Financial industry-oriented computing power development evaluation method

Abstract

The invention discloses a financial industry-oriented computing power development evaluation method, and relates to the technical field of finance, and the method comprises the following steps: constructing an evaluation index system for business expansibility, difficult substitutability and value leading of commercial banks; and carrying out weight setting on a matrix formed by the indexes at all levels by adopting an AHP (Analytic Hierarchy Process): based on the set evaluation indexes of the three fields, obtaining a three-dimensional evaluation result. According to the financial industry-oriented computing power development evaluation method, the limitation of traditional computing power development evaluation is broken through, non-traditional indexes such as business expansibility, value leading and difficult substitutability are incorporated into an evaluation system, a new thought and method are provided for financial industry computing power development evaluation, and on the basis, an AHP analytic hierarchy process is combined, so that the computing power development evaluation efficiency is improved. And weight calculation and setting are carried out, so that a more accurate and objective three-dimensional evaluation result can be obtained, and the overall development condition of financial industry computing power can be reflected more comprehensively.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and specifically provides a method for evaluating the development of computing power for the financial industry. Background Art

[0002] The financial industry is a crucial part of the modern economic system. It encompasses a wide range of activities and institutions aimed at facilitating the flow, allocation, and management of funds. By definition, the financial industry refers to the field of economic activities related to currency circulation and credit activities, including many sub - fields such as banking, securities, insurance, funds, trusts, etc. Computing power refers to the ability of a computer system to execute computing tasks, which quantifies the speed and efficiency of data processing. In the financial industry, the development of computing power is crucial because it directly affects the speed, accuracy, and security of financial transactions. With the rapid development of fintech, the demand for computing power in the financial industry is also increasing continuously. For example, high - frequency trading, big data analysis, risk management, etc. all require powerful computing power support. Therefore, the financial industry is constantly strengthening computing power construction and improving data - processing capabilities to cope with increasingly complex financial operations and market demands.

[0003] In the prior art, the evaluation of the development of computing power in the financial industry generally adopts the following methods: performance benchmark testing (evaluating performance indicators such as the processing speed and response time of a computer system by running specific test programs to reflect the computing power level), resource utilization evaluation (analyzing the utilization of computing resources and the performance of the system under different loads to evaluate the effective utilization of computing power), and energy - efficiency ratio analysis (combining computing power performance and energy consumption to calculate the energy - efficiency ratio to evaluate the energy efficiency of the computing system). However, these methods mainly focus on the hardware performance and resource utilization of the computing system, and involve less evaluation of the business expansibility, value leadership, and irreplaceability unique to the financial industry. Due to the neglect of the evaluation of aspects such as the business expansibility, irreplaceability, and value leadership unique to the financial industry, the evaluation results cannot comprehensively reflect the actual needs and future development potential of the financial industry. And due to the lack of evaluation indicators, the existing evaluation methods cannot accurately reflect the true situation of the development of computing power in the financial industry.

[0004] Therefore, it is urgent to improve this shortcoming. The present invention studies and improves the existing technology and deficiencies, and provides a method for evaluating the development of computing power for the financial industry. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for evaluating the development of computing power for the financial industry to solve the problems raised in the above - mentioned background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: A computing power development evaluation method for the financial industry, comprising the following steps: constructing an evaluation index system for business expansibility, irreplaceability, and value leadership for commercial banks; using the AHP (Analytic Hierarchy Process) method to set weights for the matrix composed of indicators at all levels.

[0007] Furthermore, the evaluation index system includes 9 first-level indicators and 26 second-level indicators, covering general data such as macroeconomics, as well as specific data such as the development strategies and asset growth of each commercial bank; and the indicators are quantified and normalized by means of hierarchical scoring and expert scoring.

[0008] Furthermore, the business expansibility is composed of 3 first-level indicators and 12 second-level indicators, specifically including 3 first-level indicators: strategic layout, asset growth, and macroeconomics; the irreplaceability is composed of 3 first-level indicators and 7 second-level indicators, specifically including 3 first-level indicators: investment in technology development, intellectual property advantages, and institutional type and financial status; the value leadership is composed of 3 first-level indicators and 7 second-level indicators, specifically including 3 first-level indicators: green finance strategy, ESG indicators, and corporate governance and finance.

[0009] Furthermore, using the AHP method to set weights for the matrix composed of indicators at all levels, including: for the indicators at each level, constructing a judgment matrix to determine the relative importance relationship between every two indicators in each first-level indicator, and calculating the weights of each second-level indicator.

[0010] Furthermore, constructing a judgment matrix to determine the relative importance relationship between every two indicators in each first-level indicator, including: constructing the first-layer judgment matrix to determine the relative importance relationship between every two indicators in the first-level indicators; constructing the second-layer judgment matrix to determine the relative importance relationship between every two indicators in the second-level indicators.

[0011] Furthermore, for the irreplaceability, first calculate the relative importance relationship of the first-level indicators: investment in technology development, intellectual property advantages, and institutional type and financial status; then calculate the relative importance relationship of the second-level indicators; based on this, calculate the weights of the irreplaceability second-level indicators: IT capital investment, other technology expenses, number of patent applications, number of patent authorizations, type of financial institution, total assets, and liquidity coverage ratio.

[0012] Furthermore, for the value leadership, first calculate the relative importance relationship of the first-level indicators: green finance strategy, ESG indicators, and corporate governance and finance; then calculate the relative importance relationship of the second-level indicators; based on this, calculate the weights of the value leadership second-level indicators: balance of green credit, intensity of green finance investment, issuance volume of ESG bonds, ESG rating, green certifications and awards, proportion of independent directors, and net interest margin.

[0013] The present invention provides a method for evaluating the computing power development in the financial industry, which has the following beneficial effects:

[0014] The present invention breaks through the limitations of traditional computing power development evaluation, incorporates non-traditional indicators such as business expansibility, irreplaceability, and value leadership into the evaluation system, provides new ideas and methods for evaluating the computing power development in the financial industry, and on this basis, combines the AHP (Analytic Hierarchy Process). By constructing a judgment matrix, the relative importance relationship between pairwise indicators at the same level is determined, and weight calculation and setting are carried out accordingly, which is conducive to obtaining a more accurate and objective three-dimensional evaluation result. This result comprehensively considers the performance of commercial banks in aspects such as business development, value creation, and unique status, and can more comprehensively reflect the overall development status of the computing power in the financial industry. At the same time, by constructing an evaluation index system, financial institutions can clarify their development directions and goals in terms of business expansion, value creation, and irreplaceability, providing clear guidance for the development of computing power. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a diagram of the evaluation index system for a method for evaluating the computing power development in the financial industry according to the present invention;

[0016] Figure 2 It is a schematic diagram of the judgment matrix of the first-level indicator of business expansibility for a method for evaluating the computing power development in the financial industry according to the present invention;

[0017] Figure 3 It is a schematic diagram of the judgment matrix of the second-level indicator of business expansibility for a method for evaluating the computing power development in the financial industry according to the present invention;

[0018] Figure 4 It is a schematic diagram of the weights of the business expansibility indicators for a method for evaluating the computing power development in the financial industry according to the present invention;

[0019] Figure 5 It is a schematic diagram of the judgment matrix of the first-level indicator of irreplaceability for a method for evaluating the computing power development in the financial industry according to the present invention;

[0020] Figure 6 It is a schematic diagram of the judgment matrix of the second-level indicator of irreplaceability for a method for evaluating the computing power development in the financial industry according to the present invention;

[0021] Figure 7 It is a schematic diagram of the weights of the irreplaceability indicators for a method for evaluating the computing power development in the financial industry according to the present invention;

[0022] Figure 8 It is a schematic diagram of the judgment matrix of the first-level indicator of value leadership for a method for evaluating the computing power development in the financial industry according to the present invention;

[0023] Figure 9Schematic diagram of the judgment matrix of the value-leading secondary indicators of a computing power development evaluation method for the financial industry according to the present invention;

[0024] Figure 10 Schematic diagram of the index weights of the value-leading indicators of a computing power development evaluation method for the financial industry according to the present invention;

[0025] Figure 11 3D distribution map of the computing power competitiveness of 18 commercial banks of a computing power development evaluation method for the financial industry according to the present invention. Detailed implementation manners

[0026] The following further describes in detail the implementation manners of the present invention with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0027] As Figures 1-11 shown, a computing power development evaluation method for the financial industry includes the following steps: constructing an evaluation index system for business expansibility, irreplaceability, and value leadership for commercial banks; using the AHP (Analytic Hierarchy Process) method to set weights for the matrix composed of indicators at all levels;

[0028] In this embodiment, the evaluation index system includes 9 first-level indicators and 26 second-level indicators, covering general data such as macroeconomics, as well as special data such as the strategic layout and asset growth of each commercial bank; and the indicators are quantified and normalized by means of hierarchical scoring and expert scoring, specifically as follows:

[0029] Hierarchical scoring: For indicators with clear quantification criteria or historical data, different levels (such as excellent, good, medium, poor, bad) are set according to the actual performance of the indicators, and corresponding scores (such as 5, 4, 3, 2, 1) are assigned to each level;

[0030] Expert scoring: For indicators that are difficult to directly quantify, industry experts or internal executives are invited to score according to experience and professional knowledge. Specifically, a score range (such as 1 - 5 points) is set, and it is required that experts give scores according to factors such as the importance and performance of the indicators;

[0031] After the indicator scores are obtained, the normalization formula is used to convert the score of each indicator into a value between 0 and 1 for comparison and comprehensive analysis. The normalization formula is: Normalized score = (Actual score - Minimum value) / (Maximum value - Minimum value).

[0032] In this embodiment, the business expansibility is composed of 3 first-level indicators and 12 second-level indicators, specifically including 3 first-level indicators of strategic layout, asset growth, and macroeconomics;

[0033] 1) The secondary indicators included in the strategic layout and their definitions are as follows:

[0034] Technology Finance Layout: Combining technology with finance to provide financial services such as financing for technology enterprises, promoting the development of technological innovation, including forms such as venture capital and technology loans.

[0035] Pension Finance Layout: Financial activities carried out around pension needs, covering pension insurance, pension wealth management products, etc., providing financial security for people's old-age life.

[0036] Green Finance Layout: Loans provided by financial institutions to support environmental protection and sustainable development projects, promoting the green transformation of the economy.

[0037] Inclusive Finance Layout: Committed to providing affordable financial services for all sectors of society, especially vulnerable groups, such as microloans and microfinance.

[0038] Digital Finance Layout: Using digital technologies to carry out financial operations, including mobile payments, online lending, digital wealth management, etc., improving the efficiency and convenience of financial services.

[0039] 2) The secondary indicators included in asset growth and their definitions are as follows:

[0040] Total Asset Growth Rate: An indicator that measures the expansion speed of the asset scale of an enterprise or financial institution. It reflects the change in the total asset volume over a period of time. A relatively high total asset growth rate may indicate that the enterprise is in a rapid development stage and expands its asset scale through investment, mergers and acquisitions, etc.

[0041] Deposit Growth Rate: An indicator used to evaluate the deposit absorption ability of a financial institution. It reflects the growth rate of the deposit amount during a specific period. A relatively high deposit growth rate indicates that the financial institution is relatively successful in attracting deposits and has more funds available for business such as lending.

[0042] Loan Growth Rate: Reflects the growth of the loan business of a financial institution. Loan growth may be to meet the financing needs in economic activities, but excessive growth may bring problems such as credit risks.

[0043] Non-Interest Income Growth Rate: Non-interest income mainly includes income from fees and commissions, investment income, etc. This indicator reflects the growth of diversified income of a financial institution beyond traditional interest business. A relatively high non-interest income growth rate helps to enhance the profitability and risk resistance ability of the financial institution.

[0044] Operating Income Growth Rate: An indicator that measures the growth speed of the operating income of an enterprise or financial institution. It reflects the change in operating income over a period of time. A relatively high operating income growth rate usually indicates smooth business expansion and an increase in market share, but factors such as cost control also need to be considered comprehensively.

[0045] 3) The secondary indicators included in the macroeconomy and their definitions are as follows:

[0046] Per capita disposable income: It refers to the total sum that residents can use for final consumption expenditure and savings, that is, the income that residents can freely dispose of. It includes cash income and in-kind income. Classified by income sources, it can be divided into wage income, net operating income, net property income, and net transfer income. Per capita disposable income is an important indicator to measure the living standards of residents, which reflects the consumption ability and economic status of residents. A relatively high per capita disposable income usually means that residents have more funds for consumption, investment, and savings, and the quality of life is relatively high.

[0047] Banking industry prosperity index: It is an indicator that reflects the overall operating conditions and development trends of the banking industry. It comprehensively evaluates the loan demand, perception of monetary policy, profitability, asset quality, etc. of the banking industry through methods such as questionnaires for bankers. If the banking industry prosperity index is relatively high, it indicates that the overall operation of the banking industry is good, business demand is strong, profitability is strong, and asset quality is stable; on the contrary, if the index is relatively low, it may mean that the banking industry is facing certain operating pressures, such as insufficient loan demand, declining profitability, and rising risks.

[0048] In this embodiment, the irreplaceability is composed of 3 first-level indicators and 7 second-level indicators, specifically including 3 first-level indicators: investment in scientific and technological development, intellectual property advantages, and institutional type and financial status;

[0049] 1) The secondary indicators included in the investment in scientific and technological development and their definitions are as follows:

[0050] IT capital investment: It refers to the investment used to purchase, upgrade, or maintain long-term technical assets, such as hardware, software, or data centers.

[0051] Other scientific and technological expenses: It includes daily operation-related expenses such as technical training, system maintenance, and technical consulting.

[0052] 2) The secondary indicators included in the intellectual property advantages and their definitions are as follows:

[0053] Number of patent applications: It measures the degree of attention of commercial banks to intellectual property.

[0054] Number of patent authorizations: It measures the achievements of commercial banks in intellectual property.

[0055] 3) The secondary indicators included in the institutional type and financial status and their definitions are as follows:

[0056] Type of financial institution: The advantages of state-owned, joint-stock, city commercial, and rural commercial banks in computing power development are different, so they have their irreplaceability.

[0057] Total assets: It measures the total amount of bank assets.

[0058] Liquidity Coverage Ratio: Measures a bank's ability to meet sudden funding needs.

[0059] In this embodiment, the value leadership is composed of 3 first-level indicators and 7 second-level indicators, specifically including 3 first-level indicators: green finance strategy, ESG indicators, and corporate governance and finance;

[0060] 1) The second-level indicators included in the green finance strategy and their definitions are as follows:

[0061] Green credit balance: Reflects the efforts of a financial institution or country in promoting environmental protection and sustainable development. If both the balance and the increment show an increasing trend, it indicates that the bank's green credit business is expanding continuously.

[0062] Intensity of green finance investment: Reflects the bank's commitment and intensity to green finance and sustainable development.

[0063] 2) The second-level indicators included in the ESG indicators and their definitions are as follows:

[0064] ESG bond issuance volume: Issues debt instruments used to finance projects or activities that meet environmental, social, and governance (ESG) standards. Aims to support sustainable development projects such as environmental protection, social responsibility, and good governance practices.

[0065] ESG rating: The ESG rating is an important tool for evaluating a bank's performance in sustainable development and value leadership. It not only reflects the bank's actual performance but also shows its leadership in promoting social and environmental responsibilities.

[0066] Green certifications and awards: Certifications and awards can reflect the relevant achievements of the bank in green finance.

[0067] 3) The second-level indicators included in corporate governance and finance and their definitions are as follows:

[0068] Proportion of independent directors: Independent directors and diverse backgrounds can bring multi-angle scrutiny, enhancing the fairness and comprehensiveness of decision-making.

[0069] Net interest margin: In a low-interest-rate environment, with lower financing costs, banks are more likely to increase investments in green and sustainable projects.

[0070] In this embodiment, for business expansibility, first calculate the relative importance relationships of the first-level indicators (strategic layout, asset growth, macro economy), and the results are shown in Figure 2 ; then calculate the relative importance relationships of the second-level indicators, and the results are shown in Figure 3 ; based on this, calculate the weights of the business expansibility indicators, such as Figure 4As shown in the figure, the weights of business expansion indicators are as follows: Science and Technology Finance Layout 25.99%, Pension Finance Layout 7.60%, Green Finance Layout 9.19%, Inclusive Finance Layout 7.60%, Digital Finance Layout 19.76%, Total Asset Growth Rate 7.71%, Deposit Growth Rate 4.63%, Loan Growth Rate 4.63%, Non-Interest Income Growth Rate 5.25%, Operating Income Growth Rate 4.84%, Per Capita Disposable Income 1.40%, Banking Industry Prosperity Index 1.40%.

[0071] Among them, the specific contents of the first-level indicators (Strategic Layout, Asset Growth, Macroeconomy) are as follows:

[0072] Strategic Layout: Evaluate the strategic planning and implementation of commercial banks in the aspects of Science and Technology Finance Layout, Pension Finance Layout, Green Finance Layout, Inclusive Finance Layout, and Digital Finance Layout. It determines the future development direction and competitive advantages of banks and is the cornerstone of business expansion.

[0073] Asset Growth: Measure the total asset growth rate, deposit growth rate, loan growth rate, non-interest income growth rate, and operating income growth rate of commercial banks, which reflects the business expansion ability and market position of banks and is a direct manifestation of business expansion.

[0074] Macroeconomy: Examine the impact of the macroeconomic environment on the business expansion of commercial banks. The macroeconomic environment is an external condition for the business expansion of banks and has an important impact on the business scale and profitability of banks.

[0075] Taking a commercial bank's plan to expand its fintech business as an example, in order to evaluate the feasibility of this business expansion, it is necessary to construct a computing power development evaluation method, and the first-level indicators of business expansion will play a key role.

[0076] When choosing a weight calculation method, it is necessary to comprehensively consider the data availability, calculation complexity, and industry consensus. Currently, common calculation methods include: Delphi method, Analytic Hierarchy Process (AHP), Entropy Weight Method, Weighted Average Method. Considering the advantages and disadvantages of the above methods and the characteristics of the financial business scenario, this case will adopt the Analytic Hierarchy Process (AHP) to calculate the weights of the first-level indicators. Because the Analytic Hierarchy Process can decompose the complex indicator system according to the hierarchical structure, which helps to comprehensively and systematically evaluate all aspects of business expansion. And by constructing a judgment matrix and calculating weights, the Analytic Hierarchy Process can clearly reflect the relative importance and logical relationship between indicators. Moreover, the Analytic Hierarchy Process has a wide application foundation in the financial field and can better adapt to the complexity and diversity of the financial business scenario.

[0077] In this embodiment, for the irreplaceability, first calculate the relative importance relationship of the first-level indicators (Science and Technology Development Investment, Intellectual Property Advantage, Institution Type and Financial Status), and the results are shown inFigure 5 ; Then calculate the relative importance relationship of the secondary indicators, and the results are shown in Figure 6 ; Based on this, calculate the weights of the irreplaceability indicators, such as Figure 7 shown. The weights of the irreplaceability indicators are as follows: IT capital investment 40.69%, other science and technology expenses 20.61%, number of patent applications 3.30%, number of patent authorizations 5.28%, type of financial institution 12.35%, total assets 11.53%, liquidity coverage ratio 6.24%.

[0078] Among them, the indicator contents of the first-level indicators (science and technology development investment, intellectual property advantages, institution type and financial status) are specifically as follows:

[0079] Science and technology development investment: Measure the scientific and technological strength and future competitiveness of commercial banks. The more investment, the stronger the bank's ability in technological innovation, and the more likely it is to develop unique competitive products and services.

[0080] Intellectual property advantages: Examine the accumulation and protection of intellectual property of commercial banks, which is one of the key factors for commercial banks to be difficult to be easily replaced by other institutions. Having a strong intellectual property portfolio means that the bank has a higher threshold and stronger competitiveness in technological innovation, product development and market expansion.

[0081] Institution type and financial status: Examine the institution type of commercial banks (such as state-owned banks, joint-stock banks, city commercial banks, etc.) and financial status (such as capital adequacy ratio, asset quality, profitability, etc.). The institution type and financial status reflect the scale and strength of commercial banks, as well as their status and influence in the financial market. These factors together determine the irreplaceability of banks in business expansion, product innovation and market competition.

[0082] Taking a commercial bank that hopes to evaluate its own irreplaceability in the field of fintech in order to better formulate development strategies and market positioning as an example. In this case, a computing power development evaluation method can be constructed, and key attention can be paid to the first-level irreplaceability indicators. By collecting and analyzing relevant data, the bank can understand its own performance in aspects such as science and technology development investment, intellectual property advantages and institution type and financial status, and then clarify its competitive advantages and potential risks.

[0083] Similar to business scalability, when selecting a weight calculation method, it is necessary to comprehensively consider the data availability, computational complexity, and industry consensus. Currently, common calculation methods include: Delphi method, Analytic Hierarchy Process (AHP), entropy weight method, and weighted average method. Considering the advantages and disadvantages of the above methods and the characteristics of the financial business scenario, this case will adopt the Analytic Hierarchy Process (AHP) to calculate the weights of the first-level indicators. Because the Analytic Hierarchy Process can decompose a complex indicator system according to the hierarchical structure, which helps to comprehensively and systematically evaluate all aspects of business scalability. And by constructing a judgment matrix and calculating weights, the Analytic Hierarchy Process can clearly reflect the relative importance and logical relationship between indicators. Moreover, the Analytic Hierarchy Process has a wide application foundation in the financial field and can better adapt to the complexity and diversity of the financial business scenario.

[0084] In this embodiment, for value leadership, first calculate the relative importance relationship of the first-level indicators (green finance strategy, ESG indicators, corporate governance and finance), and the results are shown in Figure 8 ; then calculate the relative importance relationship of the second-level indicators, and the results are shown in Figure 9 ; based on this, calculate the value leadership indicator weights. As shown in Figure 10 , the value leadership indicator weights are in turn: green credit balance 27.59%, green finance investment intensity 26.78%, ESG bond issuance volume 13.15%, ESG rating 16.55%, green certification and awards 12.11%, proportion of independent directors 2.20%, net interest margin 1.62%.

[0085] Among them, the indicator contents of the first-level indicators (green finance strategy, ESG indicators, corporate governance and finance) are specifically as follows:

[0086] Green finance strategy: Measure the situation of green credit balance and green finance investment intensity, evaluate the product innovation intensity of the bank in the green finance field, such as launching new green bonds, green trusts and other products, and examine the service quality provided by the bank in the green finance field, such as customer satisfaction, service efficiency, etc.

[0087] ESG indicators: Examine the bank's performance in the aspects of environment (E), society (S) and corporate governance (G).

[0088] Corporate Governance and Finance: Evaluate the situation of banks in terms of the proportion of independent directors and net interest margin. Taking the green credit business of a commercial bank as an example, the bank regards green credit as an important part of its green finance strategy. By increasing the credit investment in green industries, it has supported the development of multiple green projects. At the same time, the bank has also actively improved ESG indicators, strengthened environmental management and social responsibility, and improved corporate governance and financial performance. In terms of business expansion, the bank has improved its market competitiveness by continuously innovating green credit products and services. In terms of irreplaceability, due to the green credit business conforming to national policy orientations and market demands, it has a high degree of irreplaceability.

[0089] In this embodiment, based on real data, 18 commercial banks including 6 state-owned banks, 9 joint-stock banks, and 3 city commercial banks are evaluated, and the evaluation results are as Figure 11 shown.

[0090] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the present invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are selected and described to better explain the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. A computing power development evaluation method for the financial industry, characterized in that, It includes the following steps: Construct an evaluation index system for business expansion, irreplaceability, and value leadership for commercial banks; use the AHP (Analytic Hierarchy Process) to set weights for the matrix composed of indicators at all levels.

2. The computing power development evaluation method for the financial industry according to claim 1, wherein The evaluation index system includes 9 first-level indicators and 26 second-level indicators, covering general data such as macroeconomics, as well as special data such as the development strategies and asset growth of each commercial bank; and the indicators are quantified and normalized by means of hierarchical scoring and expert scoring.

3. The computing power development evaluation method for the financial industry according to claim 2, wherein The business expansion is composed of 3 first-level indicators and 12 second-level indicators, specifically including 3 first-level indicators: strategic layout, asset growth, and macroeconomics; the irreplaceability is composed of 3 first-level indicators and 7 second-level indicators, specifically including 3 first-level indicators: investment in technology development, intellectual property advantages, institutional type and financial status; the value leadership is composed of 3 first-level indicators and 7 second-level indicators, specifically including 3 first-level indicators: green finance strategy, ESG indicators, corporate governance and finance.

4. The computing power development evaluation method for the financial industry according to claim 3, wherein Use the AHP to set weights for the matrix composed of indicators at all levels, including: for the indicators at each level, construct a judgment matrix, determine the relative importance relationship between every two indicators in each first-level indicator, and calculate the weights of each second-level indicator.

5. The computing power development evaluation method for the financial industry according to claim 4, characterized in that, Construct a judgment matrix to determine the relative importance relationship between every two indicators in each first-level indicator, including: construct the first-layer judgment matrix to determine the relative importance relationship between every two first-level indicators; construct the second-layer judgment matrix to determine the relative importance relationship between every two second-level indicators.

6. The computing power development evaluation method for the financial industry according to claim 5, wherein For irreplaceability, first calculate the relative importance relationship of the first-level indicators: investment in technology development, intellectual property advantages, institutional type and financial status; then calculate the relative importance relationship of the second-level indicators; based on this, calculate the weights of the second-level indicators of irreplaceability: IT capital investment, other technology expenses, number of patent applications, number of patent authorizations, type of financial institution, total assets, liquidity coverage ratio.

7. The computing power development evaluation method for the financial industry according to claim 6, wherein For value leadership, first calculate the relative importance relationship of the first-level indicators: green finance strategy, ESG indicators, corporate governance and finance; then calculate the relative importance relationship of the second-level indicators; based on this, calculate the weights of the second-level indicators of value leadership: balance of green credit, intensity of green finance investment, issuance volume of ESG bonds, ESG rating, green certifications and awards, proportion of independent directors, net interest margin.

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