Unfavorable asset value evaluation method and related device

Abstract

The invention discloses a non-performing asset value evaluation method and a related device, and relates to the technical field of non-performing asset disposal, and the method comprises the steps: carrying out the scoring of the money return potential of non-performing assets through a standardized scoring model, obtaining the scores of the non-performing assets, carrying out the classification of the value change trends of the non-performing assets based on the scores of the non-performing assets, and obtaining the value change trends of the non-performing assets. Obtaining a value classification result of the unhealthy assets, wherein the value classification result is used for representing the recovery possibility of the unhealthy assets; and evaluating the value of the unhealthy assets by using the standardized valuation model to obtain the recovery value of the unhealthy assets. According to the method, the recovery possibility and recovery value of the unhealthy assets can be determined, and the value evaluation of the unhealthy assets is accurately completed.

Description

Technical Field

[0001] The present application relates to the technical field of non-performing asset disposal, and in particular to a non-performing asset value assessment method and related devices. Background Art

[0002] Non-performing assets mainly refer to those assets that financial institutions cannot recover the principal and interest on time due to debtor default or other reasons, or assets that credit enterprises cannot effectively dispose of in the field of financial credit, such as overdue loans and situations where the value of collateral is lower than the loan balance. The existence of non-performing assets may bring serious consequences to financial institutions, including a decline in capital adequacy ratio, weakened profitability, and damaged reputation. In serious cases, it may even lead to the risk of bankruptcy of financial institutions. In this context, in order to reduce the economic losses that may be caused by non-performing assets, financial institutions need a method to accurately assess the actual value of non-performing assets. Specifically, it is necessary to develop a technical solution that uses big data analysis and other means to conduct in-depth analysis of non-performing asset data to predict the recovery possibility and recovery value of non-performing assets, aiming to help financial institutions more accurately understand the true actual value of non-performing assets, so as to formulate more effective collection plans, ultimately reduce economic losses, and improve asset recovery efficiency.

[0003] At present, there are two main methods for valuing non-performing assets: (1) Offline pricing. Traditional offline pricing methods usually rely on manual evaluation and market comparison to determine the actual value of non-performing assets. They rely on the experience and expertise of the appraisers, which may lead to subjective judgment bias and inconsistency, especially when facing complex or non-standardized assets; offline pricing methods are often time-consuming and inefficient; due to the lack of systematic and standardized data collection and analysis processes, offline pricing methods may not fully utilize existing market data and historical records, thereby reducing the accuracy and reliability of pricing. (2) Pricing using big data analysis. Traditional big data analysis pricing methods collect and analyze massive amounts of data, use data mining, machine learning and other technical means, and use predictive models to evaluate the recovery value and recovery possibility of non-performing assets, solving the problems of offline pricing methods. Although work efficiency has been improved, there are still some limitations. Big data analysis pricing methods may not fully consider special factors in specific scenarios, which means that in some cases, valuation results may not be accurate enough. Summary of the invention

[0004] The purpose of this application is to provide a method and related device for evaluating the value of non-performing assets, which can accurately complete the value evaluation of non-performing assets.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for evaluating the value of non-performing assets, the method comprising:

[0007] The collection potential of non-performing assets is scored using a standardized scoring model to obtain a score for the non-performing assets; the primary matching field of the standardized scoring model is determined based on the customer type of the customer to whom the non-performing assets belong; if the customer type is a corporate customer and a personal loan mortgage customer, the primary matching field of the standardized scoring model includes: at least one of: debt situation analysis, principal debtor analysis, guarantor analysis, and mortgage analysis; if the customer type is a personal loan credit loan customer, the primary matching field of the standardized scoring model includes: at least one of: debt principal situation analysis and debt quality situation analysis;

[0008] Classifying the value change trend of the non-performing assets based on the scores of the non-performing assets to obtain the value classification results of the non-performing assets; the value classification results are used to characterize the possibility of recovering the non-performing assets;

[0009] The value of non-performing assets is assessed using a standardized valuation model to obtain the recovery value of the non-performing assets.

[0010] Optionally, for corporate customers, the secondary matching fields of the debt situation analysis include: basic information of the debt, debt risk situation, collection litigation situation, other stakeholders and whether there is an intended transferee; among them, the tertiary matching fields of the basic information of the debt include: type of debt, completeness of debt information and agreement on contract dispute clauses; the tertiary matching fields of the debt risk situation include: actual use of financing; the tertiary matching fields of the collection litigation situation include: litigation stage; the tertiary matching fields of other stakeholders include: attitude of the government and court and whether a criminal case is involved; the tertiary matching fields of whether there is an intended transferee include: whether there is an intended transferee;

[0011] The secondary matching fields of the principal debtor analysis include: willingness to repay, repayment ability and equity situation; among them, the tertiary matching fields of willingness to repay include: willingness to repay, whether the actual controller and the legal representative overlap and the actual controller's default limit; the tertiary matching fields of repayment ability include: repayment ability; the tertiary matching fields of equity situation include: whether it is a one-person limited company and whether the shareholder's capital contribution has not been paid in full;

[0012] When the guarantor of the non-performing assets is a legal person, the secondary matching fields of the guarantor analysis include: legal attributes of the guarantee, repayment willingness, repayment ability and equity situation; among them, the tertiary matching fields of the legal attributes of the guarantee include: guarantee type and guarantee effectiveness; the tertiary matching fields of repayment willingness include: repayment willingness, whether the actual controller and the legal representative overlap and the actual controller's default limit; the tertiary matching fields of repayment ability include: repayment ability; the tertiary matching fields of equity situation include: whether it is a one-person limited company and whether the shareholder's capital contribution has not been paid in full;

[0013] When the guarantor of the non-performing assets is a natural person, the secondary matching fields of the guarantor analysis include: legal attributes of the guarantee, repayment willingness and repayment ability; among which, the tertiary matching fields of the legal attributes of the guarantee include: guarantee type; the tertiary matching fields of repayment willingness include: repayment willingness and guarantor identity; the tertiary matching fields of repayment ability include: repayment ability;

[0014] The secondary matching fields of the mortgage analysis include: the nature and status of the mortgaged property and the defects of rights; the tertiary matching fields of the nature and status of the mortgaged property include: the status of the mortgaged property and the seizure situation; the tertiary matching fields of the defects of rights include: the effectiveness of the mortgage and the defects of rights;

[0015] For individual mortgage loan customers, the secondary matching fields of the debt situation analysis include: debt information, debt risk, collection litigation and other stakeholders; among them, the tertiary matching fields of debt information include: debt information integrity and contract dispute clause agreement; the tertiary matching fields of debt risk include: overdue time; the tertiary matching fields of collection litigation include: litigation stage and statute of limitations; the tertiary matching fields of other stakeholders include: whether a criminal case is involved and whether there is an intended transferee;

[0016] The secondary matching fields analyzed by the principal debtor include: willingness to repay and ability to repay; wherein, the tertiary matching fields of willingness to repay include: willingness to repay; the tertiary matching fields of ability to repay include: ability to repay;

[0017] The secondary matching fields analyzed by the guarantor include: willingness to repay and ability to repay; wherein, the tertiary matching fields of willingness to repay include: willingness to repay; the tertiary matching fields of ability to repay include: ability to repay;

[0018] The secondary matching fields of the mortgage analysis include: mortgage property status and rights defects; the tertiary matching fields of the mortgage property status include: house attributes and house age; the tertiary matching fields of rights defects include: registration status, mortgage status, seizure status, occupation, sole residence and rental;

[0019] For individual credit loan customers, the secondary matching fields of the debt principal situation analysis include: principal ranking;

[0020] The secondary matching fields of the debt quality analysis include: overdue time ranking, repayment ratio ranking, age ranking and loan interest rate ranking.

[0021] Optionally, for corporate customers and personal mortgage customers, the standard scoring model is used to score the potential for repayment of non-performing assets to obtain the score of non-performing assets, including:

[0022] Match the non-performing assets with all the third-level matching fields to obtain the target third-level matching fields that match the non-performing assets;

[0023] For each of the target third-level matching fields, the product of the score and the weight of the target third-level matching field is calculated, and the product of the product, the weight of the target second-level matching field to which the target third-level matching field belongs, and the weight of the first-level matching field to which the target second-level matching field belongs is calculated to obtain the score corresponding to the target third-level matching field;

[0024] The scores corresponding to all the target third-level matching fields are added together to obtain the score of the non-performing assets.

[0025] Optionally, for individual credit loan customers, the standard scoring model is used to score the potential for repayment of non-performing assets to obtain the score of non-performing assets, including:

[0026] For each secondary matching field, calculate the product of the score and the weight of the secondary matching field, and calculate the product of the product and the weight of the primary matching field to which the secondary matching field belongs, to obtain the score corresponding to the secondary matching field;

[0027] The scores corresponding to all the secondary matching fields are added together to obtain the score of the non-performing assets.

[0028] Optionally, the value change trend of the non-performing assets is classified based on the scores of the non-performing assets to obtain the value classification results of the non-performing assets, which specifically include:

[0029] Determine whether the score of the non-performing asset is less than a first preset value, and obtain a first determination result;

[0030] If the first judgment result is yes, the value classification result of the non-performing asset is "ice" assets, and the corresponding assets have a high risk of continuous impairment;

[0031] If the first judgment result is no, then judging whether the score of the non-performing asset is less than a second preset value, and obtaining a second judgment result;

[0032] If the second judgment result is yes, the value classification result of the non-performing asset is an "egg" asset, and the corresponding asset has a higher future value;

[0033] If the second judgment result is no, then judging whether the score of the non-performing asset is less than a third preset value, and obtaining a third judgment result;

[0034] If the third judgment result is yes, the value classification result of the non-performing asset is a "jade" asset, and the corresponding asset has a repairable defect;

[0035] If the result of the third judgment is no, the value classification result of the non-performing assets is a "gold" asset, and the corresponding assets have the opportunity to be transferred at a high price.

[0036] Optionally, for corporate customers and personal mortgage customers, the value of non-performing assets is assessed using a standardized valuation model to obtain the recovery value of the non-performing assets, specifically including: using a valuation map to determine the valuation of the collateral of the non-performing assets; determining the realization value of the collateral based on the valuation of the collateral; determining the collateral realization tax based on the collateral realization value; determining the amount of compensation our company can receive based on the collateral realization value, the collateral realization tax and other priorities borne by the collateral; determining the recovery value of the non-performing assets based on the amount of compensation our company can receive, the estimated number of years of discounting and internal asset transfer pricing.

[0037] Optionally, for individual credit loan customers, the value of non-performing assets is assessed using a standardized valuation model to obtain the recovery value of the non-performing assets, specifically including: obtaining the principal recovery rate of the debt through statistical analysis based on the historical transaction data of the individual credit loan business; and determining the recovery value of the non-performing assets based on the principal and the principal recovery rate.

[0038] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for assessing the value of non-performing assets.

[0039] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for assessing the value of non-performing assets.

[0040] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for assessing the value of non-performing assets.

[0041] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0042] The present application provides a method and related device for evaluating the value of non-performing assets, which uses a standardized scoring model to score the recovery potential of non-performing assets to obtain a score for the non-performing assets, and classifies the value change trend of the non-performing assets based on the score of the non-performing assets to obtain a value classification result of the non-performing assets. The value classification result is used to characterize the possibility of recovery of the non-performing assets, and the value of the non-performing assets is evaluated using a standardized valuation model to obtain the recovery value of the non-performing assets, thereby determining the recovery possibility and recovery value of the non-performing assets, and completing the value evaluation of the non-performing assets. This application targets corporate customers and personal mortgage loan customers. The first-level matching fields of the standardized scoring model include: at least one of the analysis of creditor's rights, principal debtor, guarantor and mortgage analysis; for personal credit loan customers, the first-level matching fields of the standardized scoring model include: at least one of the analysis of creditor's rights principal situation and creditor's rights quality situation. That is, when determining the possibility of recovering non-performing assets, multiple factors such as the creditor's rights situation, principal debtor, guarantor, mortgage, creditor's rights principal situation and creditor's rights quality situation are taken into consideration to determine the score of the non-performing assets, and the possibility of recovering the non-performing assets is further determined based on the score of the non-performing assets. The possibility of recovering the non-performing assets can be determined based on multiple factors, which improves the accuracy and thus accurately completes the value assessment of the non-performing assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flowchart of a method for assessing the value of non-performing assets provided in Example 1 of the present application.

[0045] Figure 2 A schematic diagram of the structure of a computer device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] Example 1

[0048] This embodiment provides a method for evaluating the value of non-performing assets. Figure 1As shown, the non-performing asset value assessment method includes:

[0049] S1. Scoring the potential for collecting payments of non-performing assets using a standardized scoring model to obtain a score for the non-performing assets; the primary matching field of the standardized scoring model is determined based on the customer type of the customer to whom the non-performing assets belong; if the customer type is a corporate customer and a personal loan mortgage customer, the primary matching field of the standardized scoring model includes: at least one of: debt situation analysis, principal debtor analysis, guarantor analysis, and mortgage analysis; if the customer type is a personal loan credit customer, the primary matching field of the standardized scoring model includes: at least one of: debt principal situation analysis and debt quality situation analysis.

[0050] S2, classifying the value change trend of the non-performing assets based on the scores of the non-performing assets to obtain the value classification results of the non-performing assets; the value classification results are used to characterize the possibility of recovering the non-performing assets.

[0051] S3, use the standardized valuation model to evaluate the value of non-performing assets and obtain the recovery value of non-performing assets.

[0052] By implementing the above-mentioned steps S1 to S3, this embodiment can determine the recovery possibility and recovery value of the non-performing assets, thereby completing the value assessment of the non-performing assets. When determining the recovery possibility of the non-performing assets, various factors such as the creditor's rights situation, the principal debtor, the guarantor, the mortgage, the creditor's rights principal situation and the creditor's rights quality situation are taken into consideration to determine the score of the non-performing assets, and the recovery possibility of the non-performing assets is further determined based on the score of the non-performing assets. The recovery possibility of the non-performing assets can be determined based on multiple factors, which improves the accuracy, so that the value assessment of the non-performing assets can be accurately completed.

[0053] This embodiment provides a standardized debt valuation model recommended for non-performing asset disposal, and the standardized debt valuation model includes a standardized scoring model and a standardized valuation model.

[0054] The standardized scoring model of this embodiment is intended to comprehensively evaluate the collection potential of debt claims. By formulating a comprehensive strategic framework, the objectivity and accuracy of the scoring are ensured. Different scoring criteria are used for different customer types, including corporate customers, personal mortgage loan customers (i.e., individuals with collateral) and personal credit loan customers (i.e., individuals without collateral).

[0055] For corporate customers and personal mortgage loan customers, the standardized scoring model is developed around core areas such as creditor's rights analysis, principal debtor analysis, guarantor analysis (legal person), guarantor analysis (natural person) and mortgage analysis. Each core area corresponds to a clear data type (i.e., first-level matching field), which constitutes the first-level directory of the standardized scoring model. The total proportion of each first-level matching field is set to 100% to ensure comprehensive coverage and balanced consideration of the scoring. Each data type is subdivided into second-level directories (i.e., data subdivision, the second-level directory includes several second-level matching fields), and in-depth analysis of specific aspects of each core area, also following the principle of setting the total proportion to 100%, to ensure in-depth and thorough analysis. Under the second-level directory, there is a third-level directory (the third-level directory includes several key fields, i.e., the third-level matching field). These key fields are directly related to the key details of the scoring. Their weight distribution is also based on rigorous logic and data analysis, and also follows the principle of setting the total proportion to 100%, which together constitute the cornerstone of the standardized scoring model. For individual credit loan customers, the standardized scoring model revolves around core areas such as debt principal analysis and debt quality analysis. Each core area corresponds to a clear data type (i.e., primary matching field), which constitutes the primary directory of the standardized scoring model. The total proportion of each primary matching field is set to 100% to ensure comprehensive coverage and balanced consideration of the scoring. Each data type is subdivided into secondary directories (i.e., data segmentation, secondary directories include several secondary matching fields), which deeply analyze the specific aspects of each core area, and also follow the principle of setting the total proportion to 100%, to ensure in-depth and thorough analysis, which together constitute the cornerstone of the standardized scoring model.

[0056] In this embodiment, for corporate customers, the first-level matching fields of the standardized scoring model include: at least one of: creditor's rights analysis, principal debtor analysis, guarantor analysis and mortgage analysis.

[0057] The secondary matching fields of the creditor's rights analysis include: basic information of creditor's rights, creditor's rights insurance situation, collection litigation situation, other stakeholders, and whether there is an intended transferee. Among them, the tertiary matching fields of the creditor's rights basic information include: type of creditor's rights, creditor's rights information integrity, and contract dispute clause agreement. The tertiary matching fields of the creditor's rights insurance situation include: actual use of financing. The tertiary matching fields of the collection litigation situation include: litigation stage. The tertiary matching fields of other stakeholders include: attitude of the government and courts, and whether a criminal case is involved. The tertiary matching fields of whether there is an intended transferee include: whether there is an intended transferee.

[0058] The secondary matching fields of the principal debtor analysis include: willingness to repay, repayment ability, and equity situation. Among them, the tertiary matching fields of willingness to repay include: willingness to repay, whether the actual controller and the legal representative overlap, and the actual controller's default limit. The tertiary matching fields of repayment ability include: repayment ability. The tertiary matching fields of equity situation include: whether it is a one-person limited company and whether the shareholder's capital contribution has not been paid.

[0059] When the guarantor of the non-performing assets is a legal person, the secondary matching fields of the guarantor analysis include: the legal attributes of the guarantee, repayment willingness, repayment ability and equity situation. Among them, the tertiary matching fields of the legal attributes of the guarantee include: guarantee type and guarantee effectiveness. The tertiary matching fields of repayment willingness include: repayment willingness, whether the actual controller and the legal representative overlap, and the actual controller's default limit. The tertiary matching fields of repayment ability include: repayment ability. The tertiary matching fields of equity situation include: whether it is a one-person limited company and whether the shareholder's capital contribution has not been paid.

[0060] When the guarantor of the non-performing assets is a natural person, the secondary matching fields of the guarantor analysis include: legal attributes of the guarantee, willingness to repay, and ability to repay. Among them, the tertiary matching fields of the legal attributes of the guarantee include: guarantee type. The tertiary matching fields of willingness to repay include: willingness to repay and identity of the guarantor. The tertiary matching fields of ability to repay include: ability to repay.

[0061] The second-level matching fields of the mortgage analysis include: the nature and status of the mortgaged property and the defects of rights. Among them, the third-level matching fields of the nature and status of the mortgage include: the status of the mortgaged property and the seizure situation. The third-level matching fields of the defects of rights include: the effectiveness of the mortgage and the defects of rights.

[0062] Table 1 Standardized scoring model for corporate customers

[0063]

[0064]

[0065]

[0066]

[0067] As shown in Table 1 above, it is the specific content of the standardized scoring model, including the three-level splitting of data types and score distribution. The first-level types include data such as creditor's rights, principal debtor, guarantor (legal person, natural person), mortgage and pledge, etc., accounting for a total of 100%. The second-level types include data such as basic creditor's rights information, creditor's rights risk situation, collection litigation situation, other stakeholders, whether there is an intended transferee, repayment willingness, repayment ability, equity situation, etc., accounting for a total of 100%. The third-level types include data such as creditor's rights type, creditor's rights information integrity, contract dispute clause agreement, litigation stage, etc., accounting for a total of 100%.

[0068] Based on Table 1 above, this embodiment performs debt situation analysis, constructs a wildcard algorithm recognizable by computer language based on data synchronization, data analysis and rule matching algorithms, applies data synchronization to complete the construction of a standardized scoring model, applies data analysis and rule matching algorithms, and combines the high computing power query and analysis capabilities of search engines to complete the scoring of non-performing assets and obtain the scoring results of the standardized scoring model. Specifically, the computer system stores the various indicator configurations of the standardized scoring model in the database, obtains the due diligence report data elements through the query interface, and fills the due diligence report data elements into the standardized scoring model. Based on the specific elements of the debt, the preset rules and algorithms are used to carefully match the three-level matching fields in the scoring system, and all the three-level matching fields that are consistent with the current debt situation are screened out to ensure the pertinence and relevance of the scoring results. Combined with the score distribution of some constraints input by the user, after matching and screening out the applicable three-level matching fields, a multi-level weighted merging algorithm is used to score and calculate each item and dump it into the database, comprehensively considering the importance of different scoring items and their mutual relationships, and through reasonable weight distribution, ensure that the scoring results can comprehensively and objectively reflect the actual situation of the debt.

[0069] For example, customer A enters the standardized scoring model, which performs standardized analysis on customer A's primary data type - creditor's rights situation. The analysis includes secondary data types - basic information on creditor's rights, creditor's rights risk situation, collection litigation situation, etc. Among them, the basic creditor's rights situation analyzed includes tertiary data types - creditor's rights type, creditor's rights information integrity and contract dispute clause agreement. The standardized scoring model analyzes the above situations according to different constraints, and scores them from 0-100 or 0-50-100. Finally, the scores are added to obtain the score of customer A in creditor's rights situation. It can be understood that creditor's rights situation analysis is only one data type analyzed by the standardized scoring model, and other data types - principal debtor, guarantor (legal person, natural person), mortgage, etc. are also analyzed using the same method.

[0070] The specific scoring process of the standardized scoring model is as follows:

[0071] (1) Establishment of unique identification of scoring items

[0072] The basis of the scoring process is clarified, that is, the first-level directory, the second-level directory, the third-level directory and the constraints are considered as the unique identifier of each scoring item. Through this step, it is ensured that each item in the scoring system can be accurately identified and distinguished, laying a solid foundation for the precise execution of subsequent scoring work.

[0073] (2) Rating item matching and screening

[0074] Based on the specific elements of the claims, the preset rules and algorithms are used to carefully match the items in the scoring system. This process aims to screen out all scoring items that are consistent with the current claims status to ensure the pertinence and relevance of the scoring results. Through this step, scoring items that do not match the actual situation of the claims can be excluded, improving the efficiency and accuracy of scoring.

[0075] (3) Application of multi-level weighted merging algorithm

[0076] After matching and screening out the applicable scoring items, a multi-level weighted combination algorithm is used to score and calculate each item. This algorithm comprehensively considers the importance of different scoring items and their interrelationships, and through reasonable weight allocation, ensures that the scoring results can fully and objectively reflect the actual situation of the debt. This step makes the scoring process more scientific and rigorous.

[0077] (4) Total score summary and output

[0078] The scores of all scoring items are summed up to obtain the final scoring result, which is not only a comprehensive assessment of the overall status of the creditor's rights, but also an important basis for subsequent decision-making and analysis. Through this step, the actual value and risk status of the creditor's rights can be clearly understood, providing strong data support for relevant parties.

[0079] During the scoring process, clear constraints and scoring standards are set for different key fields. Taking the type of debt as an example, if it is a mortgage debt, the score for this item is the full score (as set to 100 points in Table 1). Then, through the carefully designed calculation formula (score * third-level catalog proportion * second-level catalog proportion * first-level catalog proportion), that is, 100*40%*30%*15%=1.8, the score of the debt type is 1.8. Subsequently, the scores of each item are accurately summarized to obtain the final score of the non-performing assets. This calculation process ensures the accuracy and traceability of the scoring results.

[0080] It is worth noting that the scoring mechanism in Table 1 provides a detailed and scientific analysis of the scores of various constraints. For example, mortgage bonds often enjoy a higher repayment probability assessment due to their additional security, while credit bonds may face lower repayment expectations. Such differentiation not only improves the accuracy of the score, but also provides strong support for debt valuation and risk management.

[0081] For personal loan and mortgage loan customers, the first-level matching fields of the standardized scoring model include: at least one of the following: creditor's rights analysis, principal debtor analysis, guarantor analysis and mortgage analysis.

[0082] The secondary matching fields of the creditor's rights analysis include: creditor's rights data, creditor's rights insurance, collection litigation, and other interested parties. Among them, the tertiary matching fields of the creditor's rights data include: creditor's rights data integrity and contract dispute clause agreement. The tertiary matching fields of the creditor's rights insurance include: overdue time. The tertiary matching fields of the collection litigation include: litigation stage and statute of limitations. The tertiary matching fields of other interested parties include: whether a criminal case is involved and whether there is an intended transferee.

[0083] The secondary matching fields of the main debtor analysis include: willingness to repay and ability to repay. Among them, the tertiary matching fields of willingness to repay include: willingness to repay. The tertiary matching fields of ability to repay include: ability to repay.

[0084] The second-level matching fields of the guarantor analysis include: willingness to repay and ability to repay. Among them, the third-level matching fields of willingness to repay include: willingness to repay. The third-level matching fields of ability to repay include: ability to repay.

[0085] The second-level matching fields of mortgage analysis include: mortgage property status and title defects. Among them, the third-level matching fields of mortgage property status include: house attributes and house age. The third-level matching fields of title defects include: registration status, mortgage status, seizure status, occupation, sole residence and rental.

[0086] For individual credit loan customers, the first-level matching fields of the standardized scoring model include: at least one of the analysis of the principal of the debt and the analysis of the quality of the debt. The analysis of the principal of the debt accounts for 60%, and the analysis of the quality of the debt accounts for 40%.

[0087] The secondary matching fields for debt principal analysis include: principal ranking, which calculates the score based on the ranking of the debt principal balance.

[0088] The secondary matching fields for debt quality analysis include: overdue time ranking, repayment ratio ranking, age ranking and loan interest rate ranking, and the score is calculated based on the ranking of each feature.

[0089] For corporate customers and personal mortgage customers, in S1, the standardized scoring model is used to score the recovery potential of non-performing assets to obtain the score of non-performing assets, which specifically includes:

[0090] (1) Match the non-performing assets with all the third-level matching fields to obtain the target third-level matching fields that match the non-performing assets.

[0091] (2) For each target third-level matching field, the product of the score and the weight of the target third-level matching field is calculated, and the product of the score and the weight of the target second-level matching field to which the target third-level matching field belongs and the weight of the first-level matching field to which the target second-level matching field belongs is calculated to obtain the score corresponding to the target third-level matching field.

[0092] (3) Add up the scores corresponding to all target third-level matching fields to obtain the score of the non-performing assets.

[0093] For individual credit loan customers, in S1, the standardized scoring model is used to score the potential for repayment of non-performing assets to obtain the score of non-performing assets, which specifically includes:

[0094] (1) For each secondary matching field, the product of the score and the weight of the secondary matching field is calculated, and the product of the score and the weight of the primary matching field to which the secondary matching field belongs is calculated to obtain the score corresponding to the secondary matching field.

[0095] (2) Add up the scores corresponding to all secondary matching fields to obtain the score of the non-performing assets.

[0096] This embodiment also innovatively introduces a dynamic adjustment mechanism for the standardized scoring model, which enables the model to automatically or manually trigger the update and re-evaluation process of the model according to the passage of time and the dynamic changes in the asset market. Specifically, the system will dynamically adjust the score according to changes in information such as the debtor's repayment ability and willingness to repay. Through this mechanism, it is ensured that the standardized scoring model can continue to maintain the accuracy and timeliness of its evaluation, effectively respond to changes in different asset conditions, and provide more reliable and scientific decision-making support for debt evaluation.

[0097] This embodiment also introduces an automatic asset classification mechanism, which is rooted in a solid manual classification foundation and aims to achieve efficient and accurate computer classification of assets through a carefully designed automated rule system. This mechanism cleverly combines advanced information technology and business logic, which not only greatly improves the classification efficiency, but also ensures the accuracy of the classification results. Among them, the automatic classification of assets covers four categories: "ice" assets-assets with a high risk of continuous impairment, "egg" assets-assets with higher future value, "jade" assets-assets with repairable defects, and "gold" assets-assets have the opportunity to be transferred at a high price.

[0098] Then, in S2, the value change trend of non-performing assets is classified based on the scores of non-performing assets, and the value classification results of non-performing assets are obtained, which specifically include:

[0099] (1) Determine whether the score of the non-performing asset is less than a first preset value, and obtain a first determination result.

[0100] (2) If the first judgment result is yes, the value classification result of the non-performing assets is "ice" assets, and the corresponding assets have a high risk of continuous impairment.

[0101] (3) If the first judgment result is no, then determine whether the score of the non-performing asset is less than a second preset value to obtain a second judgment result.

[0102] (4) If the second judgment result is yes, the value classification result of the non-performing assets is "egg" assets, and the corresponding assets have a higher future value.

[0103] (5) If the second judgment result is no, then determine whether the score of the non-performing asset is less than a third preset value to obtain a third judgment result.

[0104] (6) If the result of the third judgment is yes, then the value classification result of the non-performing asset is a "jade" asset, and the corresponding asset has repairable defects.

[0105] (7) If the result of the third judgment is no, the value classification result of the non-performing assets is "gold" assets, and the corresponding assets have the opportunity to be transferred at a high price.

[0106] The value classification result of this embodiment is used to characterize the possibility of recovering non-performing assets. The user can decide whether to recover the assets according to needs based on the value classification result.

[0107] This embodiment can also introduce debtor characteristic tags into the automatic asset classification mechanism. At this time, the comprehensive creditor's rights scoring score and debtor characteristic tags, such as exceeding the statute of limitations, poor repayment ability, guarantor's guarantee effectiveness defects, rights defects, etc., are prioritized for classification based on asset scores. 0-2.5 points correspond to "ice" assets - assets with a high risk of continued impairment, 2.5-5 points correspond to "egg" assets - assets with higher future value, 5-7.5 points correspond to "jade" assets - assets with repairable defects, 7.5-10 points correspond to "gold" assets - assets have the opportunity to be transferred at a high price. Based on the scoring classification, the debtor characteristic tags are classified. For example, a debtor holding a tag of exceeding the statute of limitations is automatically classified as having a high risk of continued impairment. Through the classification standards of the above-mentioned asset scoring and debtor characteristic labels, the value classification results of the assets for a fixed customer or creditor's rights are automatically calculated. The value classification results include "ice" assets - assets with a high risk of continuous impairment, "egg" assets - assets with higher future value, "jade" assets - assets with repairable defects, and "gold" assets - assets with the opportunity to be transferred at a high price.

[0108] Standardized valuation models can be understood as a method for estimating the asset value of a specific customer or debt, with a deep focus on the precise measurement of asset value and forward-looking analysis of its changing trends. Standardized valuation models include valuation models for corporate customers and personal mortgage customers, and valuation models for personal credit customers.

[0109] Among them, in the valuation model for corporate customers and personal mortgage customers, automated asset value classification is a key link. It refers to the valuation map and fills in the asset data under the current address. The computer program reads the valuation map and obtains the result. It further refers to the asset value assessment formula with collateral to achieve rapid and accurate determination of asset value and store the result in the storage medium. In the valuation and pricing process, the model needs to comprehensively consider multiple key factors. At this time, the fields of the collateral asset valuation formula include the collateral valuation, collateral realization value, collateral realization taxes, other priorities borne by the collateral, the amount that our company can repay, the estimated number of years of discount and internal asset transfer pricing. The collateral realization value = 0.56*collateral valuation, the collateral realization taxes = 0.5*collateral realization value. Both values ​​(i.e. 0.56 and 0.5) support manual modification. The amount that our company can repay = collateral realization value - collateral realization taxes - other priorities borne by the collateral. The other priorities borne by the collateral are confirmed by the business personnel. The collateral value (i.e. the recovery value of non-performing assets) = the amount that our company can repay / {(1+FTP)^n}, where n is the estimated number of years of discount and FTP is the internal asset transfer pricing.

[0110] Based on the analysis of these comprehensive and detailed data points, professional valuation calculation formulas are used to scientifically and reasonably value and price the collateral. At the same time, using the valuation map as a reference standard, the computer system queries the valuation data of the search engine in the storage medium through the interface, simulates the buyer's valuation method, and obtains the valuation result. That is, using the valuation map, the model adopts a method of simulating buyer valuation. This method allows users to formulate valuation plans based on data analysis and buyer expectations, combined with the asset value assessment formula of the collateral, and finally obtains the asset pricing result and stores it in the storage medium.

[0111] For example, when evaluating the value of collateral, the valuation map tool is introduced to analyze the geographical location and item attributes of the collateral in the debt, and quickly obtain the market price of similar assets in the surrounding area. For a specific property, the prices of multiple similar assets in the surrounding area are comprehensively analyzed through statistical methods, and the average is calculated to obtain a more objective and accurate asset pricing. For example, when evaluating the value of a commercial property located in the center of Shanghai as collateral, the system will call the valuation map tool to automatically obtain the precise location information of the commercial property, and combine its area, use, construction age and other attributes. The system will compare this information with the data of similar assets in the database, analyze the latest transaction price or rental situation of similar commercial properties in the surrounding area, and thus obtain the market reference value of the collateral. For another example, when the collateral of the debt is a 100-square-meter residential house in a community in Hangzhou, the valuation model is used to automatically identify and query the current market prices of multiple similar properties in the community, and accurately price the property through statistical averaging. In terms of business applications, this method can help financial institutions quickly and accurately assess the value of collateral, thereby achieving refined management of non-performing assets, optimizing asset disposal strategies, improving asset recovery rates, reducing risk exposure, and effectively reducing the economic losses of financial institutions.

[0112] In addition, in order to effectively manage asset value risks and prevent insolvency in the process of asset realization, the model sets up an asset value change trend analysis link, combines the historical price query function of the valuation map tool, and uses year-on-year and month-on-month statistical strategies to deeply analyze the dynamic change trend of the value of the collateral and scientifically predict its future price trend. It is understandable that this link can enhance the sensitivity and foresight of asset value changes and provide model data to support subsequent risk management and decision-making.

[0113] Among them, in the valuation model for personal loan credit customers, the constructed valuation model relies on historical industry data to deeply analyze the key characteristics of personal loan business, such as overdue days, repayment ratio, borrower age, income level, etc. Based on the overdue days of non-performing assets, repayment ratio, and borrower age and income level, the asset score is estimated, and the principal recovery rate of personal loan business is further estimated. The asset value of personal loan credit = principal balance * principal recovery rate, which is automatically calculated by the model through data capture. Among them, the principal recovery rate is obtained by automatically capturing massive historical transaction data of personal loan credit business, and statistical analysis is performed to obtain the principal recovery rate of the debt, and further complete the valuation. In this way, personal loans can be quickly and accurately standardized, helping financial institutions to better understand the risk status of personal loans, optimize the disposal strategy of non-performing assets, and improve asset management efficiency, thereby effectively reducing the economic losses of financial institutions.

[0114] Then in S3, for corporate customers and personal mortgage customers, the value of non-performing assets is evaluated using a standardized valuation model to obtain the recovery value of non-performing assets, including: using a valuation map to determine the collateral valuation of non-performing assets; determining the collateral realization value based on the collateral valuation; determining the collateral realization tax based on the collateral realization value; determining the amount of compensation that our company can receive based on the collateral realization value, the collateral realization tax and other priorities borne by the collateral; determining the recovery value of non-performing assets based on the amount of compensation that our company can receive, the estimated number of years of discounting, and the internal asset transfer pricing. For personal credit loan customers, the value of non-performing assets is evaluated using a standardized valuation model to obtain the recovery value of non-performing assets, including: based on the historical transaction data of personal credit loan business, statistical analysis is performed to obtain the principal recovery rate of the debt; based on the principal and principal recovery rate, the recovery value of non-performing assets is determined.

[0115] Big data analysis pricing often focuses on the initial valuation process, but pays less attention to subsequent processing after valuation, such as asset recovery and management. In a rapidly changing market economy, asset prices often fluctuate dynamically around their intrinsic value. In view of this feature, this embodiment specifically establishes a value review mechanism to cope with the uncertainty of the price of collateral and the economic status of the debtor. As an important component of creditor's rights protection, the price of collateral is not fixed, but is affected by various market factors and the debtor's economic activities. In order to accurately reflect such changes, this embodiment adopts a combination of regular review and flexible manual review. Regular review collects and analyzes the latest data of the debtor through an automated system to ensure the timeliness of the evaluation. Specifically, when a change in a characteristic value affecting the asset value is detected, the calculation result will be automatically corrected; manual review responds quickly to special circumstances or emergencies, and provides more in-depth and detailed evaluation and analysis. Through this mechanism, changes in the price of collateral and the economic status of the debtor can be captured in a timely manner, ensuring that the evaluation of the creditor's rights and the debtor always maintains a high degree of accuracy and timeliness. This not only helps to reduce creditor's rights risks, but also provides more reliable data support for creditors' decision-making.

[0116] The value recheck mechanism means that the value of assets will change accordingly during the process of time migration (M0-M6). The standardized debt valuation model will re-evaluate the value according to the changed asset situation, which can improve the accuracy of the user's value assessment of a specific customer or debt, and then affect the accuracy of automatic asset classification. The computer system uses a scheduling strategy to re-evaluate the assets that need to be rechecked using the standardized debt valuation model to obtain the valuation results, and then recalculate its value classification results through indicators and strategies, and dump the value classification results and valuation results to the storage medium.

[0117] For example, in the M0 stage, the standardized debt valuation model performs pricing and valuation on the assets of client A, and the evaluation result is an asset appreciation opportunity. When time moves to the M6 ​​stage, affected by the changes in the market macro-environment, the standardized debt valuation model identifies that the market price of the collateral of the debt has fluctuated greatly. At this time, the standardized debt valuation model re-evaluates the value according to the changed asset situation and obtains an updated valuation result. Through indicators and strategies, the value classification result is re-calculated. The conclusion obtained through the standardized debt valuation model provides new asset disposal information for the account holder, improving the accuracy and direction of decision-making.

[0118] The standardized debt valuation model provided in this embodiment uses a standardized scoring mechanism to objectively and quickly evaluate non-performing assets. The scoring results can intuitively reflect the quality of the assets and the difficulty of collecting payments. Financial institutions can quickly identify which assets need to be focused on based on the scoring results so that they can take corresponding management measures to improve asset management efficiency. Through this standardized scoring mechanism, business personnel can clearly know the debts that need to be managed and invest their limited energy in the most valuable collection objects. Based on the dynamic valuation of assets, the collection handles are fully mastered, and the corresponding collection suggestion labels and strategy details are generated in combination with business scenarios and clues to achieve key account management, making asset management more targeted and effective, thereby improving the overall work efficiency and achieving efficiency improvement and closed loop in the asset disposal process.

[0119] This embodiment provides a standardized debt valuation model recommended for the disposal of non-performing assets, and its asset value assessment and monitoring runs through the entire business process. In the asset acceptance management stage, the model prices the assets after the initial due diligence, calculates the asset value through the standardized debt valuation model, and classifies the value according to the results; when the assets enter the collection process management stage, the asset value changes accordingly, triggering the value review mechanism of the standardized debt valuation model, sending regular review reminders, and reviewing and updating the value assessment factors. The standardized debt valuation model is a model that uses system information such as debt status, guarantor status, collateral status, and external big data support to perform a cyclic valuation of debt through automatic asset classification, asset pricing, asset scoring, and value review. It focuses on a comprehensive analysis of the value of debt, value change trends, and collection potential, aiming to provide an objective and accurate debt valuation analysis report to provide solid data support for related decisions.

[0120] Compared with traditional offline pricing asset valuation methods, this embodiment uses technical means such as data mining to collect and analyze massive business data, improve work efficiency, and make full use of existing market data and historical records through systematic and standardized data collection and analysis processes to improve pricing accuracy and reliability; compared with other methods that use big data analysis pricing and use predictive models to evaluate the potential value and recovery possibility of assets, the standardized debt valuation model of this embodiment comprehensively considers multiple key factors to provide more accurate results, and the standardized debt valuation model of this embodiment establishes a value review mechanism, which combines regular review with flexible manual review to cope with the uncertainty of the price of collateral and the economic status of the debtor.

[0121] Example 2

[0122] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 2As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for evaluating the value of non-performing assets is implemented.

[0123] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] In an exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating the value of non-performing assets described in Example 1.

[0125] Example 3

[0126] An embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating the value of non-performing assets described in Example 1 is implemented.

[0127] Example 4

[0128] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for evaluating the value of non-performing assets described in Example 1.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0130] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0131] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating the value of non-performing assets, characterized in that: The non-performing asset value assessment method includes: The collection potential of non-performing assets is scored using a standardized scoring model to obtain a score for the non-performing assets; the primary matching field of the standardized scoring model is determined based on the customer type of the customer to whom the non-performing assets belong; if the customer type is a corporate customer and a personal loan mortgage customer, the primary matching field of the standardized scoring model includes: at least one of: debt situation analysis, principal debtor analysis, guarantor analysis, and mortgage analysis; if the customer type is a personal loan credit loan customer, the primary matching field of the standardized scoring model includes: at least one of: debt principal situation analysis and debt quality situation analysis; Classifying the value change trend of the non-performing assets based on the scores of the non-performing assets to obtain the value classification results of the non-performing assets; the value classification results are used to characterize the possibility of recovering the non-performing assets; The value of non-performing assets is assessed using a standardized valuation model to obtain the recovery value of the non-performing assets.

2. The method for evaluating the value of non-performing assets according to claim 1, characterized in that: For corporate customers, the secondary matching fields of the debt situation analysis include: basic information of the debt, debt risk situation, collection litigation situation, other stakeholders and whether there is an intended transferee; among them, the tertiary matching fields of the basic information of the debt include: type of debt, completeness of debt information and agreement on contract dispute clauses; the tertiary matching fields of the debt risk situation include: actual use of financing; the tertiary matching fields of the collection litigation situation include: litigation stage; the tertiary matching fields of other stakeholders include: attitude of the government and court and whether a criminal case is involved; the tertiary matching fields of whether there is an intended transferee include: whether there is an intended transferee; The secondary matching fields of the principal debtor analysis include: willingness to repay, repayment ability and equity situation; among them, the tertiary matching fields of willingness to repay include: willingness to repay, whether the actual controller and the legal representative overlap and the actual controller's default limit; the tertiary matching fields of repayment ability include: repayment ability; the tertiary matching fields of equity situation include: whether it is a one-person limited company and whether the shareholder's capital contribution has not been paid in full; When the guarantor of the non-performing assets is a legal person, the secondary matching fields of the guarantor analysis include: legal attributes of the guarantee, repayment willingness, repayment ability and equity situation; among them, the tertiary matching fields of the legal attributes of the guarantee include: guarantee type and guarantee effectiveness; the tertiary matching fields of repayment willingness include: repayment willingness, whether the actual controller and the legal representative overlap and the actual controller's default limit; the tertiary matching fields of repayment ability include: repayment ability; the tertiary matching fields of equity situation include: whether it is a one-person limited company and whether the shareholder's capital contribution has not been paid in full; When the guarantor of the non-performing assets is a natural person, the secondary matching fields of the guarantor analysis include: legal attributes of the guarantee, repayment willingness and repayment ability; among which, the tertiary matching fields of the legal attributes of the guarantee include: guarantee type; the tertiary matching fields of repayment willingness include: repayment willingness and guarantor identity; the tertiary matching fields of repayment ability include: repayment ability; The secondary matching fields of the mortgage analysis include: the nature and status of the mortgaged property and the defects of rights; the tertiary matching fields of the nature and status of the mortgaged property include: the status of the mortgaged property and the seizure situation; the tertiary matching fields of the defects of rights include: the effectiveness of the mortgage and the defects of rights; For individual mortgage loan customers, the secondary matching fields of the debt situation analysis include: debt information, debt risk, collection litigation and other stakeholders; among them, the tertiary matching fields of debt information include: debt information integrity and contract dispute clause agreement; the tertiary matching fields of debt risk include: overdue time; the tertiary matching fields of collection litigation include: litigation stage and statute of limitations; the tertiary matching fields of other stakeholders include: whether a criminal case is involved and whether there is an intended transferee; The secondary matching fields analyzed by the principal debtor include: willingness to repay and ability to repay; wherein, the tertiary matching fields of willingness to repay include: willingness to repay; the tertiary matching fields of ability to repay include: ability to repay; The secondary matching fields analyzed by the guarantor include: willingness to repay and ability to repay; wherein, the tertiary matching fields of willingness to repay include: willingness to repay; the tertiary matching fields of ability to repay include: ability to repay; The secondary matching fields of the mortgage analysis include: mortgage property status and rights defects; the tertiary matching fields of the mortgage property status include: house attributes and house age; the tertiary matching fields of rights defects include: registration status, mortgage status, seizure status, occupation, sole residence and rental; For individual credit loan customers, the secondary matching fields of the debt principal situation analysis include: principal ranking; The secondary matching fields of the debt quality analysis include: overdue time ranking, repayment ratio ranking, age ranking and loan interest rate ranking.

3. The method for evaluating the value of non-performing assets according to claim 2, characterized in that: For corporate customers and personal mortgage customers, the standardized scoring model is used to score the potential for repayment of non-performing assets, and the scores of non-performing assets are obtained, including: Match the non-performing assets with all the third-level matching fields to obtain the target third-level matching fields that match the non-performing assets; For each of the target third-level matching fields, the product of the score and the weight of the target third-level matching field is calculated, and the product of the product, the weight of the target second-level matching field to which the target third-level matching field belongs, and the weight of the first-level matching field to which the target second-level matching field belongs is calculated to obtain the score corresponding to the target third-level matching field; The scores corresponding to all the target third-level matching fields are added together to obtain the score of the non-performing assets.

4. The method for evaluating the value of non-performing assets according to claim 2, characterized in that: For individual credit loan customers, the standardized scoring model is used to score the potential for repayment of non-performing assets, and the scores of non-performing assets are obtained, including: For each secondary matching field, calculate the product of the score and the weight of the secondary matching field, and calculate the product of the product and the weight of the primary matching field to which the secondary matching field belongs, to obtain the score corresponding to the secondary matching field; The scores corresponding to all the secondary matching fields are added together to obtain the score of the non-performing assets.

5. The method for evaluating the value of non-performing assets according to claim 1, characterized in that: Based on the rating of non-performing assets, the value change trend of non-performing assets is classified to obtain the value classification results of non-performing assets, including: Determine whether the score of the non-performing asset is less than a first preset value, and obtain a first determination result; If the first judgment result is yes, then the value classification result of the non-performing asset is that the risk of continuous asset impairment is high; If the first judgment result is no, then judging whether the score of the non-performing asset is less than a second preset value, and obtaining a second judgment result; If the second judgment result is yes, then the value classification result of the non-performing asset is that the asset has a higher future value; If the second judgment result is no, then judging whether the score of the non-performing asset is less than a third preset value, and obtaining a third judgment result; If the third judgment result is yes, the value classification result of the non-performing asset is that the asset has repairable defects; If the result of the third judgment is no, the value classification result of the non-performing assets is that the assets have the opportunity to be transferred at a high price.

6. The method for evaluating the value of non-performing assets according to claim 1, characterized in that: For corporate customers and personal mortgage loan customers, we use standardized valuation models to assess the value of non-performing assets and obtain the recovery value of non-performing assets, including: using valuation maps to determine the valuation of the collateral of non-performing assets; determining the realization value of the collateral based on the valuation of the collateral; determining the collateral realization tax based on the collateral realization value; determining the amount of compensation that our company can receive based on the collateral realization value, the collateral realization tax and other priorities borne by the collateral; determining the recovery value of non-performing assets based on the amount of compensation that our company can receive, the estimated number of years of discounting and internal asset transfer pricing.

7. The method for evaluating the value of non-performing assets according to claim 1, characterized in that: For individual credit loan customers, the value of non-performing assets is assessed using a standardized valuation model to obtain the recovery value of the non-performing assets, specifically including: based on the historical transaction data of the individual credit loan business, statistical analysis is performed to obtain the principal recovery rate of the debt; based on the principal and the principal recovery rate, the recovery value of the non-performing assets is determined.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating the value of non-performing assets according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the value of non-performing assets described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating the value of non-performing assets described in any one of claims 1 to 7 is implemented.

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