KR20110139896A - Method for recommendation the financial goods - Google Patents

Method for recommendation the financial goods Download PDF

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Publication number
KR20110139896A
KR20110139896A KR1020100059990A KR20100059990A KR20110139896A KR 20110139896 A KR20110139896 A KR 20110139896A KR 1020100059990 A KR1020100059990 A KR 1020100059990A KR 20100059990 A KR20100059990 A KR 20100059990A KR 20110139896 A KR20110139896 A KR 20110139896A
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KR
South Korea
Prior art keywords
customer
data
analysis data
propensity
financial product
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KR1020100059990A
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Korean (ko)
Inventor
신옥식
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노아에이티에스 (주)
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Priority to KR1020100059990A priority Critical patent/KR20110139896A/en
Publication of KR20110139896A publication Critical patent/KR20110139896A/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0282—Rating or review of business operators or products
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

Abstract

The present invention relates to a financial product recommendation method. This study was conducted as a result of the 2009 Cultural Contents Industry Technology Support Project of the Ministry of Culture, Sports and Tourism and the Korea Creative Content Agency. This research is supported by Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Developement Program 2009.
Financial product recommendation method according to an aspect of the present invention comprises the steps of inputting the existing customer's propensity and retained financial product data, analyzing the input customer propensity data to generate customer propensity analysis data, financing according to the customer propensity analysis data Analyzing the product data to the financial product analysis data step, storing the customer propensity analysis data and financial product analysis data in the data house, if there is new customer information data, entering the customer propensity data of the new customer, entered Analyzing customer propensity data of the new customer to generate customer propensity analysis data; and recommending a financial product according to the customer propensity analysis data of the new customer.

Description

{Method for recommendation the financial goods}

Analysis of financial markets and how to recommend financial products to customers based on them.

Recently, the number of financial products that can be purchased has increased in various ways. Accordingly, it takes a lot of time for the customer to search for the desired product. For this reason, many systems and methods are being developed to recommend products suitable for customers.

Conventionally, whenever a consultation with a customer occurs, a recommended financial product is searched on the basis of financial market analysis data, and a product suitable for the customer's tendency is found through consultation on the recommended financial products.

Specifically, referring to FIG. 1, a conventional financial product recommendation method will be described.

First, the customer propensity related information is input (150), and then the input customer propensity is analyzed (155). Next, the financial market is analyzed using the financial market analysis system using the predefined market index as a base index, and based on this, the recommended financial product is output using the product recommendation system (170).

In this case, when the number of financial products is small, the time required for recommending the financial products is relatively small, but recently, due to the increase in the number of financial products, whenever there is consultation with a customer in the same manner as before, financial If it takes a lot of time to search for and recommend products, the accuracy of the search is not reflected, and the accuracy of the search is reduced, and the ability of recommending products tailored to customer preferences depends on the individual's ability to recommend financial products. . Therefore, there is a need for a method for recommending relevant financial products in real time during customer consultation.

The technical problem to be solved by the present invention is to analyze the propensity of existing customers with financial products, analyze the financial product information according to the customer propensity analysis data, and recommend financial products according to the propensity of new customers based on this. Improve product recommendation speed and provide customized financial products for each customer.

Financial product recommendation method according to an aspect of the present invention comprises the steps of inputting the existing customer's propensity and retained financial product data, analyzing the input customer propensity data to generate customer propensity analysis data, financing according to the customer propensity analysis data Analyzing the product data to the financial product analysis data step, storing the customer propensity analysis data and financial product analysis data in the data house, if there is new customer information data, entering the customer propensity data of the new customer, entered Analyzing customer propensity data of the new customer to generate customer propensity analysis data; and recommending a financial product according to the customer propensity analysis data of the new customer.

The step of recommending a financial product according to the customer propensity analysis data of the new customer is to check whether the data matching the data of the customer propensity analysis data of the new customer is present in the customer propensity analysis data of the existing customer stored in the data house. Outputting customer propensity analysis data and financial product analysis data stored in the data house, if there is a matched data; and if there is no matched data, a product recommendation system (RPS). The method may include outputting the recommended financial instrument data using.

The method may further include storing customer propensity analysis data of the new customer and financial product information selected by the new customer in the data house.

According to this feature, the present invention analyzes the propensity of existing customers with financial products, analyzes financial product information according to customer propensity analysis data, and recommends financial products according to the propensity of new customers based on this. When the customer information is input, for example, when a new customer requests consultation about the financial product, the recommendation rate of the financial product may be improved by recommending the financial product based on the stored analysis information.

1 is a flow chart illustrating a financial product recommendation method according to the prior art. 2 is a flowchart illustrating a method for recommending a financial product based on a customer tendency according to an embodiment of the present invention. 3 is a flowchart illustrating a method for recommending a financial product based on a customer tendency according to an embodiment of the present invention. 4 is a flowchart illustrating a method for recommending a financial product based on a customer tendency according to an embodiment of the present invention. 5 is a diagram illustrating a financial product recommendation system based on a customer tendency according to an embodiment of the present invention.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and like reference numerals designate like parts throughout the specification.

With reference to the accompanying drawings will be described a financial product recommendation method and system based on customer preferences according to an embodiment of the present invention.

2 is a flowchart illustrating a method for recommending a financial product based on a customer tendency according to an embodiment of the present invention.

First, data of the propensity of the existing customers and the financial products held by the existing customers are input (210).

Next, the propensity analysis is performed based on the data of the existing propensity of the existing customer, thereby generating customer propensity analysis data (215).

Next, financial products analysis based on customer propensity analysis data based on customer propensity analysis data and data on financial products owned by existing customers, for example, analysis of preferred financial products for each customer propensity The product analysis data is generated (220).

The analyzed result, that is, the financial product analysis data including the correlation between the customer propensity analysis data and the customer propensity analysis data and the financial product are stored in the data house (Product Data House, PDH) (225).

Thereafter, when new customer information data is generated, that is, when a product consultation for a new customer is required, the customer propensity data of the new customer is input (240, 250).

Next, the propensity analysis is performed based on the data on the propensity of the new customer, which is input, to generate the propensity analysis data (255).

Finally, financial products are recommended according to the customer propensity analysis data of the new customer (270).

The recommendation of the financial product according to the customer propensity analysis data of the new customer will be described in more detail with reference to FIG. 3.

3 is a flowchart illustrating a method for recommending a financial product based on a customer tendency according to an embodiment of the present invention.

The step 355 of generating the propensity analysis data by performing the propensity analysis based on the data of the propensity of the new customer input is similar to the embodiment shown in FIG. 2.

Next, it is checked whether the data matching the generated customer propensity analysis data of the new customer exists in the customer propensity analysis data of the existing customer stored in the data house (PDH) (360).

When the matching data exists in the data house PDH, the customer disposition analysis data stored in the data house PDH and the financial product analysis data according to the same are output (370 and 375).

If the matching data does not exist in the data house PDH, the recommended financial product data is output using the product recommendation system (RPS) (380). More specifically, the financial market is analyzed using the Market Analysis System (MAS) based on the predefined Product Market Value (PMV) and based on the product recommendation system (RPS). ) To generate the data of the product recommended by the system.

In this case, before consulting with a new customer, the financial products according to the existing customer's inclination are analyzed and stored in advance, and when there are existing customers with the same inclination as the new customer's tendency when consulting with the new customer, You can recommend the right product information in real time. Therefore, the time required for product recommendation can be saved, and customized products can be recommended according to customer preferences.

Next, a financial product recommendation method according to an embodiment of the present invention will be described with reference to FIG. 4.

4 is a flowchart illustrating a method for recommending a financial product based on a customer tendency according to an embodiment of the present invention.

Similar to the financial product recommendation method according to the embodiment shown in FIG. 3, the method further includes a step 490 of storing customer propensity analysis data of the new customer and financial product information selected by the new customer in the data house PDH.

The stored customer propensity analysis data and the financial product analysis data related to the new customer are added to the customer propensity analysis data and the financial product analysis data of the existing customers stored in the data house (PDH), and then used for new customer consultation.

5 is a diagram illustrating a financial product recommendation system based on a customer tendency according to an embodiment of the present invention.

As shown in FIG. 5, the financial product recommendation system according to the present embodiment includes a user computer 10, a management system 20, a data house 30, a financial market analysis system 40, and a product recommendation system 50. Equipped.

At least one user computer 10 may exist and is a computer that is used by at least one user, that is, an Asset Manager (AM), to input customer information and consult a financial product during customer consultation. . When the user accesses the virtual world through the user computer 10, the user is connected to the management system 20 through the network.

Through the management system 20, the data house 30, the financial market analysis system 40, the product recommendation system 50, and the like are connected.

The management system 20 includes a customer disposition analysis module and a financial product analysis module according to the customer disposition, and analyzes existing customer disposition data input through the user computer 10 at the initial startup to analyze the disposition of the existing disposition of the customer. And generate the financial product analysis data by analyzing the financial product data according to the customer propensity analysis data.

The customer propensity analysis data and the financial product analysis data are stored in the data house 30.

Subsequently, when there is a consultation for a new customer, the asset manager inputs information such as the customer tendency of the new customer into the user computer 10 and generates the customer tendency analysis data of the new customer in the management system 20. In addition, if there is matching data by comparing the generated propensity analysis data of the new customer with the existing client propensity analysis data stored in the data house 30, the data house 30 may analyze the financial product analysis data for the corresponding client propensity analysis data. To call.

At this time, if there is no data in the customer propensity analysis data stored in the data house 30 that matches the customer propensity analysis data of the new customer, the financial market analysis system 40 analyzes the financial market, and based on this, the product recommendation system 50. Generates a recommended financial product and delivers it to the user computer 10 through the management system 20.

Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

10: user (AM) computer 20: management system
30: Data House (PDH) 40: Financial Market Analysis System (MAS)
50: Product Recommendation System (RPS)

Claims (4)

Entering in your existing customer preferences and financial product data you have,
Analyzing the input customer propensity data to generate customer propensity analysis data;
Analyzing the financial product data according to the customer propensity analysis data to perform financial product analysis data;
Storing the customer disposition analysis data and the financial instrument analysis data in a data house;
If there is new customer information data, entering customer orientation data for the new customer,
Analyzing the customer propensity data of the new customer entered to generate the customer propensity analysis data;
Financial product recommendation method comprising the step of recommending a financial product according to the customer disposition analysis data of the new customer In claim 1,
Recommend financial products based on the customer disposition analysis data of the new customer,
Confirming whether the data corresponding to the customer disposition analysis data of the new customer exists in the customer disposition analysis data of the existing customer stored in the data house;
And outputting the customer propensity analysis data stored in the data house and the financial product analysis data accordingly when there is matching data. In claim 1,
Recommend financial products based on the customer disposition analysis data of the new customer,
Confirming whether the data corresponding to the customer disposition analysis data of the new customer exists in the customer disposition analysis data of the existing customer stored in the data house;
And if there is no matching data, outputting the recommended financial product data using the product recommendation system (RPS). 3. The method according to claim 2 or 3,
And storing the customer propensity analysis data of the new customer and financial product information selected by the new customer in a data house.
KR1020100059990A 2010-06-24 2010-06-24 Method for recommendation the financial goods KR20110139896A (en)

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101456920B1 (en) * 2012-07-12 2014-10-31 주식회사 하나은행 Saving execution apparatus, saving execution method and computer readable recording medium recording program for implementing the method
WO2016135520A1 (en) * 2015-02-25 2016-09-01 Accenture Global Services Limited Customer management system for determining aggregate customer value
KR20190038436A (en) * 2017-09-29 2019-04-08 이다커뮤니케이션즈(주) System and method for recommending insurance products using personalized persona management server
WO2019098455A1 (en) * 2017-11-17 2019-05-23 조두영 Method for providing business start-up support service reflecting self-diagnosis result of business start-up individual
CN110415084A (en) * 2019-07-30 2019-11-05 中国工商银行股份有限公司 A kind of product intelligent recommended method and device
KR102175931B1 (en) * 2020-02-10 2020-11-09 리스펙트스몰머니 주식회사 System, apparatus and method for providing user-customized financial information
KR20220000475A (en) * 2020-06-26 2022-01-04 미래에셋증권 주식회사 System and method for recommendation of customized financial products

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101456920B1 (en) * 2012-07-12 2014-10-31 주식회사 하나은행 Saving execution apparatus, saving execution method and computer readable recording medium recording program for implementing the method
WO2016135520A1 (en) * 2015-02-25 2016-09-01 Accenture Global Services Limited Customer management system for determining aggregate customer value
KR20190038436A (en) * 2017-09-29 2019-04-08 이다커뮤니케이션즈(주) System and method for recommending insurance products using personalized persona management server
WO2019098455A1 (en) * 2017-11-17 2019-05-23 조두영 Method for providing business start-up support service reflecting self-diagnosis result of business start-up individual
CN110415084A (en) * 2019-07-30 2019-11-05 中国工商银行股份有限公司 A kind of product intelligent recommended method and device
CN110415084B (en) * 2019-07-30 2022-10-21 中国工商银行股份有限公司 Intelligent product recommendation method and device
KR102175931B1 (en) * 2020-02-10 2020-11-09 리스펙트스몰머니 주식회사 System, apparatus and method for providing user-customized financial information
KR20220000475A (en) * 2020-06-26 2022-01-04 미래에셋증권 주식회사 System and method for recommendation of customized financial products

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