KR20230049889A - Method, devce and system for generating customized financial product recommendation information based on big data and artificial intelligence - Google Patents

Abstract

According to an embodiment, a method, a device and a system for generating customized financial product recommendation information based on big data and artificial intelligence are disclosed. The method comprises the steps of: obtaining personal information, personal financial information data, financial product information, and big data information to perform financial analysis based on the needs of a customer, wherein the personal information is customer information including an age group and an occupation and the personal financial information data includes income information, credit information, and customer-owned products, and the data is collected from online and offline consultation histories from a financial company; performing the financial analysis based on the personal information, the financial product information, and the big data information to determine financial products included in a financial product list; generating a first analysis result presenting the financial product list and a second analysis result presenting a financial guideline; and providing the first analysis result and the second analysis result to a customer terminal. The financial guideline includes a list of recommended products according to the priority of the analysis results of the customer. The present invention can provide the customer with customized financial product-related information optimized for each individual.

Description

Big data and artificial intelligence-based customer-customized financial product recommendation information generation method, device, and system

The following embodiments relate to a method, apparatus, and system for generating customer-specific financial product recommendation information based on big data and artificial intelligence. More specifically, it relates to a method, apparatus, and system for obtaining information about a user and generating customized financial product recommendation information by big data analysis based on artificial intelligence.

Recently, with the growth of fintech, a service that combines finance and technology, a number of online services that broker financial products owned by multiple financial institutions to customers are appearing. These services compare the personal information entered by the customer with the conditions of the financial products held by the financial institution to determine the financial products available to the customer and recommend them to the customer, brokering financial products to the customer.

In this way, conventional services provide customers with financial products determined by comparing customer personal information and financial product conditions of financial institutions one-dimensionally. There is a problem in that customized financial product recommendation information cannot be provided to customers.

In addition, since employees who provide actual services to customers at financial company branches directly recommend products, information on recommending financial products of different quality is provided depending on the employee's capabilities, which ultimately lowers reliability and makes it easier for customers to select recommended products It is also difficult to understand and select.

Embodiments are a big data and artificial intelligence-based customer-customized financial product recommendation information generation method that searches for financial product recommendation information most suitable for the user by big data analysis based on artificial intelligence without the need for the user to analyze enormous information on financial products. , the purpose of which is to provide devices and systems.

Embodiments are intended to provide a new type of financial product recommendation information service that can provide updated financial product-related information using updated big data information as well as customer personal information.

Acquiring personal information, personal financial information data, financial product information, and big data information for performing financial analysis based on the customer's needs; The personal information is customer information including age group and occupational group, and the personal financial information data includes income information, credit information, and products owned by customers, and includes information collected from online and offline consultation details from the financial company, generating a first analysis result presenting a list of financial products and a second analysis result presenting financial guidelines by performing the financial analysis based on the personal information, the financial product information, and the big data information; and providing the first analysis result and the second analysis result to the customer terminal, wherein the financial guideline includes a product recommendation list according to the priority of the analysis result of the customer. It provides a method, device, and system for generating customer-tailored financial product recommendation information based on data and artificial intelligence.

An apparatus according to an embodiment may be combined with hardware and controlled by a computer program stored in a medium to execute any one of the methods described above.

Embodiments relate to a method, apparatus, and system for generating customer-specific financial product recommendation information based on big data and artificial intelligence, and by using big data information as well as customer personal information to reflect the latest trends in related financial products, Optimized and customized financial product-related information can be provided to customers.

In addition, the present invention can build a healthy financial market sales marketing ecosystem by attracting customers with high real potential while reducing the proportion of indiscriminate existing TV advertisements and online advertisements, thereby improving profitability and strengthening the rights and interests of financial consumers in the process of selecting financial products. there is.

On the other hand, the effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

1 is a flowchart illustrating a method, apparatus, and system for generating customer-specific financial product recommendation information based on big data and artificial intelligence according to an embodiment.
2 is a diagram of components of a server according to an embodiment.
3 is a table showing customer information to be collected according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes can be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents or substitutes to the embodiments are included within the scope of rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only, and may be modified and implemented in various forms. Therefore, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various components, such terms should only be construed for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element.

It should be understood that when an element is referred to as being “connected” to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle.

Terms used in the examples are used only for descriptive purposes and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

In addition, in the description with reference to the accompanying drawings, the same reference numerals are given to the same components regardless of reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description will be omitted.

The embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

1 is a flowchart illustrating a method, apparatus, and system for generating customer-specific financial product recommendation information based on big data and artificial intelligence according to an embodiment.

In this specification, financial products are various types of products related to finance, for example, banking products under the Banking Act, financial investment products and derivatives under the Capital Markets and Financial Investment Business Act (“Finance Act”), and insurance under the Insurance Business Act. Products, credit financial products under the Specialized Credit Financial Business Act (“Still Act”), mutual savings bank products under the Mutual Savings Bank Act, loan products under the Loan Business Act, real estate-related loan products (e.g., housing loan products) provided by many financial institutions, etc. may, but is not limited thereto.

In step S110, the analysis information acquisition module 211 of the control module 210 may acquire personal information, personal financial information data, financial product information, and big data information for performing financial analysis based on the customer's needs. .

Here, the customer is a consumer of a financial product and may be an individual or a group. The customer's needs are related to financial analysis, and may be, for example, related to the purpose of using a financial product (eg, house purchase, car purchase, loan, credit card issuance).

Personal information for performing financial analysis based on the customer's needs and the personal financial information data may be collected from online and offline consultation details from the financial company.

Here, the personal information is information about an individual customer, and may be, for example, information about a name, date of birth, address, age group, occupational group, military service information, credit rating, and the like. Financial information includes income information, credit information, products owned by customers, and other benefits insurance subscription status.

Also, the financial product information is information about a financial product provided by a financial product provider. For example, it may be information about the name of a financial institution, the name of a financial product, and detailed conditions of a financial product.

The financial product information provider may be an individual or group that provides financial products, for example, a financial institution such as an individual or bank, savings bank, insurance company, lender, etc. that provides peer to peer (P2P) financial products. , but not limited thereto.

In addition, big data information is information extracted and processed from big data, and may be, for example, information necessary for analyzing financial product suitability and information necessary for analyzing financial guidelines. At this time, big data is, for example, information disclosed or provided by domestic and foreign governments and public institutions through networks, information disclosed or provided by various private associations or organizations through networks, and various companies and individual organizations through networks. Information disclosed or provided, information disclosed on the Internet and collectable directly through a network, information disclosed and collected through SNS, etc. may be included.

In step S120, the first financial analysis execution module 212 performs the financial analysis based on the personal information, the financial product information, and the big data information, and generates a first analysis result presenting a list of financial products. do.

Here, the financial product list is a list composed of a plurality of financial products, and information related to each financial product, for example, financial institution name, financial product name, detailed conditions, statistical information of searching or using the financial product through the financial product mediation server, corresponding It may include information on customer evaluations and reviews of financial products. Here, the financial guidelines are guidelines for helping customers understand finance, and are related to information related to customers' financial understanding, for example, one or more types of big data (eg, financial big data, LBD big data, and tax law big data). A guideline interface including analysis items and adjustable objects may be included. In this case, the adjustable object may be associated with each of analysis items related to big data.

In step S130, the second financial analysis execution module 213 generates a second analysis result that presents financial guidelines by prioritizing financial products for the customer's analysis result.

The second financial analysis execution module 213 performs analysis of preset financial guidelines, determines analysis items related to one or more big data included in the financial guidelines, and prioritizes analysis items.

At this time, the second financial analysis performance module 113 considers the customer's needs (eg, the customer's needs and / or the attributes, characteristics, characteristics, etc. of objects related to the customer's needs) and considers one included in the financial guidelines. Analysis items related to the above big data can be determined, and priorities among analysis items can be set.

For example, when the customer's need is "preparing a home", the financial analysis performance module 113 performs the attribute of "preparing a home", which is the customer's need, and/or the attribute of "housing", which is an object related to the customer's need ( location is one of the important factors), LBS big data-related analysis items, financial big data-related analysis items, and tax law big data-related analysis items can be determined as analysis items included in the financial guidelines, and LBS big data-related analysis items It can be set as having this highest priority.

As an example, the criterion and method for analyzing the financial guidelines may be changed at the request of the customer. In this case, the financial product guidelines may be related to each of the analysis items related to big data.

In step S140, the product information providing module 214 provides the first analysis result and the second analysis result to the customer terminal.

The financial guidelines include a product recommendation list according to the priority of the customer's analysis result. Through the financial guidelines, it is possible to check a list of personalized and customized financial product recommendations for customers, and it is possible to check knowledge that is helpful in understanding financial products or purchasing them in the future.

2 is a diagram for explaining components of a server according to an embodiment.

The financial product recommendation information providing server 200 includes a control module 210 and a database unit including a customer information database 220 and a financial product database 230 . At this time, the control module 210 and the database unit may be connected by wire or wirelessly through a network to perform communication.

A customer, as a consumer of a financial product, may access the financial product recommendation information providing server 200 by visiting an Internet website or the like.

The control module 210 includes the components of the financial product recommendation information providing server 200 (e.g., the analysis information acquisition module 211, the first financial analysis execution module 212, the second financial analysis execution module 213, and The product information providing module 214 may be controlled, and data transmission/reception between respective components may be managed.

The database unit includes a customer information database 220 and a financial product database 230 . A database may be configured to store one or more data. Customer information or financial product information can be converted into a database and stored, and collected and processed big data information extracted from big data collected in a preset manner can be converted into a database and stored. Big data information required for financial analysis can be extracted from the collected big data and stored in the database.

The analysis information acquisition module 211 may acquire information for performing financial analysis. In addition, based on the acquired personal information, it is possible to obtain personalized financial product information and personalized big data information by searching the database to accurately obtain personal information necessary for performing financial analysis based on the customer's needs.

The analysis information acquisition module 211 may include information about the properties of financial products related to customer needs and related laws and tax regulations by using big data information. The above contents may be changed according to update of big data information.

The first financial analysis execution module 212 determines, as financial products included in the financial product list, financial products having a fitness level calculated according to a preset financial product fitness analysis that is equal to or greater than a preset value.

The second financial analysis execution module 213 may prioritize the financial products included in the financial product list according to the size of the suitability value of the financial products determined to be included in the financial product list.

As an embodiment, the second financial analysis execution module 213 may perform the financial product suitability analysis using condition matching, optimal solution derivation using a data mining algorithm, classification analysis, cluster analysis, pattern recognition technology, and the like.

The product information providing module 214 may provide the customer with the generated customer-customized financial product recommendation list. Through this, it is possible to check a list of customized financial products personalized to the customer, thereby increasing the customer's understanding of the financial product.

3 is a table 300 illustrating customer information to be collected according to an embodiment.

In step S110, information about the customer's personal information, financial information, etc. is collected in order to select a customer-tailored financial product.

The customer's personal information to be collected above includes the customer's name, date of birth, address, age group, occupational group, existing customer information, military service information, income information, credit information, interest income, financial products owned by the customer, and other income insurance subscription status. It is information.

The information to be collected may be obtained through offline or online consultation details. Account information, credit card information, card details, withdrawal details, savings status, etc. are collected through offline consultation details, and information such as credit loan details, mortgage loan details, survey details, product inquiry details, etc. is collected through online consultation details.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

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

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

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

200: Financial product recommendation information providing server
210: control module
211: analysis information acquisition module
212: first financial analysis module
213: second financial analysis module
214: product information providing module
220: customer information database
230: financial product database
300: Table showing customer information to be collected

Claims (3)

Acquiring personal information, personal financial information data, financial product information, and big data information for performing financial analysis based on the customer's needs;
The personal information is customer information including age group and occupational group, and the personal financial information data includes income information, credit information, and products owned by customers, and includes information collected from online and offline consultation details from the financial company,
generating a first analysis result presenting a list of financial products and a second analysis result presenting financial guidelines by performing the financial analysis based on the personal information, the financial product information, and the big data information; and
Providing the first analysis result and the second analysis result to the customer terminal, wherein the financial guideline includes a product recommendation list according to the priority of the customer's analysis result. and artificial intelligence-based methods, devices, and systems for generating customer-customized financial product recommendation information. According to claim 1,
The acquiring and generating steps include:
acquiring personal information required to perform the financial analysis;
obtaining personalized financial product information and personalized big data information by searching a database for input data for the acquired personal information item;
determining financial products included in the list of financial products;
generating a first analysis result by performing a pre-set financial instrument suitability analysis; and
Determining analysis items related to the big data included in the financial guidelines, and generating a second analysis result by prioritizing financial products for the customer's analysis results; recommending products according to priority, including A method, apparatus and system for generating customer-specific financial product recommendation information based on big data and artificial intelligence, characterized in that it provides a list. According to claim 1,
an analysis information acquisition module for acquiring personal information, personal financial information, financial product information, and big data information for performing financial analysis based on the customer's needs by searching the database;
a first financial analysis performing module generating a first analysis result obtained by performing a pre-set financial product suitability analysis on the financial products;
a second financial analysis execution module that performs the financial analysis based on the personal financial information, financial product information, and the big data information, and generates a second analysis result presenting a list of recommended financial products according to priority; and
A method, apparatus, and system for generating customer-specific financial product recommendation information based on big data and artificial intelligence, including a product information providing module that provides the generated customer-tailored financial product recommendation list to customers.

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