Research on Intelligent Methods and Strategies of Corporate Financial Risk Assessment in the Digital Economy Era

This paper conducts financial risk assessment of enterprises from five dimensions: profitability, solvency, operating ability, growth ability, and cash ability [22], and the overall evaluation index system is shown in Table 1. In evaluating the profitability of enterprises, we mainly use two key indicators: gross profit margin on sales and net profit margin on assets. For solvency considerations, we use the equity ratio, current ratio, quick ratio, and guaranteed interest rate ratio to make a comprehensive assessment. In terms of measuring the operating efficiency of enterprises, we choose the total assets turnover, current assets turnover, and business cycle as the core indicators. In order to evaluate the growth potential of an enterprise, we refer to the growth rate of total assets and the growth rate of main business revenue. As for the assessment of cash capacity, we rely on indicators such as the proportion of cash from sales, the proportion of cash from main business income, the proportion of cash from net profit and the proportion of cash dividend protection. It should be noted that, in addition to the equity ratio and business cycle are regarded as positive indicators, the rest of the indicators are regarded as negative indicators.

Analytic hierarchy Process (AHP) is a decision-making method for difficult quantification problems [23], the principle of operation is to disassemble a complex system with multiple goals and guiding principles in a hierarchical manner until it is refined to a single indicator. Then, by comparing the importance of the peer indicators, we determine their respective weights. However, it should be noted that when AHP is used for weight calculation, it is assumed that there is no correlation among the influencing factors, which ignores the possible mutual influence in practice. In contrast, decision lab analysis allows for more comprehensive consideration of the interaction between these factors[24]. This paper combines AHP and DEMATEL analysis methods. First, the AHP method is used to clarify the basic weight ranking of each index. Then, the DEMATEL method is used to analyze the interaction between various factors, and the comprehensive influence weight of each index is adjusted and calculated accordingly, which greatly improves the accuracy, rationality and fairness of weight distribution.

A large amount of information needs to be aggregated into one place for analysis, and this information usually has non-deterministic characteristics, including expressions of ambiguity, randomness, and complexity. In order to deal with the problem of ambiguity and randomness, the academic circle extracts the concept of "cloud" from probability theory and fuzzy mathematics, and deduces its analytic formula. The concept innovatively merges randomness and fuzziness into a single mathematical model through a normal distribution, the so-called cloud model. Since then, the model has flourished in the fields of artificial intelligence and data mining [25], in recent years, its application scope has been further expanded to various assessment scenarios. In recent years it has been extended to be applied to various types of assessments.

For a quantitative argument U , C is a qualitative concept on U . The quantitative value x is subordinate to U , x the degree of subordination of qualitative concepts C is expressed by the affiliation function μ_x ∈ [0,1], then x multiple random occurrences on the domain U become cloud, each x occurrences are counted as such, regarded as a cloud droplet, the degree of deviation from the ideal state of the cloud droplets just reveals the fuzzy characteristics of the cloud. Therefore, the cloud model, a mathematical model, contains both randomness and fuzziness.

2.3.1

Numerical Characterization of Cloud Models

The cloud model characteristic parameters are calculated as in equation (10). Where Ex, En, He denotes cloud expectation, cloud entropy and cloud super entropy respectively, and information uncertainty is reflected by the three parameters together: (10) { Ex=1n∑i=1nxiEn=1nπ2∑i=1n|xi−x¯|He=1n−1∑i=1n(xi−x¯)2−En2

where x_i and n denote the sample observations and the number of samples, respectively. Expectation Ex is the desired numerical quantity for the qualitative problem, It represents the core of the distribution of cloud droplets in the argument space. En entropy, as a bridge between qualitative and quantitative, reveals the degree of deviation between the actual value and the ideal expectation, and this deviation can reflect the fuzzy characteristics of information. Hyperentropy He is reflected in the cloud diagram as the degree of cohesive looseness of the cloud droplets. For the number of intervals for which there is an interval threshold [L_min,L_max], the characteristic parameters can be expressed as in equation (11): (11) { Ex=(Lmin+Lmax)/2En=(Lmax−Lmin)/6He=k

k is a constant, selected according to the fuzzy threshold of the variable, often take 0.01, 0.02, 1, the fuzzy threshold of the indicators in this paper this paper takes k = 0.01 . In order to realize qualitative and quantitative transformation of cloud modeling, it is necessary to use forward and reverse cloud algorithm. From qualitative to quantitative, it is often achieved through the forward cloud algorithm, that is, the transformation of abstract concepts into concrete numerical points, a typical normal cloud diagram and its digital features are shown in Figure 1.

Figure 1.

Standard cloud map

Company G is a large listed private science and technology enterprise, founded in September 2005. The high-tech company invests heavily in product research and development and market expansion, accounting for 20%-30% of the annual sales profit, and attaches great importance to the protection of current assets, which accounts for the proportion of total assets increased year by year. By the end of 2023, Company G had 380 active employees, with R&D personnel accounting for about 34%, management personnel accounting for 12%, and the rest being technicians and ordinary skilled workers. Figure 2 shows the financial status of the stock price in 2022 in an open-high-low-close curve for Company G. Company G successfully went public in 2021, and from September-November 2022, the company’s stock price experienced unprecedented turbulence with fluctuating ups and downs. The graph shows that in September-November 2022, Company G’s stock price had a high opening price of about $18 and a low closing price of about $16.40. Overall the company’s stock price started a downward trend after rising until late October. Such a stock price trend had a significant impact on the finances of Company G. The management of the company decided that it was necessary to assess the company’s financial situation and financial risk in order to clarify the direction of the enterprise’s capital management and to promote the further development of the enterprise.

Figure 2.

Stock price change

For the financial situation of Company G, according to the established enterprise financial risk assessment system, a number of experts are invited to assess the financial risk of Company G. By combining AHP and DEMATEL method, the comprehensive weights and specific index weights among factors can be obtained. Using AHP-DEMATEL grouping method, the comprehensive weights of each index can be calculated accurately, and Table 2 shows the results of weight calculation. Table 2 shows the results of the weight calculation. From the table, we can see that among the comprehensive weights of the first-level indicators, the weight of the growth capacity (A4) indicator is the highest at 0.33, while the weight of the operating capacity (A3) is the lowest at 0.07. It shows that when the enterprise develops to a certain extent, the most important factor affecting the enterprise’s financial risk is whether the enterprise has enough growth capacity or not.

Five financial risk assessment-related domain experts are invited to assign scores on the 16 indicators affecting the financial risk of Company G. The cloud model parameters of each indicator are determined according to the formula, and the cloud digital features of Company G’s financial risk assessment are determined by combining the weight values of each indicator in Table 2 as shown in Table 3, where CDC denotes the cloud digital features and ICDC denotes the cloud digital features of A1-A5.

According to the calculated cloud numerical characteristics can be obtained the evaluation cloud diagram of the first-level indicators, and the results are shown in Fig. 3, where (a)-(e) are the evaluation cloud diagrams for each first-level indicator from A1 to A5, respectively. From the figure, it can be seen that each level of indicators is in the medium range,the comprehensive score of financial risk cloud numerical characteristics calculation is above 7.0,which means that there is a low risk in the finances of G companyand overall good financial condition. Horizontally, the risk of solvency and growth capacity of Company G is relatively high, with a composite score value of 7.2 and 7.1, respectively. This is because Company G as a whole is in the expansion stage, and its solvency is more sensitive to financial risk. At the same time, the growth capacity of Company G in the expansion stage is still to be explored, and some of the development resolutions in the growth process will have a significant impact on the company’s finance. Therefore, Company G should pay great attention to matters related to solvency and growth capacity in the process of subsequent development, so as to reduce the financial risks faced by the enterprise and realize the healthy development of the enterprise.

Figure 3.

Comprehensive assessment of financial risk

In view of the low comprehensive scores of G Company’s "solvency" and "growth". It is recommended that the company strengthen credit assessment and management, and fully understand the credit status of customers and trading partners before cooperating with them, including payment history, financial status, etc. Avoid cooperating with enterprises or individuals with bad credit, improve the customer credit evaluation system, control the risk of bad debt, and reduce the uncertainty of accounts receivable. We will keep the scale and structure of debt under reasonable control. Company G shall, according to its financial strength, operating conditions and profitability, reasonably determine the amount of debt, optimize the debt structure, balance the proportion of long-term and short-term debt, and avoid the debt repayment pressure caused by excessive short-term debt. We will strengthen the monitoring and management of the liquidity of funds and improve the efficiency of the use of funds. Strengthen contract management and receivables collection by carefully reviewing the contract terms before signing, especially key terms such as payment method and time limit. Set up early warning indicators for accounts receivable risk, such as ageing and bad debt ratio. Conduct regular risk assessments of accounts receivable to identify potential risks in time. Establish a accounts receivable monitoring system to monitor the dynamic changes of accounts receivable in real time.

Actively adopting new technologies and tools can optimize the production process by introducing artificial intelligence technology, and achieve the combination of personalized and large-scale production. Pay close attention to market demand, continuously carry out product innovation, and introduce new products to meet user needs. Product development can be guided by customer feedback and market research to ensure that the product is competitive in the market. Market expansion and brand building can be achieved by entering new markets or expanding existing market share. You can consider expanding geographic markets or related industry markets through partnerships, acquisitions or investments. Focus on staff training and development to improve staff skills and capabilities to adapt to the rapidly changing market environment. The talent pool can be enriched by bringing in external talent or partnering with universities. Optimize organizational structure, improve operational efficiency, and improve management level. Ensure that the company can quickly respond to market changes and take advantage of growth opportunities. Establish and enhance the financial management system to guarantee the company’s stable financial situation. Through reasonable budget planning and cost control, improve the efficiency of capital utilization. Actively explore avenues of capital operations to raise additional funds for the company to support its growth and development.