KR102296404B1 - Planning system and method of financial contents - Google Patents

Abstract

According to a preferred embodiment of the present invention, as a financial content design method performed in a financial content design system including a financial content design server connected to a user terminal and a network and a database storing two or more financial content information, 1) the financial content design server receiving user personal information input from a user terminal; 2) determining, by the financial content design server, the degree of similarity between the user and each customer group stored in the database by a preset similarity determining method based on the user's personal information; 3) the financial content design server generates first financial content recommendation information using financial content information that can be matched to a customer group having a high degree of similarity to the user, and providing the first financial content recommendation information to the user terminal ; 4) receiving, by the financial content design server, response information to the first financial content recommendation information from the user terminal; and 5) generating, by the financial content design server, second financial content recommendation information based on the response information, and providing it to the user terminal;

Description

Financial contents design system and financial contents design method {Planning system and method of financial contents}

The present invention relates to a financial content design system and a financial content design method, and more particularly, determines a degree of similarity between a user and a customer group based on two or more preset customer group information and user input information, and a user based on the similarity It relates to a financial content design system and a financial content design method that recommends financial content suitable for users and recommends financial content again in consideration of the user's reaction (response) to the recommended financial content.

With the development of economic development and information and communication technology, countless financial contents (financial products) such as bank deposits and savings accounts, as well as credit cards and insurance, are being created and destroyed. The process of finding, comparing, and selecting financial contents suitable for one is accompanied by great difficulties.

In particular, it is not easy for consumers to select appropriate financial content on their own because it is often difficult to actually select (subscribe) even for financial content that they think is suitable for them, depending on their economic situation or credit.

Reflecting this reality, various technologies have been developed to design (recommend) appropriate financial contents to consumers among numerous financial contents and to help consumers select them.

Among the prior art related to financial content design, there are a computer program and a computer readable recording medium for performing a method and a device for providing a user-customized card recommendation service, a method for providing a user-customized card recommendation service, and a computer-readable recording medium of Korean Patent Application Laid-Open No. 10-2015-0018753 .

The prior art provides a consumption pattern input interface to the user terminal, calculates the benefit values such as discount/accumulation of all 1,400 cards according to the consumption pattern information, conducts a full investigation up to the unit of won, and sets the user benefit price for all checked cards By listing in this large order, it is configured so that the user (consumer) can check and apply for card information optimized for his or her consumption pattern by recommending the card with the greatest number of benefits such as discount / accumulation.

However, the prior art has a problem in that it may cause confusion to the user for financial content that is difficult to classify as a benefit value. There is a problem in that appropriate financial content cannot be recommended unless you input or provide your consumption pattern to a financial company.

In addition, the prior art does not consider how to design to recommend appropriate financial content to the user when requesting a recommendation again after selecting and subscribing to the recommended financial content or to receive a recommendation for other financial content instead of the recommended financial content. there is a problem.

The present invention is to solve this conventional problem, and even if the user does not input all of his/her personal information but only partially inputs the financial content optimized for the user in various ways, it is more suitable for the user based on the user's response The purpose is to design to automatically recommend financial content.

According to a preferred embodiment of the present invention for achieving this object, as a financial content design method made in a financial content design system including a financial content design server connected to a user terminal and a network and a database in which two or more financial content information is stored - the above The database includes two or more customer group information, and each customer group information is configured to be able to match at least one or more financial content information-, 1) the financial content design server receives user personal information input from the user terminal step; 2) determining, by the financial content design server, the degree of similarity between the user and each customer group stored in the database by a preset similarity determining method based on the user personal information - Each customer group is the Existing users are pre-classified by a preset clustering algorithm based on user personal information; 3) the financial content design server generates first financial content recommendation information using financial content information that can be matched to a customer group having a high degree of similarity to the user, and providing the first financial content recommendation information to the user terminal ; 4) receiving, by the financial content design server, response information to the first financial content recommendation information from the user terminal; and 5) generating, by the financial content design server, second financial content recommendation information based on the response information, and providing it to the user terminal;

According to another preferred embodiment of the present invention, before the step 2), the financial content design server calculating the user's operable assets based on the user's personal information; further comprising, The second financial content recommendation information is characterized in that it is generated based on the response information and the user's operable assets.

According to another preferred embodiment of the present invention, the response information is financial content subscription information and financial content recommendation request information according to selection of the first financial content information included in the first financial content recommendation information, and the second financial content recommendation information. The content recommendation information is characterized in that it is generated based on an amount obtained by subtracting a predetermined evaluation amount of the first financial content from the operable asset of the user.

According to another preferred embodiment of the present invention, the response information is financial content re-recommendation request information according to not selecting all financial content information included in the first financial content recommendation information, and the second financial content recommendation information is generated based on an amount obtained by subtracting the evaluation amount of the financial content having the smallest predetermined evaluation value among all the financial contents included in the first financial contents recommendation information from the user's operable assets.

According to another preferred embodiment of the present invention, the response information is financial content subscription information and financial content recommendation request information according to selection of the first financial content information included in the first financial content recommendation information, and the second financial content recommendation information. The content recommendation information includes the user and the data by a similarity determination method preset based on the user personal information including the financial content subscription information according to the selection of the first financial content information included in the response information by the financial content design server in the response information. Determining the degree of similarity with each customer group stored in the base, and the financial content design server generating second financial content recommendation information using financial content information that can be matched to a customer group having a high similarity with the user. It is characterized in that when the similarity is determined, a positive similarity weight is given to a customer group to which the first financial content can be matched.

According to another preferred embodiment of the present invention, the response information is financial content re-recommendation request information according to not selecting all financial content information included in the first financial content recommendation information, and the second financial content recommendation information determining, by the financial content design server, the degree of similarity between the user and each customer group stored in the database by a preset similarity determining method based on the user personal information; It is generated through the step of generating second financial content recommendation information using financial content information that can be matched to a customer group having a high similarity with It is characterized in that negative similarity weights are given to possible customer groups.

According to another preferred embodiment of the present invention, the clustering algorithm is characterized in that the following equation 1 is satisfied.

[Equation 1]

는 고객그룹에 속한 각각의 데이터의 중심벡터,

는 고객그룹에 속한 데이터의 집합,

는 고객그룹에 속한 특정 데이터의 벡터임.RSS is the objective function of the preset clustering algorithm (total variance), K is the number of customer groups,

is the central vector of each data belonging to the customer group,

is the set of data belonging to the customer group,

is a vector of specific data belonging to a customer group.

According to another preferred embodiment of the present invention, the similarity determining method is characterized in that the following equation 2 is satisfied.

[Equation 2]

는 고객그룹 벡터와 사용자 벡터가 이루는 각임.G is the customer group data, U is the user's data,

is the angle between the customer group vector and the user vector.

According to another preferred embodiment of the present invention, the step 3) comprises: 3-1) classifying the financial content information according to a predetermined decision-making method by the financial content design server; 3-2) extracting, by the financial content design server, financial content information that can be matched with a customer group having a high degree of similarity to the user based on the classified financial content information; and 3-3) generating, by the financial content design server, financial content recommendation information using the extracted financial content information.

According to another preferred embodiment of the present invention, the user personal information includes the user's image information, and before step 2), the financial content design server analyzes the user's image information by a preset image analysis method to do; and further generating characterized user personal information based on the analyzed result.

According to another preferred embodiment of the present invention, the image information of the user includes the appearance shape of the user, and the image analysis method is to compare the appearance shape of the user with the appearance shape of existing users to determine whether it is similar. , the textualized user personal information is characterized in that it is generated based on the personal information of an existing user having an appearance similar to that of the user.

According to another preferred embodiment of the present invention, the user's image information includes the user's face shape, and the image analysis method is to analyze the user's face shape based on coronal science to extract the user's coronal information , The textualized user personal information is characterized in that it is generated based on the user's contemplative information.

According to another preferred embodiment of the present invention, the user's image information includes the user's palm shape, and the image analysis method analyzes the user's palm shape based on water science to extract the user's palm information. and the characterized user personal information is characterized in that it is generated based on the palmistry information of the user.

According to another preferred embodiment of the present invention, before step 3), requesting the user's CB information from the financial content design server to a CB (Credit Bereau) server further includes; step 3) The financial content recommendation information generated in is characterized in that it is generated by reflecting the user's credit status included in the user's CB information.

According to another preferred embodiment of the present invention, a user terminal that performs including a function of inputting user personal information and a function of displaying financial content recommendation information; a database for storing including customer group information configured to match two or more financial content information and at least one or more financial content information; and a function of receiving the input user personal information and network connection with the user terminal, and determining the similarity between the user and each customer group stored in the database by a similarity determination method preset based on the user personal information function, a function of generating first financial content recommendation information using financial content information that can be matched to a customer group having a high degree of similarity to the user, and receiving response information to the first financial content recommendation information from the user terminal, and Financial content design comprising; a financial content design server performing a function of generating second financial content recommendation information based on the response information and a function of providing the first and second financial content recommendation information to the user terminal system is provided.

According to another preferred embodiment of the present invention, the financial content design server further includes a function of calculating the user's operable assets, and the second financial content recommendation information includes the calculated user's operable assets. It is characterized in that it is created on the basis of

The present invention can recommend financial content so that the user can easily select the best financial content suitable for the user even if the user does not input all of his or her personal information. There is an advantage that the user's intention can be effectively reflected when a recommendation is requested or when a request for recommendation of financial content other than the recommended financial content is requested.

In addition, the present invention has an advantage in that it is possible to further recommend user-customized financial contents by analyzing the user's image information.

In addition, the present invention has an advantage in that an error in selecting financial content that a user cannot subscribe to does not occur, and it is possible to quickly select financial content by allowing the user to preemptively determine the credit status of an individual.

1 is a block diagram of a financial content design system according to an embodiment of the present invention.
2 is a schematic diagram of the configuration of user personal information according to an embodiment of the present invention.
3A and 3B are flowcharts of a method for designing financial content according to an embodiment of the present invention.
4 is a schematic diagram illustrating a clustering algorithm according to an embodiment of the present invention.
5 is an exemplary diagram of a customer group classified by a clustering algorithm according to an embodiment of the present invention.
6 is a schematic diagram illustrating a similarity determining method according to an embodiment of the present invention.
7A and 7B are schematic diagrams for explaining a decision-making method according to an embodiment of the present invention.
8 is an exemplary diagram of financial content recommendation information according to an embodiment of the present invention.
9 to 11 are diagrams for explaining a method of generating characterized user personal information using user image information according to another embodiment of the present invention.
12 is a flowchart of a method for generating textualized user personal information according to another embodiment of the present invention.
13 is a flowchart of a financial content design method according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, in the description of the present invention, in the case of obscuring or obscuring the technical idea of the present invention due to the detailed description of the known configuration, the description of the known configuration will be omitted.

1 is a block diagram of a financial content design system 10 according to an embodiment of the present invention.

The financial content design system 10 according to an embodiment of the present invention comprises a user terminal 300 , a database 200 , and a financial content design server 100 .

The user terminal 300 performs including a function of inputting user personal information and a function of displaying first financial content recommendation information and second financial content recommendation information, which will be described later.

The user terminal 300 may be understood as a computer, a laptop computer, a smartphone, a tablet, and the like, and each of the input means 320 for inputting the user personal information and options, and each generated based on the user personal information. and a display means 340 for displaying financial content recommendation information.

The input means 320 and the display means 340 may be integrally formed in the form of a touch screen capable of inputting a touch.

The database 200 may store information related to various financial contents. The database 200 may store two or more pieces of financial content information, customer group information configured to match at least one piece of financial content information, and user personal information input from the user terminal 300 .

The database 200 may be separately installed and electrically connected to the financial content design server 100 as shown in FIG. 1 , and may be provided in the form of a memory inside the financial content design server 100 , , may be provided in the form of a network connection with the financial content design server 100 .

The customer group information is information about each customer group, and each customer group is formed by pre-classifying existing users by a clustering algorithm preset based on user personal information of existing users. Each customer group consists of at least one or more existing users, and the financial content that the existing users subscribed to or the financial content most suitable for the customer group, recommended by a financial content designer or computer, or newly created financial content, etc. can be matched. A detailed description of the clustering algorithm will be described later.

The financial content design server 100 is connected to the network with the user terminal 300, the user and the database by a function of receiving the input user personal information, and a similarity determination method preset based on the user personal information. A function of determining a degree of similarity with each customer group stored in 200, a function of generating first financial content recommendation information using financial content information that can be matched to a customer group having a high similarity with the user, the first financial content A function of receiving response information to the recommendation information from the user terminal 300 and generating second financial contents recommendation information based on the response information, and the first financial contents recommendation information and the second financial contents recommendation information to the user It performs including a function provided to the terminal 300 .

For example, the financial content design server 100, as shown in FIG. 1, receives the user personal information input from the operation module performing the overall management function of the server, the user terminal 300, and A user personal information management module for storing and managing in the database 200, a customer group information management module for managing two or more customer group information classified in advance by a preset clustering algorithm to be described later based on the user personal information, the data A financial content information management module that manages the financial content information stored in the base 200 and allows the newly created financial content information to be stored in the database 200, analyzes the user's image information to be described later, and collects the textualized user personal information It may be achieved by including an image information analysis module to further generate. The financial content information management module generates the first financial content recommendation information and/or the second financial content recommendation information, and the database 200 provides each user in addition to the financial content information and user personal information. 1 financial content recommendation information and/or second financial content recommendation information may be further stored.

In addition, the financial content design server 100 further includes a function of calculating the user's operable assets, and the second financial content recommendation information is based on the user's response information to the first financial content recommendation information. generated, and may be generated based on the user's operable asset information along with the response information.

For example, the user personal information further includes image information of the user including at least one of a user's external shape, a user's face shape, and a user's palm shape.

In this case, the financial content design server 100 further includes a function of analyzing the user's image information by a preset image analysis method, and generating characterized user personal information based on the analyzed result.

The user's image information serves to supplement the user's personal information when the user's personal information necessary for the financial content recommendation is not properly secured due to omission of input or the small amount of personal information that can be entered. A description will be given later.

2 is a schematic diagram of a configuration of user personal information according to an embodiment of the present invention, and FIGS. 3A and 3B are flowcharts of a financial content design method according to an embodiment of the present invention.

A financial content design method according to an embodiment of the present invention includes a financial content design server 100 connected to a user terminal 300 and a network and a database 200 in which two or more financial content information is stored in a financial content design system 10 ) is done in

First, the financial content design server 100 receives the user personal information input from the user terminal 300 (step S1).

Before the step S1, the user terminal 300 requests a financial content recommendation to the financial content design server 100 and the financial content design server 100 sends the user terminal 300 to the user of the user. The step of requesting input of personal information may be further included.

In addition, before the step S1, the user terminal 300 may further include a step of subscribing and logging in to the financial content design server 100, and user personal information input from the user terminal 300 in the subscription step. A, may be automatically provided to the financial content design server 100 in the sign-up step or log-in step.

4 is a schematic diagram illustrating a clustering algorithm according to an embodiment of the present invention.

When the personal information of the user is received by the financial content design server 100, the financial content design server 100 is next to the user and the database 200 by a preset similarity determination method based on the user personal information. A degree of similarity with each stored customer group is determined (step S2).

As described above, each of the customer groups pre-classifies existing users by a clustering algorithm preset based on user personal information of the existing users.

For example, the clustering algorithm satisfies Equation 1 below.

[Equation 1]

는 고객그룹에 속한 각각의 데이터의 중심벡터,

는 고객그룹에 속한 데이터의 집합,

는 고객그룹에 속한 특정 데이터의 벡터이다.RSS is the objective function of the preset clustering algorithm (total variance), K is the number of customer groups,

is the central vector of each data belonging to the customer group,

is the set of data belonging to the customer group,

is a vector of specific data belonging to a customer group.

The data constitutes the user personal information, and may be configured in the form of a pattern in which two or more elements (eg, age, subscribed insurance product) of the user's personal information are displayed on spatial coordinates.

The clustering algorithm may classify existing users who have subscribed to similar financial contents (eg, hold deposits) into two or more customer groups.

The RSS represents a measure (Residual Sum of Squares) of how well the center of each customer group classified by the clustering algorithm expresses data belonging to the customer group, and as shown in Equation 1, For all data belonging to it, it can be expressed as the sum of the square distances to each data and the center.

For example, as shown in FIG. 4 , after two or more elements of user personal information are input in the form of a pattern on spatial coordinates, arbitrary two or more data are selected to set an arbitrary center line between each data, Grouped into two or more customer groups based on an arbitrary centerline.

When the center point of each grouped customer group is selected and a new center line is set between each center point, a new grouping is performed based on the new center line.

The process in which a new center point of each newly grouped customer group is selected and grouping is made based on the center line set between each new center point is repeated until no new center line (or center point) is set. .

If no new center line is set, the clustering algorithm is terminated and each customer group is determined in that state, and the determined customer group information is stored in the database 200 .

The customer group information may be changed by the clustering algorithm based on newly added user personal information. As the number of accumulated and stored users increases, the degree of change in the customer group information decreases.

는 다음과 같이 정의된다.The clustering algorithm is configured to minimize the average Euclidean distance between each piece of data and the center of the customer group to which the data belongs. The central point of the customer group may be understood as an average or centroid of data belonging to the customer group, and a central vector of each data belonging to the customer group.

is defined as

[Equation 2]

는 고객그룹에 속한 데이터의 집합,

는 고객그룹에 속한 특정 데이터의 벡터임.

is the set of data belonging to the customer group,

is a vector of specific data belonging to a customer group.

The clustering algorithm may be understood as an algorithm similar to, for example, a K-means algorithm that groups given data into K clusters.

5 is an exemplary diagram of a customer group classified by a clustering algorithm according to an embodiment of the present invention.

For example, as shown in FIG. 5 , user personal information of existing users is classified into four customer groups by the clustering algorithm, and each customer group is profiled by gender, age, wage and investment propensity characteristics. can

Each of the customer groups is used to identify characteristics of customers (users) who have requested financial content recommendation through the profiling and to generate financial content recommendation information.

6 is a schematic diagram illustrating a similarity determining method according to an embodiment of the present invention.

The method for determining the similarity according to an embodiment of the present invention satisfies Equation 3 below.

[Equation 3]

는 고객그룹 벡터와 사용자 벡터가 이루는 각임.G is the customer group data, U is the user's data,

is the angle between the customer group vector and the user vector.

The similarity determination method is used to determine (measure) the similarity between each customer group and the user. Since it is not a vector calculation by using the cosine similarity, the complexity of the calculation is low, and the frequency of data appearance (e.g. , the frequency of inclusion of specific elements of user personal information) is the key, so there is an advantage that the similarity can be easily determined even if there is a difference in the amount of each user's personal information.

는 상기 고객그룹 G1 벡터와 사용자 U 벡터가 이루는 각인

이 된다.6, to determine the degree of similarity between the customer group G1 and the user U, G in Equation 3 becomes data of the customer group G1, U in Equation 3 becomes data of the user U, of Equation 3 above

is the imprint formed by the customer group G1 vector and the user U vector

becomes this

The similarity has a value between -1 and 1, and in the case of -1, the customer group G1 and the user U have opposite (not identical) data, and in the case of 1, the customer group G1 and the user U It can be understood as having identical (exactly matching) data.

Based on the determined similarity, it is possible to determine which customer group or which customer group the user is most similar to, and based on this, financial content information that can be matched with the corresponding customer group or finance that can be matched with the most similar customer group The first financial content recommendation information may be generated using the content information.

7A and 7B are schematic diagrams for explaining a decision-making method according to an embodiment of the present invention.

As shown in FIG. 3A , when the similarity between the user and each customer group is determined by the similarity determining method, the financial content design server 100 can match the user and the customer group having a high similarity to the customer group financial content information. to generate the first financial content recommendation information, and provide the first financial content recommendation information to the user terminal 300 (step S3).

For example, the step S3 includes a step S31 in which the financial content design server 100 classifies financial content information by a preset decision-making method, and the financial content design server 100 collects the classified financial content information. A step S32 of extracting financial content information that can be matched to a customer group with a high degree of similarity to the user based on the step S32 and a step S33 of the financial content design server 100 generating the first financial content recommendation information with the extracted financial content information include

In step S31 , as shown in FIG. 7A , each piece of financial content information may be classified based on several characteristics.

For example, threshold 1 of characteristic A may be set to whether the age of the profiled customer group is 50 years or older, threshold value 2 may be set to whether or not the age of the profiled customer group is 80 years or more, and threshold 1 of characteristic B is one or more credits. It may be set as whether to subscribe (maintain) the card, and the threshold value 1 of the characteristic D may be set to whether or not to be male.

If the profiled characteristic of a customer group with a high degree of similarity to the user who wants to receive financial content recommendations is a male in his 40s, and he has signed up for two or more credit cards, the user corresponds to characteristic A, characteristic B, and characteristic F. can be understood as In this case, the financial content design server 100 extracts (matchable) financial content information that satisfies all characteristics A, characteristic B, and characteristic F in step S32, and recommends first financial contents using the extracted financial contents information Information is generated, and as shown in FIG. 7B, the first financial content recommendation information consisting of financial contents such as platinum credit card, savings insurance, tax savings, free savings, secured loan, and derivative investment is the user terminal ( 300) is provided.

As an example, the decision-making method may be set to classify financial content information based on a decision tree technique, and the decision tree technique uses a target variable (first financial information) based on several input variables (user personal information). It can be understood as a known decision tree decision learning method generally used for data mining to predict the value of content recommendation information).

8 is an exemplary diagram of first financial content recommendation information according to an embodiment of the present invention.

The first financial content recommendation information is provided from the financial content design server 100 to the user terminal 300 .

The user may determine one financial content included in the first financial content recommendation information through the user terminal 300 or select one or more financial contents from among two or more financial contents.

The financial content design server 100 may further include a function of performing a procedure related to the overall subscription of the financial content selectively input to the user terminal 300 .

For example, the first financial content recommendation information includes financial content information matched to the customer group having the highest similarity, and financial content information matched to the customer group having the next highest similarity is further arranged and displayed in sequence It may be configured to be

The financial content design server 100 according to this embodiment, as shown in FIG. 8, forms first financial content recommendation information according to each financial content type (eg, credit card, insurance, etc.), but ranks first in similarity , when the financial content information corresponding to the second order of similarity and the third order of similarity is sequentially arranged, the user can identify and select the appropriate financial content information for himself or herself at a glance.

The user may select one or more financial content information among the first financial content recommendation information displayed on the user terminal 300 or may not select all financial content information, and the first financial content recommendation information according to the user's decision. Response information (eg, selection/non-selection information of the first financial content recommendation information) is transmitted to the financial content design server 100 through the user terminal 300 (step S4).

For example, the user selects one piece of financial content information (eg, first financial content information) from among the first financial content recommendation information, and subscribes to the corresponding financial content (eg, first financial content) according to a predetermined subscription procedure. can do.

In this case, it is preferable to recommend another financial content other than the first financial content for which the subscription is made to the corresponding user, and response information including the subscription information for the first financial content is designed through the user terminal 300 . transmitted to the server 100 .

On the other hand, the user may not select all the financial contents included in the first financial contents recommendation information, and the response information according to the non-selection (non-adoption) of the first financial contents recommendation information is the financial contents design server 100 ) is transferred to

That is, the user may select and subscribe to one of the financial contents recommended by the user, or he/she may stop receiving the recommendation or request another financial contents recommendation because he or she cannot find the appropriate financial contents to subscribe to among the recommended financial contents, The financial content design server 100 receives response information according to each case.

The financial content design server 100 generates second financial content recommendation information based on the response information in response to a user's request for financial content re-recommendation (or automatically after a predetermined time has elapsed), and recommends the generated second financial content The information is provided to the user terminal 300 (step S5).

The second financial content recommendation information according to an embodiment of the present invention is generated based on the response information and the user's operable assets. To this end, as shown in Fig. 3b, before the step S2, the financial content design server 100 calculates the user's operable assets based on the user's personal information (step SB2) is further Included.

The user's operable asset may be understood as the user's property in which the user can provide a certain amount to a financial institution for a certain period of time by subscribing to financial contents such as savings, savings, and insurance, for example, the user's monthly salary or deposits.

Calculation of the user's operable assets may be automatically performed through various asset evaluation methods by the financial content design server 100, or may be made through simple extraction of the asset details input by the user or a separate user input.

Whereas the first financial content recommendation information is automatically generated using user personal information received through a user terminal and financial content information that can match a customer group having a high degree of similarity to the user determined by the similarity determination method, the The second financial content recommendation information is generated based on the user's response (selection or not) and the user's operable asset status.

A user receives the first financial content recommendation information and selects specific financial content (eg, first financial content) from the first financial content recommendation information to sign up, or without selecting all of the first financial content recommendation information Since all requests for recommendations for other financial contents involve the user's intention, it is desirable to recommend appropriate financial contents according to the user's reaction.

Therefore, in the financial content design method (second financial content recommendation) according to this embodiment, the user's intention to make a selection in consideration of the user's own asset status is taken into consideration, the user's assets that can be actually operated are calculated, and the It is configured to recommend financial content that can be subscribed within the scope.

For example, the response information is financial content subscription information according to a selection of the first financial content information included in the first financial content recommendation information. That is, the financial content subscription information means information that the user selects the first financial content information and subscribes to the corresponding financial content (first financial content), and further provides the recommendation request information that the user wants to subscribe to another financial content. include

When the user selects the first financial content included in the first financial content recommendation information, and the subscription is made according to the subscription procedure determined for the first financial content, the financial content design server 100 is the user's operable The second financial content recommendation information is generated based on the amount obtained by subtracting the predetermined evaluation amount of the first financial content from the asset.

The above 'evaluation amount' is a division of financial contents into amounts. If the financial contents are deposits, the monthly payment amount is used. If the financial contents are credit cards, the average (yearly or monthly) usage amount of the customer group to which the financial contents are matched, and monthly in the case of loans. It can be understood as interest (or interest and principal) that must be repaid.

As an example, the predetermined evaluation amount of the first financial content may be an amount to be paid (provided) every month to maintain the first financial content, or, as another example, may be a total amount required for maintaining the first financial content.

When a user subscribes to the first financial content, the user must pay, deposit, or repay a certain amount monthly, regularly, or temporarily. It is preferable to recommend the second financial content. At this time, the financial content design server 100 is based on the amount obtained by subtracting the predetermined evaluation amount of the first financial content from the user's operable asset (the amount reset to the user's operable asset), the financial content according to the degree of similarity The second financial content recommendation information may be generated using the design method.

As another example, the response information is financial content re-recommendation request information according to not selecting all financial content information included in the first financial content recommendation information, and the second financial content recommendation information is from the user's operable assets. The first financial content recommendation information is generated based on an amount obtained by subtracting the evaluation amount of the smallest financial content, the predetermined evaluation amount among all the financial contents included in the recommendation information.

When the user does not find financial content to subscribe to from all the financial content information included in the first financial content recommendation information, as shown in FIG. 3B , the user may select to quit without recommending other financial content, You can also ask us to recommend content.

If a user requests to recommend other financial content, such a user is likely to find that the recommended financial content is aggressive content (eg, high-risk, high-income content) or the amount to bear is not small compared to their operable assets. Therefore, when recommending other financial contents for the user, it is necessary to consider the evaluation amount of the financial contents included in the first financial contents recommendation information.

For example, when the user's operable assets are 1 million won per month, and the predetermined evaluation amounts of financial content included in the first financial content recommendation information are 400,000 won per month, 300,000 won per month, and 250,000 won per month, respectively, the user's Based on 750,000 per month, which is the amount obtained by subtracting the evaluation amount of the financial content with the smallest predetermined evaluation value among the financial contents included in the first financial contents recommendation information from the operable assets, the second financial contents design method according to the similarity is used. It is possible to generate financial content recommendation information. In this case, it is preferable that the financial content design server 100 controls (eg, deletes) the second financial content recommendation information so that the financial content information included in the first financial content recommendation information is not repeatedly recommended.

As another example, the range of deduction of the evaluation amount may be adjusted according to the type of financial content to be recommended (eg, the evaluation amount is subtracted from the financial content having the largest predetermined evaluation amount), and a group with a high degree of similarity to the user has a high aggressive investment propensity. In this case, it is also possible to add the predetermined evaluation amount to the user's operable assets, and to generate the second financial content recommendation information based on this.

The second financial content recommendation information according to another embodiment of the present invention is generated using the above-described method for determining the degree of similarity with each customer group, and the degree of similarity is based on the user's response information to the first financial content recommendation information. It may be configured to be generated through regulation.

That is, the second financial content recommendation information according to the present embodiment is generated based on response information configured differently depending on whether the financial content in the first financial content recommendation information is selected and subscribed, and the similarity adjustment process is performed in each When using the similarity determination method with the customer group, a positive (+) or negative (-) similarity weight may be assigned. The second financial content recommendation information is configured to include financial content information that can be matched to a customer group having the highest similarity ranking according to the similarity ranking to which the similarity weight is reflected.

For example, when the user selects and subscribes to the first financial content information, the response information includes financial content subscription information and financial content recommendation request information according to the selection of the first financial content information included in the first financial content recommendation information. includes

In this case, the second financial content recommendation information is based on the user personal information including the financial content subscription information according to the selection of the first financial content information included in the response information by the financial content design server 100 in advance. Determining the degree of similarity between the user and each customer group stored in the database 200 by a set similarity determining method, and the financial content design server 100 can match the user and the customer group having a high degree of similarity. It is created through the step of generating the second financial content recommendation information using the content information. When the financial content design server 100 determines the similarity, a positive similarity weight is given to a customer group to which the first financial content can be matched.

For example, when the first financial content recommendation information is created, the user's personal information has the highest similarity with the customer group G1 (eg, 0.9), so the first financial content recommendation information consists of financial content information that can be matched with the customer group G1, and the user When the first financial content included in the first financial content recommendation information is selected and subscribed, the user personal information is updated by further including the first financial content subscription information in the user personal information.

When the similarity with each customer group is determined using the updated personal information of the user, the similarity with the existing customer group G1 is changed to have a higher similarity or a lower similarity.

If the similarity weight is not reflected, if the similarity to customer group G1 is 0.75 and the similarity to customer group G2 is 0.8, and the similarity of customer group G2 is the highest, financial content information that can be matched with customer group G2 is used to finance the second finance Content recommendation information is generated. However, when the similarity weight is set to 0.1, if a positive similarity weight is given to the customer group G1, the similarity becomes 0.85, so that the customer group G1 has the highest similarity. Accordingly, the second financial content recommendation information is generated using the financial content information that can be matched with the customer group G1, and the user's intention is reflected in the second financial content recommendation information. This reflection of the user's intention (applying similarity weight) may also be applied to other customer groups to which the first financial content can be matched.

However, it is preferable that the first financial content information to which the user has already subscribed is excluded from the second financial content recommendation information.

As another example, if the user does not select all the financial content information included in the first financial content recommendation information, the response information is financial according to not selecting all the financial content information included in the first financial content recommendation information Includes content re-recommendation request information.

In this case, the second financial content recommendation information includes the user and each customer group stored in the database 200 by the similarity determination method preset by the financial content design server 100 based on the user personal information. It is created through the steps of determining the degree of similarity of , and the financial content design server 100 generating second financial content recommendation information using financial content information that can be matched to a customer group having a high similarity with the user. When determining the similarity, the financial content design server 100 gives a negative similarity weight to a customer group to which each financial content included in the first financial content recommendation information can be matched.

For example, when the first financial content recommendation information is created, the user's personal information has the highest similarity with the customer group G1 (eg, 0.9), so the first financial content recommendation information consists of financial content information that can be matched with the customer group G1, and the user If all financial contents included in the first financial contents recommendation information are not selected, the user's personal information is not updated.

If the similarity with the customer group G2 is 0.85 and the similarity of the customer group G1 is the highest, there is a problem in that the second financial content recommendation information must be generated using the financial content information that can be matched with the customer group G1 again. However, when the similarity weight is set to 0.1, if a negative similarity weight is given to the customer group G1, the similarity of the customer group G1 becomes 0.8, so that the customer group G2 has the highest similarity (0.85). Accordingly, the second financial content recommendation information is generated using the financial content information matching the customer group G2, and the user's intention is reflected in the second financial content recommendation information. This reflection of the user's intention (similarity weighting) may be applied to other customer groups to which all financial content information included in the first financial content recommendation information can be matched.

However, it is preferable that the financial content information already recommended to the user is excluded from the second financial content recommendation information.

9 to 11 are diagrams for explaining a method of generating characterized user personal information using user image information according to another embodiment of the present invention, and FIG. 12 is characterization according to another embodiment of the present invention. It is a flowchart of a method for generating user personal information.

According to an embodiment of the present invention, in the step S1, the user personal information includes the image information of the user, and before the step S2, the financial content design server 100 sets the image information of the user in advance. It includes a step of analyzing by an analysis method (step S11) and further generating characterized user personal information based on the analyzed result (step S12).

As an example, the user's image information includes the user's external shape (NUI), and as shown in FIG. 9 , the image analysis method includes the user's external shape (NUI) and the existing users' external shape (OUI1, OUI1, OUI2 and OUI3) are compared to determine whether they are similar.

The textualized personal information of the user is generated based on personal information of an existing user having an appearance similar to that of the user.

For example, the comparison of the external shape may be understood as photo analysis by artificial intelligence (Artificial Intelligence).

For example, the financial content design server 100 according to the present invention interworks with an artificial intelligence server configured to analyze photo information about numerous people through deep learning to provide image information of the user to the artificial intelligence server. If provided, the artificial intelligence server compares the image information of the user with the image information of the existing user stored in advance to determine whether it is similar, and based on the determined information, the image information of the existing user most similar to the image information of the user is said It is provided to the financial content design server 100 .

The financial content design server 100 may extract user personal information of the existing user from the database 200 based on the image information of the existing user provided from the artificial intelligence server, and the user personal information of the existing user Some of the information may be textualized and added as user personal information of the user.

As an example, the user's image information includes a user's face shape, and the image analysis method is to extract the user's coronal information by analyzing the user's face shape based on coronal science, and the characterized user personal information is It is generated based on the user's contemplation information.

The above-mentioned contemplation is to grasp a person's personality, wealth, etc. through the appearance of a person, especially the shape of the face.

The financial content design server 100 can analyze the user's face shape based on coronal science, and the color or size of the pupil, the length of the eyebrow, the size and shape of the ear, the size and shape of the nose, the size and thickness of the mouth, Personal information according to the user's contemplation can be generated by analyzing the length of the pharynx.

As an example, a user who has a broad and flat forehead is analyzed as being wise and considerate and saving money through planned expenditure. can be added.

As another example, the image information of the user includes the shape of the user's palm, and the image analysis method is to analyze the shape of the user's palm based on water science to extract the palm information of the user, and the characterized user personal The information is generated based on the palm information of the user.

Sanghak (手相学) is to evaluate a person's personality or future by analyzing the palms formed on the hands.

The financial content design server 100 may analyze the shape of the user's palm based on water science, and analyze the shape of the palm (emotional line, brain line, life line, fate line, etc.) or the shape of the hand and finger to analyze the shape of the user's palm. You can create personal information according to

For example, the emotional line represents emotional stability, love luck, depression and heart health, and the brain line represents a learning style, communication approach, intellectual activity and desire for study, and the like. The life line may determine the degree of health of the body, and the fate line represents the degree of change in a person's life due to the external environment.

As an example, as shown in FIG. 11 , if the lifeline P is short and faint, it indicates that it is easy to be swayed by other people, so a user with such a palmistry creates user personal information that is characterized as 'high risk'. to be added to the user's personal information.

The user's image information analyzed based on ornamental science, water science, etc. may not exactly match the user's personal information. When it is difficult to provide appropriate financial content recommendation information to the user with only personal information, user personal information can be added through the analysis of image information by means of ornamental science, etc., so that user-customized financial content recommendation information can be provided.

13 is a flowchart of a financial content design method according to another embodiment of the present invention.

According to another embodiment of the present invention, before the step S3, the step of requesting the user's CB information from the financial content design server 100 to the CB (Credit Bereau) server; further comprising, the step S3 The financial content recommendation information generated in is generated by reflecting the user's credit status included in the user's CB information.

The CB server is a server provided in any one of the credit rating agencies that evaluate an individual's credit, and may be, for example, a server provided in the Korea Credit Bureau. The user's CB information includes information related to the user's credit status.

The CB server collects personal credit information from financial companies or public institutions, such as banks, credit card companies, and insurance companies, and processes it into user's CB information, and is configured to provide it according to the request of the financial content design server 100 .

When the financial content design server 100 receives the user's CB information from the CB server, the user's credit status may be automatically classified.

Among various financial contents, when the user's credit status is below a certain level, there are financial contents that the user cannot subscribe to. If the financial content recommendation information generated based on the user's personal information includes financial content that the user cannot subscribe to, even if the user selects the financial content, the user's subscription is impossible. In this case, it takes time and effort to select other financial contents, so to prevent such inconvenience in advance, the user's credit status is preemptively identified, By removing the content information, appropriate financial content can be recommended to the user.

In the above, the present invention has been described in detail with reference to specific embodiments, but since the embodiments are merely examples for easy understanding of the present invention, substitutions, additions, and modifications are made within the scope not departing from the technical spirit of the present invention. Embodiments will also fall within the protection scope of the present invention defined by the following claims.

10: financial content design system 100: financial content design server
200: database 300: user terminal

Claims (16)

A financial content design method made in a financial content design system including a financial content design server connected to a user terminal and a network and a database in which two or more financial content information is stored - The database includes two or more customer group information, and each customer The group information is configured to match at least one or more financial content information-,
1) receiving, by the financial content design server, user personal information input from a user terminal;
2) determining, by the financial content design server, the degree of similarity between the user and each customer group stored in the database by a preset similarity determining method based on the user personal information - Each customer group is the Existing users are pre-classified by a preset clustering algorithm based on user personal information;
3) the financial content design server generates first financial content recommendation information using financial content information that can be matched to a customer group having a high degree of similarity to the user, and providing the first financial content recommendation information to the user terminal ;
4) receiving, by the financial content design server, response information to the first financial content recommendation information from the user terminal; and
5) generating, by the financial content design server, second financial content recommendation information based on the response information and providing it to the user terminal;
The response information is financial content subscription information and financial content recommendation request information according to the selection of the first financial content information included in the first financial content recommendation information,
The second financial content recommendation information,
Each of the financial content design server stored in the database with the user by a preset similarity determining method based on the user personal information including the financial content subscription information according to the selection of the first financial content information included in the response information by the financial content design server It is generated through the steps of determining a degree of similarity with a customer group, and the financial content design server generating second financial content recommendation information using financial content information that can be matched to a customer group with a high degree of similarity to the user,
A method for designing financial contents, characterized in that a positive similarity weight is given to a customer group to which the first financial contents can be matched when the similarity is determined.
◈Claim 2 was abandoned when paying the registration fee.◈ According to claim 1,
Before step 2) above,
The method further includes; by the financial content design server, calculating the user's operable assets based on the user's personal information;
The second financial content recommendation information is a financial content design method, characterized in that generated based on the response information and the user's operable assets.
◈Claim 3 was abandoned when paying the registration fee.◈ 3. The method of claim 2,
The response information is financial content subscription information and financial content recommendation request information according to the selection of the first financial content information included in the first financial content recommendation information,
The second financial content recommendation information is financial content design method, characterized in that generated based on an amount obtained by subtracting a predetermined evaluation amount of the first financial content from the operable asset of the user.
◈Claim 4 was abandoned when paying the registration fee.◈ 3. The method of claim 2,
The response information is financial content re-recommendation request information according to not selecting all financial content information included in the first financial content recommendation information,
The second financial content recommendation information is generated based on an amount obtained by subtracting the evaluation amount of the financial content having the smallest predetermined evaluation amount among all the financial contents included in the first financial contents recommendation information from the user's operable asset. financial content design method.
delete ◈Claim 6 was abandoned when paying the registration fee.◈ According to claim 1,
The response information is financial content re-recommendation request information according to not selecting all financial content information included in the first financial content recommendation information,
The second financial content recommendation information,
determining, by the financial content design server, a degree of similarity between a user and each customer group stored in the database by a preset similarity determining method based on the user personal information; It is created through the step of generating second financial content recommendation information using financial content information that can be matched to a customer group with a high
When the similarity is determined, a negative similarity weight is given to a customer group to which each financial content included in the first financial content recommendation information can be matched.
◈Claim 7 was abandoned when paying the registration fee.◈ According to claim 1,
The clustering algorithm is a financial content design method, characterized in that it satisfies Equation 1 below.
[Equation 1]

RSS is the objective function of the preset clustering algorithm (total variance), K is the number of customer groups,

is the central vector of each data belonging to the customer group,

is the set of data belonging to the customer group,

is a vector of specific data belonging to a customer group.
◈Claim 8 was abandoned when paying the registration fee.◈ According to claim 1,
The method for determining the degree of similarity is a financial content design method, characterized in that it satisfies Equation 2 below.
[Equation 2]

G is the customer group data, U is the user's data,

is the angle between the customer group vector and the user vector.
◈Claim 9 was abandoned at the time of payment of the registration fee.◈ According to claim 1,
Step 3) is,
3-1) classifying, by the financial content design server, financial content information according to a preset decision-making method;
3-2) extracting, by the financial content design server, financial content information that can be matched with a customer group having a high degree of similarity to the user based on the classified financial content information; and
3-3) generating, by the financial content design server, financial content recommendation information based on the extracted financial content information;
◈Claim 10 was abandoned when paying the registration fee.◈ According to claim 1,
The user personal information includes user image information,
Before step 2) above,
analyzing, by the financial content design server, the image information of the user using a preset image analysis method; and
Financial content design method comprising a; further generating characterized user personal information based on the analyzed result.
◈Claim 11 was abandoned when paying the registration fee.◈ 11. The method of claim 10,
The user's image information includes the user's external shape,
The image analysis method is to compare the appearance shape of the user with the appearance shape of existing users to determine whether the similarity,
The textualized user personal information is a financial content design method, characterized in that it is generated based on the personal information of an existing user having an appearance similar to the user.
◈Claim 12 was abandoned when paying the registration fee.◈ 11. The method of claim 10,
The user's image information includes the user's face shape,
The image analysis method is to extract the user's coronal information by analyzing the user's face shape based on coronal science,
The textualized user personal information is a financial content design method, characterized in that generated based on the user's contemplative information.
◈Claim 13 was abandoned when paying the registration fee.◈ 11. The method of claim 10,
The user's image information includes the user's palm shape,
The image analysis method is to extract the user's palm information by analyzing the user's palm shape based on water science,
The textualized user personal information is a financial content design method, characterized in that generated based on the palm information of the user.
◈Claim 14 was abandoned at the time of payment of the registration fee.◈ According to claim 1,
Before step 3) above,
Further comprising; requesting the user's CB information from the financial content design server to a CB (Credit Bereau) server,
The financial content recommendation information generated in step 3) is a financial content design method, characterized in that it is generated by reflecting the user's credit status included in the user's CB information.
a user terminal performing including a function of inputting user personal information and a function of displaying financial content recommendation information;
a database for storing including customer group information configured to match two or more financial content information and at least one or more financial content information; and
It is connected to the network with the user terminal and determines the degree of similarity between the user and each customer group stored in the database by a function of receiving the input user personal information and a preset similarity determining method based on the user personal information. A function, a function of generating first financial content recommendation information using financial content information that can be matched to a customer group having a high degree of similarity to the user, and receiving response information to the first financial content recommendation information from the user terminal and the response A financial content design server that performs including a function of generating second financial content recommendation information based on the information and a function of providing the first and second financial content recommendation information to the user terminal;
The response information is financial content subscription information and financial content recommendation request information according to the selection of the first financial content information included in the first financial content recommendation information,
The second financial content recommendation information,
Each of the financial content design server stored in the database with the user by a preset similarity determining method based on the user personal information including the financial content subscription information according to the selection of the first financial content information included in the response information by the financial content design server It is generated through the steps of determining a degree of similarity with a customer group, and the financial content design server generating second financial content recommendation information using financial content information that can be matched to a customer group with a high degree of similarity to the user,
A financial content design system, characterized in that a positive similarity weight is given to a customer group to which the first financial content can be matched when the similarity is determined.
◈Claim 16 was abandoned when paying the registration fee.◈ 16. The method of claim 15,
The financial content design server performs further including a function of calculating the user's operable assets,
The second financial content recommendation information is a financial content design system, characterized in that generated based on the response information and the user's operable assets.

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