Risk Measurement Methods Research Articles

Distortion risk measures for tail-risk multicriteria decision-making based on a probabilistic linguistic environment

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed.

Risk Measurement, Analysis, and Prediction Methods for Online Financial Services

The analysis and research on the risk of online financial business is divided into four main areas; the construction of metrics, analysis and modeling, risk prediction, and risk monitoring and control. It mainly uses high-frequency data to update the dynamic factor model, converts the results to low-frequency data to derive the systematic risk indicators; based on the Markov interval transformation model, the unpredictable and uncertain mechanisms are used as stochastic variables to identify the characteristics of the risk changes to cope with the Internet financial risks. The study concludes that the Internet financial risk has a certain trend and periodicity, and the presented market is relatively stable; due to the close connection between Internet finance and traditional financial industry, the resonance effect can bring certain risk to the whole financial market, and it is still necessary to pay attention to the changing risk factors and take corresponding risk management measures.

Novel collision risk measurement method for multi-ship encounters via velocity obstacles and temporal proximity

The collision risk assessment of multi-ship encounters in busy waters is of great significance for collision risk early warning and the intelligent supervision of maritime traffic. In this paper, a quantitative and visual evaluation method is proposed to describe changes in multi-ship collision risk in the velocity space according to multi-ship collision risk characteristics. Non-linear velocity obstacles and closest point of approach were combined to measure the risk of ship collision from the perspective of both the difficulty and urgency of the encounter. The proposed method was verified via case studies using historical Automatic Identification System data for the East China Sea. The method advantages were confirmed via comparison to the conventional time-varying collision risk and collision risk index methods. The results show that the proposed method can quantify changes in multi-ship collision risk more intuitively and effectively. The proposed method could be further developed as a new risk indicator for maritime traffic safety management and could aid in the navigation safety of manned and unmanned ships.

Risk-constrained bidding and offering strategy for sector-coupled electricity-hydrogen systems incorporating accessibility level of mobility sector

To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.

Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, and temporal supernetworks can capture the dynamic evolution of these systems. From the perspective of network stability, supernetworks can aid in risk identification for complex systems. In this paper, an IO-SuperPageRank algorithm is proposed based on the supernetwork topological structure. This algorithm reveals network instability by calculating changes in node importance, thereby helping to identify risks in complex systems. To validate the effectiveness of this algorithm, a four-layer supernetwork composed of scale-free networks is constructed. Simulated experiments are conducted to assess the impact of changes in intralayer edge numbers, intralayer node numbers, and interlayer superedge numbers on the risk indicator IO value. Linear regression and multiple tests were used to validate these relationships. The experiments show that changes in the three network topological indicators all bring about risks, with changes in intralayer node numbers having the most significant correlation with the risk indicator IO value. Compared to traditional measures of network node centrality and connectivity, this algorithm can more accurately predict the impact of node updates on network stability. Additionally, this paper collected trade data for crude oil, chemical light oil, man-made filaments and man-made staple fibers from the UN Comtrade Database. We constructed a man-made filaments and fibers supply chain temporal supernetwork, utilizing the algorithm to identify supply chain risks from December 2020 to October 2023. The study revealed that the algorithm effectively identified risks brought about by changes in international situations such as the Russia-Ukraine war, Israel–Hamas conflict, and the COVID-19 pandemic. This demonstrated the algorithm’s effectiveness in empirical analysis. In the future, we plan to further expand its application based on different scenarios, assess risks by analyzing changes in specific system elements, and implement effective risk intervention measures.

A two-step approach for deploying heterogeneous vessels and designing reliable schedule in liner shipping services

Schedule reliability is a crucial metric for measuring liner container shipping service. Due to the inherent uncertainties at sea and ports, schedule delays and unreliable on-time performance have long plagued shipping companies. In practice, the heterogeneity of container vessels also creates challenges for deployment and schedule optimization. To address the problem, we develop a two-step approach with two programming sub-models. In the first step, we tackle the challenges of ship deployment and scheduling by considering heterogeneous vessels and capturing the interarrival time between adjacent ports of call. In the second step, we use the conditional value-at-risk (CVaR) to address the uncertainty risk avoidance in schedule design. To solve the stochastic programming models, we employ a solution framework based on the outer linear approximation method and the risk measure method, where CVaR is specified as the risk measure. We illustrate the application of our model on a case study concerning a real route operated by COSCO Shipping Lines. The results show that the total cost of liner shipping service with heterogeneous fleet on the route can be reduce by 4.77% compared with homogeneous fleet. And the cost of risk with the reliable schedule can be decreased by 8.04% compared with the ideal schedule. The research conclusions can provide some useful decision-making references for shipping companies to deploy heterogeneous vessels and design reliable schedules.

Volatility spillover features in financial industries and identification of systemically important financial institutions: A new perspective

We study the spillover effects between financial industries and the identification of systemically important financial institutions (SIFIs) from a new perspective. The total volatility is decomposed into two categories, i.e., good volatility triggered by positive returns and bad volatility triggered by negative returns, which are applied to the volatility spillover index (the DY spillover index) model (Diebold and Yilmaz, 2012; 2014) to study the volatility spillover effects between different financial industries based on different types of volatility. We construct the total, good, and bad volatility spillover networks and use PageRank to measure the systemic importance of financial institutions based on the three networks and compare them with the risks of financial institutions obtained by the three risk measurement methods of ΔCoVaR, MES, and SRISK. By decomposing total volatility into good volatility triggered by positive returns and bad volatility triggered by negative returns, the positive and negative impacts of different financial industries during different periods can be effectively identified to accurately evaluate the risk levels of different financial industries. The results indicate that the PageRank rankings of financial institutions calculated by the bad volatility spillover network have the highest correlation with the risk rankings of financial institutions calculated by ΔCoVaR, MES, and SRISK. The findings suggest the necessity of decomposing the spillover effects between financial industries into good spillover effects triggered by positive returns and bad spillover effects triggered by negative returns, providing a new way of identifying SIFIs.

Analisis Yuridis Manajemen Risiko dalam Pelaksanaan Bancassurance di Indonesia Berdasarkan Surat Edaran Otoritas Jasa Keuangan Nomor 33/SEOJK.03/2016 tentang Penerapan Manajemen Risiko pada Bancassurance

The concept of bancassurance is that banks act as intermediaries in helping insurance companies reach their target customers with the aim of increasing market share. Financial Services Authority supervision of bancas-surance needs to be carried out. In reality, the implementation of bancassurance does not necessarily always comply with the Financial Services Authority Circular Letter Number 33/SEOJK.03/2016 and allows for future risks. The implementation of bancassurance allows for operational, legal and reputation risks. Risk management is carried out to avoid the possibility of these risks occurring. Therefore, the question arises about the role of the Financial Services Authority in mitigating the implementation of bancassurance in Indonesia. This research uses a statutory and conceptual approach with a normative juridical research type. The initial mitigation in implementing bancassurance is by selecting the right insurance company partner in accordance with the advantages and benefits for carrying out cooperation to the work plan and approval from the financial services authority as the supervisory institution. Banks are required to have written procedures to manage the risks inherent in bancassurance activities which include systems and procedures as well as authority; identification of all inherent risks; risk measurement and monitoring methods; accounting recording methods; analysis of legal aspects; and transparency of information to customers.

Credibility-Supervised Dynamic Clustering and Consensus Model Involving Minority Opinions Handling in Social-Network Large-Scale Decision Making

Existing studies on social-network large-scale decision making (SN-LSDM) have overlooked two vital roles of the information credibility of decision makers (DMs): 1) Information credibility directly determines the reliability of clustering results and final decision output; 2) Consideration of minority opinions may hold the truth but may also be risky, while the credibility analysis of minority opinions can help avoid risks. To this end, this paper proposes credibility-supervised dynamic clustering and consensus model involving minority opinion processing. Firstly, DMs' information is analyzed and preprocessed to obtain DMs' credibility evaluation. Subsequently, DMs' credibility is used as one of the clustering criteria to supervise and guide the trust relationships and the clustering of large-scale group, which assists in deriving the reliable clustering result. This clustering is dynamic because the clustering result may alter as the DMs' information changes in consensus. Afterwards, an SN-LSDM consensus model with minority opinions settlement is developed. A credibility-based mechanism for identification, DMs' discussion and weight adjustment of minority opinion subgroup is constructed, including it trust risk measurement method and trust risk level-based weight adjustment. Then, a risk index is defined in feedback adjustment mechanism to adjust DMs' information, and a decision plan with a higher consensus level is obtained. Finally, the feasibility and effectiveness of the proposed model are verified through the case study of “916” Lu County earthquake relief. Discussions and comparisons are conducted by simulation experiments to explore the capabilities and advantages of our proposal.

A Collaborative Planning Method for the Source and Grid in a Distribution System That Considers Risk Measurement

The existing distribution system planning methods do not fully consider improving power supply capacity and reliability through the coordination of multiple planning factors, and they are not comprehensive enough in quantifying planning risks. Therefore, this paper proposes a collaborative planning method for sources and networks that considers risk measurement. A multi-layer planning model is first constructed that includes a grid planning layer, a power planning layer, a switch planning layer, and an operation optimization layer. In the model, a risk measurement method combining opportunity constraints and conditional value-at-risk objectives is used to comprehensively assess the risk of the node voltage and branch current exceeding the limit caused by load uncertainty. Then, a solution strategy based on a genetic algorithm and a sparrow search algorithm is proposed to coordinate the contradiction between the solution time and the accuracy of the multi-layer model. Finally, taking a planned area to be expanded as an example, the results show that compared to the existing collaborative planning methods for sources and networks, the proposed method in this paper reduces the planning risks caused by load uncertainty by more than 50% and increases the annual net income of the power distribution company and the DG operators by RMB 1.5 million and RMB 1.1 million, respectively.

Cross-jurisdictional financial crime risks: what can we learn from the UK regulatory data?

PurposeFinancial crime presents a serious threat to the stability and integrity of the global financial system. To combat illicit financial activities, regulatory bodies worldwide have implemented various measures, including the requirement for financial institutions to assess the financial crime risks they are exposed to in the jurisdictions they operate in. These risks include inadequate anti-money laundering and countering the financing of terrorism frameworks and other financial crime risks that have significant strategic implications for firms’ geographical footprints and customer risk classifications. This paper aims to make a contribution to the literature by undertaking a cross-country analysis of 158 countries to shed light on what drives perceived jurisdiction risk of the UK financial services firms.Design/methodology/approachCapturing firms’ perceptions of financial crime risk requires significant data collection efforts, including surveys and interviews with key personnel. This can be highly resource-intensive and may require access to sensitive information that firms may be reluctant to share. Furthermore, the dynamic nature of financial crime risks means that perceptions can change rapidly in response to changes in the regulatory and geopolitical landscape. As a result, capturing and monitoring firms’ perceptions of financial crime risks requires ongoing monitoring and analysis. Capturing firms’ perceptions of financial crime risks at a cross-jurisdictional level is a particularly complex and challenging task that requires careful consideration of a range of factors. As a result of data limitations, empirical investigation of the factors underlying the firms’ perceptions of jurisdiction risk is in its infancy. This paper uses regulatory financial crime data from the UK in a multivariate regression analysis, following a general-to-specific approach where any redundant variables were removed from the general model sequentially.FindingsResults suggest that perceived jurisdiction risk is significantly and positively associated with evasion of tax and regulations, while it is significantly and negatively associated with political stability and regulatory stringency. These have important implications for home and host supervisors with respect to the factors that drive perceived jurisdiction risks and the evaluation of the nature of inherent financial crime risks within regulated firms. The findings confirm the critical role of the shadow economy, political stability and regulatory rigor in shaping jurisdiction risk perceptions. From a policy standpoint, the findings support the case for taking prompt policy action to identify, prioritize and implement specific and targeted measures with respect to the shadow economy, political stability and rigor of regulations to improve international firms’ perceptions of jurisdiction risk.Originality/valueWhile there exists different measures of financial crime risk, it is notoriously challenging to capture firms’ perceptions of it, particularly at a cross-jurisdiction level. This is because financial crime risks can vary significantly across different jurisdictions due to differences in legal and regulatory frameworks, cultural norms and levels of economic development. This makes it difficult for firms to compare and evaluate the financial crime risks they face in different jurisdictions. Besides, firms’ perceptions of financial crime risks can be influenced by a range of subjective factors, including personal experiences, media coverage and hearsay. These perceptions may not always align with objective risk assessments, which are based on more systematic and empirical methods of risk measurement. This paper contributes to the existing literature by undertaking a cross-country analysis drawing on a unique set of UK regulatory financial crime data, which is based on a total of 1,900 annual financial crime data regulatory return (REP-CRIM) submissions to the UK’s Financial Conduct Authority.

Research on Network Behavior Risk Measurement Method Based on Traffic Analysis

At present, the network security problem is facing a serious threat, and network security events continue to occur. It has become an important link to prevent network attacks and ensure network security. According to the network security protection measures and security technical requirements, it has become an urgent need to establish appropriate security measurement methods and strengthen the monitoring and analysis of network security status. This study proposes a network behavior risk measurement method based on traffic analysis to accurately and objectively evaluate the security state of the network. Traffic is the most basic behavior of the network and the basis of security risk measurement. Firstly, we regard the traffic data as network behavior to build scenarios. Through differential manifold modeling, the traffic data and topology of the network system are semantically described to form a matrix. Then, after manifold dimensionality reduction, the objective risk assessment value can be obtained by manifold mapping and Riemann metric. In this study, the differential manifold theory is applied to network behavior risk measurement, and the innovation of differential manifold in the field of network behavior risk measurement is given. After giving the network behavior risk measurement theory, we first verify the effectiveness of the proposed method through the simulation experiments. Secondly, the public CIC-IDS-2017 data set is used for analysis and calculation to prove the accuracy of the proposed method.

A Novel DBN-EFA-CFA-Based Dimensional Reduation for Credit Risk Measurement

Affected by the Federal Reserve's interest rate hike and the downward pressure on the domestic economy, the phenomenon of default is still prominent. The credit risk of the listed companies has become a growing concern of the community. In this paper we present a novel credit risk measurement method based on a dimensional reduation technique. The method first extracts the risk measure indexes from the basal financial data via dimensional reduation by using deep belief network (DBN), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in turn. And then the credit risk is measured by a systemic structural equation model (SEM) and logistic distribution. To validate the proposed method, we employ the financial data of the listed companies from Q1 2019 to Q2 2022. The empirical results show its effectiveness on statistical evaluation, assessment on testing samples and credit risk forecasting.

Optimal energy portfolio allocation method for regulable hydropower plants considering the impact of new energy generation

With the growing penetration of new energy sources, the impact of new energy generation on the income of the energy portfolio is becoming more significant. This paper proposes a risk measurement method based on the Conditional Value-at-Risk (CVaR) approach to measure the income risk from new energy generation on the energy portfolio. The superiority is proved by comparing it with the method only considering spot price fluctuations. Then, we constructed an energy allocation model of the hydropower plant to maximize the combined income-risk utility. In solving the optimal allocation of the energy portfolio, the effects of inflow and water level on the risk aversion parameters of the hydropower plant are considered. The results and comparison show that the proposed risk measurement method increases the income of the energy portfolio by 12%, and the proposed risk aversion parameter’s adjustment strategy can increase the income of the energy portfolio by 5.8% and reduces the CVaR value by 14.5%. Moreover, the method that considers the income risk from new energy generation can provide favorable conditions for improved new energy accommodation and safe operation of the system.

An integrated risk assessment method for cross-regional mega construction projects

PurposeThe purpose of this paper is to develop a novel integrated risk assessment method from the system perspective to evaluate the risk of the cross-regional mega construction project (CMCP). Furthermore, this paper aims to confirm the core risk source factors and refine the risk management strategies of CMCPs through a case study.Design/methodology/approachBased on the analysis of the risk system of CMCPs, the concept model and risk assessment principles of integrated risk assessment were confirmed. The risk source factors and project objectives of the CMCP were identified from a literature review, export interview and case analysis. According to the vulnerability theory, the integrated risk assessment model was developed by involving vulnerabilities, threats, objectives and interaction of those factors synthetically. Then, ZW high-speed railway from China was analyzed to verify the feasibility and effectiveness of the proposed method.FindingsAs a result, 12 threat factors and 12 vulnerability factors were identified. Based on the case study, the main external threat comes from T13 (conflicts of interest between local governments) and T23 (harsh natural environment); the most easily exploited internal vulnerabilities were V11 (complexity of technology), V13 (lack of experience in technical application), V21 (inadequate experience) and V23 (lack of interest coordination mechanism). Moreover, the economic objective was most affected.Practical implicationsIt is essential to develop an interest coordination mechanism for CMCPs. The harsh natural environment is a critical factor, but it also promotes technological innovation and iteration. Public opinions in different regions are critical for CMCPs, and more emphasis should be placed on public opinion surveys of CMCPs. Moreover, diverse and flexible environmental protection strategies should apply in CMCPs.Originality/valueThis research has the following three contributions. First, based on vulnerability theory, an integrated risk assessment approach of CMCPs is developed, which enriches the risk measurement method system and provides inspiration for future research on risk in the construction industry. Second, the risk sources of CMCPs are identified from the perspective of vulnerability and threat to provide clear guidance for the risk management of CMCPs. Third, the core risk source factors and management strategies confirmed by the case study will be beneficial for various governments in different regions and project managers to optimize the project management scheme, as they are transferable management experiences.

Risk Management of Fuel Hedging Strategy Based on CVaR and Markov Switching GARCH in Airline Company

Using a hedging strategy to stabilize fuel price is very important for airline companies in order to reduce the cost of their main business. In this paper, we construct models for managing the risk of the hedging strategy. First, we use conditional value at risk (CVaR) to measure the risk of an airline company’s hedging strategy. Compared with the value at risk (VaR), CVaR satisfies subadditivity, positive homogeneity, monotonicity, and transfer invariance. Therefore, CVaR is a consistent method of risk measurement. Second, time-varying state transition probability is introduced into our model in order to build a Markov Switching-GARCH (MS-GARCH). MS-GARCH takes dynamic changes of market state into account, a feature which has obvious advantages over the traditional constant state model. Additionally, we use a Markov chain Monte Carlo (MCMC) algorithm to estimate the parameters of MS-GARCH based on Gibbs sampling. We use fuel oil futures data from the Shanghai Futures Stock Exchange to implement and evaluate our model. In this paper, we empirically estimate the risk of airlines’ hedging strategy and draw the conclusion that our model is obviously effective in terms of the risk management of hedging, a use which has a certain guiding significance for reality.

Climate transition risk and bank performance:Evidence from China

Under the “carbon peaking and carbon neutrality goals”, China's commercial banks are facing a severe climate transition risk. This paper proposes a climate transition risk measurement method for commercial banks, and investigates the impact of climate transition risk on bank performance based on the data of 490 commercial banks in China from 2008 to 2019. The empirical findings are as follows: firstly, the climate transition risk has an inhibitory effect on the performance of commercial banks, and the inhibitory effect weakens with the increase of bank size. Secondly, the signing of the Paris Agreement and the increase of the economic policy uncertainty in China have a positive moderating effect, which weakens the inhibitory effect of the climate transition risk. Finally, the climate transition risk inhibits the performance of commercial banks partly by reducing the scale of bank loans.

Financial Institution Prevention Financial Risk Monitoring System Under the Fusion of Partial Differential Equations

Abstract This paper proposes a financial risk monitoring system based on partial differential equations. In this paper, a functional expression of risk measurement is proposed for the uncertainty of financial assets risk of financial institutions. In this paper, a partial differential equation model of asset price based on non-extensive statistical theory is established. At the same time, this paper establishes the optimal portfolio investment model with the restriction of risk measure under partial differential equation. The experimental study shows that the financial risk measurement method under the partial differential equation proposed in this paper fully considers the financial risk uncertainty of financial assets. It satisfies the axiomatic system of consistent risk measures.

Interbank Offered Interest Rate Risk Measurement Based on Embedded Sensor Network

Interbank lending rate is the main benchmark interest rate in the domestic currency market, looking for an appropriate financial time series model to describe its stochastic fluctuation process, and choosing an appropriate risk measurement method to measure the domestic interbank lending interest rate risk. The risk management of the interest rate market has great theoretical and practical significance. This article is aimed at studying the interbank interest rate risk measurement based on the embedded sensor network and proposes the key technology of sensor network, sensor network MAC layer protocol, LEACH energy model, and other methods in the article. Related experiments have been carried out on the interbank interest rate risk measurement. The experimental results show that the interbank interest rate risk measurement model based on the embedded sensor network is much more efficient than the traditional interest rate risk measurement model. This has reduced the bank’s loss of interest rate by at least 7%.

Enterprise Financial Asset Risk Measurement Based on Embedded Microprocessor Security Analysis

As the volatility of financial markets continues to increase, the risk structure involved in financial derivatives has become more and more complex. The harmful effects of financial risks can put the company in trouble. Only by understanding the correlation between financial assets can we more accurately measure the risk of a company’s financial assets. This article is aimed at studying the company’s financial asset risk measurement method based on integrated microprocessor security analysis, so that enterprises can effectively manage the risk of financial assets. This paper randomly selects 10 variables related to demographic characteristics, customer needs, and consumption characteristics of 850 customers from the historical customer data of a bank that has been abandoned for customer market segmentation research. Based on the theoretical basis of customer value evaluation, the factor analysis method refers to a multivariate statistical analysis method that starts from the internal dependence relationship of the research index correlation matrix and reduces some variables with overlapping information and intricate relationships into a few unrelated comprehensive factors. The basic idea is to group variables according to the size of the correlation, so that the correlation between the variables in the same group is higher, but the variables of different groups are not correlated or correlated, and each group of variables represents a basic structure—the common factor. This paper uses the factor analysis model in statistical principles to analyze the main factors affecting the pricing of deposit products, and we can obtain three quantitative common factors: customers related to the customer’s willingness to pay demand value, customer loyalty factor, and customer cost factor. Then, the variance contribution rate of the three factors to all observable variables is weighted and converted into the net customer relationship value, at the same time, the embedded microprocessor evolved from the CPU in the general-purpose computer. Its characteristic is that it has a 32-bit or more processor and has high performance. In actual embedded applications, it only retains closeness to embedded applications. Related functional hardware, removing other redundant functional parts so that the special requirements of embedded applications can be realized with the lowest power consumption and resources. Before constructing the structural equation model, it is necessary to test the reliability and validity of the sample data. The data selected in this paper are panel data. In order to judge whether the data is stable, the panel unit root test should be carried out. This article chooses LLC to verify the stability of the panel data. Individual fixed effects and linear time trends are added to the test, and the lag order of the difference lag is 0. Among the intermediary business income of joint-stock commercial banks, bank card fee income accounts for the largest proportion, with China Everbright Bank accounting for 70.12% and China CITIC Bank accounting for 54.16%. The research results show that with the continuous increase of Internet penetration rate, the impact of corporate finance on commercial banks shows phased characteristics. The corporate financial risk measurement method studied in this paper can provide effective help for risk monitoring and early warning. Risk measurement is a management activity that uses certain methods to determine and quantify the possibility of a company’s future financial results deviating from expected financial goals due to various uncertain factors. In practice, it is generally analyzed by a combination of qualitative and quantitative, subjective, and objective methods.