Loan assessment method, system and application of technical enterprise

Abstract

The invention provides a loan assessment method and system for a technical enterprise, and an application. The method comprises the steps of obtaining basic information, a loan amount and a loan project of a loan requester; preliminarily evaluating the credit condition of the person in charge according to the personal credit report to obtain a first evaluation result; calling first platform data of the main responsible person, and judging a data value; if yes, performing evaluation to obtain a second evaluation result, and performing comprehensive credit evaluation by integrating the first evaluation result and the second evaluation result; carrying out authenticity evaluation on the loan project, and carrying out rationality evaluation on the loan amount; and obtaining a loan evaluation opinion of the loan requester according to the comprehensive credit, the authenticity evaluation result and the rationality evaluation result. According to the application, the credit condition, the technical capability and the project return condition of the loan requester are comprehensively evaluated by integrating the basic information, the management team information, the research and development team information, the personal credit report, the external data platform information, the patent data and other multi-dimensional information of the loan requester.

Description

Technical Field

[0001] This application relates to the field of loan credit assessment, and more specifically, to a loan assessment method, system, and application for technology companies. Background Technology

[0002] In today's rapidly evolving economic environment, technology-based enterprises, especially small and micro-sized technology companies, are playing an increasingly important role. These companies typically possess high innovation capabilities and growth potential, but they also face funding shortages. Traditional loan assessment methods often focus on a company's financial condition, asset size, and credit history, while neglecting crucial factors such as technological innovation capabilities and the quality of the management team. Therefore, traditional assessment systems may fail to accurately reflect the actual value and development potential of these companies. This makes it difficult for many promising small and micro-sized technology companies to obtain the necessary financing support, limiting their further development. Summary of the Invention

[0003] Based on the problems existing in the current technology, this application provides a loan assessment method, system, and application for technology-based enterprises. The specific solution is as follows:

[0004] Part One, this application proposes a loan assessment method for technology-based enterprises, including the following:

[0005] Obtain basic information about the loan applicant, the loan amount, and the loan project;

[0006] After confirming that the loan applicant is a technology company based on the basic information, obtain its management team information and R&D team information; based on the management team information, determine the principal and deputy principals of the loan applicant, as well as the member relationships between the principal and deputy principals.

[0007] Obtain the personal credit reports of the principal and each deputy principal, and conduct a preliminary assessment of the creditworthiness of the principal and deputy principals based on the personal credit reports to obtain a first assessment result;

[0008] The system obtains the data permissions of the principal responsible person on multiple preset external data platforms, retrieves the first platform data corresponding to the data permissions, and determines whether the first platform data has value in evaluating the principal responsible person's creditworthiness. If it does, the system evaluates the principal responsible person's creditworthiness on the external data platforms based on the first platform data to obtain a second evaluation result. The system then combines the first evaluation result and the second evaluation result to conduct a comprehensive credit assessment of the loan applicant.

[0009] Obtain the patent data of the loan applicant, conduct a technical evaluation based on the R&D team information and the patent data, and evaluate the project return of the loan project through a preset evaluation model;

[0010] The authenticity of the loan project is assessed based on the comprehensive credit assessment results, technical assessment results, and pre-submitted supporting documents. The reasonableness of the loan amount is assessed based on the project's return and fixed assets. Based on the comprehensive credit assessment results, the authenticity assessment results of the loan project, and the reasonableness assessment results of the loan amount, a loan assessment opinion is obtained from the loan applicant.

[0011] In some specific embodiments, if not available, a first responsible person who can repay the debt on behalf of the main responsible person is determined based on the member relationship and the family relationship of the main responsible person; a third assessment result is obtained by evaluating the credit status of each first responsible person on multiple external data platforms based on the data of the external data platforms.

[0012] A comprehensive credit assessment is conducted on the loan applicant based on the combined results of the first and third assessments.

[0013] In some specific embodiments, the process of obtaining the third evaluation result specifically includes:

[0014] Based on the member relationships, the degree of closeness between the primary person in charge and each secondary person in charge is determined, and the secondary person in charge whose degree of closeness is greater than a preset value is designated as the second person in charge.

[0015] Obtain the family situation of the primary person in charge and the relationship evaluation of the primary person in charge's family members by the second person in charge. Based on the family situation and the relationship evaluation, identify family members who can repay the debts on behalf of the primary person in charge, and designate these family members, together with the second person in charge, as the first person in charge.

[0016] The system obtains the data permissions of the first responsible person on multiple preset external data platforms, retrieves the second platform data corresponding to the data permissions for the first responsible person, analyzes the second platform data to determine whether the first close relative has the credit and economic conditions to repay the loan amount, and obtains a third assessment result.

[0017] In some specific embodiments, the loan project or loan requester is analyzed based on the supporting documents and preset government data to determine whether it involves policy support.

[0018] If policy support is involved, the authenticity assessment result of the loan project is deemed qualified, and the reasonableness assessment of the loan amount is conducted based on the loan amount of external loan projects with the same policy support.

[0019] In some specific embodiments, assessing the reasonableness of the loan amount specifically includes:

[0020] Based on the project's return on investment, analyze the profitability and risk profile of the loan project;

[0021] Obtain external loan projects under the same policy support, and screen out at least a number of external loan projects that are the same or similar to the loan requester in terms of the fixed assets, the first assessment result, the second assessment result and the comprehensive credit, to obtain the first loan project;

[0022] Based on the first loan project, the aforementioned profitability, and the aforementioned risk assessment, a recommended loan amount is determined for the loan project.

[0023] If the loan amount exceeds a preset percentage of the recommended loan amount, the loan amount is deemed not to have passed the reasonableness assessment.

[0024] In some specific embodiments, the external data platform includes e-commerce platforms, telecommunications operators, and social media platforms;

[0025] The consumption frequency and amount of the loan applicant are extracted from the data of the e-commerce platform. If the consumption frequency is lower than the preset frequency and the consumption amount is lower than the preset amount, it is determined that the data of the e-commerce platform is not valuable for evaluating the creditworthiness of the principal or first person in charge.

[0026] The number of times the loan applicant has defaulted is extracted from the data provided by the telecommunications operator. If the number of defaults is lower than the preset number of defaults, it is determined that the data of the telecommunications operator is not valuable for evaluating the creditworthiness of the principal or first person in charge.

[0027] The login frequency and online time interval of the loan applicant are parsed from the data provided by the social platform. If the login frequency is lower than the preset frequency, or the online time interval is concentrated in the preset time period, it is determined that the data of the social platform does not have the value to participate in the evaluation of the credit status of the principal or first person in charge.

[0028] In some specific embodiments, when the personal credit reports of all the principal responsible persons and a preset proportion of the secondary responsible persons simultaneously meet the preset credit conditions, the first assessment result is deemed qualified.

[0029] If the first assessment result is unqualified, then the overall credit assessment result is unqualified;

[0030] If either the comprehensive credit assessment or the technical assessment result is unqualified, then the authenticity assessment result of the loan project is unqualified.

[0031] Part Two, this application proposes a loan assessment system for technology companies, including:

[0032] The input unit is used to obtain basic information about the loan applicant, the loan amount, and the loan project.

[0033] The information acquisition unit is used to acquire the management team information and R&D team information of the loan applicant after confirming that the loan applicant is a technology-based enterprise based on the basic information; and to determine the principal person in charge, the deputy person in charge, and the member relationships between the principal person in charge and each deputy person in charge based on the management team information.

[0034] The first assessment unit is used to obtain the personal credit reports of the principal and the sub-principals, and to preliminarily assess the credit status of the principal and the sub-principals based on the personal credit reports to obtain the first assessment result;

[0035] The comprehensive assessment unit is used to obtain the data permissions of the principal responsible person on multiple preset external data platforms, and retrieve the first platform data about the principal responsible person corresponding to the data permissions, and determine whether the first platform data has value in evaluating the creditworthiness of the principal responsible person; if so, the principal responsible person's creditworthiness on the external data platform is evaluated based on the first platform data to obtain a second assessment result; and a comprehensive credit assessment of the loan applicant is conducted by combining the first assessment result and the second assessment result.

[0036] The technology assessment unit is used to obtain the patent data of the loan applicant, conduct a technology assessment on the R&D team information and the patent data, and evaluate the project return of the loan project through a preset assessment model.

[0037] The output unit is used to assess the authenticity of the loan project based on the comprehensive credit assessment results, technical assessment results, and pre-submitted supporting documents; to assess the reasonableness of the loan amount based on the project's return and fixed assets; and to obtain the loan assessment opinion from the loan requester based on the comprehensive credit assessment results, the authenticity assessment results of the loan project, and the reasonableness assessment results of the loan amount.

[0038] Part Three: This application discloses a computer device, said computer device comprising:

[0039] One or more processors;

[0040] Memory, used to store one or more programs;

[0041] When the one or more programs are executed by the one or more processors, the one or more processors implement a loan assessment method for technology companies as described in any of the first parts.

[0042] Part Four, this application proposes a computer program product including executable instructions for implementing, when executed by a processor, a loan assessment method for a technology company as described in any of Part One.

[0043] Beneficial Effects: This application proposes a loan assessment method, system, and application for technology-based enterprises. It comprehensively assesses the loan applicant's creditworthiness, technological capabilities, and project returns by integrating multi-dimensional information such as basic information, management team information, R&D team information, personal credit reports, external data platform information, and patent data. Preliminary assessment of the personal credit reports of the principal and deputy principals, combined with further assessment using data from external data platforms, improves the accuracy of the assessment. Rigorous credit assessment ensures the good credit standing of key personnel, reducing loan risks associated with poor credit. Technical assessment of the enterprise's technological innovation capabilities and project market prospects ensures project feasibility and market potential, reducing technological risks. A reasonable assessment of the loan amount ensures that the loan amount matches project needs, avoiding risks arising from excessively high or low loan amounts. Multi-dimensional data support enhances the transparency and credibility of the assessment results.

[0044] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a flowchart illustrating the loan assessment methodology for this application;

[0047] Figure 2 This is a schematic diagram illustrating the loan assessment method used in this application.

[0048] Figure 3 This is a schematic diagram of the loan assessment system module in this application.

[0049] Reference numerals: 1-Input unit; 2-Information acquisition unit; 3-First evaluation unit; 4-Comprehensive evaluation unit; 5-Technical evaluation unit; 6-Output unit. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0051] This application proposes a loan assessment method for technology-based enterprises, which comprehensively evaluates loan applications by integrating multi-dimensional information such as the loan applicant's basic information, management team information, R&D team information, personal credit reports, external data platform information, and patent data. This method not only better reflects the actual value and development potential of technology-based enterprises but also effectively reduces the loan risk for financial institutions, thus providing more accurate and effective financial support for small and micro technology-based enterprises. A flowchart of the loan assessment method is attached. Figure 1 As shown in the attached diagram, the principle is as follows: Figure 2 As shown, the specific solution is as follows:

[0052] A loan assessment method for technology companies includes the following steps:

[0053] 101. Obtain basic information about the loan applicant, the loan amount, and the loan project;

[0054] 102. After confirming that the loan applicant is a technology-based enterprise based on basic information, obtain its management team information and R&D team information; based on the management team information, determine the principal and deputy principals of the loan applicant, as well as the member relationships between the principal and deputy principals;

[0055] 103. Obtain the personal credit reports of the principal and deputy principals, and make a preliminary assessment of their creditworthiness based on the personal credit reports to obtain the first assessment result;

[0056] 104. Obtain the data permissions of the principal responsible person on multiple preset external data platforms, and retrieve the first platform data of the principal responsible person corresponding to the data permissions, and determine whether the first platform data has value in evaluating the creditworthiness of the principal responsible person; if so, evaluate the creditworthiness of the principal responsible person on the external data platform based on the first platform data to obtain the second evaluation result; combine the first evaluation result and the second evaluation result to conduct a comprehensive credit assessment of the loan applicant.

[0057] 105. Obtain the patent data of the loan applicant, conduct a technical evaluation based on the R&D team information and patent data, and evaluate the project return of the loan project through a preset evaluation model;

[0058] 106. Based on the comprehensive credit assessment results, technical assessment results, and pre-submitted supporting documents, conduct an authenticity assessment of the loan project; based on the project's return on investment and fixed assets, conduct a reasonable assessment of the loan amount; and obtain the loan assessment opinion from the loan requester based on the comprehensive credit assessment results, the authenticity assessment results of the loan project, and the reasonableness assessment results of the loan amount.

[0059] This loan assessment scheme is applicable to micro and small enterprises, especially early-stage technology startups. Micro and small technology enterprises are typically small in scale, and their financial data may be insufficient, making traditional loan assessment methods difficult to accurately reflect their actual value and development potential. This application, by comprehensively considering information from multiple dimensions such as the enterprise's management team, R&D team, personal credit reports, external data platform information, and patent data, provides a more comprehensive understanding of the enterprise's actual situation. Information from different dimensions can complement and verify each other, improving the accuracy of the assessment. For example, the management team's experience and technical background can reflect the enterprise's management and innovation capabilities, while personal credit reports and external data platform information can reflect the creditworthiness of key personnel. For technology-based enterprises, especially startups, technological innovation capability is their core competitiveness. By obtaining patent data and conducting technology assessments, financial institutions can better understand the enterprise's technological strength and market prospects, thereby more accurately assessing the feasibility and return potential of projects. Pre-set assessment models can help financial institutions scientifically predict project returns, providing a basis for loan decisions. This helps identify enterprises with high growth potential and reduces loan risk.

[0060] Step 101 is the first step in the loan assessment process, primarily aimed at obtaining basic information about the loan applicant, the loan amount, and the loan project. This step provides foundational data for subsequent detailed assessments. Through this information, financial institutions can gain a preliminary understanding of the company's basic situation, funding needs, and loan purpose, thus laying the groundwork for further credit and technical assessments. Basic information refers to the fundamental situation of the loan applicant (i.e., the company), including but not limited to the company's registration information, operating status, and financial condition. This information is collected through various channels, such as application materials provided by the company, public database searches, and reports from third-party institutions. The loan amount refers to the specific amount of the loan applied for by the company, usually expressed in currency. The loan amount is determined through the loan application form or other relevant documents submitted by the company. The loan project refers to the specific purpose or project for which the company is applying for the loan, which could be technology research and development, market expansion, equipment purchase, etc. The specific content of the loan project is determined through the loan application form, project plan, or other relevant documents submitted by the company.

[0061] In step 102, the management team information needs to include the personal information, positions, responsibilities, educational backgrounds, and work experience of the company's key management personnel (such as CEO, CFO, COO, etc.). The R&D team information needs to include the personal information, positions, responsibilities, educational backgrounds, and professional skills of the key members of the company's R&D department. Basic information on management team members and technical team members should be collected through application materials provided by the company, publicly available information, and reports from third-party organizations. The authenticity and accuracy of the information need to be verified, and further verification can be conducted through telephone interviews, face-to-face meetings, etc., if necessary, to understand the professional background and technical capabilities of the R&D team. Understanding the professional background and technical capabilities of the R&D team helps to verify the feasibility and market prospects of the loan project.

[0062] In this application, the principal responsible person is typically the legal representative or actual controller of the enterprise, bearing direct responsibility for the enterprise's debts. The secondary responsible person refers to other senior managers holding important positions in the enterprise's management, such as the CTO and CFO. They bear significant responsibility for the enterprise's operation and development, but are not necessarily the primary debt bearers. By identifying the principal responsible person, financial institutions can clearly identify who the primary debt bearers are, thereby better assessing loan risk. Membership relationships primarily refer to the close relationship between the principal and secondary responsible persons, including but not limited to kinship, long-term partnerships, and joint entrepreneurial experiences, allowing financial institutions to gain a deeper understanding of key personnel within the enterprise and their relationships. Through the assessment of membership relationships, financial institutions can identify which secondary responsible persons might be willing or able to assume the principal's debts when necessary, thereby reducing loan risk.

[0063] Step 103 is a crucial step in the loan assessment process. Its main purpose is to obtain the personal credit reports of the principal and subordinates, and to conduct a preliminary assessment of their creditworthiness based on these reports. Personal credit reports are documents provided by credit reporting agencies and contain information such as an individual's credit score, credit history, repayment history, debt situation, and public records. Appropriate credit reporting agencies can be selected in advance as needed. During the loan application process, written authorization from the principal and subordinates is obtained, allowing the financial institution to access their personal credit reports. The personal credit reports of the principal and subordinates are retrieved through the credit reporting agency's system or platform. Through these reports, the financial institution can gain a comprehensive understanding of the principal and subordinates' credit history and current credit status. Discovering any negative credit records or high debt situations helps identify potential credit risks.

[0064] In some embodiments, credit conditions include: if none of the following conditions exist in the personal credit reports of the principal or a predetermined proportion of the subordinates.

[0065] 1. Credit score is too low: below the minimum standard set by financial institutions.

[0066] 2. Frequent or serious overdue payment records: multiple overdue payments or long-term overdue payments.

[0067] 3. Excessive debt level: The total debt is too high as a percentage of income, which affects the ability to repay.

[0068] 4. Negative public records: Possesses significant negative records such as bankruptcy records and court judgments.

[0069] 5. Significant negative information: such as fraudulent activities, malicious debt evasion, etc.

[0070] If the credit conditions are not met, the first assessment result can be deemed unqualified.

[0071] Through these detailed assessments, financial institutions can more accurately determine a company's credit risk, thereby making more reasonable lending decisions.

[0072] The main purpose of step 104 is to further assess the creditworthiness of the principal responsible person by obtaining their data access permissions on multiple pre-set external data platforms and retrieving relevant data. This step helps financial institutions gain a more comprehensive understanding of the principal responsible person's credit status, thereby making more accurate loan decisions.

[0073] Financial institutions pre-configure third-party data platforms, such as social media, industry forums, professional databases, and publicly available government data. The principal responsible for the data needs to authorize the financial institution to access it on these platforms. Identify the external data platforms to be queried, such as LinkedIn, Weibo, Zhihu, GitHub, and Tianyancha. Obtain written authorization from the principal responsible for the data on these platforms. Retrieve relevant data from these platforms via API interfaces or other methods. Data obtained from external data platforms can provide more information dimensions, helping financial institutions to gain a more comprehensive understanding of the principal responsible for the creditworthiness. Furthermore, data from external data platforms can be cross-verified with information in personal credit reports, improving the authenticity of the information.

[0074] In practical applications, the retrieved data needs to be cleaned and organized to remove invalid or duplicate data. Data cleaning and classification ensure that the data used for evaluation is high-quality and relevant. Data can be categorized into different types, such as professional experience, social activities, technical contributions, and industry reputation. Based on the completeness and relevance of the data, it is determined whether the data has value for credit assessment. Value assessment ensures that only valuable data is used for credit evaluation, improving the accuracy of the assessment. For example, Zhang San's professional experience data is obtained from LinkedIn, his social activity data from Weibo and Zhihu, and his technical contribution data from GitHub. Professional experience and social activity data have high value and can be used for credit assessment; technical contribution data also has some reference value.

[0075] In this application, the second assessment result is an evaluation of the principal's creditworthiness based on data from an external data platform. A comprehensive assessment of the principal's creditworthiness is conducted through multi-dimensional data analysis. Combining information from the external data platform improves the accuracy and reliability of the credit assessment. For example, analyzing professional experience: Zhang San has extensive work experience at several well-known technology companies and has significant industry influence. Analyzing social activities: Zhang San is active on Weibo and Zhihu, has published numerous technical articles, and has a certain industry reputation. Analyzing technical contributions: Zhang San has multiple open-source projects on GitHub, with high contribution levels, demonstrating strong technical capabilities.

[0076] A comprehensive credit assessment combines the initial assessment from an individual's credit report with a secondary assessment from an external data platform to conduct a final credit evaluation of the loan applicant. Based on the financial institution's assessment standards, appropriate weights are assigned to the initial and secondary assessment results. A comprehensive score is then calculated to arrive at the final credit assessment result. • Assuming the initial assessment result accounts for 60% of the weight and the secondary assessment result accounts for 40% of the weight.

[0077] Overall rating:

[0078] First assessment result: 850 points (out of 900 points), weight 60%, score 510 points.

[0079] Second assessment result: 800 points (out of 900), weight 40%, score 320 points.

[0080] Overall score: 510 + 320 = 830 points (out of 900).

[0081] • Opinion formed: The overall credit assessment result is "good credit".

[0082] Step 104 involves obtaining data access permissions for the principal responsible person on multiple pre-set external data platforms and retrieving relevant data to further assess their creditworthiness. Data obtained through external data platforms provides more information dimensions, helping financial institutions gain a more comprehensive understanding of the principal responsible person's credit status. The data from external data platforms can be cross-verified with information in personal credit reports, improving the authenticity of the information. Through multi-dimensional data analysis, a comprehensive assessment of the principal responsible person's creditworthiness is conducted. Based on this multi-dimensional data support, financial institutions can make more accurate loan decisions.

[0083] In some embodiments, external data platforms include e-commerce platforms, telecommunications operators, and social media platforms; data from these platforms can provide additional dimensions of information, helping financial institutions to gain a more comprehensive understanding of the creditworthiness of the principal or first-in-command.

[0084] The data from e-commerce platforms is analyzed to determine the loan applicant's spending frequency and amount. If the spending frequency and amount are both below a preset threshold, the data is deemed unsuitable for evaluating the creditworthiness of the primary responsible person or principal. Spending frequency refers to the number of times the loan applicant makes purchases on the e-commerce platform. Written authorization from the primary responsible person is obtained, allowing the financial institution access to their data on the e-commerce platform. The primary responsible person's spending records are retrieved from the e-commerce platform via API or other means. Spending amount refers to the total amount spent by the loan applicant on the e-commerce platform. For example, if the spending frequency is below a preset threshold (e.g., less than 5 times per month), the data is deemed unsuitable for evaluating creditworthiness. If the spending amount is below a preset threshold (e.g., less than 1000 yuan per month), the data is deemed unsuitable for evaluating creditworthiness. For example, Zhang San's spending records are retrieved from e-commerce platforms such as Taobao and JD.com. It is found that Zhang San averaged 3 purchases per month over the past year, with each purchase amounting to approximately 800 yuan. Because the frequency of consumption is lower than the preset frequency (less than 5 times per month) and the amount of consumption is lower than the preset amount (less than 1,000 yuan per month), the data from the e-commerce platform is deemed not valuable for evaluating creditworthiness.

[0085] Frequent spending (e.g., more than 5 times per month) indicates stable spending habits, potentially reflecting income stability and purchasing power. High spending amounts (e.g., over 1000 yuan per month) suggest financial strength and the ability to afford higher consumption levels. Frequent spending can reflect the spending habits and financial stability of the primary or main person in charge. For example, spending more than 8 times per month indicates stable shopping needs and potentially a stable source of income. High spending amounts reflect the financial strength of the primary or main person in charge. For example, monthly spending exceeding 1000 yuan suggests sufficient financial capacity for larger purchases, which may be related to repayment ability.

[0086] The process involves analyzing data from telecommunications operators to determine the number of times a loan applicant has defaulted on payments. If the number of defaults is lower than a preset threshold, the data is deemed unsuitable for assessing the creditworthiness of the principal or primary responsible person. Written authorization from the principal is obtained, allowing the financial institution access to their data with the telecommunications operator. The principal's default records are retrieved from the telecommunications operator via API or other means. The number of defaults is then analyzed. If the number of defaults is lower than a preset threshold (e.g., less than 3 in the past year), the data is deemed unsuitable for assessing creditworthiness. A lower number of defaults (e.g., less than 3 in the past year) indicates good payment habits, potentially reflecting strong financial management and credit awareness, and is related to repayment ability and creditworthiness. For example, retrieving Zhang San's (the principal's) default records from a telecommunications operator reveals only one default in the past year. Because the number of defaults is lower than the preset threshold (less than 3 in the past year), the telecommunications operator's data is deemed unsuitable for assessing creditworthiness. However, if the number of overdue payments is 0 or very low (such as 1 time), the data can be considered valuable for evaluating creditworthiness.

[0087] Data from social media platforms is analyzed to determine the login frequency and online time range of loan applicants. If the login frequency is lower than a preset frequency, or the online time range is concentrated in a preset period, the data from the social media platform is deemed unworthy of evaluating the creditworthiness of the principal or primary responsible person. A high login frequency (e.g., more than 4 times per week) indicates stable social activities, potentially reflecting their social relationships and lifestyle. Normal online time ranges (e.g., daytime working hours) suggest a regular lifestyle, potentially reflecting job stability and routine. Frequent social activities can reflect the social relationships and lifestyle of the primary or primary responsible person. For example, logging in more than 4 times per week indicates a stable social network and potentially good interpersonal relationships and social support. Being online during normal time periods (e.g., daytime working hours) reflects the job stability and routine of the primary or primary responsible person. For example, online time concentrated between 9 am and 6 pm suggests fixed working hours, which may be related to job stability and repayment ability. For example, the login records and online time of Zhang San (the primary responsible person) can be retrieved from social media platforms such as Weibo. It was found that Zhang San logged in an average of 4 times per week over the past month, mainly online from 9:00 AM to 6:00 PM. Since the login frequency is higher than the preset frequency (more than 3 times per week) and the online time is concentrated in the normal hours (9:00 AM to 6:00 PM), the data from the social media platform is deemed valuable for evaluating creditworthiness.

[0088] High frequency and amount of spending data from e-commerce platforms can reflect the economic strength and spending habits of the primary or principal responsible person. The number of overdue payments recorded by telecommunications operators can reflect the financial discipline and credit awareness of the primary or principal responsible person. High login frequency and normal online timeframes on social media platforms can reflect the social relationships and lifestyle patterns of the primary or principal responsible person. Through this data, financial institutions can obtain more comprehensive information, thereby making more accurate and reliable credit assessment decisions. This multi-dimensional data analysis helps financial institutions gain a more comprehensive understanding of the creditworthiness of primary and principal responsible persons, thus better assessing loan risks.

[0089] The success of micro and small enterprises (MSEs) often depends on the capabilities and experience of their core management team. Evaluating the personal credit reports of management team members and information from external data platforms provides a better understanding of their creditworthiness and professional background, thereby assessing the overall management level and credit risk of the enterprise. In MSEs, the founders and core management personnel have a decisive impact on the company's development. Their credit history and industry reputation directly affect the company's creditworthiness and repayment ability. Detailed credit assessments can effectively identify potential credit risks and reduce the incidence of non-performing loans. This is particularly important for financial institutions, as MSEs are often considered high-risk. Technical assessments ensure the technical feasibility and market prospects of loan projects, reducing the risk of loan defaults due to technical risks. Assessments of the authenticity and reasonableness of projects ensure that loan funds are used for reasonable and profitable projects, reducing ineffective investments.

[0090] By comprehensively assessing creditworthiness, technical capabilities, and pre-submitted supporting documentation, the feasibility and authenticity of loan projects can be ensured. This helps prevent fraudulent applications and ineffective investments, protecting the interests of financial institutions. A reasonable assessment of loan amounts based on project returns and fixed assets ensures funds are used where they are most needed, avoiding resource waste. This helps optimize resource allocation and improve the efficiency of fund utilization.

[0091] In some embodiments, if not available, a primary responsible person who can repay the debt on behalf of the primary responsible person is determined based on membership relationships and the primary responsible person's family relationships; a third assessment result is obtained by evaluating the creditworthiness of each primary responsible person on multiple external data platforms; and a comprehensive credit assessment of the loan applicant is conducted by combining the first and third assessment results. If data obtained from e-commerce platforms, telecommunications operators, and social media platforms is insufficient to evaluate the credit value of the primary responsible person, financial institutions can further determine a primary responsible person who can repay the debt on behalf of the primary responsible person based on membership relationships and the primary responsible person's family relationships, and conduct an assessment using the external data of these primary responsible persons. Membership relationships: refer to the close relationship between the primary responsible person and the secondary responsible persons, such as kinship, long-term partnership, etc. Family relationships: refer to the relationship between the primary responsible person and their family members, such as spouse, children, parents, etc. Secondary responsible persons with close relationships to the primary responsible person can be identified based on the company's organizational chart and management team information. Family members, especially those with high economic capacity and credit history, are identified based on the primary responsible person's personal information. Membership relationships and family relationships are comprehensively considered to determine who may be willing and able to repay the debt on behalf of the primary responsible person.

[0092] For example:

[0093] Member relationships: Zhang San (the main person in charge) and Li Si (the CTO) are brothers who co-founded the company.

[0094] Family Relationships: Zhang San's wife, Wang Wu, is a successful entrepreneur with high economic capabilities and a good credit record.

[0095] Identify the alternative primary responsible persons: Based on the above relationships, Li Si and Wang Wu are identified as alternative primary responsible persons.

[0096] The process involves obtaining written authorization from each primary responsible person to allow financial institutions access to their data on external data platforms. Relevant data for each primary responsible person is then parsed. Based on pre-defined criteria, the value of this data in assessing creditworthiness is determined. If the data is valuable, the creditworthiness of each primary responsible person is evaluated based on this data, resulting in a third assessment. Weights are assigned to the first and third assessment results according to the financial institution's assessment criteria. A comprehensive score is calculated to arrive at the final credit assessment result. Through these steps, financial institutions can identify alternative primary responsible persons based on member and family relationships, and conduct assessments using the external data of these primary responsible persons, even when the data from external data platforms is insufficient to evaluate the creditworthiness of the primary responsible person. Data obtained from multiple external data platforms provides more information dimensions, helping financial institutions gain a more comprehensive understanding of the creditworthiness of primary responsible persons. Data from external data platforms can be cross-verified with information in personal credit reports, improving the authenticity of the information. Through this multi-dimensional data analysis, financial institutions can more comprehensively and accurately assess the creditworthiness and project feasibility of micro and small technology enterprises, thereby providing more precise and effective financial support.

[0097] For example:

[0098] Li Si (CTO):

[0099] E-commerce platform: 8 purchases per month, with each purchase amounting to approximately 1200 yuan.

[0100] Telecommunications operators: No outstanding payments in the past year.

[0101] Social media: Logs in 4 times a week, mainly online from 9 am to 6 pm.

[0102] Value assessment: All data has value in evaluating creditworthiness.

[0103] Creditworthiness assessment: After comprehensive assessment, Li Si's creditworthiness is good, and the third assessment result is "good credit".

[0104] Wang Wu (Zhang San's wife):

[0105] E-commerce platform: 10 purchases per month, with each purchase amounting to approximately 1,500 yuan.

[0106] Telecommunications operators: No outstanding payments in the past year.

[0107] Social media: Logs in 5 times a week, mainly online from 9 am to 6 pm.

[0108] Value assessment: All data has value in evaluating creditworthiness.

[0109] Credit assessment: After comprehensive assessment, Wang Wu's credit situation is very good, and the third assessment result is "excellent credit".

[0110] In some embodiments, the process of obtaining the third assessment result specifically includes: classifying the closeness between the principal and each deputy principal according to the member relationship, and designating the deputy principals whose closeness is greater than a preset value as the second principals; obtaining the principal's family situation and the second principals' evaluation of the principal's family members' relationships, assessing family members who can repay the debt on behalf of the principal based on the family situation and relationship evaluation, and designating such family members, together with the second principals, as the first principals; obtaining the first principal's data permissions on multiple preset external data platforms, and retrieving the second platform data corresponding to the data permissions for the first principal, analyzing the second platform data to determine whether the first close relative has the credit and economic conditions to repay the loan amount, and obtaining the third assessment result.

[0111] Based on the company's organizational chart and management team information, identify the relationship types between the primary manager and each subordinate manager (e.g., kinship, partnership, etc.). Based on the relationship type and other relevant factors (e.g., length of service together, number of collaborative projects, etc.), assess the closeness of the relationship between the primary manager and each subordinate manager. Subordinate managers whose closeness exceeds a preset value are designated as second managers. Example:

[0112] Membership Relationship:

[0113] Zhang San (the person in charge) and Li Si (the CTO) are brothers who co-founded the company and have worked together for many years.

[0114] Zhang San (the person in charge) and Wang Wu (the CFO) are longtime friends and have worked together at the same company for many years.

[0115] Assess the degree of closeness:

[0116] The degree of closeness between Zhang San and Li Si is 90 points (out of 100).

[0117] The degree of closeness between Zhang San and Wang Wu is 80 points (out of 100).

[0118] Delineation of closeness: Assuming a preset value of 85 points, then Li Si is classified as the second person in charge.

[0119] By utilizing the personal information of the primary person in charge and materials provided by family members, basic information, professional backgrounds, and economic status of family members are obtained. Through interviews or questionnaires, the second person in charge's evaluation of the relationship with the primary person in charge's family members is obtained. Example:

[0120] Family situation:

[0121] Zhang San's wife, Wang Wu, is a successful entrepreneur with high economic capabilities and a good credit record.

[0122] Zhang San's parents: Retirees with stable pension income.

[0123] Relationship evaluation:

[0124] Li Si gives Wang Wu a 95 out of 100 rating, believing that she is very willing and able to repay Zhang San's debts.

[0125] Li Si gives Zhang San's parents a score of 70 out of 100, believing that although they are willing, their financial resources are limited.

[0126] Based on an evaluation of family members' economic situation, professional background, and relationship with the secondary debtor, assess which family members possess the ability and willingness to repay the debt. Select the family member with the means to repay the debt as the primary debtor. For example, Wang Wu has high economic capacity and a good credit record, and Li Si provides strong support for her; therefore, she possesses the ability and willingness to repay the debt. Although Zhang San's parents have stable retirement income, their economic capacity is limited, and Li Si provides relatively little support; therefore, they are not selected as the primary debtor. Wang Wu is identified as the primary debtor.

[0127] Financial institutions pre-configure third-party data platforms, such as e-commerce platforms, telecommunications operators, and social media platforms. Written authorization from the primary responsible person is obtained, allowing the financial institution access to their data on these platforms. Credit conditions are analyzed as follows: Frequency and amount of spending: assessing the primary responsible person's spending habits and financial strength. Number of overdue payments: assessing the primary responsible person's financial discipline and credit awareness. Login frequency and online time intervals: assessing the primary responsible person's lifestyle and occupational stability. Financial conditions are analyzed as follows: Income level: assessing the primary responsible person's income level through spending amounts and occupational background. Debt situation: obtaining information about the primary responsible person's debts through other channels to assess their repayment ability. Comprehensive assessment: combining credit conditions and financial conditions to form a third-party assessment result.

[0128] Step 105 primarily involves assessing the loan applicant's technological capabilities and the expected returns of the loan project. This step is particularly important for small and micro-sized technology enterprises, as their core competitiveness often lies in their technological innovation capabilities and the market potential of their projects. Patent data of the loan applicant is obtained through the State Intellectual Property Office or other patent databases. The obtained patent data is then categorized and organized for subsequent analysis. Patent data reveals the enterprise's technological innovation capabilities and R&D strength. Patent data serves as proof of the enterprise's technological advantages and helps assess its competitiveness in the industry. Based on R&D team information and patent data, the enterprise's technological innovation capabilities and technological advantages are assessed. In practical applications, this includes assessing the educational background, professional skills, and work experience of R&D team members; evaluating the organizational structure and division of labor within the R&D team; assessing the technological content and innovativeness of patents; and evaluating the enterprise's patent portfolio across different technological fields. Combining R&D team information and patent data forms the technical assessment results. Through multi-dimensional data analysis, a comprehensive assessment of the enterprise's technological innovation capabilities and technological advantages is conducted. Combining R&D team information and patent data improves the accuracy and reliability of the technical assessment.

[0129] Financial institutions use pre-set models, including financial models and market analysis models, to assess project returns. Relevant data about the loan project (such as market research reports and financial forecasts) is input into the pre-set assessment model. The model runs to calculate the project's expected return. It outputs indicators such as expected returns, market prospects, and risks, forming a project return assessment result. By using pre-set assessment models, the expected returns of loan projects can be quantified, providing data support for decision-making. Based on the model's assessment results, financial institutions can make more accurate lending decisions.

[0130] Evaluating the return on loan projects for micro, small and medium-sized technology enterprises using an assessment model requires considering multiple factors, including financial indicators, market potential, and technological advantages. First, the objective of the assessment model must be clearly defined. In this case, the objective is to assess the expected return on loan projects for micro, small and medium-sized technology enterprises, including the project's profitability, market prospects, and risk level. Based on the assessment objective, appropriate assessment indicators should be selected. These indicators can be categorized into financial indicators, market indicators, and technological indicators.

[0131] Financial indicators

[0132] Net Present Value (NPV): The present value of a project's future cash flows.

[0133] Internal rate of return (IRR): The discount rate that makes the net present value of a project zero.

[0134] Payback period: The time required to recover the initial investment.

[0135] Profit margin: The ratio of net profit to sales revenue.

[0136] Cash flow: The cash inflows and outflows of the project in each year.

[0137] Market Indicators

[0138] Market growth rate: The annual growth rate of the target market.

[0139] Market share: A company's share of the target market.

[0140] Competitive analysis: the number of competitors, their market share, strengths, and weaknesses.

[0141] Customer base: Number of existing customers and number of potential customers.

[0142] Market demand: The market demand for a product or service.

[0143] Technical indicators

[0144] Number of patents: The number of patents owned by the company.

[0145] R&D team strength: the background, experience and skills of the R&D team.

[0146] Technological innovation: The innovativeness and technological advancement of the product.

[0147] Technical barriers: Technical protection measures and intellectual property protection status.

[0148] Collect data related to the above indicators. This data can be obtained from enterprise-provided materials, public market data, industry reports, etc. Build an evaluation model using appropriate tools (such as Excel, Python, R, etc.). Integrate the results of the financial, market, and technical models to form the final project return evaluation result. Assign weights to each indicator to reflect its importance in the overall evaluation. For example: financial indicators: 60%; market indicators: 30%; technical indicators: 10%. Standardize the score of each indicator to between 0 and 100 points. Calculate the weighted score for each category based on the weights. Sum the weighted scores of all categories to obtain the project's comprehensive score. Comprehensive score > 80: The project has high return potential, and loan approval is recommended. Comprehensive score 60-80: The project has medium return potential, and further investigation is recommended. Comprehensive score < 60: The project has low return potential, and loan rejection is recommended. By constructing a comprehensive evaluation model, a scientific and systematic evaluation of loan projects for micro and small technology enterprises can be conducted, helping financial institutions to more accurately judge the feasibility and risks of projects, thereby making more reasonable loan decisions.

[0149] Step 106 primarily involves verifying the overall authenticity of the loan project, the reasonableness of the loan amount, and forming a final loan assessment opinion. This step ensures the scientific rigor and reliability of the loan decision.

[0150] The evaluation criteria include: Comprehensive credit assessment results: Credit assessment results obtained by combining personal credit reports and data from external data platforms. Technology assessment results: Technology assessment results obtained based on R&D team information and patent data. Pre-submitted supporting documents: Relevant supporting documents provided by the company, such as business licenses, financial statements, market research reports, etc. The following operations are performed on the above documents and results: Verify supporting documents: Verify the authenticity and completeness of the pre-submitted supporting documents. Cross-validate: Cross-validate the supporting documents with the comprehensive credit assessment results and the technology assessment results. Assess authenticity: Assess the authenticity of the loan project by integrating all data and documents. Calculate the expected return of the project through a pre-set assessment model. Assess the company's fixed assets and determine their collateral value. Assess the reasonableness of the loan amount by combining the project return and fixed assets, ensuring that the loan amount matches the project needs and avoiding risks arising from excessively high or low loan amounts.

[0151] Finally, the results of the comprehensive credit assessment, the authenticity assessment of the loan project, and the reasonableness assessment of the loan amount are combined. Based on the comprehensive assessment results, a loan assessment opinion is formed for the loan applicant. By integrating assessment results from multiple dimensions, the comprehensiveness and scientific nature of loan decisions are ensured. With the support of multi-dimensional data, financial institutions can make more accurate loan decisions.

[0152] For example:

[0153] Overall assessment:

[0154] Overall credit assessment results: The creditworthiness of the principal and deputy principal is good.

[0155] The loan project's authenticity assessment result: The loan project is genuine and reliable.

[0156] Assessment result on the reasonableness of the loan amount: The loan amount is reasonable.

[0157] Based on the above assessment results, it is recommended that the loan application be approved. The loan assessment opinion is "Approved".

[0158] In some embodiments, the system analyzes whether a loan project or loan applicant is involved in policy support based on supporting documents and pre-set government data. If policy support is involved, the authenticity assessment result of the loan project is deemed qualified, and the loan amount is reasonably assessed based on the loan amount of external loan projects with the same policy support. Supporting documents: Relevant supporting documents provided by the enterprise, such as business licenses, financial statements, market research reports, etc. Government data: A pre-set government policy database of financial institutions, containing various policy support information. The process involves: obtaining all relevant supporting documents provided by the loan applicant; accessing the pre-set government policy database to obtain currently valid policy support information; matching the information in the supporting documents with the government data to determine whether the loan project or loan applicant meets the policy support conditions; verifying whether the loan project or loan applicant truly meets the policy support conditions through government data; improving the accuracy of loan project assessment by combining government data; determining the authenticity and reliability of the loan project based on the assessment results of supporting documents and government data; and determining the authenticity assessment result if the loan project or loan applicant meets the policy support conditions. The authenticity assessment result is recorded as a basis for subsequent assessments. Obtain loan amount data from financial institutions' databases or other publicly available sources for external loan projects with similar policy support. Compare the loan amount requested by the loan applicant with the loan amounts of these external loan projects. Based on the comparison results, assess the reasonableness of the loan amount. By comparing the loan amounts of external loan projects, ensure that the loan amount matches the project needs. Avoid risks arising from loan amounts that are too high or too low.

[0159] For example, under Policy A, the average loan amount for other high-tech enterprise R&D projects is 5 million yuan. Under Policy B, the average loan amount for other innovative enterprise projects is 3 million yuan.

[0160] Comparative Analysis: The loan applicant requested a loan amount of 4 million yuan. Compared to projects under Policy A, the loan amount is slightly lower. Compared to projects under Policy B, the loan amount is slightly higher.

[0161] Reasonableness assessment: Taking into account the support levels of Policy A and Policy B, as well as the specific circumstances of the loan applicant, the loan amount of 4 million yuan is considered reasonable.

[0162] Reasonableness assessment result: Reasonable.

[0163] Through the above steps, financial institutions can systematically assess whether loan projects or loan applicants involve policy support, and conduct authenticity and reasonableness assessments accordingly.

[0164] In some embodiments, the reasonableness assessment of the loan amount specifically includes: analyzing the profitability and risk of the loan project based on the project's return; obtaining external loan projects under the same policy support, and screening out at least several external loan projects that are the same or similar to the loan requester in terms of fixed assets, first assessment results, second assessment results, and comprehensive credit, to obtain the first loan project; assessing the recommended loan amount for the loan project based on the first loan project, profitability, and risk; if the loan amount exceeds a preset proportion of the recommended loan amount, the loan amount is deemed to have failed the reasonableness assessment. Project return data is obtained from materials provided by the loan requester (such as financial forecast reports and market research reports). Expected revenue for each year of the project is calculated based on market research and sales forecasts. Net profit for each year of the project is calculated based on cost forecasts and tax policies. Cash inflows and outflows for each year of the project are calculated based on revenue, costs, and investment plans. Factors such as market demand fluctuations and competitive landscape are analyzed. Factors such as the difficulty of technological innovation and technological barriers are assessed. Factors such as team experience and management capabilities are assessed. Expected returns of the loan project are quantified through financial models and market analysis. Risk factors that the loan project may face are identified through risk analysis. Obtain data on external loan projects with similar policy support from financial institution databases or other publicly available sources. Filter external loan projects similar to the loan applicants based on the following criteria:

[0165] Fixed assets: The company's fixed assets, such as real estate and equipment.

[0166] First assessment result: Preliminary assessment results based on personal credit report.

[0167] Second evaluation result: Data evaluation results based on external data platforms.

[0168] Comprehensive credit: A comprehensive credit assessment result combining the results of the first assessment and the second assessment.

[0169] By screening similar projects, a reference basis is provided for assessing the reasonableness of the loan amount. Ensuring high comparability of the comparison objects improves the accuracy of the assessment. Based on the initial screening, projects that are identical or similar to the loan requester in more aspects are further screened. Finally, the first loan project most similar to the loan requester is determined. Through multi-dimensional screening, a high degree of similarity between the first loan project and the loan requester is ensured. Assessment is conducted based on highly similar projects to improve the accuracy of the assessment. A detailed analysis of the first loan project's loan amount, repayment history, returns, and risks is performed. The loan requester's returns and risks are compared with the first loan project. Based on the above analysis, a reasonable loan amount is assessed. A threshold set by the financial institution is used to determine whether the loan amount is reasonable. For example, a preset ratio of 10% is set, comparing the loan amount applied for by the loan requester with the recommended loan amount. If the loan amount exceeds the preset ratio of the recommended loan amount, the loan amount is deemed to have failed the reasonableness assessment.

[0170] In some embodiments, if the personal credit reports of all principals and a predetermined proportion of secondary principals simultaneously meet the predetermined credit conditions, the first assessment result is deemed qualified; if the first assessment result is unqualified, the comprehensive credit assessment result is unqualified; if either the comprehensive credit assessment or the technical assessment result is unqualified, the authenticity assessment result of the loan project is unqualified.

[0171] A rural resident loan assessment system based on multiple data sources is shown in the attached diagram. Figure 3 As shown, the specific solutions include:

[0172] Input unit 1 is used to obtain basic information about the loan applicant, the loan amount, and the loan project;

[0173] Information acquisition unit 2 is used to obtain information about the management team and R&D team of the loan applicant after confirming that the loan applicant is a technology-based enterprise based on basic information; and to determine the principal and deputy principals of the loan applicant, as well as the member relationships between the principal and deputy principals, based on the management team information.

[0174] The first assessment unit 3 is used to obtain the personal credit reports of the principal and deputy principals, and to conduct a preliminary assessment of the credit status of the principal and deputy principals based on the personal credit reports to obtain the first assessment result;

[0175] The comprehensive assessment unit 4 is used to obtain the data permissions of the principal responsible person on multiple preset external data platforms, and retrieve the first platform data of the principal responsible person corresponding to the data permissions, and determine whether the first platform data has value in evaluating the credit status of the principal responsible person; if it does, the principal responsible person's credit status on the external data platform is evaluated based on the first platform data to obtain the second assessment result; the first assessment result and the second assessment result are combined to conduct a comprehensive credit assessment of the loan applicant.

[0176] Technology assessment unit 5 is used to obtain the patent data of the loan applicant, conduct a technology assessment based on the R&D team information and patent data, and evaluate the project return of the loan project through a preset assessment model.

[0177] Output unit 6 is used to assess the authenticity of the loan project based on the comprehensive credit assessment results, technical assessment results, and pre-submitted supporting documents; to assess the reasonableness of the loan amount based on the project's return and fixed assets; and to obtain the loan assessment opinion from the loan requester based on the comprehensive credit assessment results, the loan project's authenticity assessment results, and the loan amount's reasonableness assessment results.

[0178] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a loan assessment method for technology-based enterprises. Applying a loan assessment method for technology-based enterprises to a computer program product facilitates execution.

[0179] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a loan assessment method for a technology-based enterprise as described above.

[0180] The computer storage medium of this application can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. This application applies a loan assessment method for technology-based enterprises to a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the steps of the clothing simulation method provided in this application, which is simple, fast, easy to store, and not easily lost.

[0181] This application proposes a loan assessment method, system, and application for technology-based enterprises. It comprehensively assesses the loan applicant's creditworthiness, technological capabilities, and project return potential by integrating multi-dimensional information, including basic information, management team information, R&D team information, personal credit reports, external data platform information, and patent data. Preliminary assessments of the personal credit reports of the principal and deputy principals, combined with further assessments using data from external data platforms, improve the accuracy of the assessment. Rigorous credit assessments ensure the good credit standing of key personnel, reducing loan risks associated with poor credit. Technical assessments of the enterprise's technological innovation capabilities and project market prospects ensure project feasibility and market potential, reducing technological risks. A reasonable assessment of the loan amount ensures that the loan amount matches project needs, avoiding risks arising from excessively high or low loan amounts. Multi-dimensional data support enhances the transparency and credibility of the assessment results.

[0182] Those skilled in the art will understand that the modules described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using computer-executable program code, allowing them to be stored in a storage system for execution by the computing system. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0183] Note that the above description is merely a preferred embodiment and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the concept of this application, and the scope of this application is determined by the scope of the appended claims.

[0184] The above disclosures are only a few specific implementation scenarios of this application. However, this application is not limited to these. Any variations that can be conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A loan evaluation method for a technology-based enterprise, characterized in that: include: Obtain basic information of the loan requester, loan amount and loan project; After confirming that the loan applicant is a technology-based enterprise based on the basic information, obtain its management team information and R&D team information; determine the principal and deputy principals of the loan applicant and the relationship between the principal and deputy principals based on the management team information; Obtaining personal credit reports of the principal person in charge and each of the sub-persons in charge, and preliminarily evaluating the credit status of the principal person in charge and the sub-persons in charge based on the personal credit reports to obtain a first evaluation result; Obtain the data permissions of the principal person in charge on multiple preset external data platforms, and retrieve the first platform data about the principal person in charge corresponding to the data permissions, and determine whether the first platform data has the value to participate in the evaluation of the principal person in charge's credit status; if so, evaluate the principal person in charge's credit status on the external data platform based on the first platform data to obtain a second evaluation result; and conduct a comprehensive credit evaluation on the loan requester based on the first evaluation result and the second evaluation result; Obtain patent data of the loan requester, conduct technical evaluation on the loan requester based on the R&D team information and the patent data, and evaluate the project return of the loan project through a preset evaluation model; Conduct an authenticity assessment of the loan project based on the comprehensive credit assessment results, technical assessment results and pre-submitted supporting documents, and conduct a rationality assessment of the loan amount based on the project return and fixed assets; obtain the loan assessment opinion of the loan requester based on the comprehensive credit assessment results, the authenticity assessment results of the loan project and the rationality assessment results of the loan amount.

2. The loan assessment method according to claim 1, characterized in that: If not, determine the first person in charge who can repay the debt on behalf of the main person in charge based on the member relationship and the family relationship of the main person in charge; evaluate the credit status of each first person in charge in the external data based on the data of the multiple external data platforms to obtain a third evaluation result; A comprehensive credit assessment is performed on the loan requester based on the first assessment result and the third assessment result.

3. The loan assessment method according to claim 2, characterized in that: The process of obtaining the third evaluation result specifically includes: According to the member relationship, the closeness between the main person in charge and each sub-person in charge is divided, and the sub-person in charge whose closeness is greater than a preset value is regarded as the second person in charge; Obtaining the family situation of the principal person in charge and the relationship evaluation of the second person in charge with respect to the family members of the principal person in charge, evaluating the family member who can repay the debt on behalf of the principal person in charge based on the family situation and the relationship evaluation, and taking the family member together with the second person in charge as the first person in charge; The data permissions of the first person in charge on multiple preset external data platforms are obtained, and the second platform data about the first person in charge corresponding to the data permissions are retrieved, and the second platform data is analyzed to analyze whether the first close relative has the credit conditions and economic conditions to repay the loan amount, so as to obtain a third evaluation result.

4. The loan assessment method according to claim 1, characterized in that: Analyze whether the loan project or the loan requester involves policy support based on the aforementioned certification documents and preset government data; If policy support is involved, the authenticity assessment result of the loan project will be deemed qualified, and the rationality of the loan amount will be assessed based on the loan amount of external loan projects with the same policy support.

5. The loan assessment method according to claim 4, characterized in that: The reasonableness assessment of the loan amount specifically includes: Analyze the income and risk of the loan project based on the project return; Acquire external loan projects supported by the same policy, screen out at least a plurality of external loan projects that are identical or similar to the loan requester in terms of the fixed assets, the first assessment result, the second assessment result, and the comprehensive credit, and obtain a first loan project; Evaluate the recommended loan amount for the loan project based on the first loan project, the income situation and the risk situation; If the loan amount exceeds the preset ratio of the recommended loan amount, it is deemed that the loan amount has not passed the rationality assessment.

6. The loan assessment method according to claim 2, characterized in that: The external data platforms include e-commerce platforms, telecom operators and social platforms; The consumption frequency and consumption amount of the loan requester are parsed from the data of the e-commerce platform. If the consumption frequency is lower than the preset frequency and the consumption amount is lower than the preset amount, it is determined that the data of the e-commerce platform is not valuable for participating in the evaluation of the credit status of the principal or first person in charge; The number of arrears of the loan requester is parsed from the data provided by the telecommunications operator. If the number of arrears is lower than the preset number of arrears, it is determined that the data of the telecommunications operator is not valuable for participating in the evaluation of the credit status of the principal or first person in charge; The login frequency and online time interval of the loan requester are parsed from the data provided by the social platform. If the login frequency is lower than the preset frequency, or the online time interval is concentrated in the preset time period, it is determined that the data of the social platform has no value in participating in the evaluation of the credit status of the main person in charge or the first person in charge.

7. The loan assessment method according to claim 1, characterized in that: When the personal credit reports of all the principals and the sub-principals of the preset proportion meet the preset credit conditions at the same time, the first assessment result is deemed qualified; If the first evaluation result is unqualified, the comprehensive credit evaluation result is unqualified; If any of the comprehensive credit assessment and technical assessment results are unqualified, the authenticity assessment result of the loan project will be unqualified.

8. A loan evaluation system for technology-based enterprises, characterized in that: include: An input unit, used to obtain basic information of the loan requester, loan amount and loan items; An information acquisition unit is used to obtain the management team information and R&D team information of the loan applicant after confirming that the loan applicant is a technology-based enterprise according to the basic information; determine the principal and deputy principals of the loan applicant and the relationship between the principal and deputy principals according to the management team information; A first evaluation unit is used to obtain personal credit reports of the principal person in charge and each of the sub-persons in charge, and preliminarily evaluate the credit status of the principal person in charge and the sub-persons in charge according to the personal credit reports to obtain a first evaluation result; A comprehensive evaluation unit is used to obtain the data authority of the principal person in charge on multiple preset external data platforms, and retrieve the first platform data about the principal person in charge corresponding to the data authority, and determine whether the first platform data has the value to participate in the evaluation of the credit status of the principal person in charge; if so, the credit status of the principal person in charge on the external data platform is evaluated according to the first platform data to obtain a second evaluation result; and a comprehensive credit evaluation is performed on the loan requester based on the first evaluation result and the second evaluation result; A technology evaluation unit, which is used to obtain patent data of the loan requester, conduct a technology evaluation on the loan requester based on the R&D team information and the patent data, and evaluate the project return of the loan project through a preset evaluation model; The output unit is used to conduct an authenticity assessment on the loan project based on the comprehensive credit assessment results, the technical assessment results and the pre-submitted supporting documents, and conduct a rationality assessment on the loan amount based on the project return and fixed assets; and obtain the loan assessment opinion of the loan requester based on the comprehensive credit assessment results, the authenticity assessment results of the loan project and the rationality assessment results of the loan amount.

9. A computer device, characterized in that: The computer device comprises: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a loan assessment method for a technology-based enterprise as described in any one of claims 1-7.

10. A computer program product, characterized in that It includes executable instructions for implementing a loan assessment method for a technology-based enterprise as described in any one of claims 1-7 when executed by a processor.

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