KR20170034836A - Method, system and recording medium for evaluating the credit on the basis of financial and non-financial factor - Google Patents

Abstract

Disclosed are a method and a system for evaluating a users credit, and a recording medium. A credit evaluation method implemented with a computer comprises: a step of providing a user interface for providing an inquiry for personality evaluation; a step of receiving a response to the inquiry through the user interface; a step of extracting personality information and demographic information from information published to at least one of a social network service and a web page to which a user has subscribed; a step of calculating a personality score based on the response and the personality information; and a step of evaluating the users credit using non-financial information and financial information based on the personality score and the demographic information.

Description

FIELD OF THE INVENTION [0001] The present invention relates to a credit evaluation method, system, and recording medium based on financial information and non-financial information. BACKGROUND OF THE INVENTION [0002]

Embodiments of the present invention are directed to techniques for evaluating an individual's credit using various information in addition to the asset value of the individual.

As the credit society is settled, the importance of credit information is emphasized in the credit review process for judging the establishment and maintenance of transactions. In particular, the problem of bad credit due to delinquency, default, etc. is becoming a big social problem. It is estimated that the problem of credit failure of such individuals is caused by a method of evaluating individual credit information by a credit information evaluation institution such as a financial institution other than an individual mistake.

Generally, a credit rating agency such as a financial institution evaluates an individual's credit based on the asset value of the individual. For example, a credit rating agency evaluates an individual's credit based on fixed income such as personal property and real estate assets and benefits. Especially, when real estate prices are relatively higher than the market economy, real estate acts as a very big factor in evaluating individual 's credit.

However, when evaluating an individual 's credit based on the value of an individual' s asset value, the real estate price falls sharply due to the recession, etc.

Therefore, besides evaluating the individual's credit based on the value of the individual's assets, there is a demand for a technique that can effectively evaluate the individual's credit.

A method and system for effectively evaluating an individual's credit taking into account non-financial information in addition to the asset value of the individual, and a recording medium.

It also provides a credit evaluation method and system and a recording medium that make it possible to design an individual's financial plan even if economic knowledge is lacking, by recommending a financial product appropriate for the evaluated credit.

A computer-implemented credit evaluation method includes providing a user interface for providing a query for toughness evaluation, receiving a response to the query through the user interface, Extracting toughness information and demographic information on information posted on at least one of a service and a web page, calculating a toughness score based on the response and toughness information, And evaluating the user's credit using non-financial and financial information based on the demographic information.

According to an aspect of the present invention, the step of evaluating the credit may evaluate the credit by applying a weight to the financial information according to the amount of increase of the data of the financial information to the non-financial information.

According to another aspect, calculating the toughness score may be performed by scoring a personality factor based on a Big Five model.

According to another aspect of the present invention, the step of calculating the toughness score includes calculating a score calculated by applying a toughness evaluation algorithm based on the vocabulary included in the posted information and a score calculated by applying a toughness evaluation algorithm based on the response The toughness score can be calculated.

According to another aspect of the present invention, the step of extracting the toughness information and the demographic information includes the steps of: extracting information on at least one of a social network service and a web page, , The average number of comments per unit time, and the number of friends can be extracted as the toughness information.

According to another aspect of the present invention, the step of extracting the toughness information and the demographic information includes a step of extracting information on at least one of a social network service and a web page, , Birthplace, workplace, family structure, income, and health state of the user.

According to another aspect, the method may further include recommending at least one of the loan-related commodity and the investment-related commodity based on the credit of the evaluated user.

According to yet another aspect, there is provided a method of providing financial information, the method comprising: requesting and receiving the financial information from at least one credit information evaluation system, the financial information comprising financial asset information, asset information associated with fixed income, Or the like.

The credit rating system includes a user interface providing unit for providing a user interface for providing a query for toughness evaluation and receiving a response to the query through the user interface, a Social Network Service for which the user subscribes, An information extracting unit that extracts toughness information and demographic information from information posted on at least one of a tentative web page and a web page, a calculation unit that calculates a toughness score based on the response and toughness information, A credit evaluation unit that evaluates the user's credit using non-financial and financial information based on score and demographic information.

According to an aspect of the present invention, the system further includes at least one of a loan-related commodity and an investment-related commodity based on the evaluated user's credit, and the range of the loan term, loan amount, and loan interest rate of the loan- May be changed and set by the user within a range set in accordance with the credit of the user. In this case, the loan related product may include a selfie loan, a selfie card, a self-landing, and the like. When a self-supporting card is used, the loan period, the loan amount, Card usage limit, and card interest rate.

In addition to the financial information indicating the asset value of the individual, the individual's credit can be effectively evaluated by taking into consideration non-financial information.

In addition, by recommending financial instruments appropriate to the assessed credit, even if economic knowledge is lacking, it can contribute to the financial design of the individual.

1 shows a credit evaluation system 100 and a user terminal 101 in an embodiment of the present invention.
2 is a block diagram showing an internal structure of a credit evaluation system in an embodiment of the present invention.
3 is a flowchart showing an information providing method in an embodiment of the present invention.
4 is a diagram illustrating an example of a query using an image provided on a screen of a user terminal according to an exemplary embodiment of the present invention.
FIG. 5 is a diagram provided to illustrate an operation of calculating a toughness score of a user, according to an embodiment of the present invention. FIG.
6 is a graph showing an example of a toughness score according to the toughness evaluation algorithm performance in an embodiment of the present invention.
Figure 7 is a diagram provided to illustrate the operation of extracting demographic information in one embodiment of the present invention.
8 is a diagram for explaining an operation of assigning a weight to financial information and evaluating a user's credit in an embodiment of the present invention.
FIG. 9 is a diagram provided for explaining an operation of calculating a credit score for non-financial information in an embodiment of the present invention. FIG.
10 is a flowchart for recommending a financial instrument based on a credit score in an embodiment of the present invention.
11 is a block diagram for explaining an example of the internal configuration of a computer system in an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The present invention relates to a technique for evaluating credit using financial information and non-financial information, and includes a response to a personality evaluation quiz stored in a database, a social network service or a web page to which a user subscribes, The present invention relates to a technology for evaluating credit using non-financial information based on information posted on a credit card. In particular, the embodiments are directed to a technique for recommending loan-related products and investment-related products such as a selfie loan, a selfie card, etc., based on user's credit evaluated using financial information and non-financial information .

In this specification, 'non-financial factor' can be used to evaluate the personality that represents the personality and behavior of the user, as information representing the value of the individual's mental asset. For example, 'non-financial information' includes demographic information obtained by analyzing personality information indicating five personal factors based on the Big Five model and information posted on the SNS or web page to which the user subscribes. .

The Big Five model is a model depressing the basic personality factors of humans into five categories. The five personalities are openness, conscientiousness, extraversion, agreeableness, and neuroticism. Lt; / RTI > In particular, in the present specification, credit evaluation is performed using conscientiousness and agreeableness among the five personality factors as personality information as an example. Other personality factors besides sincerity and affinity may be used for credit evaluation.

In the present specification, the " toughness evaluation query " may indicate a problem for evaluating five personality factors based on the Big Five model. In this embodiment, the toughness evaluation query is stored and maintained in a database in a multiple-choice form, and the toughness evaluation query is provided in a multiple-choice form. However, the toughness evaluation query corresponds to the embodiment, It is possible.

In this specification, 'financial factor' is information indicating the value of a material asset of an individual. For example, financial information includes asset information related to fixed income such as personal movable property, real property, salary, It may include information about related assets (eg, savings deposits, savings insurance, savings, money deposited on principal-guaranteed investments) and loans.

In this specification, the case of evaluating an individual's credit based on financial information and non-financial information is taken as an example, but this corresponds to an embodiment, and the credit of the enterprise may be evaluated based on financial information and non-financial information. For example, a company's credit may be assessed based on the nonfinancial information of the owner and officers of the enterprise and the financial information of the enterprise.

1 shows a credit evaluation system 100 and a user terminal 101 in an embodiment of the present invention. In FIG. 1, the arrow indicates that data can be transmitted and received between the credit evaluation system 100 of the user terminal 101 and the wireless network.

The user terminal 101 may be a personal computer, a smart phone, a tablet, a wearable computer, a smart television, or the like, May refer to all terminal devices capable of installing and executing a mobile site access or service exclusive application (hereinafter referred to as a 'credit evaluation app'). At this time, the user terminal 101 can perform service-wide operations such as service screen configuration, data input, data transmission / reception, and data storage under the control of a web / mobile site or a dedicated application.

The credit rating system 100 serves as a service platform for providing a credit evaluation service to a client. In this case, the credit rating system 100 may be implemented in an application form on the user terminal 101, but is not limited thereto and may be implemented in a service platform that provides a credit evaluation service in a client-server environment It is also possible. In particular, the credit rating system 100 may evaluate a user's credit based on nonfinancial information and financial information, and recommend a financial related product based on the evaluated credit.

Hereinafter, the service environment through the credit evaluation app is described in detail, but the present invention is not limited thereto, and the credit evaluation service to which the present invention is applied may be implemented as a connection environment of a web / mobile site.

FIG. 2 is a block diagram illustrating an internal configuration of a credit evaluation system according to an exemplary embodiment of the present invention, and FIG. 3 is a flowchart illustrating an information providing method in an embodiment of the present invention.

2, the credit evaluation system 200 according to the present embodiment may include a processor 210, a bus 220, a network interface 230, a memory 240, and a database 250 . The memory 240 may include an operating system 241 and a service providing routine 242. [ The processor 210 may include an interface providing unit 211, an information extracting unit 212, a calculating unit 213, and a credit evaluating unit 214. In other embodiments, the rating system 200 may include more components than the components of FIG. 2, or some of the components may be omitted. For example, the processor 210 may further include a product recommendation unit (not shown).

The memory 240 may be a computer-readable recording medium and may include a permanent mass storage device such as a random access memory (RAM), a read only memory (ROM), and a disk drive. Also, the memory 240 may store program codes for the operating system 241 and the service providing routine 242. [ These software components may be loaded from a computer readable recording medium separate from the memory 240 using a drive mechanism (not shown). Such a computer-readable recording medium may include a computer-readable recording medium (not shown) such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, or a memory card. In other embodiments, the software components may be loaded into the memory 240 via the network interface 230 rather than from a computer readable recording medium.

The bus 220 may enable communication and data transfer between components of the credit rating system 200. [ The bus 220 may be configured using a high-speed serial bus, a parallel bus, a Storage Area Network (SAN), and / or other suitable communication technology.

Network interface 230 may be a computer hardware component for connecting credit rating system 200 to a computer network. The network interface 230 may connect the rating system 200 to the computer network via a wireless or wired connection.

The database 250 is constituted by a credit evaluation app, and stores and maintains information related to non-financial information used for credit evaluation.

As an example, the database 250 may store a toughness evaluation query for acquiring non-financial information used for credit evaluation. For example, the database 250 may store the objective-type queries for evaluating the integrity and affinity of the user on the basis of the Big Five model. In addition to the integrity and affinity, the objective-type queries for evaluating openness, extroversion, . Examples of queries for evaluating integrity can be found in Table 1 below.

Are you a reliable worker? 1. Disagree Strongly 2. Disagree a little 3. Neither agree nor disagree 4. Agree a little 5. Agree Strongly

Table 1 shows an example of a query for determining the integrity based on the Big Five model, and the database 250 can store and maintain various queries for determining the integrity. The database 250 may periodically update queries based on the Big Five model to reflect a measure of sincerity and affinity that changes over time.

As shown in Table 1, the toughness evaluation query may be provided in a text form, but may be provided in an image form, or may be provided in a mixed form of text and images. For example, a query and a multiple-choice view of the query may be stored in the database 250 in an image form, such as a visual DNA, and the query 250 may be text, the multiple- As shown in FIG.

The processor 210 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations of the credit evaluation system 200. [ The instructions may be provided by the memory 240 or the network interface 230 and to the processor 210 via the bus 220. The processor 210 may be configured to execute program codes for the interface providing unit 211, the information extracting unit 212, the calculating unit 213 and the credit evaluating unit 214. [ In another embodiment, the processor 210 executes program codes for the interface providing unit 211, the information extracting unit 212, the calculating unit 213, the credit evaluating unit 214, and the product recommending unit (not shown) . Such program code may be stored in a recording device such as memory 240. [

The processor 210 may be configured such that the interface providing unit 211, the information extracting unit 212, the calculating unit 213 and the credit evaluating unit 214 perform the steps (steps 310 to 350) 1002, and 1002).

In step 310, the interface providing unit 211 may provide a user interface for providing the user terminal 101 with the toughness evaluation query stored in the database 250. [

For example, the interface providing unit 211 may provide a user interface for providing a selected one of a plurality of toughness evaluation queries stored in the database 250 on the screen of the user terminal 101. At this time, the query may be randomly selected from among a plurality of toughness evaluation queries, and may be selected in order of a query identification number (e.g., query 1, query 2, etc.) given to each query. Subsequent queries may be selected for the remaining toughness evaluation queries other than those already provided to the user terminal 101 among the stored toughness evaluation queries. That is, the query can be selected such that the same query is not provided redundantly to the user terminal 101.

In step 320, the interface providing unit 211 may receive a response to the inquiry through the user interface.

For example, a user interaction such as a specific number / symbol touch or a specific number / symbol selection may be generated for a multiple choice query displayed on the screen of the user terminal. Then, the interface providing unit 211 can receive information corresponding to the number / symbol of the user interaction from the user terminal 101 through the user interface in response to the inquiry.

 In operation 330, the information extraction unit 212 may extract the toughness information and the demographic information through data analysis. The information extracting unit 212 may extract the toughness information and the demographic information from the information posted in the social network service (SNS) subscribed by the user and the information posted on the user's web page. As an example of a user's web page, a user's home page may be included.

In step 331, the information extracting unit 212 may extract user-related toughness information by analyzing data from the information posted on the SNS and the information posted on the web page.

For example, the information extracting unit 212 extracts data such as the average number of posts per unit time, the average number of comments per unit time, the number of friends, and the like on the information posted on the Facebook, the blog, the Instagram, It can be extracted as toughness information. For example, the greater the number of average comments per unit time (the number of user's comments) and the greater the number of friends, the higher the affinity, the higher the average number of posts per unit time and the average number of comments per unit time have.

As another example, the information extracting unit 212 may apply the morphological analysis algorithm to the information posted on the user, such as Facebook, a blog, an Instagram, and a homepage, to evaluate the previously stored personality (integrity and affinity) Can be extracted. For example, a vocabulary for assessing integrity is a vocabulary that represents a factor defined as a sub-characteristic of integrity in the Big Five model, and may include vocabulary related to competence, order, duty, achievement effort, self-control, and prudence. Vocabulary for evaluating affinity may include vocabulary related to reliability, morality, altruism, humility, meekness, and conformity.

 In step 332, the information extracting unit 212 extracts demographics by performing data analysis on the information posted on the SNS and the information posted on the web page.

For example, the information extracting unit 212 extracts information on the education level, the age, the place of residence, the place of birth, the place of work, the family member, the income, and the health status of the user by using a morphological analysis algorithm It can be extracted as information. The detailed operation of extracting demographic information will be described with reference to FIG.

In step 340, the calculation unit 213 may calculate the toughness score of the user by applying the response to the query and the toughness information to the toughness evaluation algorithm. For example, the calculation unit 213 can calculate the toughness score by scoring personality factors such as integrity and affinity based on the Big Five model through a toughness evaluation algorithm. Here, the detailed operation of calculating the toughness score of the user will be described later with reference to FIG.

The response to the query, personality information, and demographic information can be used as non-financial information to evaluate a user's credit. As the toughness score of the user is calculated based on the response to the query and the toughness information, the toughness score and demographic information can be used for the user's credit evaluation as nonfinancial information.

In step 350, the credit evaluation unit 214 can evaluate the credit of the user based on the non-financial information and the financial information. As shown in Equation 1 below, the credit evaluation unit 214 applies the personality score, the demographic information, and the financial information as input parameters of the credit evaluation algorithm, Can be evaluated. The credit evaluated through the credit evaluation algorithm can be expressed as a credit score.

In Equation (1), the credit score for non-financial information can be calculated by regression analysis of the relationship between actual credit score, toughness score, and demographic information on the basis of a total point as shown in Equation (2) below. Here, it is assumed that the relationship between actual credit score, personality score, and demographic information has a first-order linear relationship.

According to equation (2), as the sum of the credit score (Score _{non-financial)} is toughness score (Score _{personality),} and demographic information, the score (Score _demographic) for evaluating the non-financial information can be calculated.

In Equation (1), the credit score for the financial information is calculated using a credit score (e.g., Fair Isaac Corporation, FICO Score, etc.) provided by a credit information evaluation system (e.g., credit information company such as a financial institution) ) To one point.

Equation (1) can be expressed as Equation (3) below, and the credit evaluation unit 214 can evaluate the credit of the user by applying the credit evaluation algorithm according to Equation (3) below.

) 및 비재무 정보 기반의 신용 점수(

)의 합으로써, 계산될 수 있다.According to Equation (3), the final credit score of the evaluated user is the financial information based credit score

) And non-financial information based credit score

), ≪ / RTI >

At this time, the credit evaluation unit 214 can evaluate the credit of the user by applying the weights (?,?) To the non-financial information and the financial information, respectively. For example, the credit evaluation unit 214 may assign a higher weight to the financial information than the non-financial information based on the increase ratio of the non-financial information to the non-financial information, Can be given. The detailed operation of applying the weights to the financial information will be described later with reference to FIG.

In addition, the credit evaluation unit 214 may perform credit evaluation based on non-financial information and financial information by applying different weights according to the age of the user.

For example, when a user is a minor who has no fixed income, it can apply a high weight if he or she applies a small weight and makes vocational activities such as 30-40. At this time, the database 250 can match and store corresponding weights according to the age of the user (minor-weight 1, 20 to 40-weight 8, 50 to 60-weight 10, 70-weight 7 The credit evaluation unit 214 can determine the corresponding weight from the database 250 based on the extracted age of the user as the demographic information, and evaluate the credit of the user.

FIG. 5 is a diagram provided to illustrate an operation of calculating a toughness score of a user, according to an embodiment of the present invention. FIG.

According to FIG. 5, the response 501 to the toughness evaluation query and the toughness information 502 extracted through data analysis may be input parameters of the toughness evaluation algorithm. Then, the toughness evaluation algorithm can calculate the toughness score 503 by performing the toughness evaluation based on the input response 501 and the toughness information 502.

 For example, when 100 integrity-related queries and 100 affinity-related queries are provided and 100 responses are received for each query, the toughness evaluation algorithm adds the matched scores to each of the 100 sincency-related responses , And scores matched to each of the 100 affinity-related responses. For example, in relation to the sincerity related query in Table 1 above, the score is matched according to the view of the query, such as 0 for example 1, 1 for example 2, 2 for example 3, 4 for example 4, (250). ≪ / RTI > At this time, if 3 is selected as the sincerity related response, the toughness evaluation algorithm can cumulatively add 2 points corresponding to No. 3 and scores corresponding to subsequent received responses.

In addition, the toughness evaluation algorithm may calculate the toughness score 503 by adding the converted score based on the extracted toughness information such as the average number of posts per unit time, the number of comments, the number of friends, and the like.

For example, an affinity score and a sincerity score may be matched with each other by the amount of the number of posts, the number of comments, and the number of friends, and may be stored in the database 250 as shown in Table 2 below. , The sincerity and affinity score corresponding to the toughness information can be calculated by referring to Table 2 below. The toughness evaluation algorithm may calculate the toughness score by adding the calculated integrity score and the cumulative summed sincency score based on the response and adding the calculated affinity score and the cumulative summed affinity score based on the response have. That is, the toughness score can be calculated by sincerity and affinity.

Average number of posts per hour Average comments per unit time Number of Friends Integrity Affinity 0 to 5 0 to 10 0 to 10 people A1 point A2 point 6 to 20 11 to 20 11 to 30 people B1 point B2 point 21 to 50 21 to 30 31 to 60 people C1 point C2 point 51 to 99 31 to 40 61 to 99 D1 point D2 point More than 100 41 or more More than 100 E1 point E2 point

In Table 2, A1 to E1 and A2 to E2 represent numerical scores, and may be set to 1 point to 5 points, or may be set to other numerical values. The above Table 2 corresponds to an example of calculating the toughness score 503 based on the toughness information 502 and the toughness evaluation algorithm, and the average number of posts per unit time, the number of comments, the number of friends, Other sincerity and affinity scores can be changed.

The credit rating algorithm 503 includes the toughness score 503, the demographic information 504, the financial information 504, and the financial information 505. The toughness score 503, the demographic information 504, 505) to calculate a credit score 506 that represents the credit of the user.

6 is a graph showing an example of a toughness score according to the toughness evaluation algorithm performance in an embodiment of the present invention.

FIG. 6 is a graph in which toughness scores are calculated for five personal factors based on the Big Five model. At this time, the toughness score can be calculated on the basis of 100 points for each personality factor. For example, the integrity 601 of the user is 41 points on a scale of 100 points, the affinity 602 is 32 points on the basis of 100 points, openness 603 is 65 points, neuroticism 604 is 71 points, 605) can be calculated as 13 points.

Figure 7 is a diagram provided to illustrate the operation of extracting demographic information in one embodiment of the present invention.

7, it is assumed that the demographic information is extracted from the information posted in the SNS. In addition, demographic information may be extracted from the user's home page or the like.

7, the information extracting unit 212 extracts a user's work 701, education 702, a residence 703, a place of birth 704, and the like using the morphological analysis algorithm on the information 700 posted in the SNS. ) Can be extracted as demographic information.

In addition, when the information posted in the SNS is economic related information, it is possible to extract the economic knowledge related information of the user of the information extracting unit 212 as demographic information, and the information posted in the SNS may be related to the health check result , Information related to the health status of the user may be extracted as demographic information, or information related to the user's family structure may be extracted as demographic information if the information posted on the SNS is related to family travel or family photography. You may.

8 is a diagram for explaining an operation of assigning a weight to financial information and evaluating a user's credit in an embodiment of the present invention.

8 shows an example of the data increase amount of the financial information when a = 10, b = 600, and c = 100, and a, b and c are precisely adjusted as the weight related research to be applied to the financial information is advanced . Here, a, b, and c may represent parameters for controlling the ratio of financial information to total credit score. For example, by adjusting the values of a and c, it is possible to control the change in the data amount of the financial information to change the beta value, and b can represent the minimum value of the beta value. And, a, b, and c can be set differently according to individual personality characteristics, demographic characteristics, and the like. For example, the parameters a, b, and c can be set differently for the twenties and thirties who have the same number of financial information, so that the percentage of financial information in the credit score can be set differently.

Equation (4) below is a mathematical expression representing the relationship between the weight (?) For non-financial information and the weight (?) For financial information.

According to Equation (4) above, when the credit score is calculated based on the total score of the credit score of 1000, it is confirmed that the ratio of the non-financial information to the total score is changed according to the data amount of financial information (#Financial Data) . For example, as the amount of financial information increases, the ratio of financial information to total credit score increases and the ratio of nonfinancial information decreases.

According to FIG. 8, as the amount of data of the financial information increases by 10 times, the weight β used in calculating the credit score of financial information increases by 10%, and the weight α used in calculating credit score of nonfinancial information decreases by 10% have. In this way, the ratio of financial information in the amount of data and credit score of financial information can have a logarithmic function (logarithmic relation).

On the other hand, if the user does not have financial information related to the user due to reasons such as a minor age, or if the data amount of the financial information is very less than the preset reference data amount, the credit evaluation unit 214 may assign a non- Can be applied to evaluate the user's credit. For example, the weight α may have a value larger than the weight β.

FIG. 9 is a diagram provided for explaining an operation of calculating a credit score for non-financial information in an embodiment of the present invention. FIG.

9 corresponds to an example of calculating a credit score for non-financial information. In addition to the method described in FIG. 9, a credit score for non-financial information can be calculated in various ways.

In Figure 9, certain parameters may be added or deleted as demographic information and personality are enhanced. For example, age, birth place, etc. may be added. In FIG. 9, the score scale and score criteria of each item (education, family, financial knowledge, health conscientiousness, agreeableness, others) can be subdivided according to the upgrading, Can be adjusted as the degree of advancement increases.

 According to FIG. 9, the toughness score of each of the integrity and affinity can be calculated based on the response to the query, the SNS, and the toughness information extracted from the information posted on the web page. In this case, the integrity score (901) and the affinity score (902) can account for 25% of non - financial information, respectively. And demographic information can account for the remaining 50% of nonfinancial information. That is, the toughness score obtained through the toughness evaluation query and data analysis accounts for 50% of nonfinancial information, and the demographic information accounts for the remaining 50%, which can be used for credit evaluation of nonfinancial information.

At this time, each information extracted as demographic information can be equally divided and occupied 50% allocated to the demographic information. For example, if four items (education, family, financial knowledge, and health) are extracted as demographic information, 12.5% can be allocated to each item.

Hereinafter, an operation of evaluating a user's credit when there are 100 financial information (i.e., credit transaction information) related to the user will be described as an example. The user interface unit 211 can request and receive user's financial information from an external credit information evaluation system.

When the number of financial information is 100, according to FIG. 8, the weight α = 200 and β = 800 can be determined. At this time, the credit score of the user who is evaluated according to the conventional standard can be calculated as 760 points (out of 1000).

However, when non-financial information and financial information are taken into consideration, the credit evaluation unit 214 may calculate the credit score for the financial information at 608 points (800 * 760/1000) in consideration of the weight β. Then, the credit evaluating unit 214 can calculate the credit score for the non-financial information in consideration of the weight?.

For example, demographic information of users includes information related to education (college graduation), information related to family composition (household of one person), information related to economic knowledge (common economic knowledge), information related to medical expenditure , The credit evaluation unit 214 may calculate the score by digitizing each item according to the demographic information related table 910 shown in FIG. 9. For example, if the education-related information is 4 (911), which is a numerical value obtained by graduating from a university, the credit evaluation unit 21 can calculate 6 points (10 * 3/5 = 6) If the value obtained by quantifying the family structure-related information as 1 family is 5 (912), the credit evaluation unit 214 can calculate the family structure-related information as 10 points (10 * 5/5 = 10). When the economic knowledge related information is numerically expressed as 913, the credit evaluation unit 214 may calculate the economic knowledge related information as 6 points (10 * 3/5 = 6). If the value of the medical expenditure corresponds to one year of the medical expenditure of 500,000 won or more and less than 1 million won (914) and the numerical value is 3, the credit evaluation unit 214 stores the information related to the medical expenditure at 6 points (10 * 3 / 5 = 6). Finally, if the integrity-related personality score calculated from the toughness evaluation algorithm falls between 40 and 60 (915), and the affinity-related personality score falls between 20 and 40 (916) (214) can recalculate the integrity-related personality score to 15 (25 * 3/5 = 15) and the affinity related personality score to 20 (25 * 4/5 = 20).

The credit evaluation unit 214 calculates the credit score based on the non-financial information as 130 points (200 * (8 + 10 + 6 + 2)) based on the sum of the constants of each item constituting the calculated non- 6 + 15 + 20) / 100 = 130, ②). The credit evaluation unit 214 may calculate the final credit score of the user as 738 points by summing 130 credit points based on non-financial information and 760 credit scores based on financial information. The user interface unit 211 can display the calculated credit score on the screen of the user terminal 101 through the user interface. Accordingly, the user can immediately confirm his / her creditworthiness without disclosing the creditworthiness to others.

The credit score of the user thus calculated can be used to recommend the financial product to the user or to be used for various purposes.

10 is a flowchart for recommending a financial instrument based on a credit score in an embodiment of the present invention.

In FIG. 10, the steps 1001 to 1002 may be performed by the components 211, 212, 213 and 214 of FIG. 2 and the product recommendation unit (not shown).

In step 1001, the credit evaluation unit 214 calculates the credit score of the user based on the non-financial information and the financial information, and the calculated credit score can be provided on the screen of the user terminal 101. [

In step 1002, the commodity recommender (not shown) may recommend the appropriate financial product to the user based on the calculated credit score of the user. In order to recommend a financial product, the database 250 may store and maintain one or more matched financial products that are appropriate for each credit score. For example, an appropriate financial product may be matched and stored within a certain range, such as an investment product and a loan product suitable for a credit score of 701 to 800.

For example, when the user's credit score is between 731 and 800, the commodity recommender (not shown) receives an investment item corresponding to the credit score from the database 250 (eg, Insurance, etc.) and loan products (credit, home equity, etc.).

As another example, the interest rate, the payment period of the card, the card limit, and the like may be determined based on the credit score of the user in a Selfie Card, a selfie loan, a selfie landing, a crowdfunding, . The commodity recommending unit (not shown) can provide the screen of the user terminal 101 so that the interest rate, the card price, the payment term, the card limit, etc. can be changed by the user.

For example, when the user uses the self-card, a range of the card settlement period, the card usage rate, the card interest rate, etc. of the predetermined self-service card based on the user's credit score can be displayed on the screen of the user. Here, the range of the card settlement period, the degree of card use, the card interest rate, and the like can be preset to various levels for each credit score. For example, the credit scores A and B are set to 1 grade, the card settlement period is 15 to 1 month, the card limit is 5,000 to 10 million won, and the card interest rate is preset to 4% to 8% C to D are 2 grades, the card settlement period is 1 day to 1 month, the card limit is 100,000 to 1,000,000 won, and the card interest rate can be preset to 4% to 12% per year.

As described above, in addition to the card settlement period, the usage limit, and the interest rate, the rental period, the loan amount, and the loan interest rate in the case of Selphiron can have a predetermined range of the lower limit value and the upper limit value, Within the set range, the upper limit value and the lower limit value can be selected and reduced by the user. For example, if the user's credit corresponds to the second grade, the card payment period corresponding to the credit score may be from 1 day to 1 month, and the card usage limit of 100,000 to 1,000,000 possible. At this time, since the card settlement period is one day to one month, the card settlement period can be selected by the user within a given range (one day to one month). For example, when the user selects a card settlement period of one month, the card usage limit may be automatically reduced from 100,000 to 300,000 won. As the card settlement period is reduced, the card limit of the user may be reduced to the card usage limit matched in the reduced card settlement period. Similarly, when the card interest rate range is 4% to 12% per year, the card payment period is 1 to 2 months, and the interest rate is selected as 5% per year by the user, the card payment period matched to the selected interest rate 5% Can be automatically reduced to 10 days to 1 month. The commodity recommendation unit (not shown) can display information such as the card payment period selected by the user, the card usage degree automatically changed, the interest rate selected by the user, and the automatically changed card payment period on the screen of the user terminal.

As described above, the user can freely set the settlement period and the used interest rate within the range of the card settlement period, the card use limit, the card interest rate, the loan period, the loan amount, and the loan interest rate set to be available according to the user's credit score.

11 is a block diagram for explaining an example of the internal configuration of a computer system in an embodiment of the present invention.

11, a computer system 1100 includes at least one processor 1110, a memory 1120, a peripheral interface 1130, an I / O subsystem, A power circuit 1140, a power circuit 1150, and a communication circuit 1160. At this time, the computer system 1100 may correspond to a user terminal.

The memory 1120 can include, for example, a high-speed random access memory, a magnetic disk, a SRAM, a DRAM, a ROM, a flash memory, or a non-volatile memory. have. Memory 1120 may include software modules, a set of instructions, or various other data required for operation of computer system 1100. At this time, accessing the memory 1120 from other components, such as the processor 1110 or the peripheral device interface 1130, may be controlled by the processor 1110.

The peripheral device interface 1130 may couple the input and / or output peripheral devices of the computer system 1100 to the processor 1110 and the memory 1120. Processor 1110 may execute a variety of functions and process data for computer system 1100 by executing a software module or set of instructions stored in memory 1120. [

The input / output subsystem 1140 can couple various input / output peripheral devices to the peripheral interface 1130. For example, input / output subsystem 1140 may include a controller for coupling a peripheral device such as a monitor, keyboard, mouse, printer, or a touch screen or sensor, as needed, to peripheral interface 1130. According to another aspect, the input / output peripheral devices may be coupled to the peripheral device interface 1130 without going through the input / output subsystem 1140.

The power circuitry 1150 can provide power to all or a portion of the components of the terminal. For example, the power circuitry 1150 may include one or more power supplies, such as a power management system, a battery or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, And may include any other components for creation, management, distribution.

The communication circuitry 1160 may enable communication with other computer systems using at least one external port. Or as described above, the communication circuitry 1160 may communicate with other computer systems by sending and receiving RF signals, also known as electromagnetic signals, including RF circuits.

11 is merely an example of the computer system 1100, and the computer system 1100 may include additional components not shown in FIG. 11, some components shown in FIG. 11 may be omitted, Lt; RTI ID = 0.0 > components. ≪ / RTI > For example, in addition to the components shown in FIG. 11, a computer system for a communication terminal in a mobile environment may further include a touch screen, a sensor, etc., and various communication methods (WiFi, 3G, LET , Bluetooth, NFC, Zigbee, etc.). Components that may be included in computer system 1100 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing or application specific integrated circuits.

The methods according to embodiments of the present invention may be implemented in the form of a program instruction that can be executed through various computer systems and recorded in a computer-readable medium.

The program according to the present embodiment can be configured as a PC-based program or an application dedicated to a mobile terminal. The credit evaluation application according to the present embodiment may be implemented as a program operating independently or in an in-app form of a specific application, and may be implemented to operate on the specific application.

In addition, the methods according to embodiments of the present invention may be performed by a credit evaluation application controlling a user terminal. Such an application can be installed in a user terminal through a file provided by a file distribution system. For example, the file distribution system may include a file transfer unit (not shown) for transferring the file at the request of the user terminal.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (10)

In a computer implemented credit rating method,
Providing a user interface for providing a query for toughness evaluation;
Receiving a response to the query through the user interface;
Extracting personality information and demographic information on information posted on at least one of a social network service and a web, to which a user subscribes;
Calculating a toughness score based on the response and toughness information; And
Evaluating the user's credit using non-financial information and financial information based on the toughness score and demographic information
. The method according to claim 1,
The step of evaluating the credit comprises:
Evaluating the credit by applying a weight to the financial information according to the amount of increase in the amount of data of the financial information compared to the non-financial information
And a credit rating method. The method according to claim 1,
Wherein calculating the toughness score comprises:
Scoring Personality Factors Based on the Big Five Model
And a credit rating method. The method according to claim 1,
Wherein calculating the toughness score comprises:
Calculating the toughness score based on the score calculated by applying the toughness evaluation algorithm based on the vocabulary included in the posted information and the score calculated by applying the toughness evaluation algorithm based on the response
And a credit rating method. The method according to claim 1,
Wherein the step of extracting the toughness information and the demographic information comprises:
Extracting the average number of posts per unit time, the average number of comments per unit time, and the number of friends as the toughness information on information posted on at least one of a social network service and a web page
And a credit rating method. The method according to claim 1,
Wherein the step of extracting the toughness information and the demographic information comprises:
Age, place of residence, place of birth, workplace, family composition, income, and health status of at least one of information published on at least one of a social network service and a web page, Extraction as demographic information
And a credit rating method. The method according to claim 1,
And recommending at least one of a loan-related commodity and an investment-related commodity based on the evaluated user's credit
Further comprising: The method according to claim 1,
Requesting and receiving the financial information from at least one credit information evaluation system
Further comprising:
The financial information includes:
Including at least one of financial asset related asset information, asset information related to fixed income, and loan related information
And a credit rating method. A user interface providing unit for providing a user interface for providing a query for toughness evaluation and receiving a response to the query through the user interface;
An information extracting unit for extracting personality information and demographic information on information posted on at least one of a social network service and a web page to which the user subscribes;
A calculation unit calculating a toughness score based on the response and toughness information; And
A credit evaluation section for evaluating the user's credit using non-financial information and financial information based on the toughness score and demographic information,
And a credit rating system. 10. The method of claim 9,
Based on the evaluated user's credit, at least one of the loan-related commodity and the investment-
Further comprising:
The range of the loan term, the loan amount, and the loan interest rate of the loan related product is changed and set by the user within the range set according to the user's credit
Credit rating system.

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