Intelligent risk assessment method for enterprise financing business

Abstract

The invention provides an intelligent risk assessment method for enterprise financing businesses, and relates to the technical field of intelligent risk assessment, and the method comprises the steps: carrying out the independent risk assessment according to the business attribute information corresponding to each financing business, and obtaining a plurality of independent risk indexes; performing feature identification on the plurality of financing businesses according to the business attribute information; training a collaborative risk resistance evaluation model according to the financing scale dispersity, the financing channel similarity and the financing type centrality, and outputting a first collaborative risk resistance index; and performing fitting evaluation on the first collaborative risk resistance index according to the plurality of independent risk indexes, and outputting a financing risk index. According to the method and the device, the technical problem that the risk is concentrated into decision-making deviation due to the fact that risk assessment is carried out mostly for single financing in enterprise financing business risk assessment in the prior art can be solved, and the technical target of identifying financing business risk resistance is achieved; the technical effects of improving the risk assessment reliability and formulating the risk management strategy are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent risk assessment, and in particular to a method for intelligent risk assessment of corporate financing business. Background Art

[0002] Risk assessment of corporate financing business refers to the assessment and management of risks involved in corporate financing activities. At present, in the existing risk assessment process of corporate financing business, most of them do not conduct risk assessment through multiple financing businesses. Single financing risk assessment leads to risk concentration in specific businesses, ignoring the risk diversification effect of the overall financing strategy of the enterprise. If a single financing business has problems, it will have a serious impact on the overall financial health of the enterprise. Evaluating each financing business separately cannot capture the interaction and potential risk synergy between different financing activities. For example, multiple financing businesses may be sensitive to market changes at the same time, resulting in an underestimated overall risk. The risk assessment results based on a single financing business will mislead the financing decision of the enterprise, causing the enterprise to choose a suboptimal financing strategy, increase financing costs or take unnecessary risks. Therefore, a method is needed to solve the above problems.

[0003] In summary, the existing technology has technical problems in that most of the risk assessment processes for corporate financing business focus on single financing, which leads to risk concentration and lack of a global perspective, further causing decision-making bias. Summary of the invention

[0004] The purpose of this application is to provide an intelligent risk assessment method for corporate financing business, so as to solve the technical problems in the prior art that risk assessment of corporate financing business is mostly carried out on a single financing, resulting in risk concentration and lack of a global perspective, which further causes decision-making deviations.

[0005] In view of the above problems, the present application provides a method for intelligent risk assessment of corporate financing business.

[0006] The present application provides a method for intelligent risk assessment of enterprise financing business, wherein the method includes: obtaining multiple financing businesses of a target enterprise; obtaining business attribute information corresponding to each of the multiple financing businesses, including financing scale, financing channel and financing type; performing independent risk assessment according to the business attribute information corresponding to each financing business, and obtaining multiple independent risk indicators; performing feature identification on the multiple financing businesses according to the business attribute information, and obtaining the dispersion of financing scale, similarity of financing channels and concentration of financing types; training a collaborative risk resistance assessment model according to the dispersion of financing scale, similarity of financing channels and concentration of financing types, and outputting a first collaborative risk resistance indicator according to the collaborative risk resistance assessment model; performing fitting assessment on the first collaborative risk resistance indicator according to the multiple independent risk indicators, and outputting a financing risk indicator.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: By acquiring multiple financing businesses of the target enterprise; acquiring business attribute information corresponding to each of the multiple financing businesses, including financing scale, financing channel and financing type; performing independent risk assessment according to the business attribute information corresponding to each financing business, and obtaining multiple independent risk indicators; performing feature identification on the multiple financing businesses according to the business attribute information, and obtaining the dispersion of financing scale, similarity of financing channels and concentration of financing types; training a collaborative risk resistance assessment model according to the dispersion of financing scale, similarity of financing channels and concentration of financing types, and outputting a first collaborative risk resistance indicator according to the collaborative risk resistance assessment model; performing fitting assessment on the first collaborative risk resistance indicator according to the multiple independent risk indicators, and outputting a financing risk indicator, ultimately achieving the technical goal of identifying the risk resistance of multiple financing businesses, and achieving the technical effect of improving the reliability of risk assessment and formulating more effective risk management strategies.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A flowchart of a risk intelligent assessment method for corporate financing business for this application; Figure 2 A flowchart of obtaining multiple independent risk indicators in a risk intelligent assessment method for corporate financing business is provided for this application. DETAILED DESCRIPTION

[0011] This application provides a method for intelligent risk assessment of corporate financing business, which solves the technical problem that the risk assessment of corporate financing business is mostly carried out on a single financing in the prior art, resulting in risk concentration and lack of a global perspective, which further causes decision-making bias. It achieves the technical goal of identifying risk resistance for multiple financing businesses, and achieves the technical effect of improving the reliability of risk assessment and formulating more effective risk management strategies.

[0012] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them. Example

[0013] Please refer to the attached Figure 1 The present application provides a method for intelligent risk assessment of corporate financing business, wherein the method specifically comprises the following steps: Step 1: Obtain multiple financing businesses of the target enterprise.

[0014] Specifically, multiple financing business data of the target enterprise are obtained, including various financing activities carried out by the target enterprise through different channels, such as bank loans, bond issuance, equity financing, etc.

[0015] Step 2: Obtain business attribute information corresponding to each of the multiple financing businesses, including financing scale, financing channel and financing type.

[0016] Specifically, for each financing business, obtain the corresponding business attribute information, including financing scale, financing channel and financing type. Financing scale refers to the amount of funds raised by the target enterprise, which helps to assess the risk level of the financing business, because large-scale financing may result in higher debt repayment pressure and risk. Financing channel refers to the way the target enterprise obtains funds, such as commercial banks, non-bank financial institutions, stock markets or bond markets. Different financing channels have different risk characteristics and costs. Financing types include debt financing (such as loans, bonds), equity financing (such as issuing stocks) or other financial instruments. Different types of financing have different impacts on the capital structure and risk status of the target enterprise.

[0017] Step 3: Conduct independent risk assessment based on the business attribute information corresponding to each financing business and obtain multiple independent risk indicators.

[0018] Specifically, for each financing business, an independent risk assessment is conducted based on business attribute information (such as financing scale, financing channel and financing type). Through quantitative analysis, multiple independent risk indicators are output to reflect the impact of different business attributes on financing risks.

[0019] Step 4: Identify the characteristics of the multiple financing businesses based on the business attribute information to obtain the dispersion of financing scales, similarity of financing channels, and concentration of financing types.

[0020] Specifically, using business attribute information, we identify the characteristics of multiple financing businesses to obtain the dispersion of financing scale, similarity of financing channels, and concentration of financing types. The dispersion of financing scale reflects the diversity of the financing scale of enterprises. The similarity of financing channels reveals the similarity between different financing channels. The concentration of financing types shows the degree of dependence of enterprises on different financing types.

[0021] Step 5: Train a collaborative risk resistance assessment model based on the dispersion of financing scale, similarity of financing channels, and concentration of financing types, and output a first collaborative risk resistance indicator based on the collaborative risk resistance assessment model.

[0022] Specifically, the dispersion of financing scale, similarity of financing channels and concentration of financing types are used as features to train the collaborative risk resistance assessment model built based on the machine learning model. This model is used to comprehensively assess the collaborative risks of financing businesses and output the first collaborative risk resistance indicator, which provides a higher-level risk view and takes into account the interactions between different financing businesses.

[0023] Step 6: Perform fitting evaluation on the first collaborative risk resistance indicator according to the multiple independent risk indicators, and output a financing risk indicator.

[0024] Specifically, multiple independent risk indicators are used to conduct a fitting assessment of the first collaborative risk resistance indicator, verify and improve the accuracy of the collaborative risk resistance assessment model, ensure the comprehensiveness and reliability of the risk assessment, and output a comprehensive financing risk indicator based on the results of the fitting assessment, providing a comprehensive risk assessment for the target enterprise and helping the target enterprise better manage risks in financing decisions.

[0025] The intelligent risk assessment method for corporate financing business can achieve the technical goal of identifying risk resistance for multiple financing businesses, and achieve the technical effect of improving the reliability of risk assessment and formulating more effective risk management strategies.

[0026] Further, if Figure 2 As shown, this application also includes: According to the business attribute information corresponding to each financing business, the scale risk factor, channel risk factor and type risk factor are identified; the scale risk factor, channel risk factor and type risk factor are quantified respectively, and the scale risk indicator, channel risk indicator and type risk indicator are output; the scale risk indicator, the channel risk indicator and the type risk indicator are weighted and multiple independent risk indicators are output.

[0027] Specifically, according to the business attribute information corresponding to each financing business, identify the scale risk factor, channel risk factor and type risk factor. Among them, large-scale financing increases the debt burden of the enterprise and affects its debt repayment ability. Then, according to the impact of the financing scale on the financial stability of the enterprise, identify the scale risk factor. Different financing channels have different risk levels, and then identify the channel risk factor. For example, bank loans require mortgages or guarantees, while market financing is affected by market fluctuations. Debt financing and equity financing have different impacts on the capital structure and risk status of the enterprise, and identify the type risk factor. For example, debt financing increases the financial leverage of the enterprise, while equity financing dilutes the equity of the original shareholders.

[0028] Then, the identified risk factors are quantified through historical data analysis, expert scoring, model calculation and other methods to generate corresponding risk indicators. For example, the scale risk indicator is calculated based on the ratio of financing scale to corporate asset scale, income or profit. Channel risk indicators are quantified by evaluating factors such as default rate, interest rate changes, and liquidity risk of each financing channel. Type risk indicators are quantified by analyzing the interest burden and debt repayment period of debt financing and the equity dilution effect of equity financing.

[0029] Next, each risk indicator is weighted to reflect its relative importance in the overall risk assessment. The weights are determined based on historical data, expert opinions, or statistical models. By weighting, the scale risk indicator, channel risk indicator, and type risk indicator are combined to output multiple independent risk indicators, each of which integrates different aspects of risk information.

[0030] Independent risk indicators can provide enterprises with multi-dimensional risk assessments, helping them better understand and manage financing risks. At the same time, as a decision-making support tool, it helps enterprises choose the most appropriate financing strategy, optimize capital structure, and reduce financing costs.

[0031] Furthermore, the present application also includes: Obtain the initial scale weight, initial channel weight and initial type weight; establish a loss cost function, call the loss cost function to calculate the loss cost of the scale risk indicator, the channel risk indicator and the type risk indicator respectively, and output the scale cost, channel cost and type cost; according to the scale cost, channel cost and type cost, perform weight update configuration on the initial scale weight, initial channel weight and initial type weight, and output the updated scale weight, updated channel weight and updated type weight; perform weight calculation on the scale risk indicator, the channel risk indicator and the type risk indicator according to the updated scale weight, updated channel weight and updated type weight, and output multiple independent risk indicators.

[0032] Specifically, the initial impact of financing scale on overall risk is determined based on historical data, industry standards and expert opinions. Initial channel weights are assigned based on the historical performance and risk characteristics of different financing channels. Initial type weights are assigned based on the different impacts of debt financing and equity financing on the company's financial status.

[0033] Then, the loss cost function is used to evaluate the difference between the model prediction and the actual result. In risk assessment, the error between the predicted risk and the actual risk is calculated. Using the established loss cost function, the scale risk index, channel risk index and type risk index are calculated to obtain the cost of each risk. The scale cost reflects the prediction error of the model when evaluating the financing scale risk. The channel cost reflects the prediction error of the model when evaluating the financing channel risk. The type cost reflects the prediction error of the model when evaluating the financing type risk.

[0034] Then, the initial weights are updated based on the loss cost calculation results through optimization algorithms such as gradient descent to adjust the weights to minimize the loss cost. Among them, the scale weights are adjusted and updated according to the scale cost to better reflect the impact of the financing scale on the risk. The channel weights are adjusted and updated according to the channel cost to more accurately reflect the risks of different financing channels. The type weights are adjusted and updated according to the type cost to more accurately reflect the risks of different financing types.

[0035] Next, use the updated scale weight, updated channel weight and updated type weight to re-weight and calculate the scale risk indicator, channel risk indicator and type risk indicator to obtain more accurate risk assessment results and output multiple independent risk indicators, which reflect the optimal weight configuration obtained by the model through learning data, and can provide more reliable financing risk assessment for target enterprises.

[0036] Improve the efficiency and effectiveness of risk management by optimizing the risk assessment model.

[0037] Furthermore, the present application also includes: Acquire multiple financing scales corresponding to the multiple financing businesses; collect the historical financing scale range of the target enterprise; obtain the distribution positions of the multiple financing scales based on the historical financing scale range, identify the dispersion according to the distribution position, and output the financing scale dispersion.

[0038] Specifically, for each financing business of the target enterprise, record the size of the financing. Analyze the historical financing data of the target enterprise and determine the interval division of the financing scale. Divide according to the size of the financing scale, such as small financing, medium financing and large financing. The interval division can be based on statistical analysis, such as using quantiles to determine the boundaries of the interval.

[0039] Then, for each financing scale of the current financing business, determine its position in the historical financing scale interval. This is achieved by comparing the relative position of the current financing scale with the historical interval to obtain the distribution position of the financing scale.

[0040] Next, the dispersion of financing scale is evaluated based on the distribution position of each financing scale in the historical interval. Dispersion can be quantified based on various statistical indicators, such as standard deviation, coefficient of variation, or entropy. If the financing scale is distributed in multiple different intervals, the dispersion is high; if the financing scale is concentrated in a few intervals, the dispersion is low. The dispersion of financing scale is output in the form of an indicator for use in risk assessment. The financing scale dispersion indicator can help evaluate whether the company's financing strategy is too concentrated or too dispersed, thereby potentially increasing or reducing risks.

[0041] By obtaining the distribution of financing scale and taking risk diversification into account when formulating financing strategies, enterprises can more comprehensively assess and manage financing risks and ensure financial stability and long-term sustainable development.

[0042] Furthermore, the present application also includes: Acquire multiple financing channels corresponding to the multiple financing businesses; establish similarity detection groups based on the multiple financing channels, wherein each similarity detection group includes two different financing channels; establish a similarity twin network, wherein the similarity twin network includes channel type, channel condition and channel term; perform similarity detection on the similarity detection group based on the similarity twin network to obtain a similarity index, and obtain the similarity of the financing channels based on the similarity index.

[0043] Specifically, for each financing business of the target enterprise, the information of the financing channel is recorded, and the financing channels of multiple financing businesses are obtained.

[0044] Then, different financing channels are paired to form similarity detection groups. Each detection group contains two different financing channels to compare similarities.

[0045] Next, a similarity twin network is established. The similarity twin network is a neural network architecture used to determine whether two inputs are similar. In the financing channel process, the similarity twin network is used to compare the similarities of different financing channels. By learning the characteristics of financing channels in historical data sets, the similarity twin network can output a similarity index to reflect the degree of similarity between two financing channels. The similarity twin network includes channel type, channel conditions, and channel term, which correspond to different characteristics of financing channels and are used for network learning and similarity judgment.

[0046] Next, the similarity twin network is used to perform similarity detection on the financing channels of each similarity detection group. According to the similarity index, the similarity between the financing channels is evaluated to obtain the similarity of the financing channels. If the similarity index is high, it means that the two financing channels are similar in type, conditions or term; if the similarity index is low, it means that the two financing channels are quite different.

[0047] By obtaining similarities between financing channels, risk diversification can be taken into account when formulating financing strategies. For example, if an enterprise relies on financing channels that are too similar, it will increase sensitivity to market conditions, thereby increasing overall risk. Conversely, diversified financing channels can reduce this risk. Therefore, the assessment of financing channel similarity helps the target enterprise to more comprehensively assess and manage financing risks.

[0048] Furthermore, the present application also includes: Acquire multiple financing types corresponding to the multiple financing businesses; identify the business product objects of each financing type in the multiple financing types; perform correlation analysis on the business product objects of each financing type in the multiple financing types to obtain correlation indicators; and obtain financing type concentration based on the correlation indicators.

[0049] Specifically, for each financing business of the target enterprise, the information of the financing type is recorded, such as debt financing, equity financing, hybrid financing, etc., and the financing types of multiple financing businesses are obtained.

[0050] Then, for each financing type, identify the business product objects associated with it, including specific financial instruments, contract terms, interest rate arrangements, etc.

[0051] Next, the business product objects of each financing type are subjected to correlation analysis to evaluate the interrelationship and dependence between different financing types. Correlation analysis can be carried out using statistical analysis, network analysis or machine learning methods, such as cluster analysis, association rule mining, etc. Based on correlation analysis, correlation indicators are output, which reflect the correlation between different financing types and help target enterprises understand the complexity and diversity of their financing structure.

[0052] Next, based on the correlation index, evaluate the concentration of financing types. If the correlation index shows that the financing types are highly correlated or concentrated, it indicates that the company's financing structure may be too dependent on the financing type. The financing type concentration index can help the target company identify potential risk points and take measures to diversify risks.

[0053] By obtaining the distribution of financing types and taking risk diversification into account when formulating financing strategies, enterprises can more comprehensively assess and manage financing risks and ensure financial stability and long-term sustainable development.

[0054] Furthermore, the present application also includes: Obtain the risk complementarity of the multiple independent risk indicators; update the multiple independent risk indicators according to the risk complementarity, and output the updated multiple independent risk indicators; perform fitting based on the updated multiple independent risk indicators to obtain a second collaborative risk resistance indicator; perform fitting and evaluation on the first collaborative risk resistance indicator based on the second collaborative risk resistance indicator, and output a financing risk indicator.

[0055] Specifically, the relationship between multiple independent risk indicators is analyzed to determine whether there is complementarity and obtain risk complementarity. Risk complementarity is manifested in that the high value of a risk indicator can offset the low value of other risk indicators, or the change of some indicators can predict the change of other indicators.

[0056] Then, based on risk complementarity, multiple independent risk indicators are updated, including adjusting the indicator weights or correcting the indicator values, to better reflect the relationship between the indicators and ensure that the risk indicators can comprehensively and accurately reflect the risk characteristics of the financing business.

[0057] Next, using the updated multiple independent risk indicators, a higher-level indicator, namely the second collaborative risk resistance indicator, is fitted through statistical methods or machine learning algorithms, which integrates the information of multiple risk indicators to provide a more comprehensive financing risk assessment.

[0058] Next, the second synergistic risk resistance indicator is used to perform a fitting assessment on the first synergistic risk resistance indicator to verify and improve the accuracy of the initial risk assessment. Based on the fitting assessment results of the second synergistic risk resistance indicator, the financing risk indicator is output to provide the target enterprise with a comprehensive assessment of financing risks, helping the target enterprise to better manage risks when making decisions.

[0059] By identifying and managing financing risks, the robustness and flexibility of financing strategies are ensured, and the accuracy and practicality of risk assessment are improved.

[0060] Furthermore, the present application also includes: Collect the historical financing data set of the target enterprise; obtain scale risk cost samples, channel risk cost samples and type risk cost samples based on the historical financing data set; establish a loss cost function based on the scale risk cost samples, the channel risk cost samples and the type risk cost samples.

[0061] Specifically, we collect the financing data of the target enterprise in the past period of time, including successful financing cases and failed cases. The historical financing data set contains detailed information on each financing business, such as the financing scale, financing channel, financing type, and the final financing result (whether it is successful, whether it is in default, etc.).

[0062] Then, extract data points related to the financing scale from the historical data set, including relevant risk results (such as default probability, financial cost, etc.), and obtain scale risk cost samples. Extract data points and risk results related to different financing channels, and obtain channel risk cost samples. Extract data points and risk results related to different financing types, and obtain type risk cost samples.

[0063] Next, based on statistical methods, such as mean square error or mean absolute error, or the cross entropy loss function in the machine learning model, the acquired risk cost samples are used to establish or train the loss cost function, quantify the difference between the model prediction and the actual results, consider the distribution characteristics of the data and outlier processing, and ensure that the loss cost function can effectively reflect the risk cost.

[0064] By establishing a loss cost function based on historical data, the accuracy of model predictions can be evaluated according to actual risk results, thereby improving the performance of the risk assessment model.

[0065] In summary, the risk intelligent assessment method for corporate financing business provided by this application has the following technical effects: By acquiring multiple financing businesses of the target enterprise; acquiring business attribute information corresponding to each of the multiple financing businesses, including financing scale, financing channel and financing type; performing independent risk assessment according to the business attribute information corresponding to each financing business, and obtaining multiple independent risk indicators; performing feature identification on the multiple financing businesses according to the business attribute information, and obtaining the dispersion of financing scale, similarity of financing channels and concentration of financing types; training a collaborative risk resistance assessment model according to the dispersion of financing scale, similarity of financing channels and concentration of financing types, and outputting a first collaborative risk resistance indicator according to the collaborative risk resistance assessment model; performing fitting assessment on the first collaborative risk resistance indicator according to the multiple independent risk indicators, and outputting a financing risk indicator, ultimately achieving the technical goal of identifying the risk resistance of multiple financing businesses, and achieving the technical effect of improving the reliability of risk assessment and formulating more effective risk management strategies.

[0066] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0067] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A risk intelligent assessment method for corporate financing business, characterized in that: The method comprises: Obtain multiple financing businesses from target companies; Obtaining business attribute information corresponding to each of the multiple financing businesses, including financing scale, financing channel, and financing type; Conduct independent risk assessment based on the business attribute information corresponding to each financing business and obtain multiple independent risk indicators; Performing feature identification on the multiple financing businesses according to the business attribute information to obtain the dispersion of financing scales, similarity of financing channels, and concentration of financing types; Training a collaborative risk resistance assessment model according to the dispersion of financing scale, similarity of financing channels, and concentration of financing types, and outputting a first collaborative risk resistance indicator according to the collaborative risk resistance assessment model; The first collaborative risk resistance indicator is fitted and evaluated according to the multiple independent risk indicators, and a financing risk indicator is output.

2. The method according to claim 1, characterized in that Perform independent risk assessment based on the business attribute information corresponding to each financing business to obtain multiple independent risk indicators. The methods include: Identify scale risk factors, channel risk factors and type risk factors based on the business attribute information corresponding to each financing business; Quantify the scale risk factor, channel risk factor and type risk factor respectively, and output the scale risk index, channel risk index and type risk index; The scale risk indicator, the channel risk indicator and the type risk indicator are weighted and a plurality of independent risk indicators are output.

3. The method according to claim 2, characterized in that The method of weighting the scale risk indicator, the channel risk indicator and the type risk indicator includes: Obtain initial scale weight, initial channel weight and initial type weight; Establishing a loss cost function, calling the loss cost function to calculate the loss cost of the scale risk indicator, the channel risk indicator and the type risk indicator respectively, and outputting the scale cost, the channel cost and the type cost; According to the scale cost, channel cost and type cost, weight update configuration is performed on the initial scale weight, initial channel weight and initial type weight, and the updated scale weight, updated channel weight and updated type weight are output; The scale risk indicator, the channel risk indicator and the type risk indicator are weighted according to the update scale weight, the update channel weight and the update type weight, and a plurality of independent risk indicators are output.

4. The method according to claim 1, characterized in that Obtaining financing scale dispersion includes: Obtaining multiple financing scales corresponding to the multiple financing businesses; Collect the historical financing scale range of the target enterprise; Obtain the distribution positions of the multiple financing scales based on the historical financing scale intervals, identify the dispersion according to the distribution positions, and output the financing scale dispersion.

5. The method according to claim 4, characterized in that Obtaining similarity of financing channels includes: Acquire multiple financing channels corresponding to the multiple financing businesses; Establishing similarity detection groups according to the multiple financing channels, wherein each similarity detection group includes two different financing channels; Establishing a similarity twin network, wherein the similarity twin network includes channel type, channel condition and channel term; Based on the similarity twin network, similarity detection is performed on the similarity detection group to obtain a similarity index, and based on the similarity index, the similarity of the financing channels is obtained.

6. The method according to claim 4, characterized in that The types of financing obtained include: Acquire multiple financing types corresponding to the multiple financing businesses; Identifying a business product object for each financing type in the plurality of financing types; Performing a correlation analysis on the business product objects of each financing type in the multiple financing types to obtain a correlation index; According to the correlation index, the concentration of financing types is obtained.

7. The method according to claim 1, characterized in that The first collaborative risk resistance indicator is fitted and evaluated according to the multiple independent risk indicators, the method comprising: Obtaining risk complementarity of the plurality of independent risk indicators; Updating the plurality of independent risk indicators according to the risk complementarity, and outputting the updated plurality of independent risk indicators; Fitting is performed based on the updated multiple independent risk indicators to obtain a second coordinated risk resistance indicator; The first synergistic risk resistance indicator is fitted and evaluated according to the second synergistic risk resistance indicator, and a financing risk indicator is output.

8. The method according to claim 5, characterized in that Establish a loss cost function, the method includes: Collecting historical financing data sets of the target enterprise; According to the historical financing data set, obtaining a scale risk cost sample, a channel risk cost sample, and a type risk cost sample; A loss cost function is established based on the scale risk cost samples, the channel risk cost samples and the type risk cost samples.

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