Data processing method, device and equipment based on financial product, and storage medium

Abstract

The application discloses a data processing method and device based on financial products, equipment and storage medium, comprising obtaining the consumption information of a user, determining the income type of the user according to the consumption information, determining the fund circulation path of the user according to the user information of the user, inputting the user information, the income type and the fund circulation path into the trained personalized financial product prediction model for prediction, obtaining the information of the personalized financial product corresponding to the user, generating the corresponding personalized financial strategy according to the information of the personalized financial product, and executing the personalized financial strategy on the personalized financial product in response to the confirmation instruction of the user. By adopting the embodiment of the application, the personalized financial product conforming to the actual situation of each bank user can be accurately provided.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data processing method, apparatus, device, and storage medium based on financial products. Background Technology

[0002] Currently, the mainstream way to manage wealth is by purchasing wealth management products to obtain returns from investments. Wealth management products are products designed and issued by commercial banks and formal financial institutions. The funds raised are invested in relevant financial markets and used to purchase relevant financial products according to the product contract. After obtaining investment returns, they are distributed to investors according to the contract.

[0003] However, due to the diverse types of wealth management products and their large data volumes and rapid updates, coupled with the massive user base of banks, the typical approach of pushing the same one or more wealth management products to all users for selection is insufficient. This lack of personalized recommendations makes it difficult to accurately tailor products to each user's individual circumstances. Consequently, this uniform approach reduces user interest in purchasing wealth management products and hinders the development of financial technology in the banking sector.

[0004] Therefore, how to accurately recommend personalized financial products to bank users is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a data processing method, apparatus, device, and storage medium based on financial products to solve the technical problem of low accuracy in recommending personalized financial products to bank users.

[0006] In a first aspect, embodiments of the present invention provide a data processing method based on financial products, including:

[0007] Obtain the user's consumption information and determine the user's revenue type based on the consumption information;

[0008] Based on the user's user information, determine the user's fund transfer path;

[0009] The user information, the type of income, and the flow path of funds are input into a trained personalized financial product prediction model to make a prediction and obtain information on personalized financial products corresponding to the user.

[0010] Based on the information of the personalized financial products, a corresponding personalized financial strategy is generated;

[0011] In response to the user's confirmation instruction, the personalized financial management strategy is executed on the personalized financial product.

[0012] Furthermore, obtaining the user's consumption information and determining the user's revenue type based on the consumption information includes:

[0013] Obtain the user's credit card statement and determine the user's spending information based on the credit card statement;

[0014] If the consumption information meets the preset consumption conditions, the corresponding revenue type for the user is determined from the preset revenue strategy pool.

[0015] Furthermore, the preset consumption conditions include a first consumption condition, a second consumption condition, and a third consumption condition; the revenue strategy pool includes a first revenue type and a second revenue type; and if the consumption information meets the preset consumption conditions, the revenue type corresponding to the user is determined from the preset revenue strategy pool, including:

[0016] If the consumption information meets the first consumption condition, then the second revenue type in the preset revenue strategy pool will be used as the user's revenue type.

[0017] If the consumption information meets the second consumption condition, then the first revenue type in the preset revenue strategy pool will be used as the user's revenue type.

[0018] If the consumption information meets the third consumption condition, then the second revenue type in the preset revenue strategy pool will be used as the user's revenue type.

[0019] Wherein, the consumption amount corresponding to the first consumption condition is 0, the consumption amount corresponding to the second consumption condition is greater than 0 and less than the consumption amount corresponding to the third consumption condition, and the first income type is less than the second income type.

[0020] Furthermore, the fund transfer path includes a credit card path and a debit card path. Determining the user's fund transfer path based on the user's user information includes:

[0021] Extract the user's transaction information from the user's user information;

[0022] The number of fund transfers for the credit card path and the debit card path is determined based on the transaction information;

[0023] The path with the most fund transfers among the credit card path and the debit card path is taken as the user's fund transfer path.

[0024] Furthermore, before the step of inputting the user information, the type of income, and the fund flow path into the trained personalized financial product prediction model for prediction to obtain information about the personalized financial product corresponding to the user, the method further includes:

[0025] Acquire data on multiple users who have engaged in financial management and earned returns. The user data includes target user information, target return type, target fund flow path, and target financial product information.

[0026] The target user information, target return type, and target fund flow path are used as inputs to the model, and the target financial product information is used as the output of the model. Based on the input and output of the model, the personalized financial product prediction model to be trained is trained.

[0027] The model parameters of the personalized financial product prediction model to be trained are optimized according to the preset target loss function until the personalized financial product prediction model to be trained converges, thus obtaining the trained personalized financial product prediction model.

[0028] Furthermore, the step of generating a corresponding personalized financial strategy based on the information of the personalized financial product includes:

[0029] Based on the product information of the personalized financial product, generate trial funds and trial period corresponding to the personalized financial product;

[0030] The trial funds and the trial period will be used as the user's personalized financial management strategy.

[0031] Furthermore, the step of executing the personalized financial strategy on the personalized financial product in response to the user's confirmation instruction includes:

[0032] In response to the user's confirmation instruction, the user uses the trial funds to experience the personalized financial product within the trial period and generates corresponding return information.

[0033] The data processing method based on financial products further includes:

[0034] The amount corresponding to the revenue information will be transferred to the user according to the described fund transfer path.

[0035] Secondly, embodiments of the present invention provide a data processing apparatus based on financial products, comprising:

[0036] The first acquisition module is used to acquire the user's consumption information and determine the user's revenue type based on the consumption information.

[0037] The determination module is used to determine the user's fund transfer path based on the user's user information;

[0038] The prediction module is used to input the user information, the income type, and the fund flow path into the trained personalized financial product prediction model to make a prediction and obtain information on the personalized financial product corresponding to the user.

[0039] The generation module is used to generate a corresponding personalized financial strategy based on the information of the personalized financial product.

[0040] The processing module is used to execute the personalized financial strategy on the personalized financial product in response to the user's confirmation instruction.

[0041] Thirdly, embodiments of the present invention provide an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the data processing method based on financial products described above.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the data processing method based on financial products described above.

[0043] This invention provides a data processing method, apparatus, device, and storage medium based on financial products. It determines a user's return type based on their consumption information and their fund flow path based on their user information. Then, it inputs the user information, return type, and fund flow path into a trained personalized financial product prediction model for prediction. This yields information on personalized financial products corresponding to the user. Based on this information, it generates a personalized financial strategy for the user, achieving the goal of accurately providing each user with personalized financial products tailored to their specific circumstances, thus improving the user experience. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a data processing method based on financial products provided in an embodiment of the present invention;

[0045] Figure 2 This is another flowchart illustrating the data processing method based on financial products provided in this embodiment of the invention;

[0046] Figure 3 This is a flowchart illustrating a training method for a personalized financial product prediction model provided in an embodiment of the present invention.

[0047] Figure 4 This is a schematic diagram of a data processing device based on financial products provided in an embodiment of the present invention;

[0048] Figure 5 This is another schematic diagram of the data processing device based on financial products provided in an embodiment of the present invention;

[0049] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention;

[0050] Figure 7 This is another structural schematic diagram of the electronic device provided in the embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0053] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0054] In related technologies, the mainstream way to manage finances is through purchasing financial products to obtain returns. However, due to the wide variety of financial products and their characteristics of large data volumes and rapid updates, and because banking operations involve a massive user base, they typically push the same one or more financial products to all users for them to choose from, making it impossible to intelligently recommend products tailored to each individual user. Therefore, this uniform approach cannot accurately recommend financial products that match the specific circumstances of each user, which reduces users' desire to purchase financial products and hinders the development of banking financial technology.

[0055] To address the technical problems existing in related technologies, embodiments of the present invention provide a data processing method based on financial products. Please refer to [link to relevant documentation]. Figure 1 , Figure 1 This is a flowchart of a data processing method based on financial products provided in an embodiment of the present invention, which includes steps 101 to 105.

[0056] Step 101: Obtain the user's consumption information and determine the user's revenue type based on the consumption information.

[0057] In this embodiment, the consumption information includes the user's consumption details within a target time period, such as the consumption amount in January. The revenue type includes a revenue percentage corresponding to the consumption amount, where the higher the consumption amount, the higher the revenue percentage.

[0058] The transaction details can be either debit card or credit card transactions at the bank. Since debit card activity is typically higher than credit card activity in banking, this embodiment will use credit card transaction details as the user's spending information to illustrate the purpose of increasing credit card activity.

[0059] Specifically, this embodiment uses big data to obtain users' credit card spending details in real time. Then, based on the spending amount within a target time period, it determines the return rate of subsequent investment and wealth management products. For example, if a user's credit card spending in January exceeds a preset amount, such as 10,000 yuan, the return rate of subsequent investment and wealth management products is determined to be 0.05%; if the spending amount is less than 10,000 yuan, the return rate is determined to be 0.03%. Therefore, different investment returns can be determined in real time for different credit card spending amounts of different bank users, thereby effectively stimulating bank users to increase credit card activity and improving user experience.

[0060] Step 102: Determine the user's fund transfer path based on the user's user information.

[0061] In this embodiment, user information includes the user's card information and user behavior information, wherein the card information includes debit card information and credit card information, and the user behavior information includes debit card fund transfer behavior information and credit card fund transfer information.

[0062] Specifically, when the user information contains only debit card information, the fund transfer path of the debit card can be determined as the fund transfer path of the user; when the user information includes both debit card information and credit card information, the fund transfer path of the debit card and the fund transfer path of the credit card can be selected according to the actual application needs, and the selected fund transfer path is determined as the fund transfer path of the user.

[0063] Step 103: Input the user information, the type of income, and the fund flow path into the trained personalized financial product prediction model for prediction to obtain information on the personalized financial product corresponding to the user.

[0064] In this embodiment, the trained personalized wealth management product prediction model is mainly used to predict the most suitable wealth management product for the target user based on user information, return type, and fund flow path. Specifically, the personalized wealth management product prediction model provided in this embodiment learns from a large amount of user data, including user information, return type, fund flow path, and selected wealth management products. Therefore, the personalized wealth management product prediction model can determine the most similar user data in the trained user data based on the similarity with the target user's user data, and use the wealth management product selected by the user in that user data as the prediction result. This eliminates the need for the target user to spend a lot of time and energy searching for suitable wealth management products, and can accurately provide each bank user with personalized wealth management products that match their actual situation, effectively improving the user experience.

[0065] Step 104: Generate a corresponding personalized financial strategy based on the information of the personalized financial product.

[0066] In this embodiment, different personalized financial products correspond to different personalized financial strategies. The personalized financial strategy provided in this embodiment can be a first-stage strategy with a limited amount of trial funds. Under this first-stage strategy, users can obtain returns within a limited time using limited trial funds. The personalized financial strategy provided in this embodiment can also be a second-stage strategy with unlimited trial funds. Under this second-stage strategy, users can invest in financial products at any time using unlimited trial funds and can query the corresponding return information in real time. It should be noted that under the second-stage strategy, users cannot obtain actual returns, but only virtual return information corresponding to the real investment, for the user's investment reference. Specifically, the personalized financial strategy can be selected according to actual application needs, and no limitation is made here.

[0067] Step 105: In response to the user's confirmation instruction, execute the personalized financial management strategy on the personalized financial product.

[0068] After generating a personalized financial management strategy, a corresponding prompt message will be sent to the user's terminal to remind the user whether they wish to experience the personalized financial management strategy. If a confirmation instruction is received from the user to experience the personalized financial management strategy, the personalized financial management product will be invested and managed according to the personalized financial management strategy, and the experience result information will be returned after the personalized financial management strategy is completed.

[0069] Thus, the data processing method based on financial products provided in this embodiment is completed. It can not only accurately provide each bank user with personalized financial products that meet their actual situation, but also return experience results information in real time for users to view, effectively improving user experience, increasing users' desire to purchase financial products, and promoting the development of banking financial technology.

[0070] Please see Figure 2 , Figure 2 This is another flowchart illustrating a data processing method based on financial products provided in an embodiment of the present invention, which includes steps 201 to 210.

[0071] Step 201: Obtain the user's credit card statement and determine the user's spending information based on the credit card statement.

[0072] In this embodiment, user credit card statements are obtained through big data to determine the spending information of users from various banks.

[0073] Step 202: If the consumption information meets the preset consumption conditions, then determine the corresponding user's revenue type from the preset revenue strategy pool.

[0074] In this embodiment, the preset consumption conditions include a first consumption condition, a second consumption condition, and a third consumption condition, and the revenue strategy pool includes a first revenue type and a second revenue type. Specifically, the step of determining the corresponding revenue type for the user from the preset revenue strategy pool if the consumption information meets the preset consumption conditions is as follows: if the consumption information meets the first consumption condition, the second revenue type in the preset revenue strategy pool is used as the user's revenue type; if the consumption information meets the second consumption condition, the first revenue type in the preset revenue strategy pool is used as the user's revenue type; if the consumption information meets the third consumption condition, the second revenue type in the preset revenue strategy pool is used as the user's revenue type.

[0075] Wherein, the consumption amount corresponding to the first consumption condition is 0, the consumption amount corresponding to the second consumption condition is greater than 0 and less than the consumption amount corresponding to the third consumption condition, and the first income type is less than the second income type.

[0076] Setting different reward types based on users' spending amounts effectively combines their actual situations, allowing for the selection of appropriate reward types and improving user experience. For example, if a user's spending is zero, they are considered a new user as they have never used a credit card before. In this case, their reward type can be upgraded to a second reward type, increasing the attractiveness of credit card promotions to new users. If a user's spending meets the second spending condition (e.g., 5000) but not the third condition (e.g., 10000), they are considered an existing user who doesn't use credit cards frequently or has insufficient funds. In this case, their reward type can be upgraded to a first reward type, increasing the attractiveness of credit card promotions. If a user's spending meets the third condition (e.g., 10000), they are considered an existing user who uses credit cards frequently or has sufficient funds. In this case, their reward type can be upgraded to a second reward type, increasing the attractiveness of credit card promotions to users with sufficient funds.

[0077] In this way, by increasing the attractiveness of credit card promotions to bank users, the activity rate of credit cards held by bank users can be effectively improved, thereby driving the development of bank financial technology.

[0078] Step 203: Extract the user's transaction information from the user's user information.

[0079] In this embodiment, the user's user information can be used to determine the main transaction method used by the user, such as credit card transaction, WeChat transaction, Alipay transaction, debit card transaction, etc.

[0080] Step 204: Determine the number of fund transfers for the credit card path and the debit card path based on the transaction information.

[0081] After determining the main transaction methods used by users, the frequency of each transaction method is counted. For example, credit card transactions are 5 times and debit card transactions are 8 times.

[0082] It should be noted that this embodiment is not limited to determining the number of fund transfers, i.e., the number of transactions, for credit card and debit card transactions, but can also count the number of transactions for applications with transaction functions such as WeChat transactions and Alipay transactions.

[0083] Step 205: The path with the most fund transfers among the credit card path and the debit card path is taken as the user's fund transfer path.

[0084] Since there were 5 credit card transactions and 8 debit card transactions, this embodiment determines that the debit card transaction path is the user's fund transfer path.

[0085] Step 206: Input the user information, the type of income, and the fund flow path into the trained personalized financial product prediction model for prediction, and obtain information on the personalized financial product corresponding to the user.

[0086] In this embodiment, by inputting user information such as industry information, credit information, and other personal information, income type such as the second income type, and fund transfer path such as the debit card transaction path into a trained personalized financial product prediction model, matching user information with a similarity greater than a similarity threshold such as 90% can be obtained. The financial product selected in the matching user information is then used as the personalized financial product corresponding to the user, which can accurately provide the user with the most suitable financial product for their actual situation, thereby improving the user experience.

[0087] Step 207: Based on the product information of the personalized financial product, generate trial funds and trial period corresponding to the personalized financial product.

[0088] In this embodiment, product information may include the risk level of the financial product. Therefore, the higher the risk level of the financial product, the more trial funds or the longer the trial period can be generated; the lower the risk level of the financial product, the less trial funds or the shorter the trial period can be generated, thereby providing users with different investment and financial management experiences and improving the user experience.

[0089] Optionally, based on the product information of the personalized financial products, not only can trial funds and trial periods corresponding to the personalized financial products be generated, but also coupons, repayment funds, etc., can be generated to form multiple credit card promotional activities, thereby improving user experience and promoting the development of user financial technology.

[0090] Step 208: Use the trial funds and the trial period as the personalized financial management strategy corresponding to the user.

[0091] Step 209: In response to the user's confirmation instruction, the user uses the trial funds to experience the personalized financial product within the trial period and generates corresponding return information.

[0092] In this embodiment, once a user confirms their experience with the personalized financial management strategy, they can use the trial funds to invest in and experience the personalized financial management product. After the trial period, the return information for this investment experience is generated for the user to view.

[0093] Step 210: Transfer the amount corresponding to the revenue information to the user according to the fund transfer path.

[0094] After generating the return information of the user's investment experience, the system will automatically transfer the amount corresponding to the return information to the user according to the fund transfer path, such as to the user's debit card.

[0095] In this embodiment, to avoid the isolated operation of multiple business segments within banking fintech, such as credit cards, debit cards, and wealth management, and to promote the development of banking fintech, this embodiment directly integrates consumption, wealth management, and funds. Through big data support, it recommends different personalized wealth management strategies to different customer groups, effectively increasing bank users' desire to purchase wealth management products and successfully improving their activity levels across credit cards, debit cards, and wealth management, thus effectively promoting the development of banking fintech.

[0096] As an optional embodiment, please refer to Figure 3 , Figure 3 This is a flowchart illustrating a training method for a risk assessment model provided in an embodiment of the present invention, such as... Figure 3 As shown, the training method for the personalized financial product prediction model provided in this embodiment includes steps 301 to 303;

[0097] Step 301: Obtain data on multiple users who have invested and earned returns.

[0098] The user data includes target user information, target return type, target fund flow path, and target financial product information.

[0099] In this embodiment, by acquiring a large amount of user information on their investment experiences with financial products through big data, and using this user information as training data, the authenticity of investment and financial management can be improved, and the prediction accuracy of the personalized financial product prediction model can be enhanced.

[0100] Step 302: The target user information, target return type, and target fund flow path are used as inputs to the model, and the target financial product information is used as the output of the model. The personalized financial product prediction model to be trained is trained based on the input and output of the model.

[0101] Step 303: Optimize the model parameters of the personalized financial product prediction model to be trained according to the preset target loss function until the personalized financial product prediction model to be trained converges, and obtain the trained personalized financial product prediction model.

[0102] In this embodiment, the target loss function can be an L1 function or other loss functions that can improve the accuracy of the model.

[0103] In one implementation, a preset number of training iterations, such as 10,000, can be set. Once the personalized financial product prediction model has been trained 10,000 times, it can be determined that the model has converged, thus obtaining a well-trained personalized financial product prediction model.

[0104] In another implementation, a preset recognition accuracy, such as 90%, can be set. Thus, when the personalized financial product prediction model is validated, if its recognition accuracy reaches 90% or higher, the personalized financial product prediction model can be considered converged, and a well-trained personalized financial product prediction model can be obtained.

[0105] In summary, this invention provides a data processing method based on wealth management products, including acquiring user consumption information, determining the user's return type based on the consumption information, determining the user's fund transfer path based on the user's user information, inputting the user information, return type, and fund transfer path into a trained personalized wealth management product prediction model for prediction, obtaining information on personalized wealth management products corresponding to the user, generating corresponding personalized wealth management strategies based on the personalized wealth management product information, and executing the personalized wealth management strategy on the personalized wealth management product in response to the user's confirmation instruction. Using this invention, personalized wealth management products tailored to each bank user's specific circumstances can be accurately provided.

[0106] Based on the method described in the above embodiments, this embodiment will be further described from the perspective of a data processing device based on financial products. This data processing device based on financial products can be implemented as an independent entity or integrated into an electronic device, such as a terminal, which may include a mobile phone, a tablet computer, etc.

[0107] Please see Figure 4 , Figure 4 This is a schematic diagram of a data processing device based on financial products provided in an embodiment of the present invention, such as... Figure 4 As shown, the data processing device 400 based on financial products provided in this embodiment of the invention includes:

[0108] The first acquisition module 401 is used to acquire the user's consumption information and determine the user's revenue type based on the consumption information.

[0109] In this embodiment, the first acquisition module 401 is specifically used to: acquire the user's credit card statement, determine the user's consumption information based on the credit card statement; if the consumption information meets preset consumption conditions, determine the corresponding user's revenue type from a preset revenue strategy pool.

[0110] Optionally, the preset consumption conditions include a first consumption condition, a second consumption condition, and a third consumption condition. The revenue strategy pool includes a first revenue type and a second revenue type. The first acquisition module 401 is further configured to: if the consumption information meets the first consumption condition, then use the second revenue type in the preset revenue strategy pool as the user's revenue type; if the consumption information meets the second consumption condition, then use the first revenue type in the preset revenue strategy pool as the user's revenue type; if the consumption information meets the third consumption condition, then use the second revenue type in the preset revenue strategy pool as the user's revenue type.

[0111] Wherein, the consumption amount corresponding to the first consumption condition is 0, the consumption amount corresponding to the second consumption condition is greater than 0 and less than the consumption amount corresponding to the third consumption condition, and the first income type is less than the second income type.

[0112] The determination module 402 is used to determine the user's fund transfer path based on the user's user information.

[0113] In this embodiment, the fund transfer path includes a credit card path and a debit card path. The determining module 402 is specifically used to: extract the user's transaction information from the user's user information; determine the number of fund transfers for the credit card path and the debit card path based on the transaction information; and take the path with the most fund transfers among the credit card path and the debit card path as the user's fund transfer path.

[0114] The prediction module 403 is used to input the user information, the income type and the fund flow path into the trained personalized financial product prediction model to make a prediction and obtain information on the personalized financial product corresponding to the user.

[0115] The generation module 404 is used to generate a corresponding personalized financial strategy based on the information of the personalized financial product.

[0116] In this embodiment, the generation module 404 is specifically used to: generate trial funds and trial period corresponding to the personalized financial product based on the product information of the personalized financial product; and use the trial funds and trial period as the personalized financial strategy corresponding to the user.

[0117] The processing module 405 is used to execute the personalized financial management strategy on the personalized financial management product in response to the user's confirmation instruction.

[0118] In this embodiment, the processing module 405 is specifically used to: respond to the user's confirmation instruction, use the trial funds to experience the personalized financial product within the experience period, and generate corresponding return information.

[0119] Please see Figure 5 , Figure 5 This is another structural schematic diagram of the data processing device based on financial products provided in the embodiments of the present invention, such as... Figure 5 As shown, the data processing device 400 based on financial products provided in this embodiment of the invention further includes:

[0120] The transfer module 406 is used to transfer the amount corresponding to the income information to the user according to the fund transfer path.

[0121] The second acquisition module 407 is used to acquire data on multiple users who have engaged in financial management and obtained returns. The user data includes target user information, target return type, target fund flow path, and target financial product information.

[0122] The training module 408 is used to take the target user information, target return type, and target fund flow path as input to the model, and the target financial product information as output to the model, and train the personalized financial product prediction model to be trained based on the input and output of the model.

[0123] The optimization module 409 is used to optimize the model parameters of the personalized financial product prediction model to be trained according to the preset target loss function until the personalized financial product prediction model to be trained converges, thereby obtaining the trained personalized financial product prediction model.

[0124] In specific implementation, the above modules and / or units can be implemented as independent entities, or they can be arbitrarily combined and implemented as the same or several entities. For the specific implementation of the above modules and / or units, please refer to the previous method embodiments. For the specific beneficial effects that can be achieved, please also refer to the beneficial effects in the previous method embodiments, which will not be repeated here.

[0125] Additionally, please see Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device can be a mobile terminal such as a smartphone, tablet computer, or other similar device. Figure 6 As shown, the electronic device 600 includes a processor 601 and a memory 602. The processor 601 and the memory 602 are electrically connected.

[0126] The processor 601 is the control center of the electronic device 600. It connects various parts of the electronic device through various interfaces and lines. By running or loading the application program stored in the memory 602 and calling the data stored in the memory 602, it performs various functions of the electronic device 600 and processes data, thereby monitoring the electronic device 600 as a whole.

[0127] In this embodiment, the processor 601 in the electronic device 600 loads the instructions corresponding to the processes of one or more applications into the memory 602 according to the following steps, and the processor 601 runs the applications stored in the memory 602 to realize various functions.

[0128] The electronic device 600 can implement the steps in any embodiment of the data processing method based on financial products provided in the embodiments of the present invention. Therefore, it can achieve the beneficial effects that any data processing method based on financial products provided in the embodiments of the present invention can achieve. For details, please refer to the previous embodiments, which will not be repeated here.

[0129] Please see Figure 7 , Figure 7 This is another structural schematic diagram of the electronic device provided in the embodiments of the present invention, such as... Figure 7 As shown, Figure 7 A specific structural block diagram of an electronic device provided in an embodiment of the present invention is shown. This electronic device can be used to implement the data processing method based on financial products provided in the above embodiments. The electronic device 700 can be a mobile terminal such as a smartphone or a laptop computer.

[0130] RF circuit 710 is used to receive and transmit electromagnetic waves, converting electromagnetic waves into electrical signals and vice versa, thereby enabling communication with communication networks or other devices. RF circuit 710 may include various existing circuit elements used to perform these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, Subscriber Identity Module (SIM) cards, memory, etc. RF circuit 710 can communicate with various networks such as the Internet, corporate intranets, and wireless networks, or communicate with other devices via wireless networks. The aforementioned wireless networks may include cellular telephone networks, wireless local area networks (WLANs), or metropolitan area networks (MANs). The aforementioned wireless networks may use various communication standards, protocols, and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging, and short messages, and any other suitable communication protocols, including those that have not yet been developed.

[0131] The memory 720 can be used to store software programs and modules, such as the program instructions / modules corresponding to the data processing method based on financial products in the above embodiment. The processor 780 executes various functional applications and data processing based on financial products by running the software programs and modules stored in the memory 720.

[0132] Memory 720 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, memory 720 may further include memory remotely located relative to processor 780, which can be connected to electronic device 700 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0133] The input unit 730 can be used to receive input digital or character information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, the input unit 730 may include a touch-sensitive surface 731 and other input devices 732. The touch-sensitive surface 731, also known as a touch display screen or touchpad, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch-sensitive surface 731), and drive the corresponding connection device according to a pre-set program. Optionally, the touch-sensitive surface 731 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, sends it to the processor 780, and can receive and execute commands sent by the processor 780. In addition, the touch-sensitive surface 731 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch-sensitive surface 731, the input unit 730 may also include other input devices 732. Specifically, other input devices 732 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc.

[0134] Display unit 740 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of electronic device 700. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Display unit 740 may include display panel 741, optionally configured as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or other similar forms. Further, touch-sensitive surface 731 may cover display panel 741. When touch-sensitive surface 731 detects a touch operation on or near it, it transmits the information to processor 780 to determine the type of touch event. Subsequently, processor 780 provides corresponding visual output on display panel 741 according to the type of touch event. Although in the figures, touch-sensitive surface 731 and display panel 741 are implemented as two separate components to achieve input and output functions, in some embodiments, touch-sensitive surface 731 and display panel 741 can be integrated to achieve input and output functions.

[0135] The electronic device 700 may also include at least one sensor 750, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 741 according to the ambient light level, and the proximity sensor can generate an interruption when the flip is closed or shut down. As a type of motion sensor, a gravity acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that recognize the phone's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc. Other sensors that may be configured in the electronic device 700, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0136] Audio circuitry 760, speaker 761, and microphone 762 provide an audio interface between the user and electronic device 700. Audio circuitry 760 converts received audio data into electrical signals and transmits them to speaker 761, where speaker 761 converts them into sound signals for output. Conversely, microphone 762 converts collected sound signals into electrical signals, which are then received by audio circuitry 760, converted back into audio data, and processed by processor 780. The audio data is then transmitted via RF circuitry 710 to, for example, another terminal, or output to memory 720 for further processing. Audio circuitry 760 may also include an earphone jack to facilitate communication between peripheral headphones and electronic device 700.

[0137] Electronic device 700, through transmission module 770 (e.g., Wi-Fi module), can help users receive requests, send information, etc., providing users with wireless broadband internet access. Although transmission module 770 is shown in the figure, it is understood that it is not an essential component of electronic device 700 and can be omitted as needed without changing the essence of the invention.

[0138] The processor 780 is the control center of the electronic device 700. It connects to various parts of the phone via various interfaces and lines, and performs various functions and processes data of the electronic device 700 by running or executing software programs and / or modules stored in the memory 720, and by calling data stored in the memory 720, thereby providing overall monitoring of the electronic device. Optionally, the processor 780 may include one or more processing cores; in some embodiments, the processor 780 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 780.

[0139] The electronic device 700 also includes a power supply 790 (such as a battery) that supplies power to various components. In some embodiments, the power supply may be logically connected to the processor 780 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. The power supply 790 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0140] Although not shown, the electronic device 700 also includes cameras (such as front-facing cameras and rear-facing cameras), Bluetooth modules, etc., which will not be described in detail here. Specifically, in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors. One or more programs contain instructions for performing operations.

[0141] In practice, the above modules can be implemented as independent entities or combined in any way to be implemented as the same or several entities. For the specific implementation of the above modules, please refer to the previous method implementation examples, which will not be repeated here.

[0142] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. Therefore, embodiments of the present invention provide a storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any embodiment of the data processing method based on financial products provided by the present invention.

[0143] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0144] Since the instructions stored in the storage medium can execute the steps in any embodiment of the data processing method based on financial products provided in the embodiments of the present invention, the beneficial effects that any data processing method based on financial products provided in the embodiments of the present invention can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0145] The foregoing has provided a detailed description of a data processing method, apparatus, device, and computer-readable storage medium based on financial products, as provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application. Moreover, those skilled in the art can make several improvements and modifications without departing from the principles of this invention, and these improvements and modifications are also considered to be within the scope of protection of this invention.

Claims

1. A data processing method based on financial products, characterized in that, include: Obtain the user's credit card statement and determine the user's spending information based on the credit card statement; If the consumption information meets preset consumption conditions, then the corresponding revenue type for the user is determined from a preset revenue strategy pool. The preset consumption conditions include a first consumption condition, a second consumption condition, and a third consumption condition. The revenue strategy pool includes a first revenue type and a second revenue type. Determining the corresponding revenue type for the user if the consumption information meets the preset consumption conditions includes: if the consumption information meets the first consumption condition, then the second revenue type in the preset revenue strategy pool is used as the user's revenue type; if the consumption information meets the second consumption condition, then the first revenue type in the preset revenue strategy pool is used as the user's revenue type; if the consumption information meets the third consumption condition, then the second revenue type in the preset revenue strategy pool is used as the user's revenue type. Wherein, the consumption amount corresponding to the first consumption condition is 0, the consumption amount corresponding to the second consumption condition is greater than 0 and less than the consumption amount corresponding to the third consumption condition, and the first revenue type is less than the second revenue type. Extract the user's transaction information from the user's user information; The number of fund transfers via credit card and debit card paths is determined based on the transaction information. The path with the most fund transfers among the credit card path and the debit card path is taken as the user's fund transfer path; wherein, the fund transfer path includes the credit card path and the debit card path; The user information, the type of income, and the flow path of funds are input into a trained personalized financial product prediction model to make a prediction and obtain information on personalized financial products corresponding to the user. Based on the information of the personalized financial products, a corresponding personalized financial strategy is generated; In response to the user's confirmation instruction, the personalized financial management strategy is executed on the personalized financial product; The consumption information includes credit card spending details; the type of income is determined in real time based on the credit card spending details.

2. The data processing method based on financial products as described in claim 1, characterized in that, Before the step of inputting the user information, the income type, and the fund flow path into the trained personalized financial product prediction model for prediction to obtain information about the personalized financial product corresponding to the user, the method further includes: Acquire data on multiple users who have engaged in financial management and earned returns. The user data includes target user information, target return type, target fund flow path, and target financial product information. The target user information, target return type, and target fund flow path are used as inputs to the model, and the target financial product information is used as the output of the model. Based on the input and output of the model, the personalized financial product prediction model to be trained is trained. The model parameters of the personalized financial product prediction model to be trained are optimized according to the preset target loss function until the personalized financial product prediction model to be trained converges, thus obtaining the trained personalized financial product prediction model.

3. The data processing method based on financial products as described in claim 1 or 2, characterized in that, The step of generating a corresponding personalized financial strategy based on the information of the personalized financial product includes: Based on the product information of the personalized financial product, generate trial funds and trial period corresponding to the personalized financial product; The trial funds and the trial period will be used as the user's personalized financial management strategy.

4. The data processing method based on financial products as described in claim 3, characterized in that, The step of responding to the user's confirmation instruction and executing the personalized financial strategy on the personalized financial product includes: In response to the user's confirmation instruction, the user uses the trial funds to experience the personalized financial product within the trial period and generates corresponding return information. The data processing method based on financial products further includes: The amount corresponding to the revenue information will be transferred to the user according to the described fund transfer path.

5. A data processing device based on financial products, characterized in that, include: A first acquisition module is used to acquire a user's credit card statement and determine the user's consumption information based on the credit card statement. If the consumption information meets preset consumption conditions, the module determines the corresponding revenue type for the user from a preset revenue strategy pool. The preset consumption conditions include a first consumption condition, a second consumption condition, and a third consumption condition. The revenue strategy pool includes a first revenue type and a second revenue type. Determining the corresponding revenue type for the user if the consumption information meets the preset consumption conditions includes: if the consumption information meets the first consumption condition, using the second revenue type in the preset revenue strategy pool as the user's revenue type; if the consumption information meets the second consumption condition, using the first revenue type in the preset revenue strategy pool as the user's revenue type; if the consumption information meets the third consumption condition, using the second revenue type in the preset revenue strategy pool as the user's revenue type. Wherein, the consumption amount corresponding to the first consumption condition is 0, the consumption amount corresponding to the second consumption condition is greater than 0 and less than the consumption amount corresponding to the third consumption condition, and the first revenue type is less than the second revenue type. The determination module is used to extract the user's transaction information from the user's user information; determine the number of fund transfers for the credit card path and the debit card path based on the transaction information; and select the path with the most fund transfers among the credit card path and the debit card path as the user's fund transfer path; wherein, the fund transfer path includes the credit card path and the debit card path; The prediction module is used to input the user information, the income type, and the fund flow path into the trained personalized financial product prediction model to make a prediction and obtain information on the personalized financial product corresponding to the user. The generation module is used to generate a corresponding personalized financial strategy based on the information of the personalized financial product. The processing module is used to execute the personalized financial management strategy on the personalized financial product in response to the user's confirmation instruction; The consumption information includes credit card spending details; the type of income is determined in real time based on the credit card spending details.

6. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1 to 4.

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