KR101979785B1 - Credit rating assessment and total finance service offering system reflecting intangible assets evaluation result - Google Patents

Abstract

The present invention relates to a system for evaluating a credit rating and providing a total finance service to which an intangible assets evaluation result is reflected and, more specifically, to a system for evaluating a credit rating and providing a total finance service which provides a total credit rating calculated by adding credit rating weight based on an intangible assets evaluation result to an existing credit rating evaluation index and can perform a guide for an appropriate loan condition or a loan product. In addition, the system for evaluating a credit rating and providing a total finance service comprises a big data unit, an evaluation factor extraction unit, a learning unit, an input unit, a calculation unit, and an output unit.

Description

Credit rating and comprehensive financial service provision system reflecting the result of intangible asset evaluation

The present invention relates to a credit rating system and a comprehensive financial service provision system in which an intangible asset evaluation result is reflected.

More specifically, the present invention relates to a method for presenting a credit rating calculated by adding a credit weight based on an intangible asset evaluation result to an existing credit rating index to a user, Credit rating and comprehensive financial service provision system.

For financial institutions, guarantee institutions, and investors, credit analysis is crucial to confirm the possibility of bankruptcy of investors or customers.

In the past, attempts have been made to objectively determine the creditworthiness of a firm based on management or financial information in order to check the creditworthiness of a company or an individual. Among these, attempts have been made to evaluate the creditworthiness of a company to be evaluated by using multivariate analysis, statistical method, And a method of setting a credit rating.

On the other hand, there are cases where companies or individuals hold intangible assets such as patents, trademarks and designs in their business and generate business profits that are not reflected in the financial statements based on them. The value of assets is often not reflected in credit rating indicators.

In addition, when a company or individual receives a valuation of their own intangible assets, it should be referred to a separate technical valuation agency. In this case, a lot of time is required for the investigation, It is likely that the individual's subjective involvement is likely to intervene, which makes it somewhat difficult to prove it clearly after evaluating the technology value.

Recently, a system has been developed to evaluate the technical value of intangible assets. However, there is still a need to develop a system that can more accurately and systematically assess creditworthiness by reflecting the results of evaluation of intangible assets in the overall creditworthiness of a company or an individual, and provide a comprehensive financial service suited to that.

Korean Patent Publication No. 10-1488242

In the present invention, quantitative evaluation indexes of intangible assets are derived through the use of big data and machine learning, converted to credit weighted weights, and ultimately added to the overall credit rating, And provides a credit rating and a comprehensive financial service provision system that provide products.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it relates to a credit rating system and a comprehensive financial service provision system.

The credit rating system and the comprehensive financial service provision system may include a big data unit for storing a quantified value evaluation index for the technicality, marketability, and rights of the intangible asset, An evaluation factor extracting unit for extracting the technical evaluation factor, the marketability evaluation factor and the rightness evaluation factor from the intangible asset data stored in the big data unit through a text mining technique; A learning unit for learning an intangible asset value quantification model for each technical field through machine learning based on the extracted evaluation factor and a quantified value evaluation index; An input unit for receiving information on a user possessed intangible asset; The intangible asset evaluation index is derived by performing an evaluation of the technicality, marketability, and rights of the user's possessed intangible asset through the model learned by the learning unit based on the information about the intangible asset inputted to the input unit, An operation unit for calculating a credit weight corresponding to an asset evaluation index and adding the credit weight to a credit score of the user; And an output unit for providing a credit condition or financial instrument corresponding to the integrated credit rating.

In one example, the intangible asset data may be an open publication or a registered publication of a patent.

In one example, the technical evaluation element is a core keyword of an invention extracted through a text mining technique from the contents described in the publication of the patent or the problem to be solved, the solution of the problem or the effect of the invention on the registration bulletin, The marketability evaluation element is a market scale of a target product extracted from a patent disclosure publication or a description in a registration publication through a text mining technique, and the rights evaluation element is a patent disclosure disclosure document or a patent application And may be a billing component extracted from the content through a text mining technique.

In one example, the learning unit can learn the intangible asset value quantification model by technology field through a support vector machine or deep learning based on the extracted evaluation factor and the quantified value evaluation index.

In one example, the operation unit extracts technicality, marketability, and rightness evaluation factors from the user's possessed intangible asset data through a text mining technique and applies the evaluation factors to the intangible asset quantification model by technology field, And the second parameter.

In one example, the arithmetic unit may be characterized in that a credit weighting value is calculated by weighting the contribution of technical index, marketability, or rightness evaluation index among the intangible asset evaluation indexes according to the technical field.

In one example, the operation unit may calculate a credit weight value corresponding to the intangible asset evaluation index according to Equation (1).

[Equation 1]

Cr = (t x s1 + m x s2 + r x s3) x R

In Equation (1), Cr is a weighted credit score, t is a technical evaluation index conversion factor, m is a marketability evaluation index conversion factor, r is a compatibility evaluation index conversion factor, s1, s2, and s3 are technical characteristics, Marketability and rights weights, and R is the creditworthiness weight conversion factor.

In one example, the operation unit may calculate a credit weight value corresponding to the intangible asset evaluation index according to Equation (2).

[Equation 2]

Cr = 3 x min I x s x R

In Equation (2), Cr is a credit weighting factor and minI is a conversion factor of an evaluation index that has the smallest contribution to the intangible asset evaluation index among technological, marketability, and rightness evaluation indexes, s denotes a weight of minI, Credit-weighted conversion factor.

In one example, the operation unit may be configured to adjust the weight occupied by the creditworthiness weight among the total creditworthiness values to within the range of the following expression (3).

[Equation 3]

0.01Ct < Cr < 0.5C

In Equation (3), Ct is a total credit rating, Cr is a credit rating, and C is a credit rating indicator of the user.

The present invention also relates to a credit rating and a method of providing a comprehensive financial service.

The credit rating and comprehensive financial service provisioning method includes the steps of: storing a quantified valuation index of technicality, marketability, and rights of an intangible asset with matching intangible asset data; Extracting an evaluation factor for extracting the technical evaluation factor, the marketability evaluation factor and the rightness evaluation factor from the stored intangible asset data through a text mining technique; Learning an intangible asset value quantification model for each technical field through machine learning based on the extracted evaluation factor and a quantified value evaluation index; Receiving information on a user's possessed intangible asset; An intangible asset evaluation index is derived by evaluating the technicality, marketability, and rights of the user's possessed intangible asset by applying the model learned in the learning unit based on the information about the intangible asset inputted to the input unit, Calculating a credit weight corresponding to the intangible asset evaluation index and adding the credit weight to the credit score of the user; And providing credit terms or financial instruments corresponding to the credit rating.

A credit evaluation and comprehensive financial service system according to the present invention; According to the credit evaluation method and the method of providing comprehensive financial service, the value of the intangible asset of a person who wants to receive the financial service can be quantitatively calculated and reflected in the credit evaluation index. Therefore, It is possible to present a more accurate result of the credit rating.

Further, the credit evaluation and comprehensive financial service system according to the present invention; According to the credit rating and the method of providing the comprehensive financial service, it is possible to provide a financial service more suitable for a person who wants to receive the financial service based on the result of calculating the total credit amount.

1 is a block diagram for explaining a credit rating and comprehensive financial service system according to the present invention.
FIG. 2 is a block diagram for explaining a credit rating and a method for providing a comprehensive financial service according to the present invention.

Hereinafter, the present invention will be described in more detail with reference to drawings and examples.

As used herein, the term &quot; general term &quot; is used to refer to functions of the present invention. However, the present invention is not limited to the intention or the precedent of the present invention. Also, in certain cases, there may be a term selected arbitrarily by the applicant, in which case the meaning thereof will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term, not on the name of a simple term, but on the entire contents of the present invention.

Embodiments of the present invention are capable of various transformations and may have various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the description. It should be understood, however, that it is not intended to limit the scope to any particular embodiment, but is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the following description of the embodiments of the present invention,

The terms first, second, etc. in this specification may be used to describe various components, but the components are not to be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

In this specification, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

As used herein, the terms "comprise", "comprising" and / or "comprising" are intended to specify the presence of stated features, integers, steps, operations, elements, components or combinations thereof, But do not preclude the presence or addition of features, numbers, steps, operations, components, parts, or combinations thereof.

In the present invention, quantitative evaluation indexes of intangible assets are derived through the use of big data and machine learning, converted to credit weighted weights, and ultimately added to the overall credit rating, A credit rating system for providing products, a comprehensive financial service provision system, a credit rating system using the same, and a method for providing a comprehensive financial service.

Financial services providers, such as financial institutions, provide financial services such as lending or presentation of financial instruments based on customer credit ratings. Such credit ratings are based on the statements in the financial statements of the entity , Or credit rating indicators such as financial service usage history and annual income including personal debt status and delinquency status.

On the other hand, as a result of changes to the intellectual property societies, companies and individuals often possess certain intangible assets. In addition, intangible asset holders would like to receive quantitative assessments of their own intangible assets and seek financial services such as loans or the provision of financial products based on them. However, there has been no attempt to provide a quantitative assessment of these intangible assets and reflect them in the credit score.

Accordingly, the present inventor has developed a credit rating system capable of quantitatively evaluating the intangible assets of a person who wishes to receive financial services through big data and machine learning, converting the intangible assets into credit weighted values, In addition, we have developed a system that can provide comprehensive financial services by presenting credit terms and financial products according to credit rating results.

FIG. 1 is a block diagram for explaining a credit evaluation and a comprehensive financial service providing system according to the present invention in more detail.

As shown in FIG. 1, the credit rating system and the integrated financial service provision system according to the present invention match a quantified value evaluation index 102 of the technicality, marketability, and rights of an intangible asset with the intangible asset data 101 A big data portion 100 for storing the large data portion 100; Extracting an evaluation factor element 201, a marketability evaluation factor 202 and a rightness evaluation factor 203 from the intangible asset data 101 stored in the big data unit 100 through a text mining technique (200); A learning unit 300 for learning the intangible asset value quantification model 303 according to the technical field through machine learning based on the extracted evaluation factor 302 and the quantified value evaluation index 301; An input unit 400 for receiving information 401 about a user's possessed intangible assets; The model is applied to the learning unit 300 based on the information about the intangible asset input to the input unit 400 to evaluate the technicality, marketability, and rights of the intangible asset of the user, (501), calculates a credit weighting value (502) corresponding to the intangible asset evaluation index (501), adds the credit weighting value (502) to the credit rating index of the user, An operation unit 500 for operating the display unit; And an output unit 600 for providing a credit condition 601 or a financial product 602 corresponding to the integrated credit degree 503.

The credit rating system and the comprehensive financial service provision system according to the present invention largely include a big data and machine learning based learning area for quantitative evaluation of intangible assets; An operation area for calculating the quantitative evaluation index for the intangible asset by applying the learning result to the intangible asset information of the actual user, converting the converted value into the credit weight value, and ultimately adding the integrated credit value; And a financial service provision area for providing credit terms and financial products based on the calculation in the calculation area.

The learning area includes a big data part 100, an evaluation element extracting part 200, and a learning part 300.

The big data unit 100 extracts the intangible asset quantitative evaluation factor and stores data for learning the quantitative model from the extracted quantitative evaluation factor. The big data unit 100 stores a quantified value evaluation index (hereinafter referred to as &quot; intangible asset &quot; 102) with the intangible asset data (101).

That is, the big data unit 100 stores and stores the result of valuation of the intangible asset (for example, an intellectual property right such as a patent) and the intangible asset data to which the valuation result is applied (for example, patent publication) . In addition, the result of the evaluation includes a quantified value evaluation index 102 for the technicality, marketability, and the rightness. The value evaluation index 102 numerically indicates the quantitative value of the intangible asset data 101 do.

The method in which the big data unit 100 stores and stores the intangible asset data 101 and the value evaluation index 102 in a matching manner stores the result of valuation of the intangible asset created by various valuation institutions together with information on the intangible asset a method of loading the toner particles, and the like.

Meanwhile, the big data unit 100 may store the information about the user's possessed intangible asset, which is input from the input unit, which will be described later, and the intangible asset evaluation index calculated by the operation unit described later, in a matching manner. That is, the system according to the present invention can be applied as a basic source of the intangible asset evaluation information and the intangible asset evaluation index, which are input through the evaluation element extracting unit and the learning unit, through the intangible asset quantification modeling.

Value valuation of the system according to the present invention Quantitative model learning can be technically focused, for example, in quantitative valuation of patents among intangible assets. Accordingly, the intangible asset data may be a patent publication or a patent publication.

The evaluation element extracting unit 200 extracts the descriptive evaluation element 201, the marketability evaluation element 202 and the rightness evaluation element 203 from the intangible asset data 101 stored in the big data unit 100, Mining technique.

Specifically, the evaluation element extracting unit 200 extracts the intangible asset data 101 stored in the big data 100, for example, the content disclosed in the patent publication or patent registration bulletin, , Cohesion among words, and the like, and extracts the technical evaluation element 201, the marketability evaluation element 202 and the rightness evaluation element 203.

More specifically, the technical evaluation element 201 is a core keyword of an invention extracted through a text mining technique from the contents described in the publication of the patent or the problem to be solved by the registration publication, the solution of the problem, or the effect of the invention , The marketability evaluation element 202 is a market scale of a target product extracted from textual mining techniques from disclosure contents of a patent publication or a registration publication, May be a claim component extracted from textual mining techniques as described in the claims of the registration gazette.

The technical evaluation element 201 may be a core keyword extracted from a word or a sentence repeatedly described in the publication of the patent or the publication to be solved, the solution to the problem, or the effect of the invention . In addition, the core keyword can be classified into a plurality of keywords by a text mining technique based on a key keyword extraction element such as a repetition number, a position on a sentence, or a positional relationship between a specific word (purpose, effect, characteristic, technical core idea, It may be provided by extraction.

The marketability evaluation element 202 may be constructed from, for example, a technical classification system such as IPC, CPC, F-term, and the like described in a patent publication or publication, The market size of the target product extracted through the text mining technique. In addition, the market size of the target product is firstly extracted from the technical classification system such as IPC, CPC, and F-term, the technical field, the background technology, And may be secondarily extracted based on market size data.

The rightness evaluation element 203 may be configured to include a component discriminating element ("" "" "" "" "and" "or" "" "" Quot; configuration &quot;, etc.) that are extracted through a text mining technique.

The technological evaluation element 201, the marketability evaluation element 202 and the rightness evaluation element 203 extracted by the evaluation element extraction unit 200 as described above are used together with the intangible asset data and intangible assets of the learning unit 300 It can be the primary source of the value quantification model.

The learning unit 300 learns the intangible asset value quantification model for each technical field through machine learning based on the extracted evaluation factor and the quantified value evaluation index.

Specifically, the learning unit 300 can learn the intangible asset value quantification model for each technical field through the support vector machine or the deep learning based on the extracted evaluation factor and the quantified value evaluation index.

More specifically, the learning unit 300 can convert the extracted evaluation element into an evaluation element vector, and learn a support vector machine or a deep learning model for the value evaluation index from the converted evaluation element vector .

The learning unit 300 can establish a quantification model of the value of the intangible asset by the technical field by identifying the correlation between the extracted evaluation factor and the value evaluation index through machine learning and building the related evaluation model.

The intangible asset value estimation quantification model of the learning unit can be used to derive the intangible asset evaluation index corresponding to the intangible asset information input through the input unit described later and the value evaluation factors extracted from the corresponding intangible asset data . &Lt; / RTI &gt;

As described above, the learning area is configured to display, based on the intangible asset information that can be newly input, the intangible asset evaluation index (index) through the above-described configuration (the big data unit 100, the evaluation element extraction unit 200, and the learning unit 300) Which can be derived from the model.

The system for providing credit evaluation and comprehensive financial services according to the present invention is a system for providing a credit evaluation and a comprehensive financial service by calculating a quantitative evaluation index of an intangible asset by applying a learning result to an intangible asset information of an actual user and converting it into a credit weighting value, And may include an operation area.

In the computation domain, information on the user's possessed intangible assets is input, and based on the input information, evaluation on the technicality, marketability, and rights of the user's possessed intangible assets is performed through a model learned in the learning section The intangible asset evaluation index is calculated, the credit weighted value corresponding to the intangible asset evaluation index is calculated, and the creditworthiness weight is added to the user credit rating index to calculate the total creditworthiness.

That is, the calculation region includes an input unit 400 and an operation unit 500.

The input unit 400 receives information on the intangible assets of the user. The information on the user's possessed intangible assets includes, for example, the date of filing of the patent, the application number, the applicant, the registration date, the registration number, the registration owner or the notification number of the registered patent publication or the public publication containing the information can do. Meanwhile, the input unit 400 may be provided with the personal information of the user to calculate the general credit rating index of the user. The personal information may include a name of a user, a resident number or an address.

In order for the user to input his / her personal information into the input unit 400, the user may go through an authentication procedure. One or more authentication procedures using the authentication procedure password, Internet personal identification number (i-PIN), one time passwaord (OTP), mobile terminal, e-mail, authorized certificate, biometric or combinations thereof.

Based on the information about the intangible asset received from the input unit 400, the operation unit 500 derives the intangible asset evaluation index 501. The intangible asset evaluation index 501 evaluates the technicality, marketability, and rights of the user's possessed intangible assets through the model learned in the learning unit 300.

Specifically, the operation unit 500 obtains the intangible asset data, such as patent publications or open publications, based on the information 401 about the intangible assets of the user through online web crawling, Through the mining technique, the evaluation factors of technicality, marketability and rights are extracted and the evaluation factors of intangible assets can be derived by applying the evaluation factors to the quantitative model of intangible assets by technology field.

The method for extracting the technical merit, marketability, and entitlement evaluation factors from the retained intangible asset data through the text mining technique may be performed by, for example, extracting intangible asset data stored in the big data unit in the extracting unit 200 Element, the marketability evaluation element, and the rightness evaluation element.

When the extracted evaluation elements are applied to the intangible asset quantification model of the learning unit 300, the intangible asset evaluation index can be derived. The concrete method for this is to classify the technical fields based on the extracted evaluation factors, A plurality of specific intangible asset data in the intangible asset quantification model of the learning unit 300 corresponding to the field is adopted and the plurality of specific intangible asset data And the method in which the intangible asset evaluation index is derived based on the valuation index of the intangible asset.

The intangible asset valuation index can be, for example, numerically showing the valuation of an intangible asset, or displaying it as a grade, and the method is not particularly limited. In addition, the intangible asset evaluation index may be classified into technical index, marketability index, and rightness index, so that the intangible asset index may include technical index, marketability index, and rightness index.

The operation unit 500 calculates a credit weight weight corresponding to the derived intangible asset evaluation index 501. [

Specifically, the operation unit 500 may calculate the credit weighting weight by considering the intangible asset evaluation index 501 and the intangible asset data. More specifically, the operation unit 500 may calculate the creditworthiness weight based on the intangible asset evaluation index 501 The creditworthiness weight can be calculated by weighting the contribution of technical, marketability, or rights evaluation indicators.

Depending on the technology field of the intangible asset, one of the indices of technical, marketability, and rightness evaluations can have a significant impact on the quantitative valuation of the intangible asset data. Apart from that, the value of the intangible asset and its corresponding credit weight There may be cases where it is necessary to assign a larger contribution weight to one of the evaluation indexes of technology, marketability, and rights.

In other words, in calculating the credit weight corresponding to the intangible asset valuation index, if the contribution weight is assigned to any of the intangible asset valuation indexes according to the technology field, more accurate credit weighting can be calculated, and ultimately, This can realistically quantify the portion of intangible assets.

In one example, a specific technical field may have a high contribution to intangible asset value of the evaluation index on the right, while the contribution of the intangible asset value of the evaluation index on the technicality is low depending on the degree of development of the technology, By assigning a weight to the contribution and calculating the credit weight, it is possible to calculate an accurate total credit considering the characteristics of the technology field.

That is, the calculation unit may calculate a credit weight value corresponding to the intangible asset evaluation index according to Equation (1).

[Equation 1]

Cr = (t x s1 + m x s2 + r x s3) x R

In Equation (1), Cr is a weighted credit score, t is a technical evaluation index conversion factor, m is a marketability evaluation index conversion factor, r is a compatibility evaluation index conversion factor, s1, s2, and s3 are technical characteristics, Marketability and rights weights, and R is the creditworthiness weight conversion factor.

The above Equation (1) is obtained by adding the credit weighting conversion factor (t) to the sum of the product of the conversion factor (t, m, r) for applying the technicality, marketability and the rightness evaluation index to the creditworthiness weight, , Which means that weighting is given to any one of technical skill, marketability, and rightness evaluation index according to the technical field in ultimately deriving the creditworthiness weight.

In the above, the conversion factor means a factor adopted to control the ratio of the intangible asset evaluation index and the corresponding credit weight in the integrated credit rating described later, so that the non-numeric expression of each factor accounts for the credit weight in the overall credit rating And a factor that converts into a numerical expression that is set in consideration of the ratio.

On the other hand, if one of intangible assets, technology, marketability, and rights evaluation indexes is lower than the other two, the overall intangible asset valuation index is high, but if it is not practical value of the intangible asset and it is difficult to calculate the corresponding credit weight .

Accordingly, in calculating the credit weight corresponding to the intangible asset evaluation index, the calculation unit 500 may calculate the minimum credit weight based on the evaluation index having less contribution among the technicality, marketability, and rightness evaluation indexes.

That is, the operation unit 500 may calculate a credit weight value corresponding to the intangible asset evaluation index according to Equation (2).

[Equation 2]

Cr = 3 x min I x s x R

In Equation (2), Cr is a credit weighting factor and minI is a conversion factor of an evaluation index that has the smallest contribution to the intangible asset evaluation index among technological, marketability, and rightness evaluation indexes, s denotes a weight of minI, Credit-weighted conversion factor.

As shown in Equation (2), when the credit weight is evaluated based on the evaluation index that minimizes the contribution to the intangible asset evaluation index among the technicality, marketability, and rightness evaluation indexes, it is possible to prevent the calculation of the excessively weighted creditworthiness , We can present a credit weighting value that corresponds to a more substantive intangible asset valuation index.

The calculation of the creditworthiness weight in the same manner as in Equation (2) can be applied when, for example, one of the intangible asset valuation indexes contributes to the overall intangible asset valuation index excessively.

In one example, calculating the creditworthiness weight in the same manner as in Equation (2) can be made by calculating the creditworthiness weight by multiplying the contribution of the intangible asset evaluation index among technological, marketability, More than 1.5 times, or more than 2 times, but is not limited thereto.

The calculation unit 500 calculates the total creditworthiness by adding the credit rating index of the user to the credit weight calculated by the above method. On the other hand, the user's credit rating index is calculated by the existing credit rating method. If the index of the intangible asset contributes to the credit rating index more than the credit rating index, the reliability of the overall credit rating will deteriorate. have.

Accordingly, the weight of the credit weight corresponding to the intangible asset evaluation index can be adjusted within a predetermined range in the overall credit rating. Specifically, the calculation unit 500 calculates the weight of the credit weight among the total credit ratings within the range of Can be adjusted.

[Equation 3]

0.01Ct < Cr < 0.5C

In Equation (3), Ct is a total credit rating, Cr is a credit rating, and C is a credit rating indicator of the user.

The adjustment of the credit weight can be performed by, for example, the credit-weighted conversion factor in the above-described Equations 1 and 2, but is not limited thereto.

On the other hand, the credit rating indicator necessary for calculating the overall credit rating may be data provided by an external credit rating agency on the basis of the personal information of the user who accessed the system, or by a component in the operation part storing a separate credit rating It may be a derived credit rating indicator.

According to the system according to the present invention, a more accurate quantitative evaluation of the intangible asset and a corresponding comprehensive credit evaluation are possible through the above-described configurations.

The credit evaluation and comprehensive financial service provision system according to the present invention includes an output unit 600 for providing credit terms or financial products corresponding to the total credit rating presented by the calculation unit 500.

The output unit 600 provides a comprehensive financial service based on the total creditworthiness and the total creditworthiness to the user. The credit conditions of the comprehensive financial service may mean, for example, a loan interest rate or a credit ceiling, and the financial product may mean an insurance product or an investment product.

The credit rating system and the comprehensive financial service provision system according to the present invention may be stored in, for example, an external server or a computer-readable recording medium. The user may access the system through a separate terminal, Results and appropriate comprehensive financial services.

In one example, after a user accesses a credit evaluation system and a comprehensive financial service provision system stored in an external server through a separate mobile terminal, information on his / her intangible asset is inputted, A comprehensive credit rating and corresponding comprehensive financial services can be provided to the user.

In another example, after a user accesses a credit evaluation and comprehensive financial service provision system stored in a computer-readable recording medium, information on his / her intangible assets is input, and the received system receives a series of processes Credibility and corresponding comprehensive financial services to users.

The present invention also relates to a credit rating and a method of providing a comprehensive financial service.

FIG. 2 is a block diagram of the credit rating and integrated financial service provisioning method.

As shown in FIG. 2, the credit evaluation and integrated financial service provisioning method according to the present invention includes a step (S1) of matching a quantified value evaluation index of technicality, marketability, and rights of an intangible asset with intangible asset data, ; Extracting an evaluation factor for extracting the technical evaluation factor, the marketability evaluation factor and the rightness evaluation factor from the stored intangible asset data through a text mining technique; (S3) learning an intangible asset value quantification model for each technology field through machine learning based on the extracted evaluation factor and a quantified value evaluation index; (S4) receiving information on a user's possessed intangible asset; The intangible asset evaluation index is derived by performing an evaluation of the technicality, marketability, and rights of the user's possessed intangible asset through the model learned by the learning unit based on the information about the intangible asset inputted to the input unit, Calculating a credit weight corresponding to the asset evaluation index, and adding the credit weight to the credit score of the user (S5); And presenting a credit condition or financial product corresponding to the integrated credit rating (S6).

According to the credit rating and the comprehensive financial service provisioning method according to the present invention, the value of the intangible asset of a person who desires to receive a financial service can be quantitatively calculated and reflected in the credit rating index, To provide more accurate results of his / her credit evaluation. In addition, based on the result of the calculation of the total creditworthiness, it is possible to provide a financial service more suitable for a person who wants to receive the financial service.

100: Big data portion
200:
300:
400: input unit
500:
600: Output section

Claims (10)

A Big Data Department that stores quantified valuation indicators of the technical, marketability and rights of intangible assets matching intangible asset data;
An evaluation factor extracting unit for extracting the technical evaluation factor, the marketability evaluation factor and the rightness evaluation factor from the intangible asset data stored in the big data unit through a text mining technique;
A learning unit for learning the intangible asset value quantification model by technical field through a support vector machine or deep learning based on the extracted evaluation factor and the quantified value evaluation index;
An input unit for receiving information on a user possessed intangible asset;
An intangible asset evaluation index is derived by evaluating the technicality, marketability, and rights of the user's possessed intangible asset by applying the model learned in the learning unit based on the information about the intangible asset inputted to the input unit, An arithmetic unit for calculating a credit weight corresponding to the intangible asset evaluation index and adding the credit weight to the credit score of the user; And
And an output unit for providing a credit condition or financial instrument corresponding to the integrated credit rating,
The intangible asset data is an open publication or registration bulletin of a patent,
According to the technical field of the present invention, the operation unit assigns a weight to the contribution of the technical property, marketability, or rightness evaluation index among the intangible asset evaluation indexes, calculates the credit weighted weights corresponding to the intangible asset evaluation index according to Equation 1 below, A credit rating and a comprehensive financial service provision system are provided,
[Equation 1]
Cr = (tx s1 + mx s2 + rx s3) x R
In Equation (1), Cr is a weighted credit score, t is a technical evaluation index conversion factor, m is a marketability evaluation index conversion factor, r is a compatibility evaluation index conversion factor, s1, s2, and s3 are technical characteristics, Marketability and rights weights, and R is the credit weighted conversion factor;
[Equation 3]
0.01Ct < Cr &lt; 0.5C
In Equation (3), Ct is a total credit rating, Cr is a credit rating, and C is a credit rating indicator of the user. delete The method according to claim 1,
The above-
A core keyword of an invention extracted through a text mining technique from the contents described in the publication of the patent or the problem to be solved, the solution of the problem or the effect of the invention,
The marketability evaluation element may include:
The market size of the target product extracted from the contents of the patent disclosure publication or the registration publication by text mining technique,
Wherein the rightness evaluation element comprises:
The credit rating and comprehensive financial service provision system, which is a claim component extracted through text mining techniques from the disclosure of the patent or the contents of the claims of the registration application. The method according to claim 1,
Wherein,
A credit evaluation and comprehensive financial service provision system for converting the extracted evaluation element into an evaluation element vector and learning a support vector machine or a deep learning model for the value evaluation index from the converted evaluation element vector. The method according to claim 1,
The operation unit,
Marketability and entitlement evaluation factors are extracted from the user's possessed intangible asset data through a text mining technique and the evaluation factors are applied to the intangible asset quantification model for each technical field to derive intangible asset evaluation indexes And comprehensive financial service delivery system. delete delete The method according to claim 1,
The operation unit,
Wherein the credit rating weighting value corresponding to the intangible asset evaluation index is calculated according to Equation (2) below:
[Equation 2]
Cr = 3 x min I xsx R
In Equation (2), Cr is a credit weighting factor and minI is a conversion factor of an evaluation index that has the smallest contribution to the intangible asset evaluation index among technological, marketability, and rightness evaluation indexes, s denotes a weight of minI, Credit-weighted conversion factor. delete delete

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