Financial risk early warning of airlines based on convolutional neural network models

With the rapid advancement of the informatization process, enterprise informatization management has received more and more attention. Facing the increasingly complex and changeable social and economic environment, the difficulty of enterprise risk management has gradually increased. How to establish an efficient risk management mechanism for early warning of corporate risks is the goal that companies seek. Traditional statistical analysis can no longer satisfy the processing of massive financial data. Therefore, how to find useful information for the financial risk early warning management of the enterprise from the large amount of financial data information generated by the business activities of the enterprise is a problem that enterprises urgently need to solve at present. The continuous improvement and innovation of data mining technology and the good performance of research and analysis of massive data have made the two closely linked. First, this study introduces the theories of financial risk early warning and data mining technology; second, it introduces the research process of financial risk early warning model and elaborates the three data mining techniques used in this study; then combined with the actual situation of listed companies in my country, it constructs financial risk early warning index system; and finally, 77 listed manufacturing companies and their matching companies that were first processed by ST in 2005‐2007 were used as research samples, based on the financial data of the 2.4 years before being processed by ST and CXISP. It is found that the financial risk early warning model established by data mining technology has strong early warning capabilities. From the perspective of the prediction capabilities of the three models, the closer the time to ST, the higher the accuracy of the prediction; from the perspective of short‐term early warning, the three models have better prediction effects, but from the perspective of long‐term early warning, the prediction effects of neural networks and decision trees are better than logistic regression of statistical analysis; data mining techniques based on knowledge discovery are not only suitable for short‐term early warning but also for longer‐term early warning. Therefore, data mining can be applied to financial risk early warning analysis to achieve the purpose of using data mining technology for decision support.

Research on optimization of an enterprise financial risk early warning method based on the DS-RF model

Financial risk, as one of the most influential and destructive risks in business, will make enterprises unable to escape the fate of bankruptcy if not warned and prevented in time. In the paper, we conducted research on the financial risk early warning of listed companies. A total of 250 companies were randomly selected from the Chinese A-share market from 2019 to 2021. By building the 26 financial indicators of listed companies and constructing the PCA-BP neural network, we compared the financial risk early warning effects among PCA-BPNN, SVM, and Logistic. It is found that the financial data processed by PCA can better adapt to the financial risk early warning model. The PCA-BPNN model improved the prediction accuracy of the financial risk early warning, which has strong generalization ability for the prediction of financial risk. Research findings have certain reference significance for precise judgment on the financial risk of companies.

As the development of economic globalization deepens, the early warning and management of corporate financial risks have become increasingly crucial. This paper addresses the characteristics of the baijiu (Chinese liquor) industry and proposes a federated learning-based financial risk early warning model to achieve a balance between data sharing and risk prediction among baijiu enterprises. Through federated learning, different enterprises can collaboratively train an early warning model while safeguarding data privacy, thereby enhancing prediction accuracy and comprehensiveness. This paper begins by reviewing federated learning and the field of financial risk early warning. It subsequently presents the model's design and implementation, and concludes by demonstrating the model's effectiveness and superiority in baijiu enterprise financial risk early warning through experiments.

In this study, company A is taken as A case, and the efficacy coefficient method is adopted to deeply study the financial situation of new energy photovoltaic enterprises from the perspective of financial risk early warning. As A leader in the photovoltaic industry, its financial health has an important impact on the stability and sustainable development of the whole industry. By establishing the efficacy coefficient model, comprehensively considering the financial indicators, market environment and industry trends of the enterprise, the financial risks of Company A are comprehensively evaluated. The research not only focuses on the traditional financial indicators such as the profitability, solvency and operating efficiency of enterprises, but also integrates the internal environment, external environment, scientific and technological innovation and environmental protection into the model, thus improving the comprehensiveness and reliability of the early warning model. In the empirical analysis, we found that Company A has shown good financial health in recent years, but it also faces some risks and challenges in the face of increasingly fierce competition in the industry. Through the application of the efficacy coefficient method, we can grasp the financial situation of enterprises more comprehensively and accurately, find the potential risk signs in advance, and provide scientific decision-making basis for the decision makers of enterprises. This research provides a new perspective and method for the financial risk management of photovoltaic enterprises and the new energy industry, and provides strong support for the enterprise decision makers to formulate scientific and effective financial strategies. The future research direction can further explore the new financial early warning model on the basis of the efficacy coefficient method, so as to better serve the sustainable development and risk prevention of enterprises.

The financial risk early warning model is an effective means of risk prevention. This paper’s linear partial differential equation is innovatively applied to financial institutions’ financial risk early warning system. At the same time, we construct a partial differential equation, linear discriminant model, based on the extreme value principle. The system can effectively avoid the problem of fractional model failure. The simulation results show that the algorithm in this paper improves the accuracy and speed of financial risk early warning and significantly reduces the two-class classification error rate of the model.

In the current era, the market competition is becoming increasingly fierce, complicated and unpredictable. Based on the interaction of various factors, the probability of financial risks of listed companies is significantly improved. Because of its unique characteristics, the listed companies’ operating status affects the overall operation of China’s market economy and occupies a fundamental position in the national economic system. If listed companies have financial risks, it will cause great trauma to our economy. Based on the financial risk evaluation theory of listed companies, this paper analyzes the financial indicators of listed companies through random effect model, and puts forward the risk analysis and prediction index system of listed companies from theoretical and empirical angles, thus constructing a financial risk early warning model based on linear random effect model, and studying the financial risk early warning of listed companies with practical cases. The research results show that the financial risk early warning model of random effect model is feasible and effective, which can help listed companies to carry out financial risk early warning management and improve financial management level.

With the rapid development of the economy, a large amount of financial data will be generated during the continuous growth of enterprises. However, due to the explosive growth of the financial data range index, the use of machine learning methods to mine and analyse financial data is extremely important. Among them, accurate financial risk evaluation is an effective measure to prevent and resolve corporate financial crises. In this article, we use fuzzy clustering method to establish a financial risk early warning and evaluation model. Specifically, we use fuzzy C‐mean (FCM), half‐suppressed FCM, and interval FCM clustering algorithms‐based state construction financial risk early warning and evaluation models, to give an evaluation from two aspects of corporate financial indicators and non‐financial indicators system. In order to verify the feasibility and effectiveness of the fuzzy clustering algorithms used in financial data mining, we conducted experiments in financial data mining and early warning in real estate companies and ST companies. The experimental results show that the fuzzy clustering algorithms represented by the FCM clustering algorithm has achieved good results in financial data mining, and can achieve good results in financial risk analysis and financial risk early warning.

It has become urgent to find a solution for the problem of how to use the financial risk early warning system to effectively evaluate the financial risk of enterprises and determine whether the risk threshold is standard across different industries (Lei, Menghao, & N, 2021). This paper applies the Z-Score model and focuses on the medical device industry. Using a total of 48 listed companies on the Science and Technology Innovation Board as observation samples, empirical analysis of the companies’ financial risks is conducted in addition to establishing a more reliable and practical financial risk early warning model suitable for the medical device industry. APT Medical and Endovastec were selected as observation samples, followed by an empirical analysis using traditional indicators and the Z-Score model. The results show that the applicability of the Z-Score model to a certain company in financial risk early warning is low. Furthermore, the threshold of the financial risk early warning model should be dynamically adjusted according to various industries to assist enterprises in realizing the financial risk crisis in time to take adequate measures. (Lu & Zhan, 2018)

With the rapid development of the financial market, the volume of financial data has become increasingly large and complex, and the advantages of using big data methods to analyze systemic financial risks have become increasingly prominent. This paper deeply discusses the application of financial big data in systemic risk early warning, aiming to provide theoretical support and practical guidance for improving financial risk prevention and control ability.Traditional systemic risk analysis mainly focuses on individual financial institutions, which has limitations and cannot fully capture the correlation risk exposure and the potential domino effect threat to the stability of financial markets. It is therefore crucial to assess exposures at the level of the financial system as a whole. However, the complexity of the financial system is increasing, and the financial data faced by risk analysis is characterized by huge volume, complex types and strong correlation, which brings challenges to the prevention and control of financial risks.Big data methods, including machine learning, data visualization, information extraction and other technologies, provide new solutions for systemic financial risk early warning. Information extraction technology helps to extract valuable information from unstructured text data, such as investor behavior, sentiment changes, etc. These data can reflect changes in the financial market in real time, and is an important source of supplementary information for systemic financial risk analysis. Machine learning algorithms can integrate multi-source data, automatically learn and identify different credit risk levels, and improve the accuracy and efficiency of risk assessment. Data visualization technology can clearly and intuitively show financial micro-data in the form of graphs and charts, and help analysts quickly understand the relationship and trend between data.This paper analyzes the application cases of big data technology in financial contagion mechanism analysis, financial supervision and other aspects, and demonstrates the practical effect of big data methods in systemic risk early warning. By integrating financial micro-data of various financial sectors and even different countries, and analyzing financial risks as a whole based on the data, big data methods can more comprehensively capture the potential threat of systemic risks and provide timely and accurate early warning information for financial regulators.At the same time, this paper also points out the challenges faced by big data in the early warning of financial systemic risk, such as data security and privacy protection, technical threshold and capital investment, and puts forward corresponding solutions. It is emphasized that financial institutions need to strengthen data protection measures, establish a sound risk prevention and control system, increase scientific and technological investment and personnel training, and ensure that the application of big data technology in the financial industry achieves positive results.To sum up, financial big data plays an important role in the early warning of systemic risks and provides strong support for improving the ability to prevent and control financial risks. In the future, with the continuous development and improvement of big data technology, its application prospects in financial risk early warning will be broader.

With the deep penetration of artificial intelligence (AI) technology into the field of financial risk control, its application in systemic risk early warning is attracting widespread attention from academia and regulatory agencies. This study focuses on the dual characteristics of AI technology in financial risk early warning: on the one hand, through multi-source data fusion, complex algorithm models, and intelligent decision support, AI technology has significantly improved its ability to identify high-dimensional nonlinear risks, realizing a paradigm shift from static assessment to dynamic early warning; on the other hand, ethical issues such as algorithmic bias, black-box decision-making, and data privacy violations are becoming increasingly prominent, and may even give rise to new systemic risks due to model homogeneity. This paper constructs a three-dimensional analysis system of "technology application-ethical boundaries-governance framework," and uses case analysis and comparative research methods to demonstrate the necessity of establishing a responsible AI early warning mechanism. The study finds that effective risk governance requires the organic combination of technological empowerment and ethical regulation. By developing explainable artificial intelligence, constructing an algorithm audit system, and improving cross-departmental collaborative supervision, while leveraging the early warning advantages of AI technology, it is essential to ensure that its application complies with the fundamental requirements of financial stability and fairness and justice. This provides an important reference for building a new paradigm of intelligent financial supervision in the digital age.

In the process of development, enterprises need to focus on two major issues, namely the increasingly fierce competition and the problems in the financial management of the enterprise. If you don't want the financial situation of the enterprise to deteriorate, you need to pay attention to the internal operation and management of the enterprise, because if there are problems in the internal financial management of the enterprise,it will lead to the change of the market environment and the lack of timely and accurate information, so as to make wrong decisions. Therefore,it is necessary to build a financial risk early warning model to reduce losses. Taking Shandong Longda Meishi Co., Ltd. as an example, this paper uses the entropy method and the efficacy coefficient method to construct a financial risk early warning model, and gives a risk early warning to the financial data of Shandong Longda Meishi Co., Ltd. from 2018 to 2022. The empirical results show that the early warning results are consistent with the actual situation of the enterprise, which proves that the early warning model built by this method is scientific and effective.

Financial risk early warning (FREW) is critical for developing Higher Educational Institutions (HEIs). This review uses the Systematic Literature Review (SLR) method to discuss the current research status, leading causes, early warning techniques, and algorithms of financial risk management in HEIs. Based on the WoS database, 139 articles meeting the research criteria were selected from 451 relevant literature for in-depth analysis. The results show that the current research on financial risk management in HEIs mainly focuses on developing risk identification, assessment, and early warning models. The primary sources of university financial risk include the instability of fundraising and distribution, decreased financial allocation, and intensified market competition. In response to these risks, scholars have proposed various early warning models and technologies, such as univariate, multivariable, and artificial neural network models, to predict and manage these risks better. In terms of methodology, this review provides a comprehensive perspective on the study of university financial risk through quantitative and qualitative analysis. This study reveals this field's main research trends and gaps through literature screening and cluster analysis. Finally, this study discusses the practical significance of financial risk management in HEIs, highlighting its role in the stability and growth of these institutions. It suggests future research directions, especially in improving the accuracy and applicability of the Early Warning System (EWS), to further enhance the financial stability of HEIs. This literature review has crucial theoretical value for the academic community and provides practical guidance for HEI administrators.

Keywords:financial risk; neural network model;early warning mechanism Abstract.Establishment and optimization of system and the early warning system of financial risk index model were the hot issues in the research of financial risk management. Research institutions and government agencies cooperation made economic early warning methods gradually mature and application. Through the research on the financial risk index system and early-warning model and summarizes, it could accurately measure the economic indicatorsto deal with the financial risk early warning. The use of artificial intelligence technology method, the application of Internet and data analysis and information technology in the financial risk index system and early-warning study could resolve the traditional model's ability to handle the problem of insufficient qualitative indexes, and improved the adaptive ability of the system, the intelligent information processing technology, economic early warning model constructed by combining financial risk early warning system had become a hot and new research directions.

The world economy has recently moved in a fresh era, where the financial world is rapidly developing. Various economic crises, such as banking, economic, and currency crises, impose high economic costs, and harm the entire society. This necessitates the creation of an early warning system for financial crisis that can be adaptively analyzed using past information. Early warning systems could prevent the occurrence of business and economic crises by providing a systematic prediction of unfavorable events. Early warning systems are mainly used to detect crises before they do damage and to reduce false alarms of impending crises. Because of the above, this paper studies early warning of the financial crisis of listed companies based on random forest and time series. Besides, it constructs a random forest and Boruta-Random forest (BRF) model with Benford factor to deal with the impact of financial data quality on the financial risk early-warning model. Our model can effectively improve the prediction accuracy of the financial early warning model. The experiments show that, in comparison to RF, BRF can increase the accuracy of financial risk early warning, expand the applicability of RF, as well as provide a fresh perspective for research on listed company financial risk early warning.