Financial product push method, device, equipment, storage medium and product

Abstract

The present application provides a method, apparatus, device, storage medium and product for pushing financial products, the method comprising: determining customer type information based on the questionnaire response information. Determining initial financial product information based on the customer type information. Obtaining an expected rate of return. Determining combined financial product information based on the expected rate of return and the initial financial product information; obtaining expected parameters. Determining product price change data based on the expected parameters, pre-stored product parameters and a pre-stored fractional Brownian motion model, and determining product value data based on the product price change data, expected parameters, pre-stored product parameters and a pre-built calculation model. Determining target combined financial product information based on the product value data, combined financial product information and expected rate of return, and pushing the target combined financial product information to a client. The method of the present application improves the transaction efficiency and transaction rate of financial products.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, specifically to the field of intelligent robot technology in the financial industry, and in particular to a method, device, equipment, storage medium and product for pushing financial products. Background Art

[0002] With the rapid development of the financial industry, a wide variety of financial products have emerged to meet people's growing financial needs. Faced with this wide variety of financial products, how to help customers quickly find the financial products they desire has become one of the most pressing issues facing the financial industry.

[0003] In the existing technology, the push of financial products generally involves first collecting relevant information about customers' product preferences through questionnaires, then analyzing the collected information to determine whether the customer belongs to the conservative, stable or aggressive type, and then pushing corresponding financial products based on the customer's type.

[0004] However, the inventors discovered that the existing technology has at least the following technical problems: since most customers lack the professional knowledge related to financial products, the accuracy of financial products pushed using existing technology is low, resulting in customers having more concerns and taking longer to consider when finally purchasing financial products, resulting in lower transaction efficiency and transaction rate of financial products. Summary of the Invention

[0005] The present application provides a financial product push method, apparatus, device, storage medium and product to solve the problem that the accuracy of pushed financial products is low, thereby reducing the transaction efficiency and transaction rate of financial products.

[0006] In a first aspect, the present application provides a method for pushing financial products, comprising:

[0007] Receive questionnaire response information sent by the client, and determine client type information based on the questionnaire response information;

[0008] determining initial financial product information based on the customer type information, and sending the initial financial product information to the client, so that the client generates first feedback information in response to a preliminary selection operation on the initial financial product information;

[0009] receiving first feedback information sent by the client, and if determining that the first feedback information is acceptance information, sending pre-deposit yield collection information to the client, so that the client obtains an expected yield in response to an input operation of the pre-deposit yield collection information;

[0010] receiving the expected rate of return sent by the client, and determining combined financial product information based on the expected rate of return and the initial financial product information;

[0011] sending the combined financial product information to a client, so that the client generates second feedback information in response to a check operation on the combined financial product information;

[0012] receiving second feedback information sent by the client, and if determining that the second feedback information is acceptance information, sending pre-stored parameter collection information to the client, so that the client obtains expected parameters in response to a parameter input operation on the pre-stored parameter collection information;

[0013] receiving the expected parameters sent by the client, and determining product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, and determining product value data based on the product price change data, the expected parameters, the pre-stored product parameters, and a pre-built computational model;

[0014] Target combination financial product information is determined based on the product value data, the combination financial product information and the expected rate of return, and the target combination financial product information is pushed to the client.

[0015] In one possible implementation, the initial financial product information includes the prices and pre-deposit yields of multiple single financial products; accordingly, determining the combined financial product information based on the expected yield and the initial financial product information includes: determining multiple combined weight allocation coefficient information based on the expected yield and the pre-deposit yield of each single financial product, wherein each combined weight coefficient allocation information includes the weight coefficients of multiple single financial products; and confirming multiple combined financial product information based on each combined weight coefficient allocation information and the price of each single financial product.

[0016] In one possible implementation, the expected parameters include expected product retention time data; accordingly, determining the product price change data based on the expected parameters, pre-stored product parameters and the pre-stored fractional Brownian motion model includes: determining the initial financial product price based on the expected product retention time data and the pre-stored product price parameters; determining the product price change data based on the expected product retention time data, the initial financial product price, the pre-stored fractional Brownian motion model and the pre-stored product parameters.

[0017] In one possible implementation, the pre-stored product parameters include a pre-stored product price volatility, a pre-stored product risk-free rate, and a pre-stored product estimated rate of return; accordingly, the product price change data is determined based on the expected product holding time data, the initial financial product price, the pre-stored fractional Brownian motion model, and the pre-stored product parameters, and the calculation formula is:

[0018] S(t)=S0exp(σB H (t)+μt-0.5σ 2 t 2H )

[0019] Where B0 is the initial price of the financial product when the expected product holding time is t=0, B H (t) is the pre-deposit fractional Brownian motion model, σ is the price volatility of the pre-deposit product, μ is the estimated rate of return of the pre-deposit product, H is the index describing the relationship between the increments of the fractional Brownian motion, t is the expected product retention time data, and S(t) is the product price change data.

[0020] In one possible implementation, the product value data is determined based on the product price change data, the expected parameters, the pre-stored product parameters and the pre-built operation model, including: determining the product dividend rate based on the pre-stored product parameters; determining the product value data based on the product dividend rate, the pre-stored product parameters, the expected parameters and the pre-built operation model.

[0021] In one possible implementation, the pre-stored product parameters further include a pre-stored per-unit product fee and a pre-stored unit time interval; accordingly, the product value data is determined based on the product dividend rate, the pre-stored product parameters, the expected parameters, and the pre-built calculation model, and the calculation formula is:

[0022]

[0023] Where H is the index describing the relationship between the increments of fractional Brownian motion, σ is the price volatility of the pre-stored product, t is the customer's expected product retention time data, k is the pre-stored cost per unit of product, S t is the product price change data, δt is the preset unit time interval, r is the risk-free interest rate of the pre-deposited product, q is the product dividend rate, and V is the product value data.

[0024] In a possible implementation, after receiving the first feedback information sent by the client, the method further includes:

[0025] If it is determined that the initial feedback information meets the preset rejection condition, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

[0026] In one possible implementation, after receiving the second feedback information sent by the client, the method further includes: if it is determined that the second feedback information is rejection information, sending the financial product self-selection information to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

[0027] In a possible implementation, before receiving the questionnaire response information sent by the client, the method further includes: sending the questionnaire to the client, so that the client generates the questionnaire response information in response to the response operation to the questionnaire.

[0028] In one possible implementation, after pushing the target combination financial product information to the client, it also includes: receiving business processing information sent by the client, and generating processing success information based on the business processing information, wherein the business processing information is generated by the client in response to the business processing operation of the target combination financial product information; sending the processing success information to the client so that the client displays the processing success message.

[0029] In a second aspect, this application also provides a method for pushing financial products, including:

[0030] Sending the questionnaire response information to the server, so that the server determines the customer type information based on the questionnaire response information, and determines the initial financial product information based on the customer type information;

[0031] receiving the initial financial product information sent by the server, and generating first feedback information in response to a preliminary selection operation on the initial financial product information;

[0032] Sending the first feedback information to the server, so that if the server determines that the first feedback information is acceptance information, it obtains the pre-deposit yield collection information;

[0033] receiving the pre-deposit rate of return collection information sent by the server, and obtaining an expected rate of return in response to an input operation of the pre-deposit rate of return collection information;

[0034] Sending the expected rate of return to the server, so that the server determines combined financial product information based on the expected rate of return and the initial financial product information;

[0035] receiving the combined financial product information sent by the server, and generating second feedback information in response to a check operation on the combined financial product information;

[0036] sending the second feedback information to the server, so that if the server determines that the second feedback information is acceptance information, it obtains pre-stored parameter collection information;

[0037] Receiving pre-stored parameter collection information sent by the server, and obtaining expected parameters in response to a parameter input operation on the pre-stored parameter collection information;

[0038] Sending the expected parameters to the server, so that the server determines product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, and determines product value data based on the product price change data, the customer expected parameters, the pre-stored product parameters, and a pre-built computational model, and determines target combination financial product information based on the product value data, the combination financial product information, and the expected rate of return;

[0039] Receive the target combination financial product information pushed by the server.

[0040] In a possible implementation, after sending the first feedback information to the server, the method further includes: receiving the self-selected financial product information sent by the server, and obtaining the self-selected financial product information in response to a self-selection operation on the self-selected financial product information.

[0041] In a possible implementation, after sending the second feedback information to the server, the method further includes: receiving the financial product self-selection information sent by the server, and obtaining the self-selected financial product information in response to the self-selection operation on the financial product self-selection information.

[0042] In a possible implementation, before sending the questionnaire response information to the server, the method further includes: receiving the questionnaire sent by the server, and generating the questionnaire response information in response to a response operation to the questionnaire.

[0043] In one possible implementation, after receiving the target combination financial product information, it also includes: obtaining business processing information in response to a business processing operation on the target combination financial product information; sending the business processing information to the server so that the server generates a processing success information based on the business processing information; receiving the business processing success information sent by the server, and displaying the business processing success information.

[0044] In a third aspect, the present application provides a financial product push device, comprising:

[0045] A first receiving module is configured to receive questionnaire response information sent by a client and determine client type information based on the questionnaire response information;

[0046] an initial financial product determination module, configured to determine initial financial product information based on the customer type information;

[0047] a first sending module, configured to send the initial financial product information to the client, so that the client generates first feedback information in response to a preliminary selection operation on the initial financial product information;

[0048] The first receiving module is further configured to receive first feedback information sent by the client. If the first feedback information is determined to be acceptance information, the first sending module is further configured to send pre-deposit yield collection information to the client, so that the client obtains an expected yield in response to an input operation of the pre-deposit yield collection information.

[0049] The first receiving module is further configured to receive the expected rate of return sent by the client, and determine the combined financial product information based on the expected rate of return and the initial financial product information;

[0050] The first sending module is further configured to send the combined financial product information to a client, so that the client generates second feedback information in response to a check operation on the combined financial product information;

[0051] The first receiving module is further configured to receive second feedback information sent by the client, and if the second feedback information is determined to be acceptance information, the first sending module is further configured to send pre-stored parameter collection information to the client, so that the client obtains expected parameters in response to a parameter input operation on the pre-stored parameter collection information;

[0052] The first receiving module is further configured to receive the expected parameters sent by the client;

[0053] The calculation module is used to determine product price change data based on the expected parameters, pre-stored product parameters and a pre-stored fractional Brownian motion model, and to determine product value data based on the product price change data, the expected parameters, the pre-stored product parameters and a pre-built calculation model;

[0054] The push module is configured to determine target combination financial product information based on the product value data, the combination financial product information, and the expected rate of return, and push the target combination financial product information to the client.

[0055] Fourthly, the present application further provides a financial product push device, comprising:

[0056] a second sending module, configured to send the questionnaire response information to a server, so that the server determines the customer type information based on the questionnaire response information, and determines the initial financial product information based on the customer type information;

[0057] A second receiving module, configured to receive the initial financial product information sent by the server;

[0058] a feedback information generating module, configured to generate first feedback information in response to a preliminary selection operation on the initial financial product information;

[0059] The second sending module is further configured to send the first feedback information to the server, so that if the server determines that the first feedback information is acceptance information, it obtains the pre-deposit yield collection information;

[0060] The second receiving module is further configured to receive the pre-deposit yield collection information sent by the server;

[0061] A collection module, configured to obtain an expected rate of return in response to an input operation of the pre-deposit rate of return collection information;

[0062] The second sending module is further configured to send the expected rate of return to the server, so that the server determines the combined financial product information based on the expected rate of return and the initial financial product information;

[0063] The second receiving module is further configured to receive the combined financial product information sent by the server;

[0064] The feedback information generating module is further configured to generate second feedback information in response to a check operation on the combined financial product information;

[0065] The second sending module is further configured to send the second feedback information to the server, so that if the server determines that the second feedback information is acceptance information, it obtains pre-stored parameter collection information;

[0066] The second receiving module is further configured to receive pre-stored parameter collection information sent by the server;

[0067] The acquisition module is configured to obtain expected parameters in response to a parameter input operation on the pre-stored parameter acquisition information;

[0068] The second sending module is further configured to send the expected parameters to the server, so that the server determines product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, determines product value data based on the product price change data, the customer expected parameters, the pre-stored product parameters, and a pre-built calculation model, and determines target combination financial product information based on the product value data, the combination financial product information, and the expected rate of return;

[0069] The second receiving module is further configured to receive the target combination financial product information pushed by the server.

[0070] In a fifth aspect, the present application provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the processor;

[0071] The memory stores computer-executable instructions;

[0072] The at least one processor executes the computer-executable instructions stored in the memory to implement the financial product push method as described in the first aspect or the second aspect.

[0073] In a sixth aspect, the present application provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the financial product push method described in the first aspect or the second aspect.

[0074] In a seventh aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the financial product push method described in the first aspect or the second aspect.

[0075] The present application provides a method, apparatus, device, storage medium, and product for pushing financial products. Initial financial product information is pushed for the first time, followed by obtaining a first feedback message, to find initial financial product information that meets customer requirements. Combination financial product information is then adjusted based on the customer's expected rate of return. Secondary feedback is then obtained to find combination financial product information that meets the customer's basic expectations. Expected parameters sent by the client are then obtained based on the combination financial product information. Product value data is determined based on the expected parameters, pre-stored product parameters in the financial market, and a pre-stored fractional Brownian motion model. Product value data is then determined based on product price change data, the expected parameters, the pre-stored product parameters, and a pre-built computational model. Finally, target combination financial product information is determined based on the product value data, the combination financial product information, and the expected rate of return. Based on the combination financial product that meets the customer's basic expectations, target combination financial product information that better meets the customer's needs is obtained. This allows the pushed target combination financial product to better match the customer's needs in terms of its rate of return and expected parameters, thereby improving the transaction efficiency and transaction rate of financial products. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0077] Figure 1 A schematic diagram of an application scenario of the financial product push method provided in an embodiment of the present application;

[0078] Figure 2 Schematic diagram of the process of the financial product push method provided in the embodiment of this application Figure 1 ;

[0079] Figure 3 Schematic diagram of the process of the financial product push method provided in the embodiment of this application Figure 2 ;

[0080] Figure 4 A schematic diagram of the interaction flow of the financial product push method provided in an embodiment of the present application;

[0081] Figure 5 Schematic diagram of the structure of the financial product push device provided in the embodiment of this application Figure 1 ;

[0082] Figure 6 Schematic diagram of the structure of the financial product push device provided in the embodiment of this application Figure 2 ;

[0083] Figure 7 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0084] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0085] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0086] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0087] Currently, with the rapid development of the financial industry, a wide variety of financial products have emerged to meet people's growing financial needs. Faced with this vast array of financial products, how to quickly help customers find the ones they desire has become a pressing issue for the financial industry. Prior art methods for promoting financial products typically involve first collecting information about customers' product preferences through questionnaires. This information is then analyzed to determine whether the customer falls into the conservative, steady, or aggressive categories, and then the corresponding financial products are promoted based on the customer's type. Furthermore, prior art methods have also been proposed to improve the accuracy of promoted financial products by selecting a combination of financial products based on customer type. However, the inventors have discovered that due to the rapid development and rapid pace of change in the financial market, in addition to low product accuracy, customers also have concerns about the timing of buying and selling financial products, leading to dissatisfaction with the yield of promoted financial products. This can lead customers to forgo purchasing the products, resulting in reduced transaction efficiency.

[0088] To solve the above technical problems, the embodiments of the present application provide the following technical concepts for solving the problems: First, based on the fractional Brownian motion model, partial differential equations are cited to optimize the pricing combination of financial products. Volatility is taken into account in the partial differential equations satisfied by options under standard Brownian motion. Then, based on the customer's input parameters, the price changes of financial products over time are calculated, and more accurate combination financial products and appropriate buying and selling times for the combination financial products are pushed to customers, so that the pushed combination financial products are more in line with customer needs, thereby improving the transaction efficiency of financial products.

[0089] The data transmission method provided in this application is intended to solve the above technical problems in the prior art.

[0090] The following specific embodiments are used to describe in detail the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Figures 1 to 7 , describes the embodiments of the present application.

[0091] Please refer to Figure 1 , Figure 1 This is a schematic diagram of an application scenario of the financial product push method provided in the embodiment of the present application, such as Figure 1 As shown, it includes: a client 101 and a server 102.

[0092] Client 101 can be used to display and allow customers to view financial products and input corresponding parameters and selection information. Client 101 can be a server such as a computer or a mobile terminal such as a tablet, mobile phone, or laptop. Server 102 can be used to access real-time product data related to financial markets, interact with client 101, and perform corresponding operations based on the information sent by client 101 to push the corresponding financial products to client 101. Server 102 can be a physical server or a cloud server.

[0093] Please refer to Figure 2 , Figure 2 Schematic diagram of the process of the financial product push method provided in the embodiment of this application Figure 1 , the execution subject of this embodiment can be Figure 1 The server 102 in the embodiment shown may also be other computer-related devices, and this embodiment does not impose any particular restrictions on this. Figure 2 As shown, the financial product push method includes:

[0094] S201: Receive questionnaire response information sent by the client, and determine customer type information based on the questionnaire response information.

[0095] In this embodiment, the questionnaire response information may be basic information filled out by the customer on the client side. This basic information may include descriptive information such as preferences for financial products, the amount of loss they can tolerate, and their expected minimum return. The customer type information may describe the customer's purchasing power. For example, the customer type may include conservative, stable, balanced, and aggressive.

[0096] S202: Determine initial financial product information based on the customer type information, and send the initial financial product information to the client, so that the client generates first feedback information in response to a preliminary selection operation on the initial financial product information.

[0097] In this embodiment, the initial financial product information may include product information of multiple single financial products, and the product information of each single financial product may include the real-time price, annual yield, and price change information over time of the financial product. When the initial financial product information is sent to the client, it can be pushed in the form of a pop-up window. After the client sees the pop-up prompt message on the client, the client can choose whether to accept the pushed initial financial product. Whether to accept the push corresponds to the preliminary selection operation, which can be an operation gesture indicating the customer's willingness to choose, for example, the operation gesture can be a click or input. The first feedback information can be the interactive information generated by a key on the client, for example, the interactive information can be "accept" or "reject".

[0098] S203: Receive first feedback information sent by the client, and determine whether the first feedback information is acceptance information.

[0099] S203a: If the first feedback information is determined to be acceptance information, the pre-deposit rate of return collection information is sent to the client, so that the client obtains the expected rate of return in response to the input operation of the pre-deposit rate of return collection information.

[0100] In this embodiment, the pre-deposited yield collection information may be a page or content input box for collecting expected yields, where the client can enter the expected yield information on the client. The expected yield is the predicted future yield of an asset under uncertain conditions. For example, the expected yield entered by client A may be 20%.

[0101] In an optional embodiment of the present application, after receiving the first feedback information sent by the client in step S203, the method further includes:

[0102] Step A: If the second feedback information is determined to be rejection information, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

[0103] In this embodiment, the self-selected financial product information may be a financial product display interface containing a search box and selection options. The self-selection operation may be a keystroke or gesture that generates a corresponding response on the client side, such as a check, click, or search gesture. Self-selected financial product information is generated by customers directly selecting corresponding financial products from the financial market based on their purchasing power and purchasing needs. Typically, customers with some professional financial knowledge or prior knowledge of specific financial products are able to self-select financial product information that they find most satisfactory.

[0104] S204: Receive the expected rate of return sent by the client, and determine the combined financial product information based on the expected rate of return and the initial financial product information.

[0105] In this embodiment, the combined financial product information may include information of at least two or more single financial products. The combined financial product information may be obtained by applying a weight allocation method to the expected rate of return and the initial financial product information.

[0106] Specifically, in an optional embodiment of the present application, step S204 includes:

[0107] S204a: Determine a plurality of combination weight allocation coefficient information based on the expected rate of return and the pre-deposit rate of return of each single financial product, wherein each combination weight coefficient allocation information includes weight coefficients of a plurality of single financial products.

[0108] S204b: Confirm multiple combination financial product information based on the weight coefficient allocation information of each combination and the price of each single financial product.

[0109] In this embodiment, the weight coefficient can represent the importance of a certain indicator in the indicator system, and the sum of the weight coefficients of the individual financial products in each combined weight distribution coefficient is 1. For example, if the weight coefficient corresponding to financial product 1 is a and the weight coefficient corresponding to financial product 2 is b, then a + b = 1.

[0110] For example, assuming that the expected rate of return in this embodiment is 20%, the current rate of return of financial product 1 is 10%, and the rate of return of financial product 2 is 30%, and the customer wants to purchase 10,000 units of financial products, then the corresponding weight coefficient a=0.5, the weight coefficient b=0.5, and the combined financial product information is to purchase 50,000 units of financial product 1 and 50,000 units of financial product 2. The final possible profit is 120,000 units of financial products, and the expected rate of return is 20%.

[0111] S205: Send the combined financial product information to the client, so that the client generates second feedback information in response to a check operation on the combined financial product information.

[0112] In this embodiment, the check operation may be an operation gesture indicating the customer's selection intention, such as a click or input. The second feedback information may be interaction information generated by a key pressed by the client, such as "accept" or "reject".

[0113] S206: Receive the second feedback information sent by the client, and determine whether the second feedback information is acceptance information.

[0114] S206a: If it is determined that the second feedback information is acceptance information, the pre-stored parameter collection information is sent to the client, so that the client obtains the expected parameters in response to the parameter input operation of the pre-stored parameter collection information.

[0115] In this embodiment, the pre-stored parameter collection information can be an interface including multiple input boxes or selection buttons, and the expected parameters can be the data that the customer wants to enter when receiving the interface, such as: annual yield value, buying time, selling time, buying price or selling price, etc.

[0116] Based on the above embodiment, in an optional embodiment of the present application, after receiving the second feedback information sent by the client in step S206, the method further includes:

[0117] Step B: If the second feedback information is determined to be rejection information, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

[0118] In this embodiment, the implementation principle and calculation effect of step B are similar to those of step A, so they will not be described again in detail in this embodiment.

[0119] S207: Receive the expected parameters sent by the client, and determine the product price change data based on the expected parameters, pre-stored product parameters and the pre-stored fractional Brownian motion model, and determine the product value data based on the product price change data, the expected parameters, the pre-stored product parameters and the pre-built calculation model.

[0120] In this embodiment, the pre-stored fractional Brownian motion model can be: based on the existing fractional Brownian motion model FBM, let (Ω, F, F t , P) is a probability space with σ flow, B H ={B H (t)} t≥0 is a fractional Brownian motion on (Ω, F, P), where F t is the fractional Brownian motion model {B H (t)} t≥0 The generated σ-algebraic flow, P is the risk center measure, and σ is the price volatility of financial products.

[0121] When determining the product price change data in step S207, it can be obtained by solving the exponential function. During the solution process, the expected parameters, pre-stored product parameters and pre-stored fractional Brownian motion model are used as independent variables, and the product price change data is used as the dependent variable.

[0122] Specifically, in an optional embodiment of the present application, the expected parameters may include the expected product shelf life. Accordingly, in step S207, product price change data is determined based on the expected parameters, pre-stored product parameters, and the pre-stored fractional Brownian motion model, including:

[0123] S207a: Determine the initial financial product price based on the expected product retention time data and pre-stored product price parameters.

[0124] In this embodiment, the expected product holding time may include a purchase time node, a sale time node, and the time data between the purchase time and the sale time. The pre-stored product price parameters may be associated data of the price and time changes of each individual financial product. The initial financial product price may be the price of a single financial product at the customer's expected purchase time (generally referring to the time node when the customer enters the desired parameters on the client).

[0125] S207b: Determine product price change data based on expected product holding time data, initial financial product price, pre-stored fractional Brownian motion model, and pre-stored product parameters.

[0126] In this embodiment, after adding the fractional Brownian motion model to a pre-stored exponential function, for example, the exponential function model can be an exponential function with the natural constant e as the base, the expected product holding period data, the initial financial product price, and the pre-stored product parameters are substituted into the exponential function to obtain all product price change data.

[0127] Based on the above embodiment, in an optional embodiment of the present application, determining the product value data in step S207 based on the product price change data, the expected parameters, the pre-stored product parameters, and the pre-built calculation model includes:

[0128] S207c: Determine the product bonus rate based on pre-stored product parameters;

[0129] S207d: Determine product value data based on product dividend rate, pre-stored product parameters, expected parameters and pre-built calculation model.

[0130] In this embodiment, the product dividend rate can be the ratio of the dividend to the product price. A too low dividend rate indicates that the product price is too high and the expected rate of return may be low. The pre-built computational model can be a partial differential equation, such as the Black-Scholes option pricing model, which is satisfied by the product value data driven by fractional Brownian motion. The product value data can be product option value data. To determine the product value data, the product dividend rate, pre-stored product parameters, and expected parameters can be input into the pre-built computational model and then solved using a numerical or analytical algorithm. For example, when the solution method is a numerical method, the finite difference method can be used.

[0131] S208: Determine target combination financial product information based on product value data, combination financial product information, and expected rate of return, and push the target combination financial product information to the client.

[0132] In this embodiment, after obtaining the value data of each single financial product, the target combination financial product information is obtained based on the product value data, the combination financial product information and the expected rate of return using the tool or software described in step S204.

[0133] Based on the above embodiment, in an optional embodiment of the present application, after step S208, the following steps are further included:

[0134] Step C: receiving the business processing information sent by the client, and generating a processing success message according to the business processing information, wherein the business processing information is generated by the client in response to the business processing operation on the target combination financial product information.

[0135] Step D: Send the successful processing information to the client so that the client can display the successful processing message.

[0136] In this embodiment, the transaction information may include transaction-related information such as the quantity and price of the financial product to be purchased. After the customer verifies that the information is correct on the client, they can directly process the transaction through the client. After the transaction is successfully processed, a successful message will be displayed on the client.

[0137] In summary, the financial product push method provided in the embodiments of the present application finds initial financial product information that meets the customer's requirements by first receiving a first feedback message after initially pushing initial financial product information. Combination financial product information is then adjusted based on the customer's expected rate of return. Secondary feedback is then obtained to find combination financial product information that meets the customer's basic expectations. Expected parameters sent by the client are then obtained based on the combination financial product information. Product value data is determined based on the expected parameters, pre-stored product parameters in the financial market, and a pre-stored fractional Brownian motion model. Product value data is then determined based on product price change data, expected parameters, pre-stored product parameters, and a pre-built computational model. Finally, target combination financial product information is determined based on the product value data, combination financial product information, and expected rate of return. Based on the combination financial product that meets the customer's basic expectations, target combination financial product information that better meets the customer's needs is obtained. This allows the customer to better match the pushed target combination financial product with the customer's needs in terms of the target combination financial product's rate of return and expected parameters, thereby improving the transaction efficiency and transaction rate of financial products.

[0138] At the same time, by using the pre-built partial differential equation based on the pre-stored fractional Brownian motion model, the product value data corresponding to the target combination financial product is solved, taking volatility into account, and successively obtaining product price change data and product value data. When obtaining the product value data, the product dividend rate is also determined to improve the stability of the pushed target combination financial products, obtain a more stable target combination financial product, and then obtain a target combination financial product that is more closely matched with customer needs, so as to improve transaction efficiency and transaction rate.

[0139] At the same time, by sending the self-selected information of financial products to the client, customers can perform self-selection operations on the client and obtain self-selected financial product information, so that customers with professional financial knowledge can choose financial products that they are more satisfied with, further improving the transaction efficiency and transaction rate of financial products.

[0140] On the basis of the above embodiment, in an optional embodiment of the present application, the pre-stored product parameters include the pre-stored product price volatility, the pre-stored product risk-free interest rate and the pre-stored product estimated rate of return. Accordingly, the calculation formula of step S207b is:

[0141] S(t)=S0exp(σ+μt-0.5σ 2 t 2H )

[0142] Where S0 is the initial price of the financial product when the expected product holding time is t=0, B H(t) is the pre-deposit fractional Brownian motion model, σ is the price volatility of the pre-deposit product, μ is the estimated rate of return of the pre-deposit product, H is the index describing the relationship between the increments of the fractional Brownian motion, t is the expected product retention time data, and S(t) is the product price change data.

[0143] In this embodiment, exp represents the exponential function of the product price change data S(t) with the natural constant e as its base. The pre-stored product price volatility can be used to measure the degree of variability in the return on the purchase of a financial product. A lower product price volatility indicates a more stable financial product in the process of earning returns, while a higher volatility indicates a wider range of variability, resulting in potential losses or significant gains in returns. For example, Financial Product 3 in the above embodiment can be a relatively high-yield financial product with a pre-stored product volatility between 20% and 30%.

[0144] The estimated rate of return of pre-deposited products is the value estimated by valuation software for each single financial product based on the existing price data, time and the relationship between price and time.

[0145] Based on the above embodiment, as an optional embodiment of the present application, the calculation formula of step S207d is:

[0146]

[0147] Where H is the index describing the relationship between the increments of fractional Brownian motion, σ is the price volatility of the pre-stored product, t is the customer's expected product retention time data, k is the pre-stored cost per unit of product, S t is the product price change data, δt is the preset unit time interval, r is the risk-free interest rate of the pre-deposited product, q is the product dividend rate, and V is the product value data.

[0148] In this embodiment, the product value data V may be the option value of a single financial product, and the index H describing the relationship between the increments of the fractional Brownian motion may be a value in the set (0, 1). For example, when H = 0.5, the B in the above embodiment is H(t) is a standard Brownian motion. The pre-deposited per-unit product fee k can be a transaction fee. Each unit of a financial product purchased will require a corresponding transaction fee. The preset unit time interval δt is a manually set time value. The strategy for buying or selling the financial product will be adjusted every preset unit time interval δ. For example, δt can be 0.01 seconds, 0.1 seconds, or 0.5 seconds. This can make the solution results of the pre-built operation model in the above embodiment more accurate, and thus make the subsequent pushed financial products more satisfactory to customers, thereby improving the transaction efficiency of financial products. The pre-deposited product risk-free interest rate r is the interest rate that can be obtained by using the principal to purchase a certain risk-free financial product. The risk-free interest rate has a synchronous impact on the option price of the financial product. When the risk-free interest rate rises, the option price rises.

[0149] Based on the above embodiment, as an optional embodiment of the present application, before step S201, the following steps are further included:

[0150] Step E: Send the questionnaire to the client, so that the client generates questionnaire response information in response to the response operation to the questionnaire.

[0151] In this embodiment, the questionnaire is a questionnaire on purchasing preferences. The questionnaire can be pushed to the client in the form of a pop-up window, and the customer can choose to evaluate or not evaluate on the client. The response operation can be a response action of the user's input, click, or slide gesture.

[0152] In summary, the financial product push method provided in this embodiment obtains questionnaire response information related to the customer's latest purchasing preferences, product requirements and other basic parameters by sending a questionnaire to the client, thereby avoiding the use of old questionnaire response information for subsequent financial product push, improving the matching degree between the pushed target combination financial products and customer needs, and further improving the transaction efficiency and transaction rate of financial products.

[0153] Please refer to Figure 3 , Figure 3 Schematic diagram of the process of the financial product push method provided in the embodiment of this application Figure 2 , the execution subject of this embodiment can be Figure 1 The client 101 in the embodiment shown may also be other computer devices or mobile terminals with computing capabilities, and this embodiment does not impose any particular restrictions on this. Figure 3 As shown, the financial product push method includes:

[0154] S301: Sending questionnaire response information to the server, so that the server determines customer type information based on the questionnaire response information, and determines initial financial product information based on the customer type information.

[0155] In this embodiment, the action of sending the questionnaire response information to the server is an operation completed on the client side according to the client's own wishes.

[0156] S302: Receive initial financial product information sent by the server, and generate first feedback information in response to a preliminary selection operation on the initial financial product information.

[0157] S303: Send the first feedback information to the server, so that if the server determines that the first feedback information is acceptance information, it obtains the pre-deposit yield collection information.

[0158] S304: receiving the pre-deposit rate of return collection information sent by the server, and obtaining the expected rate of return in response to the input operation of the pre-deposit rate of return collection information.

[0159] In this embodiment, the expected rate of return reflects the expected value set by the customer, and the expected rate of return is collected by the client in accordance with the customer's own wishes.

[0160] S305: Send the expected rate of return to the server, so that the server determines the combined financial product information based on the expected rate of return and the initial financial product information.

[0161] S306: Receive the combined financial product information sent by the server, and generate second feedback information in response to a check operation on the combined financial product information.

[0162] S307: Send the second feedback information to the server, so that if the server determines that the second feedback information is acceptance information, it obtains the pre-stored parameter collection information.

[0163] S308: Receive the pre-stored parameter collection information sent by the server, and obtain the expected parameters in response to the parameter input operation of the pre-stored parameter collection information.

[0164] S309: Send the expected parameters to the server so that the server can determine the product price change data based on the expected parameters, pre-stored product parameters and pre-stored fractional Brownian motion model, and determine the product value data based on the product price change data, customer expected parameters, pre-stored product parameters and pre-built operation model, and determine the target combination financial product information based on the product value data, combination financial product information and expected rate of return.

[0165] S3010: Receive the target combination financial product information pushed by the server.

[0166] In this embodiment, the nouns related to financial products and the method steps completed on the server side have been explained in the above embodiments, so they will not be repeated here in this embodiment.

[0167] In summary, the financial product push method provided in the embodiment of the present application obtains the combined financial product information with a higher degree of match to the customer's needs by pushing the initial financial product information and the combined financial product information on the client in sequence, and obtains the customer's expected rate of return and expected parameters by responding to the operations performed based on the customer's needs on the client. On this basis, the computing power of the server is utilized to obtain the product price change data and product value data in accordance with the expected rate of return, expected parameters, pre-stored financial product parameters, fractional Brownian motion model and pre-built operation model. Finally, the target combined financial product information is determined based on the product value data, combined financial product information and expected rate of return. In this way, the target combined financial product with a higher degree of match to the customer's needs is obtained and pushed, which reduces the time for the customer to hesitate, thereby improving the transaction efficiency and transaction rate of the product.

[0168] Based on the above embodiment, as an optional embodiment of the present application, after sending the first feedback information to the server in step S303, the following steps are further included:

[0169] Step a: receiving the financial product self-selection information sent by the server, and obtaining the self-selected financial product information in response to a self-selection operation on the financial product self-selection information.

[0170] In this embodiment, the self-selection operation on the financial product self-selection information may be: after the customer sees the financial product self-selection information in the pop-up message displayed on the client, the customer performs a gesture operation on the client to complete the action.

[0171] In summary, the financial product push method provided in the embodiment of the present application, by providing customers with self-selected information on financial products, ultimately meets the financial product purchasing needs of customers who have professional financial knowledge or have been interested in certain financial products for a long time, thereby improving the transaction efficiency and transaction rate of financial products.

[0172] Based on the above embodiment, in an optional embodiment of the present application, after sending the second feedback information to the server in step S307, the following steps are further included:

[0173] Step b: receiving the financial product self-selection information sent by the server, and obtaining the self-selected financial product information in response to the self-selection operation on the financial product self-selection information.

[0174] In this embodiment, the technical effect and implementation process are similar to those of step a, so this embodiment will not be repeated here.

[0175] On the basis of an optional aspect of the present application, before step S301, the following steps are further included:

[0176] Step c: receiving the questionnaire sent by the server, and generating questionnaire response information in response to the response operation to the questionnaire.

[0177] In this embodiment, the answering operation to the questionnaire can be: after the customer sees the financial product selection information in the pop-up message displayed by the client, he or she performs a gesture operation on the client to complete the action. Of course, the questionnaire answer information can also be empty.

[0178] In summary, the financial product push method provided in the embodiments of the present application generates the latest and most relevant matching information for the financial products purchased by the customer at the time of the questionnaire response, providing a more accurate basis for determining the subsequent target combination of financial products.

[0179] In an optional embodiment of the present application, after step S3010, the method further includes: obtaining business processing information in response to a business processing operation on the target combination financial product information;

[0180] Step d: Send the business processing information to the server, so that the server generates a processing success message based on the business processing information;

[0181] Step e: Receive the business processing success information sent by the server and display the business processing success information.

[0182] In this embodiment, the technical effects and implementation principles of step d and step e are similar to those of step E in the above embodiment, so they are not repeated here in this embodiment.

[0183] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the interactive process of the financial product push method provided in the embodiment of this application. Figure 4 As shown, the financial product push method includes the following steps:

[0184] S401: The server sends a questionnaire to the client.

[0185] S402: The client generates questionnaire response information in response to the evaluation operation performed by the client on the client according to the questionnaire.

[0186] S403: The client sends the evaluation questionnaire response information to the server.

[0187] S404: The server determines the customer type information based on the questionnaire response information, and determines the initial financial product information based on the customer type information.

[0188] S405: The server sends initial financial product information to the client.

[0189] S406: The client determines first feedback information in response to the preliminary selection operation on the initial financial product information.

[0190] S407: The client sends first feedback information to the server.

[0191] S408: If the server determines that the initial feedback information is acceptance information, it sends the pre-deposit rate of return collection information to the client.

[0192] S409: The client obtains the expected rate of return in response to the input operation for collecting information on the pre-deposit rate of return.

[0193] S4010: The client sends the expected rate of return to the server.

[0194] S4011: The server determines the combined financial product information based on the expected rate of return and the initial financial product information.

[0195] S4012: The server sends the combined financial product information to the client.

[0196] S4013: The client generates second feedback information in response to the check operation on the combined financial product information.

[0197] S4014: The client sends second feedback information to the server.

[0198] S4015: If the server determines that the second feedback information is acceptance information, it sends pre-stored parameter collection information to the client.

[0199] S4016: The client obtains expected parameters in response to the parameter input operation on the pre-stored parameter collection information.

[0200] S4017: The client sends expected parameters to the server.

[0201] S4018: The server determines product price change data based on expected parameters, pre-stored product parameters and a pre-stored fractional Brownian motion model, and determines product value data based on the product price change data, expected parameters, pre-stored product parameters and a pre-built calculation model.

[0202] S4019: The server determines the target combination financial product information based on the product value data, combination financial product information and expected rate of return.

[0203] S4020: The server pushes the target portfolio financial product information to the server.

[0204] The above is the relevant content of the financial product push method provided in the embodiment of this application. Based on the above content, it can be seen that the customer can select a combination financial product that meets the expected needs based on the pushed target combination financial product information on the client, and can combine the price change data in the target combination financial product information to estimate the price trend after purchasing the financial product, choose a better buying or selling time, and improve the rate of return.

[0205] Please refer to Figure 5 , Figure 5 Schematic diagram of the structure of the financial product push device provided in the embodiment of this application Figure 1 .like Figure 5 As shown, the financial product pushing device includes: a first receiving module 51 , an initial financial product determining module 52 , a first sending module 53 , a combined financial product information confirming module 54 , a calculation module 55 and a pushing module 56 .

[0206] The first receiving module 51 is configured to receive questionnaire response information sent by the client, and determine client type information based on the questionnaire response information.

[0207] The initial financial product determination module 52 is configured to determine initial financial product information based on the customer type information.

[0208] The first sending module 53 is configured to send the initial financial product information to the client, so that the client generates first feedback information in response to a preliminary selection operation on the initial financial product information.

[0209] The first receiving module 51 is also used to receive the first feedback information sent by the client. If the first feedback information is determined to be acceptance information, the first sending module 53 is also used to send the pre-deposit yield collection information to the client, so that the client responds to the input operation of the pre-deposit yield collection information and obtains the expected yield.

[0210] The first receiving module 51 is further configured to receive the expected rate of return sent by the client.

[0211] The combined financial product information confirmation module 54 determines the combined financial product information according to the expected rate of return and the initial financial product information.

[0212] The first sending module 53 is further configured to send the combined financial product information to the client, so that the client generates second feedback information in response to a check operation on the combined financial product information.

[0213] The first receiving module 51 is further used to receive second feedback information sent by the client. If the second feedback information is determined to be acceptance information, the first sending module 53 is further used to send the pre-stored parameter collection information to the client, so that the client responds to the parameter input operation of the pre-stored parameter collection information and obtains the expected parameters.

[0214] The first receiving module 51 is further configured to receive expected parameters sent by the client.

[0215] The calculation module 55 is used to determine product price change data based on expected parameters, pre-stored product parameters and a pre-stored fractional Brownian motion model, and to determine product value data based on the product price change data, expected parameters, pre-stored product parameters and a pre-built calculation model.

[0216] The push module 56 is used to determine target combination financial product information based on product value data, combination financial product information and expected rate of return, and push the target combination financial product information to the client.

[0217] In an optional embodiment of the present application, the initial financial product information includes the prices and pre-deposit yields of multiple individual financial products. Accordingly, the combined financial product information confirmation module 54 is specifically configured to determine multiple combined weight allocation coefficient information based on the expected yield and the pre-deposit yield of each individual financial product, where each combined weight allocation coefficient information includes the weight coefficients of multiple individual financial products. The combined financial product information is then confirmed based on each combined weight allocation coefficient information and the price of each individual financial product.

[0218] In an optional embodiment of the present application, the expected parameters include expected product shelf life data. Accordingly, the calculation module 55 is specifically configured to determine an initial financial product price based on the expected product shelf life data and pre-stored product price parameters. Furthermore, the calculation module 55 is configured to determine product price change data based on the expected product shelf life data, the initial financial product price, a pre-stored fractional Brownian motion model, and the pre-stored product parameters.

[0219] In an optional embodiment of the present application, the pre-stored product parameters include the pre-stored product price volatility, the pre-stored product risk-free interest rate, and the pre-stored product estimated rate of return. Accordingly, the calculation formula for determining the product price change data by the operation module 55 is:

[0220] S(t)=S0exp(σB H (t)+μt-0.5σ 2 t 2H )

[0221] Where S0 is the initial price of the financial product when the expected product holding time is t=0, B H (t) is the pre-deposit fractional Brownian motion model, σ is the price volatility of the pre-deposit product, μ is the estimated rate of return of the pre-deposit product, H is the index describing the relationship between the increments of the fractional Brownian motion, t is the expected product retention time data, and S(t) is the product price change data.

[0222] In an optional embodiment of the present application, the calculation module 55 is specifically configured to: determine the product dividend rate based on pre-stored product parameters, and determine product value data based on the product dividend rate, pre-stored product parameters, expected parameters, and a pre-built calculation model.

[0223] In an optional embodiment of the present application, the pre-stored product parameters further include pre-stored per-unit product payable fees and pre-stored unit time intervals. Accordingly, the calculation formula for determining the product value data by the operation module 55 is:

[0224]

[0225] Where H is the index describing the relationship between the increments of fractional Brownian motion, σ is the price volatility of the pre-stored product, t is the customer's expected product retention time data, k is the pre-stored cost per unit of product, S t is the product price change data, δt is the preset unit time interval, r is the risk-free interest rate of the pre-deposited product, q is the product dividend rate, and V is the product value data.

[0226] In an optional embodiment of the present application, after the first receiving module 51 receives the first feedback information sent by the client, the first sending module 53 is further configured to:

[0227] If it is determined that the initial feedback information meets the preset rejection condition, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

[0228] In an optional embodiment of the present application, after the first receiving module 51 receives the second feedback information sent by the client, the first sending module 53 is also used to: if it is determined that the second feedback information is a rejection information, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

[0229] In an optional embodiment of the present application, before the first receiving module 51 receives the questionnaire response information sent by the client, the first sending module 53 is also used to: send the questionnaire to the client so that the client generates questionnaire response information in response to the questionnaire response operation.

[0230] In an optional embodiment of the present application, after the push module 56 pushes the target combination financial product information to the client, the first receiving module 51 is also used to: receive the business processing information sent by the client, and generate a processing success message based on the business processing information, wherein the business processing information is generated by the client in response to the business processing operation of the target combination financial product information.

[0231] The first sending module 53 is further configured to send the successful processing information to the client, so that the client can display the successful processing message.

[0232] The financial product push device provided in this embodiment can be used to implement the technical solution of the above-mentioned method embodiment. Its implementation principle and technical effect are similar, and will not be repeated here in this embodiment.

[0233] Please refer to Figure 6 , Figure 6 Schematic diagram of the structure of the financial product push device provided in the embodiment of this application Figure 2 .like Figure 6As shown, the financial product push device includes: a second sending module 61 , a second receiving module 62 , a feedback information generating module 63 and a collecting module 64 .

[0234] The second sending module 61 is used to send the questionnaire response information to the server, so that the server can determine the customer type information based on the questionnaire response information, and determine the initial financial product information based on the customer type information.

[0235] The second receiving module 62 is configured to receive the initial financial product information sent by the server.

[0236] The feedback information generating module 63 is configured to generate first feedback information in response to a preliminary selection operation on the initial financial product information.

[0237] The second sending module 61 is further configured to send the first feedback information to the server, so that if the server determines that the first feedback information is acceptance information, it will obtain the pre-deposit yield collection information.

[0238] The second receiving module 62 is further configured to receive the pre-deposit yield collection information sent by the server.

[0239] The collection module 64 is configured to obtain an expected rate of return in response to an input operation of the pre-stored rate of return collection information.

[0240] The second sending module 61 is further configured to send the expected rate of return to the server, so that the server determines the combined financial product information based on the expected rate of return and the initial financial product information.

[0241] The second receiving module 62 is further configured to receive the combined financial product information sent by the server.

[0242] The feedback information generating module 63 is further configured to generate second feedback information in response to a check operation on the combined financial product information.

[0243] The second sending module 61 is further configured to send the second feedback information to the server, so that if the server determines that the second feedback information is acceptance information, it will obtain the pre-stored parameter collection information.

[0244] The second receiving module 62 is further configured to receive pre-stored parameter collection information sent by the server.

[0245] The acquisition module is used to obtain expected parameters in response to a parameter input operation on pre-stored parameter acquisition information.

[0246] The second sending module 61 is also used to send the expected parameters to the server, so that the server can determine the product price change data based on the expected parameters, pre-stored product parameters and the pre-stored fractional Brownian motion model, and determine the product value data based on the product price change data, customer expected parameters, pre-stored product parameters and the pre-built operation model, and determine the target combination financial product information based on the product value data, the combination financial product information and the expected rate of return.

[0247] The second receiving module 62 is further configured to receive target combination financial product information pushed by the server.

[0248] In an optional embodiment of the present application, after the second sending module 61 sends the first feedback information to the server, the second receiving module 62 is further configured to receive the self-selected financial product information sent by the server, and obtain the self-selected financial product information in response to the self-selection operation on the self-selected financial product information.

[0249] In an optional embodiment of the present application, after the second sending module 61 sends the second feedback information to the server, the second receiving module 62 is further used to: receive the self-selected financial product information sent by the server, and obtain the self-selected financial product information in response to the self-selection operation on the self-selected financial product information.

[0250] In an optional embodiment of the present application, before the second sending module 61 sends the questionnaire response information to the server, the second receiving module 62 is also used to: receive the questionnaire sent by the server, and generate questionnaire response information in response to the response operation to the questionnaire.

[0251] In an optional embodiment of the present application, after the second receiving module 62 receives the target combination financial product information, the second receiving module 62 is further configured to obtain business processing information in response to a business processing operation on the target combination financial product information.

[0252] The second sending module 61 is used to send the business processing information to the server, so that the server generates a processing success message according to the business processing information.

[0253] The second receiving module 62 is further used to receive the business processing success information sent by the service end and display the business processing success information.

[0254] Please refer to Figure 7 , Figure 7 The hardware structure diagram of the electronic device provided in the embodiment of the present application is as follows: Figure 7 As shown, the device includes: at least one processor 701 and a memory 702.

[0255] The processor 701 is used to store computer-executable instructions.

[0256] The memory 702 is configured to execute computer-executable instructions stored in the memory to implement the various steps involved in the above method embodiment. For details, please refer to the relevant description in the above method embodiment.

[0257] Optionally, the memory 702 may be independent or integrated with the processor 701 .

[0258] When the memory 702 is independently provided, the device further includes a bus 703 for connecting the memory 702 and the processor 701 .

[0259] An embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned financial product push method is implemented.

[0260] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above financial product push method when executed by a processor.

[0261] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules described above is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0262] The modules described above as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected to implement the solution of this embodiment according to actual needs.

[0263] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each module may exist physically separately, or two or more modules may be integrated into a single unit. The units composed of the above modules may be implemented in the form of hardware or hardware plus software functional units.

[0264] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium and includes a number of commands for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute some steps of the method of each embodiment of the present application.

[0265] It should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), or application-specific integrated circuits (ASICs). A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0266] The memory may include a high-speed RAM memory, and may also include non-volatile storage NVM, such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk.

[0267] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0268] The storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0269] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0270] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented by program commands to related hardware. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0271] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0272] It should be noted that the financial product push methods, devices, equipment, storage media, and products provided in the embodiments of this application can be used in the field of artificial intelligence technology, especially the field of intelligent robotics in the financial industry. They can also be used in any field other than artificial intelligence technology. The application fields of the financial product push methods, devices, equipment, storage media, and products provided in the embodiments of this application are not limited.

[0273] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for pushing financial products, characterized in that: include: Receive questionnaire response information sent by the client, and determine client type information based on the questionnaire response information; determining initial financial product information based on the customer type information, and sending the initial financial product information to the client, so that the client generates first feedback information in response to a preliminary selection operation on the initial financial product information; receiving first feedback information sent by the client, and if determining that the first feedback information is acceptance information, sending pre-deposit yield collection information to the client, so that the client obtains an expected yield in response to an input operation of the pre-deposit yield collection information; receiving the expected rate of return sent by the client, and determining combined financial product information based on the expected rate of return and the initial financial product information; sending the combined financial product information to a client, so that the client generates second feedback information in response to a check operation on the combined financial product information; receiving second feedback information sent by the client, and if determining that the second feedback information is acceptance information, sending pre-stored parameter collection information to the client, so that the client obtains expected parameters in response to a parameter input operation on the pre-stored parameter collection information; receiving the expected parameters sent by the client, and determining product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, and determining product value data based on the product price change data, the expected parameters, the pre-stored product parameters, and a pre-built computational model; Target combination financial product information is determined based on the product value data, the combination financial product information and the expected rate of return, and the target combination financial product information is pushed to the client.

2. The method according to claim 1, characterized in that The initial financial product information includes the prices and pre-deposit yields of multiple single financial products; Accordingly, determining the combined financial product information based on the expected rate of return and the initial financial product information includes: Determining a plurality of combination weight allocation coefficient information based on the expected rate of return and the pre-deposit rate of return of each single financial product, wherein each combination weight coefficient allocation information includes weight coefficients of a plurality of single financial products; Confirm multiple combination financial product information based on the weight coefficient allocation information of each combination and the price of each single financial product.

3. The method according to claim 1, characterized in that The expected parameters include expected product shelf life data; Accordingly, determining product price change data according to the expected parameters, pre-stored product parameters, and the pre-stored fractional Brownian motion model includes: Determine the initial price of financial products based on expected product retention time data and pre-stored product price parameters; Product price change data is determined based on the expected product holding time data, the initial financial product price, the pre-stored fractional Brownian motion model, and the pre-stored product parameters.

4. The method according to claim 3, characterized in that The pre-deposit product parameters include the pre-deposit product price volatility, the pre-deposit product risk-free interest rate and the pre-deposit product estimated rate of return; Accordingly, the calculation formula for determining the product price change data based on the expected product holding time data, the initial financial product price, the pre-stored fractional Brownian motion model and the pre-stored product parameters is: Where, Keep time for expected products The initial financial product price at time is the pre-stored fractional Brownian motion model, To store product price volatility, Estimated rate of return for pre-deposit products, is an exponent describing the relationship between the increments of fractional Brownian motion, Keep time data for expected products, Product price change data.

5. The method according to claim 1, characterized in that The determining of product value data based on the product price change data, the expected parameters, the pre-stored product parameters, and the pre-built calculation model includes: Determine the product bonus rate based on the pre-stored product parameters; Product value data is determined based on the product dividend rate, the pre-stored product parameters, the expected parameters and the pre-built operation model.

6. The method according to claim 5, characterized in that The pre-stored product parameters also include pre-stored per-unit product payable fees and pre-stored unit time intervals; Accordingly, the product value data is determined based on the product dividend rate, the pre-stored product parameters, the expected parameters and the pre-built operation model, and the calculation formula is: Where, is an exponent describing the relationship between the increments of the fractional Brownian motion, To store product price volatility, Keep time data for customer expected products, To pre-deposit the cost per unit of product, Product price change data, is the preset unit time interval, r is the risk-free interest rate of the pre-deposit product, q is the product dividend rate, and V is the product value data.

7. The method according to claim 1, characterized in that After receiving the first feedback information sent by the client, the method further includes: If it is determined that the first feedback information meets the preset rejection condition, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

8. The method according to claim 1, characterized in that After receiving the second feedback information sent by the client, the method further includes: If it is determined that the second feedback information is rejection information, the financial product self-selection information is sent to the client, so that the client responds to the self-selection operation on the financial product self-selection information and obtains the self-selected financial product information.

9. The method according to claim 1, characterized in that Before receiving the questionnaire response information sent by the client, the method further includes: The questionnaire is sent to the client, so that the client generates questionnaire answer information in response to an answer operation on the questionnaire.

10. The method according to any one of claims 1 to 9, characterized in that After pushing the target combination financial product information to the client, the method further includes: receiving business processing information sent by the client, and generating processing success information according to the business processing information, wherein the business processing information is generated by the client in response to a business processing operation on the target combination financial product information; The successful processing information is sent to the client, so that the client displays the successful processing message.

11. A method for pushing financial products, characterized in that: include: Sending the questionnaire response information to the server, so that the server determines the customer type information based on the questionnaire response information, and determines the initial financial product information based on the customer type information; receiving the initial financial product information sent by the server, and generating first feedback information in response to a preliminary selection operation on the initial financial product information; Sending the first feedback information to the server, so that if the server determines that the first feedback information is acceptance information, it obtains the pre-deposit yield collection information; receiving the pre-deposit rate of return collection information sent by the server, and obtaining an expected rate of return in response to an input operation of the pre-deposit rate of return collection information; Sending the expected rate of return to the server, so that the server determines combined financial product information based on the expected rate of return and the initial financial product information; receiving the combined financial product information sent by the server, and generating second feedback information in response to a check operation on the combined financial product information; sending the second feedback information to the server, so that if the server determines that the second feedback information is acceptance information, it obtains pre-stored parameter collection information; Receiving pre-stored parameter collection information sent by the server, and obtaining expected parameters in response to a parameter input operation on the pre-stored parameter collection information; Sending the expected parameters to the server, so that the server determines product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, and determines product value data based on the product price change data, the customer expected parameters, the pre-stored product parameters, and a pre-built computational model, and determines target combination financial product information based on the product value data, the combination financial product information, and the expected rate of return; Receive the target combination financial product information pushed by the server.

12. The method according to claim 11, characterized in that After sending the first feedback information to the server, the method further includes: The self-selected financial product information is received from the server, and in response to a self-selection operation on the self-selected financial product information, the self-selected financial product information is obtained.

13. The method according to claim 11, characterized in that After sending the second feedback information to the server, the method further includes: The financial product self-selection information sent by the server is received, and the self-selected financial product information is obtained in response to a self-selection operation on the financial product self-selection information.

14. The method according to claim 11, characterized in that Before sending the questionnaire response information to the server, the method further includes: The questionnaire sent by the server is received, and questionnaire response information is generated in response to a response operation to the questionnaire.

15. The method according to any one of claims 11 to 14, characterized in that After receiving the target combination financial product information, the method further includes: In response to a business handling operation on the target combination financial product information, obtaining business handling information; Sending the business processing information to the server, so that the server generates a processing success message according to the business processing information; Receive the business processing success information sent by the service end and display the business processing success information.

16. A financial product push device, characterized in that: include: A first receiving module is configured to receive questionnaire response information sent by a client and determine client type information based on the questionnaire response information; an initial financial product determination module, configured to determine initial financial product information based on the customer type information; a first sending module, configured to send the initial financial product information to the client, so that the client generates first feedback information in response to a preliminary selection operation on the initial financial product information; The first receiving module is further configured to receive first feedback information sent by the client. If the first feedback information is determined to be acceptance information, the first sending module is further configured to send pre-deposit yield collection information to the client, so that the client obtains an expected yield in response to an input operation of the pre-deposit yield collection information. The first receiving module is further configured to receive the expected rate of return sent by the client, and determine the combined financial product information based on the expected rate of return and the initial financial product information; The first sending module is further configured to send the combined financial product information to a client, so that the client generates second feedback information in response to a check operation on the combined financial product information; The first receiving module is further configured to receive second feedback information sent by the client, and if the second feedback information is determined to be acceptance information, the first sending module is further configured to send pre-stored parameter collection information to the client, so that the client obtains expected parameters in response to a parameter input operation on the pre-stored parameter collection information; The first receiving module is further configured to receive the expected parameters sent by the client; a calculation module, configured to determine product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, and to determine product value data based on the product price change data, the expected parameters, the pre-stored product parameters, and a pre-built calculation model; The push module is used to determine target combination financial product information based on the product value data, the combination financial product information and the expected rate of return, and push the target combination financial product information to the client.

17. A financial product push device, characterized in that: include: a second sending module, configured to send the questionnaire response information to a server, so that the server determines the customer type information based on the questionnaire response information, and determines the initial financial product information based on the customer type information; A second receiving module, configured to receive the initial financial product information sent by the server; a feedback information generating module, configured to generate first feedback information in response to a preliminary selection operation on the initial financial product information; The second sending module is further configured to send the first feedback information to the server, so that if the server determines that the first feedback information is acceptance information, it obtains the pre-deposit yield collection information; The second receiving module is further configured to receive the pre-deposit yield collection information sent by the server; A collection module, configured to obtain an expected rate of return in response to an input operation of the pre-deposit rate of return collection information; The second sending module is further configured to send the expected rate of return to the server, so that the server determines the combined financial product information based on the expected rate of return and the initial financial product information; The second receiving module is further configured to receive the combined financial product information sent by the server; The feedback information generating module is further configured to generate second feedback information in response to a check operation on the combined financial product information; The second sending module is further configured to send the second feedback information to the server, so that if the server determines that the second feedback information is acceptance information, it obtains pre-stored parameter collection information; The second receiving module is further configured to receive pre-stored parameter collection information sent by the server; The acquisition module is configured to obtain expected parameters in response to a parameter input operation on the pre-stored parameter acquisition information; The second sending module is further configured to send the expected parameters to the server, so that the server determines product price change data based on the expected parameters, pre-stored product parameters, and a pre-stored fractional Brownian motion model, determines product value data based on the product price change data, the customer expected parameters, the pre-stored product parameters, and a pre-built calculation model, and determines target combination financial product information based on the product value data, the combination financial product information, and the expected rate of return; The second receiving module is further configured to receive the target combination financial product information pushed by the server.

18. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the processor; The memory stores computer-executable instructions; The at least one processor executes the computer-executable instructions stored in the memory to implement the financial product push method according to any one of claims 1 to 10 or any one of claims 11 to 15.

19. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the financial product push method according to any one of claims 1 to 10 or any one of claims 11 to 15.

20. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the financial product push method described in any one of claims 1 to 10 or any one of claims 11 to 15.

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