risks and reduce losses in time. Interpretability: The results inferred from separating metric spatial data are well interpretable, which can help enterprises better understand the correlation and influence between risk factors, and thus formulate more scientific and reasonable risk management strategies. 3.2. Disadvantages of risk management strategy based on data inference of separation metric space Data quality: the accuracy of data inference in separation metric space depends largely on the quality of data. If there are problems such as noise, abnormal values or missing data, it will have a bad influence on the analysis results. Applicability of the model: The method of separating metric spatial data inference is subjective and empirical, and different feature extraction methods and model selection will have an impact on the analysis results. Therefore, the applicability and generalization ability of the model need to be fully verified. Computational complexity: The method of separating metric spatial data inference usually involves large-scale data processing and high-dimensional data analysis, which has high computational complexity and needs high-performance computing resources and professional technicians to implement. Privacy and security: separating metric space data inference requires processing and analyzing 269 a large amount of data, which often contains sensitive and confidential information of enterprises, so effective privacy protection measures need to be taken to ensure the security and privacy of data. 4. The Application of Separating Metric Space Data Inference in Risk Management Strategy Formulation In the process of making risk management strategy, separating metric space data inference can play an important role. As shown in Table 1, the separation metric spatial data inference in practical application is embodied. Table 1. Inference of separation metric space data in practical application Application scenario Risk management issues Inference method of separating metric space data Effect Financial risk management Identify and evaluate credit risk By analyzing the financial data and credit records of customers, the risk characteristics are extracted and the credit risk evaluation model is constructed. Improve the accuracy and efficiency of credit risk assessment, and help financial institutions better manage credit risk. Supply chain risk management Identify and evaluate supply chain risks Analyze the data in the supply chain network, extract the characteristics of supply chain risk, and build a supply chain risk assessment model. Improve the accuracy and efficiency of supply chain risk assessment and help enterprises to formulate more scientific and reasonable supply chain risk management strategies. Medical risk management Identify and evaluate medical risks Analyze the hospital medical data and patients' medical records, extract the characteristics of medical risks, and build a medical risk assessment model. Improve the accuracy and efficiency of medical risk assessment and help hospitals manage medical risks better. Energy risk management Identify and evaluate energy market risks Analyze the price data, supply and demand data of the energy market, extract the risk characteristics of the energy market, and construct the risk assessment model of the energy market. Improve the accuracy and efficiency of energy market risk assessment, and help energy enterprises better manage market risks. Network security risk management Identify and evaluate network security risks Analyze network traffic data, vulnerability scanning data, etc., extract network security risk characteristics, and build a network security risk assessment model. Improve the accuracy and efficiency of network security risk assessment, and help enterprises better manage network security risks. The following is the discussion of its specific application: (1) Risk identification: By separating metric spatial data inference, it can help enterprises identify risk-related features and patterns from a large number of complex data. These characteristics and patterns may involve different data sources, including finance, operation, market and so on. Using the data inference method of separation metric space, these complex data can be transformed into an easy-to- understand form, and the accuracy and efficiency of risk identification can be improved. (2) Risk assessment and measurement: On the basis of risk identification, separating metric spatial data inference can help enterprises to assess and measure the size and impact of risks. By constructing classification, regression or clustering models, risk factors can be associated with corresponding results, so as to quantitatively analyze risks. In addition, the separation of metric space data inference can also help enterprises understand the correlation and influence between risk factors, so as to better grasp the overall picture of risk. (3) Risk monitoring and early warning: Through the method of inferring from separated metric spatial data, enterprises can monitor the changes of risk factors in real time and find and deal with new risks in time. For the existing risks, we can predict and warn their development trend, so as to take corresponding measures in advance to avoid or reduce the impact of risks. (4) Risk management decision-making: Based on the results inferred from the separated metric spatial data, enterprises can make more scientific and reasonable risk management decisions. For example, when weighing multiple risks, separating metric space data inference can help enterprises understand the correlation and influence between different risks, so as to make more comprehensive decisions. 5. Potential and Challenge of Separating Metric Spatial Data Inference in Risk Management 5.1. Potential of separating metric spatial data inference in risk management Comprehensive risk identification: separating metric space data inference can analyze multi-source and multi- dimensional data and help enterprises comprehensively identify various types of risks. Traditional risk management methods often only focus on one aspect of risk, such as financial risk and market risk, while separating metric space data inference can integrate multiple aspects of data and improve the comprehensiveness and accuracy of risk identification. Refined risk assessment: By inferring from separated metric spatial data, enterprises can more accurately assess the probability and influence degree of various risks. Traditional risk management methods often only pay 270 attention to the influence degree of risk, but ignore the probability and uncertainty of risk. The data inference of separation metric space can comprehensively consider various factors of risk through data mining and analysis, so as to obtain more refined risk assessment results. Real-time monitoring and early warning: separating metric spatial data inference can monitor and analyze risk data in real time, and discover and deal with new risk factors in time. In addition, by building an early warning model, enterprises can give early warning before risks occur, so as to take corresponding measures in advance to avoid or reduce the impact of risks. Support decision-making: Based on the inference results of separated metric spatial data, enterprises can make more scientific and reasonable risk management decisions. For example, when weighing multiple risks, separating metric space data inference can help enterprises understand the correlation and influence between different risks, so as to make more comprehensive decisions. 5.2. The challenge of separating metric spatial data inference in risk management Data quality and processing: the accuracy of data inference in separation metric space depends largely on data quality and processing methods. If there are problems such as noise, abnormal values or missing data, it will have a bad influence on the analysis results. Therefore, it is necessary to collect and process the data with high quality to ensure the accuracy and integrity of the data. Model selection and evaluation: It is necessary to select a suitable model for data analysis in order to separate metric space data inference, and different models have different characteristics and application scope. In addition, it is necessary to evaluate and optimize the performance of the model to ensure that the model can accurately reflect the actual situation. Technical threshold and cost: separating metric spatial data inference requires certain technical threshold and cost, and requires knowledge and skills in data analysis and machine learning. In addition, a lot of time and resources need to be invested in data processing and analysis, which also requires corresponding cost investment. Data security and privacy protection: separating metric spatial data inference requires processing and analysis of a large amount of data, which often contains sensitive and confidential information of enterprises, so effective privacy protection measures are needed to ensure data security and privacy. 6. Conclusions This paper discusses the risk management strategy based on data inference of separated metric space, and provides scientific basis for risk management decision-making by extracting valuable information from a large number of data. The research results show that the risk management strategy based on spatial data inference of separation metric has many advantages, such as data-driven, high-dimensional data analysis, real-time monitoring and interpretability. However, there are also some shortcomings, such as data quality, model applicability, computational complexity and privacy security. Therefore, enterprises need to weigh the advantages and disadvantages according to their actual situation and needs, and choose appropriate risk management strategies. At the same time, it is also necessary to strengthen the research and application practice of related technologies and improve the inference of spatial data of separation metrics. Generally speaking, the inference of separated metric spatial data has a wide application prospect in the process of formulating risk management strategies. It can help enterprises to better identify, evaluate and monitor risks, so as to make more scientific and reasonable risk management decisions. However, in practical application, the data reliability, the applicability of the model and the limitation of computing resources need to be considered. 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