Financial Risk Control Research Articles

A Brief Analysis of Internal Control Strategies for Financial Risks in Listed Companies

In an era of intensifying competition, a more complex socio-economic environment, and rapid technological advancement, financial risks throughout all economic activities of enterprises have become a critical concern. These risks affect not only the stability of individual businesses and financial markets but also the economic security of countries. Effective financial risk control requires a systematic approach and the integration of efforts across various functional departments to ensure stable and sustainable development of enterprises. This paper provides recommendations on managing financial and operational risks based on internal control theories, China’s regulatory norms, and perspectives from economics and management.

Research on Informatization and Intelligent Transformation of Financial Management in Financial Enterprises

This paper focuses on the financial management information and intelligent transformation of financial enterprises. Based on the analysis of the characteristics and challenges of financial management in financial enterprises, this paper expounds the application status of information technology and intelligent technology in financial management, and analyzes the driving factors of the transformation of information technology and intelligent financial management. At the same time, this paper focuses on the application of big data, artificial intelligence, blockchain and other key technologies in financial data processing, decision support, risk control, data security and other aspects. Finally, it puts forward the implementation path of financial management informatization and intelligent transformation, including top-level design, process optimization, talent training and so on.

Technology-Driven Financial Risk Management: Exploring the Benefits of Machine Learning for Non-Profit Organizations

This study explores how machine learning can optimize financial risk management for non-profit organizations by evaluating various algorithms aimed at mitigating loan default risks. The findings indicate that ensemble learning models, such as random forest and LightGBM, significantly improve prediction accuracy, thereby enabling non-profits to better manage financial risk. In the context of the 2008 subprime mortgage crisis, which underscored the volatility of financial markets, this research assesses a range of risks—credit, operational, liquidity, and market risks—while exploring both traditional machine learning and advanced ensemble techniques, with a particular focus on stacking fusion to enhance model performance. Emphasizing the importance of privacy and adaptive methods, this study advocates for interdisciplinary approaches to overcome limitations such as stress testing, data analysis rule formulation, and regulatory collaboration. The research underscores machine learning’s crucial role in financial risk control and calls on regulatory authorities to reassess existing frameworks to accommodate evolving risks. Additionally, it highlights the need for accurate data type identification and the potential for machine learning to strengthen financial risk management amid uncertainty, promoting interdisciplinary efforts that address broader issues like environmental sustainability and economic development.

Semantic Web-Based Prediction Method for Goodwill Impairment in Listed Companies Using Mamba

In the context of globalization and intense market competition, mergers and acquisitions (M&A) have become a common strategy for enterprises to expand and transform. However, goodwill issues in M&A are increasingly concerning. Traditional goodwill impairment prediction models face drawbacks like reliance on precise predictions, large datasets, complexity, poor interpretability, high computational costs, and data acquisition difficulties. This paper proposes a prediction model based on an improved Mamba algorithm. By processing financial data from listed companies in the CSMAR database, the model constructs lagged and rolling statistical features to reflect performance trends. The Mamba algorithm dynamically adjusts input parameters through a Selective State-Space Model (SSM), capturing dependencies in long-sequence data and improving prediction accuracy and timeliness. Experimental results show the Mamba algorithm excels in handling long-sequence data, offering valuable guidance for financial management and risk control.

Financial risk control model based on multi-swarm multi-objective root system growth algorithms

Financial risk control model based on multi-swarm multi-objective root system growth algorithms

Research on Internet Financial Risk Control Based on Deep Learning Algorithms with Human-Computer Interaction

Research on Internet Financial Risk Control Based on Deep Learning Algorithms with Human-Computer Interaction

The Impact and Response of China's Capital Market Opening Policy on the Financial System

Due to the liberalization of the Chinese capital market in recent years, many foreign investors have entered the country's capital market. This article outlines the history and present state of China's relative policy, gathers and evaluates pertinent policies, examines the policy's turning points, and finally highlights the difficulties and issues at hand. Based on this, it examines how this act affects financial institutions and presents three arguments for why bringing in foreign money will strengthen the financial market's vibrancy, encourage its modernization, and increase its size. Drawing from the aforementioned analysis, this article puts forth a response strategy aimed at promoting financial institutions' risk control and institutional innovation through capital market liberalization. Additionally, it suggests that the state enhance its oversight of foreign capital inflows to ensure a consistent flow of capital. To sum up, as China's capital market becomes more open to the outside world, it will be important to take advantage of the chances for innovation and growth while also managing the risks and difficulties that come with it. This paper seeks to be able to offer pertinent recommendations for the market's continued development and liberalization.

Research on the Implementation of Financial Risk Control Models and Algorithms Based on Machine Learning

Nowadays, machine learning has been rapidly applied to the financial services industry. With the applications of machine learning, it will bring challenges to the stable development of the financial industry. This paper studies the user behavior analysis algorithm based on operator big data, and provides financial risk control mechanism for financial enterprises. Taking finance as the data collection object, in the user behavior prediction algorithm, the experiment uses the spectral clustering algorithm to cluster the non-convex data samples, and converts the samples to the Laplacian feature space. It not only reduces the dimension, but also clusters the data, and obtains the global optimal solution. The proposed algorithm is analyzed experimentally, and the tool prototype is realized. The developed gadgets have also been applied in enterprises. The research provides a method for the construction of a risk control model in the financial industry, thereby helping financial enterprises to better control the risk of lending. This paper also provides reference for the performance improvement of the model.

Financial Risk Control of Listed Enterprises Based on Risk Warning Model

Financial Risk Control of Listed Enterprises Based on Risk Warning Model

Research on Financial Risk Evaluation of Tobacco Companies Based on Entropy Weight TOPSIS

Domestic economic development has switched from high-speed growth to a new stage of high-quality development, which makes domestic enterprises have to study the connotation of high-quality development. Financial risk assessment is one of the important basis for high-quality development, which promotes the relationship between balanced development and risk. This paper takes tobacco enterprises as the research object, through collecting the existing financial risk evaluation content of tobacco enterprises, and building the financial risk evaluation system, using entropy weight TOPSIS method, calculates the advantages and disadvantages of the financial risk work of each company under the same standard system, promotes benchmarking learning between different tobacco companies, improves the level of financial risk control, and promotes the high-quality development of tobacco enterprises.

Financial Risk Identification and Control in the Openness Context: A System Dynamics Approach to Renminbi Internationalization

In an attempt to control a series of domestic and foreign financial risks amid the internationalization of Renminbi (RMB), China’s currency, this article categorizes these risks into five types: excessive volatility risk of exchange rate, balance of payments risks, policy risks, crisis contagion risks, and excessive volatility risk of financial asset prices. By employing system dynamics simulation models and sensitivity analysis, this paper examines strategies for financial risk control. Results indicate that the impacts of crisis contagion risk and balance of payments risk are the most significant, and that financial risks are extremely sensitive in such aspects as the degree of capital openness, exchange rate volatility, and foreign trade dependency. The paper suggests adopting strategies like gradually opening China’s capital accounts, maintaining its exchange rate fluctuations within a reasonable range, stabilizing its dependency on foreign trade, and ensuring that its growth rate of M2 matches that of the nominal GDP – a combined effort to control the complicated financial risks arising from various sources.

An analysis of risk control in the financial sector using big data technology

The rapid development of information technology has ushered in a new era of big data in the financial industry, which has provided financial institutions with a plethora of novel tools for risk management, while also playing a pivotal role in the industry's risk control landscape. This paper analyzes the public data of existing financial institutions, combines relevant literature, and employs big data technology to assess and identify risks, which aims to explore how big data technology can improve the risk management ability of the financial industry. In addition, the paper examines the challenges and problems faced by big data technology in the application of financial risk control, including data privacy and security, technology costs, and the demand for specialized talents. The research demonstrates that big data technology improves the speed and accuracy of risk identification, but also points out the challenges of data privacy and security management. Future risk control models need to better integrate big data analytics with traditional risk management methods, thus promoting the further development and application of big data technology in the field of risk control.

Impacts of the energy transition on public health in the context of country risk: From an international perspective

The global climate issue is becoming increasingly serious, and studying the energy transition (ET) has proven to be an effective solution to this problem. The essence of climate mitigation is to address the issue of ensuring the public health (pH) of human beings. Still, the definitions of ET in existing literature are inconsistent, and Many researches overlook the definitions of ET in official documents and discuss the relationship between ET and pH. Therefore, this research utilizes panel data from 107 countries over the period 2000–2020 to construct an energy transition indicator based on the definition from the World Economic Forum (WEF). It also examines the link between ET and pH using a two-way fixed effects regression. In the current turbulent international environment, this paper also investigates this link in the context of composite risk control (CRC), financial risk control (FRC), economic risk control (ERC), and political risk control (PRC). The results show that ET can significantly improve pH, a conclusion that remains significant after being subjected to a range of robustness tests. The study exploring the mechanism impact on the relationship between ET and pH shows that CRC, FRC, ERC, and PRC all strengthen the effect of ET on pH, and the mechanism effect varies considerably between high-income and low- and middle-income countries (LMICs); ET is more significantly affected by the mechanism in LMICs than in high-income countries. Furthermore, this paper illustrates that ET does not differ by gender in improving pH among infants, while there are gender differences in adults by using the instrumental variable regression and GMM regression to mitigate the endogenous and heteroscedasticity issue for heterogeneity test. The study in this paper can not only inspire policy researchers on which aspects to promote ET, but also provide a direction for improving pH.

Research on Financial Risk Control Model Based on Deep Neural Network

Research on Financial Risk Control Model Based on Deep Neural Network

Controlling Financial Risks' Impact on Accounting Information Quality: Evidence from Iraqi banks

Controlling risk management strategies and implementing preventive actions are essential for creating a secure and conducive environment for individuals and organisations. The study aims to showcase the influence of financial risk control on the quality of accounting information in Iraqi banks. This will be achieved by identifying and assessing risks, as well as designing and implementing suitable procedures to manage hazards. The goal is to produce reliable accounting information that stakeholders can trust. The study employed 154 questions that were delivered to accountants, internal auditors, external auditors, and academics specialising in Iraqi institutions. It consisted of two axes. The initial objective was to evaluate the management of financial risk control in Iraqi banks. The first axis comprised 17 specific questions, while the second axis assessed the accuracy of its accounting information and consisted of 21 questions. The study variables were assessed utilising the statistical software SPSS version 26, while the hypotheses were examined by simple linear regression. The study found that implementing robust procedures and policies to manage financial risks in banks improves the reliability and transparency of their accounting information. This includes effectively addressing various risks, such as geopolitical concerns.

Strengthen Financial Risk Prevention and Control Capabilities and Improve the Quality and Efficiency of Financial Supervision

The book Research on the Formation and Prevention of Systemic Risks in the Banking Industry from the Perspective of Platform Finance combines the complex network characteristics of the real banking industry and brings the two relatively independent financial markets of platform finance and banking industry into the same complex network framework. The analysis breaks through the traditional systemic risk research paradigm, fills the shortcomings of traditional economic methods in analyzing the internal structure of the system, provides a new way to study the topological structure characteristics of bank networks, and broadens the perspective of systemic risk research.

Study on Supply Shain Financing Scheme of A Vietnamese Coffee Company in China

This paper takes G7 Coffee, a Vietnamese coffee company, as an example to design a supply chain financing scheme suitable for the company. First of all, on the basis of explaining the concept of supply chain, supply chain financing and other related concepts, supply chain management theory, supply chain financing model, fuzzy comprehensive evaluation model and other theoretical foundations, based on the financing situation of a Vietnamese coffee company G7 coffee in China, combined with the financing problems of the company and the company's capital needs, The feasibility of supply chain financing of G7 Coffee in China for a Vietnamese coffee company is proposed. Secondly, this paper introduces the evaluation method of supply chain financing scheme of G7 Coffee in China of a Vietnamese coffee company. Finally, on the basis of clear objectives and principles of financing scheme design, an evaluation system of supply chain financing mode of a Vietnamese coffee company based on AHP is constructed. The applicability of accounts receivable supply chain financing, inventory supply chain financing and prepayment supply chain financing adopted by the company is further analyzed by fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation results show that The accounts receivable financing model is the best financing model for the supply chain financing of G7 Coffee in China of a Vietnamese coffee company. In order to ensure the smooth implementation of the supply chain financing plan of a Vietnamese coffee company G7 Coffee in China, this paper proposes to strengthen the guarantee from three aspects: financial system, credit management and risk control.

The prospect exploration of artificial intelligence technology and its application

With the rapid development of science and technology, artificial intelligence technology has become one of the most popular research fields in today's society. From intelligent voice assistants to self-driving cars, to medical diagnostics and financial risk control, AI technology has shown huge potential and value in all fields. This paper deeply explores the current situation, development trend and its application prospect of artificial intelligence technology in various industries. This paper briefly introduces the development process of artificial intelligence technology, including its origin, development status and key technologies. Then, it focuses on the application cases of AI technology in healthcare, finance, education, industry and other fields, and evaluates its impact and potential value. It discusses the challenges of artificial intelligence technology, such as data security and privacy protection, algorithm fairness and moral considerations. In view of these problems, the corresponding solution strategies and development suggestions are put forward. The future development trend of artificial intelligence technology is discussed, including the research and application of cutting-edge technologies such as deep learning, natural language processing and emotional computing. The conclusion pointed out that artificial intelligence technology will continue to change human life in the future and bring more innovation and breakthrough to various industries.

Retraction Note: Research on Financial Risk Evaluation and Control of Tourism Enterprises Based on Improved GA Algorithm

Retraction Note: Research on Financial Risk Evaluation and Control of Tourism Enterprises Based on Improved GA Algorithm

The advantage of artificial intelligence application in financial risk assessment and management

The increasing perfection of artificial intelligence technology has brought subversive changes to the field of financial risk management. The application of artificial models such as neural networks, support vector machines, and mixed intelligence in financial risk management can improve the speed of data processing, provide deep insight into data analysis, reduce human labour costs, and hence improve the efficiency of financial risk control. Meanwhile, the increasing amount of data and the application of AI also bring new challenges to financial risk management, such as the risk of program error and information security. This paper introduces in detail the application status of three models, including Support Vector Machine, Support Vector Machine, and Large Language Model in risk management Based on this, this paper analyses the advantages of AI applications in promoting and reforming the financial industry. The goal is to provide an in-depth examination of present implementations and their respective benefits, as well as to investigate potential future advances in this sector.