financial risk management
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Enterprise financial risks are analyzed utilizing the theory of organizational behavior, and a financial risk management system is constructed to improve the design and algorithm of the enterprise risk management system. Base on the CCER (China Center for Economic Research) database, the early warning model for enterprise financial risk management containing five indices is proposed for enterprises. Through Logistic regression analysis, the design principle of the financial risk management system based on AI (Artificial Intelligence) technology is explained. The proposed system innovatively introduces the AI integrated learning method, optimizes objective function through XGBoost (eXtreme Gradient Boosting) algorithm, and trains the model through BP (Backpropagation) NN (Neural Network). Finally, following comparative analysis, the effectiveness of the proposed method is verified.

The motivation of this research consists in the following: the traditional commercial approach to financial risk management amid economic crises implies the reduction of corporate social responsibility, based on the assumption that this responsibility raises the financial risk of business. Due to this, the contribution of business to the achievement of the SDGs is not stable and is often negative, since practices of business management contradict the SDGs in crisis periods and hinder their achievement in society and the economy. However, the refusal from corporate social responsibility during a crisis does not guarantee the following increase in the level of business development in the period of stability. A study of the case experience of integrating the SDGs into corporate strategies of the largest Russian companies during the COVID-19 crisis improved the understanding of the contribution of corporate social responsibility to financial risk management of the business. Dynamic modelling showed that, in a crisis period, corporate social responsibility leads to a reduction of the financial risks of business—it is commercially profitable, similarly to the phase of stability, and critically important. Based on this, an alternative (new) approach to financial risk management is developed, which allows raising the effectiveness of this management amid economic crises (including the COVID-19 crisis) through the integration of the SDGs into corporate strategies and the manifestation of high social responsibility during crises.

Every country is concerned with the problem of corruption. Selfish misuse of public office undermines the people's confidence in government and institutions, makes public policies less effective and fair, and misuses taxpayer funds that could be used to build schools, roads and hospitals. Financial risk management in public administration is based on risk assessments and finding tools to help influence them. To develop practical tools for financial risk management, the authors studied the economic practises that determine the features of financial risk assessment. They identified the elements that can motivate financial risks, including three stages of identification and analysis of risks, analyzed the main stages and indicators of assessing an organization's financial condition based on the balance sheet, produced a risk assessment algorithm and risk minimization methods in managing financial activities, and conducted a comparative analysis of financial risk assessment methods. Based on the Professional Integrity Framework proposed by the Organization for Economic Cooperation and Development, the authors developed basic measures to mitigate the potential negative consequences and proposed seven financial risk management tools for public administration. The proposed anti-corruption tools will help significantly reduce financial risks in public administration and corruption in general.

This paper aims to study enterprise Financial Risk Management (FRM) through Big Data Mining (BDM) and explore effective FRM solutions by introducing information fusion technology. Specifically, big data technology, Support Vector Machine (SVM), Logistic regression, and information fusion approaches are employedto study the enterprise financial risks in-depth.Among them, the selection offinancial risk indexes has a great impact on the monitoring results of the SVM-based FRM model; the Logistic regression-based FRM model can efficientlyclassify financial risks; theinformation fusion-based FRM model uses a fusion algorithm to fuse different information sources. The results show that the SVM-based and Logistic regression-based FRM models can manage and classify enterprise financial risks effectively in practice, with a classification accuracy of 90.22% and 90.88%, respectively; by comparison, the information fusion-based FRM modelbeats SVM-based and Logistic regression-based FRM models by presenting a classification accuracy as high as 95.18%. Therefore, it is concluded that the information fusion-based FRM is better than the SVM-based and Logistic regression-based models; it can integrate and calculate multiple enterprise financial risk data from different sources and obtain higher accuracy; besides, big data technology can provide important research methods for enterprise financial risk problems; SVM-based FRM model and Logistic regression-based FRM model can well classify enterprise financial risks, with relatively high accuracy.

The implementation of an entrepreneurial initiative is inevitably associated with the uncertainty of the conditions for its implementation. The market model of the economic system, which determines the conditions for the behavior of business entities, is characterized by a significant number of areas of such uncertainty. The risk, which is its measure, accompanies the activities of a commercial organization at all stages of its life cycle.