WO2019071906A1 - Procédé et dispositif de recommandation de produit financier et support d'informations lisible par ordinateur - Google Patents

Procédé et dispositif de recommandation de produit financier et support d'informations lisible par ordinateur Download PDF

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Publication number
WO2019071906A1
WO2019071906A1 PCT/CN2018/077631 CN2018077631W WO2019071906A1 WO 2019071906 A1 WO2019071906 A1 WO 2019071906A1 CN 2018077631 W CN2018077631 W CN 2018077631W WO 2019071906 A1 WO2019071906 A1 WO 2019071906A1
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Prior art keywords
financial product
target customer
feature
customer
classification model
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PCT/CN2018/077631
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English (en)
Chinese (zh)
Inventor
刘睿恺
吴振宇
王建明
肖京
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平安科技（深圳）有限公司
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Publication of WO2019071906A1 publication Critical patent/WO2019071906A1/fr

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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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance
    • 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations

Definitions

  • the present application relates to the field of information processing technologies, and in particular, to a financial product recommendation apparatus, method, and computer readable storage medium.
  • the marketing plan adopted by most banks is based on the traditional marketing system, which counts the customer's transaction data, selects a certain number of customers as potential customers, and recommends bank-designated finance to these customers by phone or SMS.
  • Products and with the development of the financial industry and the Internet industry, there are more and more types of financial products, including funds, wealth management, precious metals, insurance and other products, and each product contains several products, which As a result, it is difficult for customers to choose a product that suits their needs in the face of a large number of financial products.
  • For banks due to lack of manpower, etc., it is impossible to fully promote the products, and generally choose a small number of products.
  • the promotion of more popular financial products, in addition to this marketing model is not targeted, often combined with the business rules of the product to select some customers in batches, to carry out mass marketing, without in-depth mining of customer trading behaviors, personalized precision marketing .
  • the present application provides a financial product recommendation device, method and computer readable storage medium, the main purpose of which is to improve the recommendation success rate of a financial product.
  • the present application provides a financial product recommendation device including a memory and a processor, wherein the memory stores a financial product recommendation program executable on the processor, the financial product recommendation program being The processor implements the following steps when executed:
  • the financial product is recommended to the target customer via the common contact medium.
  • the present application further provides a financial product recommendation method, the method comprising:
  • the financial product is recommended to the target customer via the common contact medium.
  • the present application further provides a computer readable storage medium, where the financial product recommendation program is stored, and the financial product recommendation program can be executed by one or more processors. To implement the steps of the financial product recommendation method as described above.
  • the financial product recommendation device, method and computer readable storage medium proposed by the present application calculate the satisfaction level of the target customer's financial products according to the characteristics of the target customer according to the preset classification model according to the acquired feature characteristics. Then, according to the product data of the financial product held by the target customer and the satisfaction level of the financial product held, the target customer is selected for the financial product to be recommended.
  • the target customer is selected for the financial product to be recommended.
  • the target customer is selected with the appropriate contact medium. In this way, all the financial products of the bank can be integrated, and targeted recommendations can be made for a certain customer to improve the recommendation success rate of the product.
  • FIG. 1 is a schematic diagram of a preferred embodiment of a financial product recommendation device of the present application.
  • FIG. 2 is a schematic diagram of functional modules of a financial product recommendation program in an embodiment of a financial product recommendation device of the present application
  • FIG. 3 is a flow chart of a preferred embodiment of a financial product recommendation method of the present application.
  • the application provides a financial product recommendation device.
  • FIG. 1 a schematic diagram of a preferred embodiment of a financial product recommendation device of the present application is shown.
  • the financial product recommendation device may be a PC (Personal Computer), or may be a portable terminal device having a display function such as a smart phone, a tablet computer, or a portable computer.
  • PC Personal Computer
  • portable terminal device having a display function such as a smart phone, a tablet computer, or a portable computer.
  • the financial product recommendation device includes a memory 11, a processor 12, a communication bus 13, and a network interface 14.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like.
  • the memory 11 may be an internal storage unit of the financial product recommendation device, such as a hard disk of the financial product recommendation device, in some embodiments.
  • the memory 11 may also be an external storage device of the financial product recommendation device in other embodiments, such as a plug-in hard disk equipped with a financial product recommendation device, a smart memory card (SMC), and a secure digital (Secure Digital, SD) card, flash card, etc.
  • the memory 11 may also include both an internal storage unit of the financial product recommendation device and an external storage device.
  • the memory 11 can be used not only for storing application software installed in the financial product recommendation device and various types of data, such as codes of the financial product recommendation program, but also for temporarily storing data that has been output or will be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing stored in the memory 11. Data, such as the implementation of financial product recommendation procedures.
  • CPU Central Processing Unit
  • controller microcontroller
  • microprocessor or other data processing chip for running program code or processing stored in the memory 11.
  • Data such as the implementation of financial product recommendation procedures.
  • Communication bus 13 is used to implement connection communication between these components.
  • the network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the device and other electronic devices.
  • a standard wired interface such as a WI-FI interface
  • Figure 1 shows only financial product recommendation devices having components 11-14 and financial product recommendation procedures, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.
  • the device may further include a user interface
  • the user interface may include a display
  • an input unit such as a keyboard
  • the optional user interface may further include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like.
  • the display may also be suitably referred to as a display screen or display unit for displaying information processed in the financial product recommendation device and a user interface for displaying the visualization.
  • a financial product recommendation program is stored in the memory 11; when the processor 12 executes the financial product recommendation program stored in the memory 11, the following steps are implemented:
  • the target customer's satisfaction level with the financial products held is calculated according to the target customer's transaction characteristics.
  • a matter information table is used in advance for storing each customer transaction item and other various items that occur based on the transaction item, for example, a complaint item, a compensation item, a comment item, and a surrender item. Wait.
  • the above transactions include transactions for fund purchases, insurance purchases, etc. for various financial products.
  • various financial product-related matters will be recorded in the above information table.
  • the contact medium at the time of the event is also recorded.
  • the contact medium is, in this embodiment, mainly includes the following channels: a PC client issued by a bank, an APP client, and a telemarketing channel.
  • the identity information of the target customer may be determined when receiving the request for product recommendation to the target customer, wherein the identity information may be identification information of the unique customer in the database by the ID card number or the mobile phone number. According to its identity information, all matters of the customer or items recorded in the past period of time are extracted from the item information table.
  • the step of extracting the item feature corresponding to the target customer from the item information table includes: extracting all items of the target customer from the item information table according to the identity information of the target customer, and filtering out the pre-predetermined items from the extracted items.
  • a matter of the item category; the corresponding item feature is extracted from the item belonging to the preset item category.
  • the items are subjected to dimensionality reduction processing, and the items belonging to the preset category are retained, and the items are not filtered out.
  • a statement that reflects the customer's satisfaction with financial products where one or more items may correspond to a financial product that a user is holding or has held.
  • the following categories of items are set in advance: an account opening category, a buying category, an insurance class, a surrender item, a complaint category, a compensation category, and the like.
  • You can set up more event categories in advance as needed.
  • a record of the surrender item is generated in the item information table, and it can be inferred that the customer has low satisfaction with the insurance product; or If a customer purchases the fund products and insurance products of the bank after opening an account with the bank, an account opening item and two purchase items will be generated correspondingly in the item information table, thereby inferring the customer.
  • High satisfaction with related financial products It can be seen that different issues reflect the different levels of satisfaction of customers with existing financial products. Therefore, in the comprehensive evaluation of the target customers' satisfaction with the financial products they are currently holding or the financial products they have held. When evaluating all historical matters of the target customer.
  • the item feature extraction process in the embodiment mainly extracts content having relevance from the customer satisfaction content as a matter feature from the recorded content content, for example, for the item for purchasing the insurance product, the item feature may be the insurance item.
  • Each item recorded in the item information table has corresponding information items, such as the item name, the item of the item, and the attribute of the item. Therefore, the information items to be extracted in the items under each item category can be set in advance, and the content of the information items set in advance can be extracted when the item features are extracted.
  • the classification model needs to be trained when calculating the satisfaction level corresponding to the target customer by using the preset classification model.
  • the support vector machine classification model is selected as a preset classification model. Obtaining a feature training set, each item feature in the item feature training set has a corresponding satisfaction level, that is, the feature in the training set needs to be artificially pre-determined to reflect the characteristics of each item; The support vector machine classification model is trained to obtain model parameters of the classification model.
  • the satisfaction degree is set to a plurality of levels in advance.
  • the satisfaction degree is set to five levels. The higher the level, the user is interested in the financial product currently held or has been held. The higher the satisfaction of financial products. Extract all the items belonging to the preset categories in the item information table and extract the item characteristics from them, and manually evaluate the satisfaction level of the financial products held by each user according to the characteristics of the items, and set the characteristics of the items. After the label of the satisfaction level is associated, the item feature database is created, and 80% of the item features are selected as the training set for training the model, and the remaining 20% of the item features are used as the verification set.
  • the training set is input into the support vector machine classification model to train the model, and the model parameters are obtained.
  • the training results are evaluated through the verification set.
  • the more users in the training set the more accurate the training parameters are.
  • the resulting model parameters reflect the correlation between the user's default type of matter and its level of satisfaction with the financial products currently held or financial products that have been held.
  • the training set can be continuously adjusted, and the model optimal parameters are obtained after multiple iterations.
  • the extracted target characteristics of the target customer are input into the trained support vector machine classification model, and the satisfaction level of the target customer to the held financial product is calculated.
  • the mapping relationship between each financial product and other one or more financial products under different satisfaction levels is established in advance, and when the recommended product is selected, the user is selected according to the mapping relationship. Financial products.
  • the financial product is recommended to the target customer via the common contact medium.
  • the contact medium used by the customer After determining the financial product to be recommended, analyzing the existing customer group of the financial product, obtaining the contact medium used by each existing customer corresponding to the financial product to be recommended;
  • the contact medium used by the customer performs statistics to determine the probability of distribution of the product to be recommended on each contact medium, and the distribution probability of the contact medium is large, indicating that the probability of the customer purchasing the financial product through the contact medium is higher, and the distribution will be distributed.
  • the most probable contact medium serves as a common contact medium for the target customer. To improve the recommendation success rate.
  • the financial product recommendation device proposed in this embodiment calculates the satisfaction level of the target customer's financial products according to the acquired classification characteristics according to the characteristics of the target customer according to the predetermined classification model, and then according to the target customer's holding level.
  • the product data of the financial products and the level of satisfaction with the financial products held select the financial products to be recommended for the target customers.
  • the target customer is selected with the appropriate contact medium. In this way, all the financial products of the bank can be integrated, and targeted recommendations can be made for a certain customer to improve the recommendation success rate of the product.
  • the financial product recommendation program may also be divided into one or more modules, one or more modules are stored in the memory 11 and are processed by one or more processors (this embodiment) Illustrated by the processor 12) to complete the application, a module referred to herein refers to a series of computer program instructions that are capable of performing a particular function for describing the execution of a financial product recommendation program in a financial product recommendation device.
  • FIG. 2 it is a schematic diagram of a function module of a financial product recommendation program in an embodiment of the financial product recommendation device of the present application.
  • the financial product recommendation program may be divided into an acquisition module 10 and a calculation module 20,
  • the selection module 30 and the recommendation module 40 are exemplarily:
  • the obtaining module 10 is configured to: extract, from the item information table, a feature feature corresponding to the target customer;
  • the calculating module 20 is configured to: according to a preset classification model, calculate a satisfaction level of the target customer to the held financial product according to the target feature of the target customer;
  • the selecting module 30 is configured to: obtain product data of the financial product held by the target customer, and according to the product data of the financial product held by the target customer and the satisfaction level of the held financial product, the target is The customer selects the financial product to be recommended;
  • the obtaining module 10 is further configured to: acquire a contact medium used by an existing customer corresponding to the financial product to be recommended, and predict a recommended contact medium of the customer according to the acquired contact medium;
  • the recommendation module 40 is configured to: recommend the financial product to the target customer through the common contact medium.
  • the present application also provides a financial product recommendation method.
  • FIG. 3 it is a flowchart of the first embodiment of the financial product recommendation method of the present application.
  • the financial product recommendation method includes:
  • step S10 the item characteristics corresponding to the target customer are extracted from the item information table.
  • step S20 according to the preset classification model, the satisfaction level of the target customer to the held financial product is calculated according to the characteristics of the target customer.
  • the method of the embodiments of the present application may be performed by a device, which may be implemented by software and/or hardware.
  • a device information table for storing each customer transaction item and other various items occurring based on the transaction item, such as a complaint item, a compensation item, a comment item, and a surrender item, are pre-established in the apparatus.
  • the above transactions include transactions for fund purchases, insurance purchases, etc. for various financial products.
  • various financial product-related matters will be recorded in the above information table.
  • the contact medium at the time of the event is also recorded.
  • the contact medium is, in this embodiment, mainly includes the following channels: a PC client issued by a bank, an APP client, and a telemarketing channel.
  • the identity information of the target customer may be determined when receiving the request for product recommendation to the target customer, wherein the identity information may be identification information of the unique customer in the database by the ID card number or the mobile phone number. According to its identity information, all matters of the customer or items recorded in the past period of time are extracted from the item information table.
  • the step of extracting the item feature corresponding to the target customer from the item information table includes: extracting all items of the target customer from the item information table according to the identity information of the target customer, and filtering out the pre-predetermined items from the extracted items.
  • a matter of the item category; the corresponding item feature is extracted from the item belonging to the preset item category.
  • the items are subjected to dimensionality reduction processing, and the items belonging to the preset category are retained, and the items are not filtered out.
  • a statement that reflects the customer's satisfaction with financial products where one or more items may correspond to a financial product that a user is holding or has held.
  • the following categories of items are set in advance: an account opening category, a buying category, an insurance class, a surrender item, a complaint category, a compensation category, and the like.
  • You can set up more event categories in advance as needed.
  • a record of the surrender item is generated in the item information table, and it can be inferred that the customer has low satisfaction with the insurance product; or If a customer purchases the fund products and insurance products of the bank after opening an account with the bank, an account opening item and two purchase items will be generated correspondingly in the item information table, thereby inferring the customer.
  • High satisfaction with related financial products It can be seen that different issues reflect the different levels of satisfaction of customers with existing financial products. Therefore, in the comprehensive evaluation of the target customers' satisfaction with the financial products they are currently holding or the financial products they have held. When evaluating all historical matters of the target customer.
  • the item feature extraction process in the embodiment mainly extracts content having relevance from the customer satisfaction content as a matter feature from the recorded content content, for example, for the item for purchasing the insurance product, the item feature may be the insurance item.
  • Each item recorded in the item information table has corresponding information items, such as the item name, the item of the item, and the attribute of the item. Therefore, the information items to be extracted in the items under each item category can be set in advance, and the content of the information items set in advance can be extracted when the item features are extracted.
  • the classification model needs to be trained when calculating the satisfaction level corresponding to the target customer by using the preset classification model.
  • the support vector machine classification model is selected as a preset classification model. Obtaining a feature training set, each item feature in the item feature training set has a corresponding satisfaction level, that is, the feature in the training set needs to be artificially pre-determined to reflect the characteristics of each item; The support vector machine classification model is trained to obtain model parameters of the classification model.
  • the satisfaction degree is set to a plurality of levels in advance.
  • the satisfaction degree is set to five levels. The higher the level, the user is interested in the financial product currently held or has been held. The higher the satisfaction of financial products. Extract all the items belonging to the preset categories in the item information table and extract the item characteristics from them, and manually evaluate the satisfaction level of the financial products held by each user according to the characteristics of the items, and set the characteristics of the items. After the label of the satisfaction level is associated, the item feature database is created, and 80% of the item features are selected as the training set for training the model, and the remaining 20% of the item features are used as the verification set.
  • the training set is input into the support vector machine classification model to train the model, and the model parameters are obtained.
  • the training results are evaluated through the verification set.
  • the more users in the training set the more accurate the training parameters are.
  • the resulting model parameters reflect the correlation between the user's default type of matter and its level of satisfaction with the financial products currently held or financial products that have been held.
  • the training set can be continuously adjusted, and the model optimal parameters are obtained after multiple iterations.
  • step S30 the extracted target feature of the target customer is input into the trained support vector machine classification model, and the satisfaction level of the target customer to the held financial product is calculated.
  • pre-establishing a mapping relationship between each financial product and other one or more financial products under different satisfaction levels when selecting a recommended product, selecting a suitable financial product for the user according to the mapping relationship .
  • Step S40 Acquire a contact medium used by an existing customer corresponding to the financial product to be recommended, and predict a recommended contact medium of the customer according to the acquired contact medium.
  • Step S50 recommending the financial product to the target customer through the common contact medium.
  • the contact medium used by the customer After determining the financial product to be recommended, analyzing the existing customer group of the financial product, obtaining the contact medium used by each existing customer corresponding to the financial product to be recommended;
  • the contact medium used by the customer performs statistics to determine the probability of distribution of the product to be recommended on each contact medium, and the distribution probability of the contact medium is large, indicating that the probability of the customer purchasing the financial product through the contact medium is higher, and the distribution will be distributed.
  • the most probable contact medium serves as a common contact medium for the target customer. To improve the recommendation success rate.
  • the financial product recommendation method proposed in this embodiment calculates the satisfaction level of the target customer's financial products according to the acquired classification characteristics according to the characteristics of the target customer according to the predetermined classification model, and then according to the target customer's holding level.
  • the product data of the financial products and the level of satisfaction with the financial products held select the financial products to be recommended for the target customers.
  • the target customer is selected with the appropriate contact medium. In this way, all the financial products of the bank can be integrated, and targeted recommendations can be made for a certain customer to improve the recommendation success rate of the product.
  • the embodiment of the present application further provides a computer readable storage medium, where the financial product recommendation program is stored, and the financial product recommendation program can be executed by one or more processors to implement the following operating:
  • the financial product is recommended to the target customer via the common contact medium.
  • the step of extracting a feature feature corresponding to the target customer from the item information table includes:
  • Extracting corresponding event features from the items belonging to the preset item category Extracting corresponding event features from the items belonging to the preset item category.
  • the step of obtaining the contact medium used by the existing customer corresponding to the financial product to be recommended, and predicting the recommended contact medium of the customer according to the acquired contact medium includes:
  • the contact medium used by each existing customer is counted, the distribution probability of the product to be recommended on each contact medium is determined, and the contact medium with the highest distribution probability is used as the common contact medium of the target customer.
  • the preset classification model is a support vector machine classification model
  • the processor is further configured to execute the financial product recommendation program to further extract the item feature corresponding to the target customer from the item information table.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

L'invention concerne un dispositif de recommandation de produit financier. Ce système comprend une mémoire et un processeur. Un programme de recommandation de produit financier qui s'exécute sur le processeur est enregistré dans la mémoire, le programme mettant en œuvre, lors de leur exécution par le processeur, les étapes suivantes consistant : à extraire, d'une table d'informations d'articles, des caractéristiques d'article correspondant à un client cible ; à calculer, sur la base d'un modèle de classification préétabli et en fonction des caractéristiques d'article du client cible, un niveau de satisfaction relatif à des produits financiers détenus par le client cible ; à sélectionner, selon les données de produit des produits financiers détenus par le client cible et le niveau de satisfaction relatif aux produits financiers détenus, un produit financier à recommander au client cible ; à obtenir des supports de contact correspondant à chaque article de la table d'informations d'articles du client cible, et à prédire, en fonction des supports de contact obtenus, un support de contact commun du client ; à recommander le produit financier au client cible par l'intermédiaire du support de contact commun. L'invention concerne également un procédé de recommandation de produit financier et un support d'informations lisible par ordinateur. La présente invention améliore les taux de réussite de recommandation concernant des produits financiers.
PCT/CN2018/077631 2017-10-09 2018-02-28 Procédé et dispositif de recommandation de produit financier et support d'informations lisible par ordinateur WO2019071906A1 (fr)

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CN109255715A (zh) * 2018-09-03 2019-01-22 平安科技（深圳）有限公司 电子装置、产品推荐方法和计算机可读存储介质
CN109447728A (zh) * 2018-09-07 2019-03-08 平安科技（深圳）有限公司 金融产品推荐方法、装置、计算机设备及存储介质
CN110009159A (zh) * 2019-04-11 2019-07-12 湖北风口网络科技有限公司 基于网络大数据的金融借贷需求预测方法及系统
CN110135942A (zh) * 2019-04-12 2019-08-16 深圳壹账通智能科技有限公司 产品推荐方法、装置及计算机可读存储介质
CN110415123A (zh) * 2019-06-06 2019-11-05 财付通支付科技有限公司 金融产品推荐方法、装置和设备及计算机存储介质
CN111429232A (zh) * 2020-04-12 2020-07-17 中信银行股份有限公司 产品推荐方法、装置和电子设备及计算机可读存储介质
CN111858686B (zh) * 2020-07-08 2024-05-28 深圳市富途网络科技有限公司 数据显示方法、装置、终端设备及存储介质
CN112669136A (zh) * 2020-12-10 2021-04-16 前海飞算科技(深圳)有限公司 基于大数据的金融产品推荐方法、系统、设备及存储介质
CN113468421A (zh) * 2021-06-29 2021-10-01 平安信托有限责任公司 基于向量匹配技术的产品推荐方法、装置、设备及介质
CN115619436A (zh) * 2022-11-14 2023-01-17 平安银行股份有限公司 一种金融产品的推荐方法、装置、计算机设备及存储介质

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