Financing data processing method and device based on product matching

Abstract

The present invention discloses a method and device for processing financing data based on product matching, wherein the method comprises: obtaining user information and credit data of a target user; determining a target agent that best matches the target user from multiple agents based on the user information and the credit data; the target agent is used to contact the target user and obtain the financing demand data of the target user; obtaining historical financing evaluation information of the target user; the historical financing evaluation information is the evaluation information of the agent corresponding to the financing operation performed by the target user in the historical time period; determining a target financing product that best matches the target user from multiple financing products based on the financing demand data, the credit data and the historical financing evaluation information of the target user; the target financing product is used to recommend to the target user. It can be seen that the present invention can provide users with more intelligent financing services.

Description

Technical Field

[0001] The present invention relates to the technical field of product recommendation algorithms, and in particular to a financing data processing method and device based on product matching. Background Art

[0002] As the financing needs of small and medium-sized enterprises or individuals increase, more and more financing service companies are using electronic systems to automatically process financing data and recommend financing products. However, existing electronic systems generally only use manual or simple label matching methods to process financing data and recommend financing products when performing these tasks. This processing method requires complex manual judgment and is severely limited by the operator's experience level. It is inefficient and has a high error rate. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a financing data processing method and device based on product matching, which can improve the accuracy of financing task allocation and the effectiveness of financing product recommendation, and provide users with more intelligent financing services.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a financing data processing method based on product matching, the method comprising:

[0005] Obtain user information and credit data of target users;

[0006] According to the user information and the credit data, a target agent that best matches the target user is determined from a plurality of agents; the target agent is used to contact the target user and obtain the financing demand data of the target user;

[0007] Acquire historical financing evaluation information of the target user; the historical financing evaluation information is evaluation information of agents corresponding to financing operations performed by the target user in a historical time period;

[0008] According to the financing demand data, the credit data and the historical financing evaluation information of the target user, a target financing product that best suits the target user is determined from a plurality of financing products; the target financing product is used to recommend to the target user.

[0009] As an optional implementation, in the first aspect of the present invention, the user information includes at least one of name information, regional information, asset information and industry information; and / or the type of credit data includes at least one of business information data, tax declaration data, tax collection data, investor data, branch data, business change data, tax arrears data, violation of laws and regulations data, legal case data and asset data; and / or the financing demand data includes at least one of financing amount, financing purpose and financing object.

[0010] As an optional implementation, in the first aspect of the present invention, determining the target agent that best matches the target user from a plurality of agents based on the user information and the credit data includes:

[0011] Inputting the user information and the credit data into a combined classification neural network model to determine the user type corresponding to the target user;

[0012] According to a preset type-agent mapping relationship, determining a target agent group corresponding to the user type from a plurality of preset agent groups;

[0013] According to the historical agent data of all agents in the target agent group, a target agent that best matches the target user is determined from the target agent group.

[0014] As an optional implementation, in the first aspect of the present invention, the combined classification neural network model includes a user information processing network model and a credit data processing network model; and the step of inputting the user information and the credit data into the combined classification neural network model to determine the user type corresponding to the target user includes:

[0015] The user information is input into the user information processing network model to obtain a plurality of first user types and a plurality of corresponding first probabilities; the user information processing network model is trained by a training data set including a plurality of training user information and corresponding annotated user types; the user information processing network model includes a first convolutional network and a first classification layer; the first classification layer is used to process feature data processed by the first convolutional network on the user information to obtain the first probability that the feature data belongs to any of the first user types;

[0016] The credit data is input into the credit data processing network model to obtain a plurality of second user types and a plurality of corresponding second probabilities; the credit data processing network model is trained by a training data set including a plurality of training credit data and corresponding annotated user types; the credit data processing network model includes a second convolutional network and a second classification layer; the second classification layer is used to process feature data processed by the second convolutional network on the user information to obtain the second probability that the feature data belongs to any of the second user types;

[0017] The user type corresponding to the target user is determined according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities.

[0018] As an optional implementation manner, in the first aspect of the present invention, determining the user type corresponding to the target user according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities, includes:

[0019] Determining a first weight corresponding to the first probability according to a first historical prediction accuracy rate of the user information processing network model; the first weight is proportional to the first historical prediction accuracy rate;

[0020] determining a second weight corresponding to the second probability according to the second historical prediction accuracy of the credit data processing network model; the second weight is proportional to the second historical prediction accuracy; the sum of the first weight and the second weight is 1;

[0021] For any one of the user types, determining the first probability and the second probability of the user type according to a correspondence between the user type and any one of the first user type and the second user type;

[0022] Calculate a weighted average of the first probability and the second probability of the user type according to the first weight and the second weight;

[0023] It is determined that the user type with the largest weighted sum average among all the user types is the user type corresponding to the target user.

[0024] As an optional implementation, in the first aspect of the present invention, determining the target agent that best matches the target user from the target agent group based on the historical agent data of all agents in the target agent group includes:

[0025] For any agent in the target agent group, the success rate and average financing difference of the financing behaviors of the agent in the historical period are calculated based on the historical agency data of the agent; the average financing difference is the average value of the difference between the final financing amount and the financing demand amount of all the financing behaviors;

[0026] Calculate the weighted sum of the success rate and the average financing difference to obtain the agent evaluation parameter of the agent; the sum of the weights of the success rate and the average financing difference is 1; the weight of the success rate is greater than the weight of the average financing difference;

[0027] The agent with the highest agent evaluation parameter among all agents in the target agent group is determined as the target agent.

[0028] As an optional implementation manner, in the first aspect of the present invention, the historical financing evaluation information is text evaluation information; and the determining, from a plurality of financing products, a target financing product that best suits the target user based on the financing demand data, the credit data and the historical financing evaluation information of the target user comprises:

[0029] Based on a part-of-speech tagging algorithm, determining a credit evaluation parameter corresponding to the historical financing evaluation information; the credit evaluation parameter is used to indicate the agent's credit evaluation of the target user;

[0030] Inputting the financing demand data and the credit data into a product recommendation neural network model to determine a plurality of recommended financing products corresponding to the target user; the product recommendation neural network model is trained by a training data set including a plurality of training financing demand data and training credit data and corresponding training financing products;

[0031] According to a preset evaluation-product mapping relationship, determining a target financing product group corresponding to the credit evaluation parameter from a plurality of preset financing product groups;

[0032] A target financing product that is most suitable for the target user is determined according to the target financing product group and the plurality of recommended financing products.

[0033] As an optional implementation manner, in the first aspect of the present invention, determining the target financing product that best suits the target user according to the target financing product group and the plurality of recommended financing products includes:

[0034] Calculating at least one financing product that overlaps between the target financing product group and the plurality of recommended financing products, and determining the product as the target financing product;

[0035] and / or,

[0036] Calculating a similarity between any first financing product in the target financing product group and any second financing product in the plurality of recommended financing products;

[0037] The first financing product and / or the second financing product whose similarity is higher than a preset similarity threshold is determined as a target financing product.

[0038] The second aspect of the present invention discloses a financing data processing device based on product matching, which comprises:

[0039] A first acquisition module, used to acquire user information and credit data of a target user;

[0040] A first determination module is used to determine a target agent that best matches the target user from multiple agents based on the user information and the credit data; the target agent is used to contact the target user and obtain the financing demand data of the target user;

[0041] The second acquisition module is used to acquire the historical financing evaluation information of the target user; the historical financing evaluation information is the evaluation information of the agent corresponding to the financing operation performed by the target user in the historical time period;

[0042] The second determination module is used to determine the target financing product that best suits the target user from multiple financing products based on the financing demand data, credit data and historical financing evaluation information of the target user; the target financing product is used to recommend to the target user.

[0043] As an optional implementation, in the second aspect of the present invention, the user information includes at least one of name information, regional information, asset information and industry information; and / or the type of credit data includes at least one of business information data, tax declaration data, tax collection data, investor data, branch data, business change data, tax arrears data, violation of laws and regulations data, legal case data and asset data; and / or the financing demand data includes at least one of financing amount, financing purpose and financing object.

[0044] As an optional implementation, in the second aspect of the present invention, the specific manner in which the first determination module determines the target agent that best matches the target user from a plurality of agents based on the user information and the credit data includes:

[0045] Inputting the user information and the credit data into a combined classification neural network model to determine the user type corresponding to the target user;

[0046] According to a preset type-agent mapping relationship, determining a target agent group corresponding to the user type from a plurality of preset agent groups;

[0047] According to the historical agent data of all agents in the target agent group, a target agent that best matches the target user is determined from the target agent group.

[0048] As an optional implementation, in the second aspect of the present invention, the combined classification neural network model includes a user information processing network model and a credit data processing network model; and the first determination module inputs the user information and the credit data into the combined classification neural network model to determine the specific manner of the user type corresponding to the target user, including:

[0049] The user information is input into the user information processing network model to obtain a plurality of first user types and a plurality of corresponding first probabilities; the user information processing network model is trained by a training data set including a plurality of training user information and corresponding annotated user types; the user information processing network model includes a first convolutional network and a first classification layer; the first classification layer is used to process feature data processed by the first convolutional network on the user information to obtain the first probability that the feature data belongs to any of the first user types;

[0050] The credit data is input into the credit data processing network model to obtain a plurality of second user types and a plurality of corresponding second probabilities; the credit data processing network model is trained by a training data set including a plurality of training credit data and corresponding annotated user types; the credit data processing network model includes a second convolutional network and a second classification layer; the second classification layer is used to process feature data processed by the second convolutional network on the user information to obtain the second probability that the feature data belongs to any of the second user types;

[0051] The user type corresponding to the target user is determined according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities.

[0052] As an optional implementation manner, in the second aspect of the present invention, the first determination module determines the specific manner of the user type corresponding to the target user according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities, including:

[0053] Determining a first weight corresponding to the first probability according to a first historical prediction accuracy rate of the user information processing network model; the first weight is proportional to the first historical prediction accuracy rate;

[0054] determining a second weight corresponding to the second probability according to the second historical prediction accuracy of the credit data processing network model; the second weight is proportional to the second historical prediction accuracy; the sum of the first weight and the second weight is 1;

[0055] For any one of the user types, determining the first probability and the second probability of the user type according to a correspondence between the user type and any one of the first user type and the second user type;

[0056] Calculate a weighted average of the first probability and the second probability of the user type according to the first weight and the second weight;

[0057] It is determined that the user type with the largest weighted sum average among all the user types is the user type corresponding to the target user.

[0058] As an optional implementation, in the second aspect of the present invention, the first determination module determines the target agent that best matches the target user from the target agent group based on the historical agent data of all agents in the target agent group, including:

[0059] For any agent in the target agent group, the success rate and average financing difference of the financing behaviors of the agent in the historical period are calculated based on the historical agency data of the agent; the average financing difference is the average value of the difference between the final financing amount and the financing demand amount of all the financing behaviors;

[0060] Calculate the weighted sum of the success rate and the average financing difference to obtain the agent evaluation parameter of the agent; the sum of the weights of the success rate and the average financing difference is 1; the weight of the success rate is greater than the weight of the average financing difference;

[0061] The agent with the highest agent evaluation parameter among all agents in the target agent group is determined as the target agent.

[0062] As an optional implementation manner, in the second aspect of the present invention, the historical financing evaluation information is text evaluation information; and the specific manner in which the second determination module determines the target financing product that best suits the target user from a plurality of financing products according to the financing demand data, the credit data and the historical financing evaluation information of the target user includes:

[0063] Based on a part-of-speech tagging algorithm, determining a credit evaluation parameter corresponding to the historical financing evaluation information; the credit evaluation parameter is used to indicate the agent's credit evaluation of the target user;

[0064] Inputting the financing demand data and the credit data into a product recommendation neural network model to determine a plurality of recommended financing products corresponding to the target user; the product recommendation neural network model is trained by a training data set including a plurality of training financing demand data and training credit data and corresponding training financing products;

[0065] According to a preset evaluation-product mapping relationship, determining a target financing product group corresponding to the credit evaluation parameter from a plurality of preset financing product groups;

[0066] A target financing product that is most suitable for the target user is determined according to the target financing product group and the plurality of recommended financing products.

[0067] As an optional implementation manner, in the second aspect of the present invention, the second determination module determines the specific manner of the target financing product that best suits the target user according to the target financing product group and the plurality of recommended financing products, including:

[0068] Calculating at least one financing product that overlaps between the target financing product group and the plurality of recommended financing products, and determining the product as the target financing product;

[0069] and / or,

[0070] Calculating a similarity between any first financing product in the target financing product group and any second financing product in the plurality of recommended financing products;

[0071] The first financing product and / or the second financing product whose similarity is higher than a preset similarity threshold is determined as a target financing product.

[0072] The third aspect of the present invention discloses another financing data processing device based on product matching, the device comprising:

[0073] A memory storing executable program code;

[0074] a processor coupled to the memory;

[0075] The processor calls the executable program code stored in the memory to execute part or all of the steps in the product matching-based financing data processing method disclosed in the first aspect of the embodiment of the present invention.

[0076] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0077] In an embodiment of the present invention, user information and credit data of a target user are obtained; based on the user information and the credit data, a target agent that best matches the target user is determined from multiple agents; the target agent is used to contact the target user and obtain the financing demand data of the target user; the historical financing evaluation information of the target user is obtained; the historical financing evaluation information is the evaluation information of the agent corresponding to the financing operation performed by the target user in the historical time period; based on the financing demand data, the credit data and the historical financing evaluation information of the target user, a target financing product that best matches the target user is determined from multiple financing products; the target financing product is used to recommend to the target user. It can be seen that the present invention can improve the degree of automation of financing task allocation and financing product recommendation through agents, and improve the accuracy of financing task allocation and the effectiveness of financing product recommendation, so as to provide users with more intelligent financing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 It is a flowchart of a financing data processing method based on product matching disclosed in an embodiment of the present invention;

[0080] Figure 2 It is a structural schematic diagram of a financing data processing device based on product matching disclosed in an embodiment of the present invention;

[0081] Figure 3 It is a structural schematic diagram of another financing data processing device based on product matching disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0082] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0083] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0084] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0085] The present invention discloses a financing data processing method and device based on product matching, which can improve the automation of financing task allocation and financing product recommendation through agents, and improve the accuracy of financing task allocation and the effectiveness of financing product recommendation, so as to provide users with more intelligent financing services. The following are detailed descriptions.

[0086] Embodiment 1

[0087] See also Figure 1 , Figure 1 : is a flow chart of a method for processing financing data based on product matching disclosed in an embodiment of the present invention. Figure 1 The described method can be applied to a corresponding financing data processing terminal, financing data processing device or financing data processing server, and the server can be a local server or a cloud server. Figure 1 As shown, the financing data processing method based on product matching may include the following operations:

[0088] 101. Obtain the user information and credit data of the target user.

[0089] Optionally, the target user may be a corporate user or an individual user who needs financing. Optionally, the user information may include at least one of name information, region information, asset information and industry information. Optionally, the type of credit data may include at least one of business information data, tax declaration data, tax collection data, investor data, branch data, business change data, tax arrears data, illegal and irregular data, legal case data and asset data.

[0090] 102. Determine a target agent that best matches the target user from multiple agents based on the user information and credit data.

[0091] Among them, the target agent is used to contact the target user and obtain the target user's financing demand data. Optionally, the target user's financing demand data may include at least one of the financing amount, financing purpose and financing object. Optionally, the agent can be a company or individual that acts as an agent for related financing business, such as an offline company organization that can promote and undertake financing business with its own brand, or a channel organization, that is, a company or individual that has accumulated a certain amount of customers on hand and cooperates with the financing platform to achieve mutual benefit and win-win results, or a corporate financial advisor, that is, an individual who provides financing, financial planning, tax planning and other services to the enterprise, or other related service organizations that cooperate with the financing platform, such as: banks, small loan companies, training institutions, legal institutions, intellectual property institutions and marketing institutions. Optionally, the way in which the target agent contacts the target user can be offline contact or contact through a preset communication method.

[0092] 103. Obtain historical financing evaluation information of the target user.

[0093] Among them, the historical financing evaluation information is the evaluation information of the agent corresponding to the financing operations performed by the target user in the historical time period. Optionally, the historical financing evaluation information can be the evaluation made by the agent on the target user after acting as the agent for the target user's financing business. The agent can follow up on the loan approval status, withdrawal, repayment, etc., or follow up on the customer's financial status, discover potential risks, and conduct corresponding evaluations on the customer, such as scoring, labeling, writing comments, etc., to record the understanding of the agent's response and integrity in all aspects of the customer, so as to facilitate subsequent data processing.

[0094] 104. Determine the target financing product that best suits the target user from multiple financing products based on the target user's financing demand data, credit data, and historical financing evaluation information.

[0095] Specifically, the target financing product is used to recommend to the target user for financing operations or subsequent further financing consultation.

[0096] It can be seen that through the above scheme, the most suitable agent can be matched according to the target user's information to contact and obtain the user's financing needs, and then the most suitable financing product can be matched according to the target user's needs, thereby improving the automation of financing task allocation and financing product recommendation through agents, and improving the accuracy of financing task allocation and the effectiveness of financing product recommendation, providing users with more intelligent financing services.

[0097] As an optional implementation, in the above step 102, determining the target agent that best matches the target user from multiple agents based on the user information and credit data includes:

[0098] Inputting user information and credit data into a combined classification neural network model to determine the user type corresponding to the target user;

[0099] According to a preset type-agent mapping relationship, a target agent group corresponding to the user type is determined from a plurality of preset agent groups;

[0100] According to the historical agent data of all agents in the target agent group, the target agent that best matches the target user is determined from the target agent group.

[0101] As an optional implementation, the combined classification neural network model includes a user information processing network model and a credit data processing network model; and in the above step, inputting the user information and credit data into the combined classification neural network model to determine the user type corresponding to the target user includes:

[0102] Inputting the user information into the user information processing network model to obtain a plurality of first user types and a corresponding plurality of first probabilities;

[0103] Inputting the credit data into a credit data processing network model to obtain a plurality of second user types and a plurality of corresponding second probabilities;

[0104] The user type corresponding to the target user is determined according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities.

[0105] Among them, the user information processing network model is obtained by training a training data set including multiple training user information and corresponding labeled user types. Specifically, the user information processing network model includes a first convolutional network and a first classification layer, wherein the first classification layer is used to process the feature data after the first convolutional network processes the user information to obtain a first probability that the feature data belongs to any first user type.

[0106] Among them, the credit data processing network model is obtained by training a training data set including multiple training credit data and corresponding labeled user types. Specifically, the credit data processing network model includes a second convolutional network and a second classification layer, wherein the second classification layer is used to process the feature data after the second convolutional network processes the user information to obtain a second probability that the feature data belongs to any second user type.

[0107] Through the above scheme, the classification model with two neural network models can be used to simultaneously process credit data and user information to obtain the probability that the target user belongs to different user types, and then combine these probabilities to finally determine the type of the target user to improve the classification accuracy of the target user.

[0108] As an optional implementation manner, in the above step, determining the user type corresponding to the target user according to multiple first user types and corresponding multiple first probabilities, and multiple second user types and corresponding multiple second probabilities, includes:

[0109] Determine a first weight corresponding to the first probability according to a first historical prediction accuracy of the user information processing network model; wherein the first weight is proportional to the first historical prediction accuracy;

[0110] Determine a second weight corresponding to the second probability according to the second historical prediction accuracy of the credit data processing network model; wherein the second weight is proportional to the second historical prediction accuracy, and the sum of the first weight and the second weight is 1;

[0111] For any user type, determining a first probability and a second probability of the user type according to a correspondence between the user type and any first user type and second user type;

[0112] Calculate a weighted average of the first probability and the second probability of the user type according to the first weight and the second weight;

[0113] The user type with the largest weighted sum average among all user types is determined as the user type corresponding to the target user.

[0114] The first user type and the second user type may actually be any user type, so there must be a corresponding relationship between them. The first and the second here are only used to identify the difference in their sources, and are not used to refer to the difference in their contents.

[0115] In some cases, a user type may correspond to only one first user type / second user type but no corresponding second user type / first user type, and in this case, its second probability / first probability is zero.

[0116] Through the above scheme, the weights of the prediction probabilities of the two network models can be determined by their respective historical prediction accuracies, and then the prediction results of the models are weighted and summed to finally determine the user type of the user, so as to improve the classification accuracy of the target user.

[0117] As an optional implementation, in the above step, determining the target agent that best matches the target user from the target agent group according to the historical agent data of all agents in the target agent group includes:

[0118] For any agent in the target agent group, the success rate and average financing difference of the financing behaviors of the agent in the historical period are calculated based on the historical agency data of the agent; wherein the average financing difference is the average value of the difference between the final financing amount and the financing demand amount of all financing behaviors;

[0119] Calculate the weighted sum of the success rate and the average financing difference to obtain the agent evaluation parameter of the agent; the sum of the weights of the success rate and the average financing difference is 1, and the weight of the success rate is greater than the weight of the average financing difference;

[0120] The agent with the highest agent evaluation parameter among all agents in the target agent group is determined as the target agent.

[0121] Through the above scheme, the agent's characterization parameters can be determined based on the agent's success rate of financing behavior and the average financing difference in the agent's historical time period, and the top priority agent can be determined to serve the customer based on this characterization parameter, so that more suitable and excellent agents can be recommended to users.

[0122] As an optional implementation, the historical financing evaluation information is text evaluation information, which can be in any language or in numbers, and its style, font and text data format are not limited. Accordingly, in the above step 104, the target financing product that best suits the target user is determined from multiple financing products based on the target user's financing demand data, credit data and historical financing evaluation information, including:

[0123] Based on the part-of-speech tagging algorithm, determine the credit evaluation parameters corresponding to the historical financing evaluation information;

[0124] Optionally, the reputation evaluation parameter is used to indicate the agent's reputation evaluation of the target user;

[0125] Input the financing demand data and credit data into the product recommendation neural network model to determine multiple recommended financing products corresponding to the target user;

[0126] The product recommendation neural network model is trained by a training data set including a plurality of training financing demand data, training credit data and corresponding training financing products;

[0127] According to the preset evaluation-product mapping relationship, determine the target financing product group corresponding to the credit evaluation parameter from the preset multiple financing product groups;

[0128] Based on the target financing product group and multiple recommended financing products, determine the target financing product that best suits the target user.

[0129] Optionally, based on a part-of-speech tagging algorithm, determining the credit evaluation parameters corresponding to the historical financing evaluation information may include:

[0130] Based on the part-of-speech tagging algorithm and the preset part-of-speech database, determine the number of positive words and the number of negative words in the historical financing evaluation information;

[0131] Determine a first weight corresponding to the number of the commendatory words according to a first sum of the distances of all the commendatory words from the nouns related to the user information of the target user; the first weight is proportional to the first sum;

[0132] Determine a second weight corresponding to the number of derogatory words according to the second sum of the distances of all derogatory words from the nouns related to the user information of the target user; the second weight is proportional to the second sum; and the sum of the first weight and the second weight is 1;

[0133] The number of positive words and the number of negative words are summed according to the first weight and the second weight to obtain the credit evaluation parameter corresponding to the historical financing evaluation information.

[0134] As an optional implementation, in the above step, determining the target financing product that best suits the target user according to the target financing product group and the multiple recommended financing products includes:

[0135] At least one financing product that overlaps between the target financing product group and the plurality of recommended financing products is calculated and determined as the target financing product.

[0136] As an optional implementation, in the above step, determining the target financing product that best suits the target user according to the target financing product group and the multiple recommended financing products includes:

[0137] Calculating the similarity between any first financing product in the target financing product group and any second financing product in the plurality of recommended financing products;

[0138] The first financing product and / or the second financing product whose similarity is higher than a preset similarity threshold is determined as a target financing product.

[0139] Among them, the method for calculating the similarity between financing products can be:

[0140] Vectorizing the product information of the first financing product to obtain a first description vector;

[0141] Vectorizing the product information of the second financing product to obtain a second description vector;

[0142] The Euclidean distance between the first description vector and the second description vector is calculated to obtain the similarity between the financing products.

[0143] The product information of the financing product may include at least one of product introduction, product type, product indicators and other data.

[0144] Embodiment 2

[0145] See also Figure 2 , Figure 2 : is a structural diagram of a financing data processing device based on product matching disclosed in an embodiment of the present invention. Figure 2 The described device can be applied to a corresponding financing data processing terminal, financing data processing equipment or financing data processing server, and the server can be a local server or a cloud server, which is not limited in the embodiment of the present invention. Figure 2 As shown, the device may include:

[0146] The first acquisition module 201 is used to acquire user information and credit data of a target user;

[0147] The first determination module 202 is used to determine the target agent that best matches the target user from multiple agents based on the user information and credit data; the target agent is used to contact the target user and obtain the financing demand data of the target user;

[0148] The second acquisition module 203 is used to acquire historical financing evaluation information of the target user; the historical financing evaluation information is evaluation information of the agent corresponding to the financing operations performed by the target user in the historical time period;

[0149] The second determination module 204 is used to determine the target financing product that best suits the target user from multiple financing products based on the target user's financing demand data, credit data, and historical financing evaluation information; the target financing product is used to recommend to the target user.

[0150] As an optional implementation, user information includes at least one of name information, regional information, asset information and industry information; and / or, the type of credit data includes at least one of business information data, tax declaration data, tax collection data, investor data, branch data, business change data, tax arrears data, violation data, legal case data and asset data; and / or, financing demand data includes at least one of financing amount, financing purpose and financing object.

[0151] As an optional implementation, the specific method of the first determining module 202 determining the target agent that best matches the target user from multiple agents based on the user information and credit data includes:

[0152] Inputting user information and credit data into a combined classification neural network model to determine the user type corresponding to the target user;

[0153] According to a preset type-agent mapping relationship, a target agent group corresponding to the user type is determined from a plurality of preset agent groups;

[0154] According to the historical agent data of all agents in the target agent group, the target agent that best matches the target user is determined from the target agent group.

[0155] As an optional implementation, the combined classification neural network model includes a user information processing network model and a credit data processing network model; and the first determination module 202 inputs the user information and credit data into the combined classification neural network model to determine the specific method of the user type corresponding to the target user, including:

[0156] Inputting user information into a user information processing network model to obtain a plurality of first user types and corresponding plurality of first probabilities; the user information processing network model is trained by a training data set including a plurality of training user information and corresponding annotated user types; the user information processing network model includes a first convolutional network and a first classification layer; the first classification layer is used to process feature data processed by the first convolutional network on the user information to obtain a first probability that the feature data belongs to any first user type;

[0157] The credit data is input into a credit data processing network model to obtain a plurality of second user types and a plurality of corresponding second probabilities; the credit data processing network model is trained by a training data set including a plurality of training credit data and corresponding annotated user types; the credit data processing network model includes a second convolutional network and a second classification layer; the second classification layer is used to process feature data processed by the second convolutional network on user information to obtain a second probability that the feature data belongs to any second user type;

[0158] The user type corresponding to the target user is determined according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities.

[0159] As an optional implementation manner, the first determination module 202 determines a specific manner of determining a user type corresponding to the target user according to a plurality of first user types and corresponding plurality of first probabilities, and a plurality of second user types and corresponding plurality of second probabilities, including:

[0160] Determine a first weight corresponding to the first probability according to a first historical prediction accuracy rate of the user information processing network model; the first weight is proportional to the first historical prediction accuracy rate;

[0161] Determine a second weight corresponding to the second probability according to the second historical prediction accuracy of the credit data processing network model; the second weight is proportional to the second historical prediction accuracy; the sum of the first weight and the second weight is 1;

[0162] For any user type, determining a first probability and a second probability of the user type according to a correspondence between the user type and any first user type and second user type;

[0163] Calculate a weighted average of the first probability and the second probability of the user type according to the first weight and the second weight;

[0164] The user type with the largest weighted sum average among all user types is determined as the user type corresponding to the target user.

[0165] As an optional implementation, the first determining module 202 determines the target agent that best matches the target user from the target agent group according to the historical agent data of all agents in the target agent group, including:

[0166] For any agent in the target agent group, the success rate and average financing difference of the financing behaviors of the agent in the historical period are calculated based on the historical agency data of the agent; the average financing difference is the average value of the difference between the final financing amount and the financing demand amount of all financing behaviors;

[0167] Calculate the weighted sum of the success rate and the average financing difference to obtain the agent evaluation parameter of the agent; the sum of the weights of the success rate and the average financing difference is 1; the weight of the success rate is greater than the weight of the average financing difference;

[0168] The agent with the highest agent evaluation parameter among all agents in the target agent group is determined as the target agent.

[0169] As an optional implementation, the historical financing evaluation information is text evaluation information; and the second determination module 204 determines the target financing product that best suits the target user from multiple financing products according to the financing demand data, credit data and historical financing evaluation information of the target user, including:

[0170] Based on the part-of-speech tagging algorithm, determine the credit evaluation parameters corresponding to the historical financing evaluation information; the credit evaluation parameters are used to indicate the agent's credit evaluation of the target user;

[0171] Inputting the financing demand data and the credit data into the product recommendation neural network model to determine a plurality of recommended financing products corresponding to the target user; the product recommendation neural network model is trained by a training data set including a plurality of training financing demand data and training credit data and corresponding training financing products;

[0172] According to the preset evaluation-product mapping relationship, determine the target financing product group corresponding to the credit evaluation parameter from the preset multiple financing product groups;

[0173] Based on the target financing product group and multiple recommended financing products, determine the target financing product that best suits the target user.

[0174] As an optional implementation, the second determination module 204 determines the specific method of the target financing product that best suits the target user according to the target financing product group and the multiple recommended financing products, including:

[0175] Calculating at least one financing product that overlaps between the target financing product group and the plurality of recommended financing products, and determining the product as the target financing product;

[0176] and / or,

[0177] Calculating the similarity between any first financing product in the target financing product group and any second financing product in the plurality of recommended financing products;

[0178] The first financing product and / or the second financing product whose similarity is higher than a preset similarity threshold is determined as a target financing product.

[0179] Embodiment 3

[0180] See also Figure 3 , Figure 3 is a structural diagram of another financing data processing device based on product matching disclosed in an embodiment of the present invention. Figure 3 As shown, the device may include:

[0181] A memory 301 storing executable program codes;

[0182] a processor 302 coupled to the memory 301;

[0183] The processor 302 calls the executable program code stored in the memory 301 to execute part or all of the steps in the financing data processing method based on product matching disclosed in the first embodiment of the present invention.

[0184] Embodiment 4

[0185] An embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the financing data processing method based on product matching disclosed in the first embodiment of the present invention.

[0186] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0187] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0188] The apparatus, device, non-volatile computer-readable storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device, and non-volatile computer storage medium will not be repeated here.

[0189] In the 1990s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0190] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0191] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0192] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0193] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0194] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0198] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0199] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0200] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0201] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0202] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0203] Finally, it should be noted that the financing data processing method and device based on product matching disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A financing data processing method based on product matching, characterized in that: The method comprises: Obtain user information and credit data of target users; According to the user information and the credit data, a target agent that best matches the target user is determined from a plurality of agents; the target agent is used to contact the target user and obtain the financing demand data of the target user; Acquire historical financing evaluation information of the target user; the historical financing evaluation information is evaluation information of agents corresponding to financing operations performed by the target user in a historical time period; Determining a target financing product that best suits the target user from a plurality of financing products according to the financing demand data, the credit data and the historical financing evaluation information of the target user; the target financing product is used to recommend to the target user; The historical financing evaluation information is text evaluation information; and determining the target financing product that best suits the target user from a plurality of financing products based on the financing demand data, the credit data and the historical financing evaluation information of the target user comprises: Based on the part-of-speech tagging algorithm and the preset part-of-speech database, determine the number of positive words and the number of negative words in the historical financing evaluation information; Determine a first weight corresponding to the number of the commendatory words according to a first sum of the distances of all the commendatory words from the nouns related to the user information of the target user; the first weight is proportional to the first sum; Determine a second weight corresponding to the number of derogatory words according to the second sum of the distances of all derogatory words from the nouns related to the user information of the target user; the second weight is proportional to the second sum; and the sum of the first weight and the second weight is 1; The number of positive words and the number of negative words are summed according to the first weight and the second weight to obtain a credit evaluation parameter corresponding to the historical financing evaluation information; the credit evaluation parameter is used to indicate the credit evaluation of the target user by the agent; Inputting the financing demand data and the credit data into a product recommendation neural network model to determine a plurality of recommended financing products corresponding to the target user; the product recommendation neural network model is trained by a training data set including a plurality of training financing demand data and training credit data and corresponding training financing products; According to a preset evaluation-product mapping relationship, determining a target financing product group corresponding to the credit evaluation parameter from a plurality of preset financing product groups; A target financing product that is most suitable for the target user is determined according to the target financing product group and the plurality of recommended financing products.

2. The method for processing financing data based on product matching according to claim 1, characterized in that: The user information includes at least one of name information, region information, asset information and industry information; and / or, the type of credit data includes at least one of business information data, tax declaration data, tax collection data, investor data, branch data, business change data, tax arrears data, violation data, legal case data and asset data; and / or, the financing demand data includes at least one of financing amount, financing purpose and financing object.

3. The method for processing financing data based on product matching according to claim 2, characterized in that: The step of determining a target agent that best matches the target user from a plurality of agents based on the user information and the credit data includes: Inputting the user information and the credit data into a combined classification neural network model to determine the user type corresponding to the target user; According to a preset type-agent mapping relationship, determining a target agent group corresponding to the user type from a plurality of preset agent groups; According to the historical agent data of all agents in the target agent group, a target agent that best matches the target user is determined from the target agent group.

4. The method for processing financing data based on product matching according to claim 3, characterized in that: The combined classification neural network model includes a user information processing network model and a credit data processing network model; And, inputting the user information and the credit data into a combined classification neural network model to determine the user type corresponding to the target user includes: The user information is input into the user information processing network model to obtain a plurality of first user types and a plurality of corresponding first probabilities; the user information processing network model is trained by a training data set including a plurality of training user information and corresponding annotated user types; the user information processing network model includes a first convolutional network and a first classification layer; the first classification layer is used to process feature data processed by the first convolutional network on the user information to obtain the first probability that the feature data belongs to any of the first user types; The credit data is input into the credit data processing network model to obtain a plurality of second user types and a plurality of corresponding second probabilities; the credit data processing network model is trained by a training data set including a plurality of training credit data and corresponding annotated user types; the credit data processing network model includes a second convolutional network and a second classification layer; the second classification layer is used to process feature data processed by the second convolutional network on the user information to obtain the second probability that the feature data belongs to any of the second user types; The user type corresponding to the target user is determined according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities.

5. The method for processing financing data based on product matching according to claim 4, characterized in that: The determining the user type corresponding to the target user according to the multiple first user types and the corresponding multiple first probabilities, and the multiple second user types and the corresponding multiple second probabilities, includes: Determining a first weight corresponding to the first probability according to a first historical prediction accuracy rate of the user information processing network model; the first weight is proportional to the first historical prediction accuracy rate; determining a second weight corresponding to the second probability according to the second historical prediction accuracy of the credit data processing network model; the second weight is proportional to the second historical prediction accuracy; the sum of the first weight and the second weight is 1; For any one of the user types, determining the first probability and the second probability of the user type according to a correspondence between the user type and any one of the first user type and the second user type; Calculate a weighted average of the first probability and the second probability of the user type according to the first weight and the second weight; It is determined that the user type with the largest weighted sum average among all the user types is the user type corresponding to the target user.

6. The method for processing financing data based on product matching according to claim 5, characterized in that: The step of determining the target agent that best matches the target user from the target agent group based on the historical agent data of all agents in the target agent group includes: For any agent in the target agent group, the success rate and average financing difference of the financing behaviors of the agent in the historical period are calculated based on the historical agency data of the agent; the average financing difference is the average value of the difference between the final financing amount and the financing demand amount of all the financing behaviors; Calculate the weighted sum of the success rate and the average financing difference to obtain the agent evaluation parameter of the agent; the sum of the weights of the success rate and the average financing difference is 1; the weight of the success rate is greater than the weight of the average financing difference; The agent with the highest agent evaluation parameter among all agents in the target agent group is determined as the target agent.

7. The method for processing financing data based on product matching according to claim 1, characterized in that: The step of determining the target financing product that best suits the target user based on the target financing product group and the plurality of recommended financing products includes: Calculating at least one financing product that overlaps between the target financing product group and the plurality of recommended financing products, and determining the product as the target financing product; and / or, Calculating a similarity between any first financing product in the target financing product group and any second financing product in the plurality of recommended financing products; The first financing product and / or the second financing product whose similarity is higher than a preset similarity threshold is determined as a target financing product.

8. A financing data processing device based on product matching, characterized in that: The device comprises: A first acquisition module, used to acquire user information and credit data of a target user; A first determination module is used to determine a target agent that best matches the target user from multiple agents based on the user information and the credit data; the target agent is used to contact the target user and obtain the financing demand data of the target user; The second acquisition module is used to acquire the historical financing evaluation information of the target user; the historical financing evaluation information is the evaluation information of the agent corresponding to the financing operation performed by the target user in the historical time period; A second determination module is used to determine a target financing product that best suits the target user from a plurality of financing products according to the financing demand data, the credit data and the historical financing evaluation information of the target user; the target financing product is used to recommend to the target user; The historical financing evaluation information is text evaluation information; and the second determination module determines the target financing product that best suits the target user from a plurality of financing products according to the financing demand data, the credit data and the historical financing evaluation information of the target user, specifically including: Based on the part-of-speech tagging algorithm and the preset part-of-speech database, determine the number of positive words and the number of negative words in the historical financing evaluation information; Determine a first weight corresponding to the number of the commendatory words according to a first sum of the distances of all the commendatory words from the nouns related to the user information of the target user; the first weight is proportional to the first sum; Determine a second weight corresponding to the number of derogatory words according to the second sum of the distances of all derogatory words from the nouns related to the user information of the target user; the second weight is proportional to the second sum; and the sum of the first weight and the second weight is 1; The number of positive words and the number of negative words are summed according to the first weight and the second weight to obtain a credit evaluation parameter corresponding to the historical financing evaluation information; the credit evaluation parameter is used to indicate the credit evaluation of the target user by the agent; Inputting the financing demand data and the credit data into a product recommendation neural network model to determine a plurality of recommended financing products corresponding to the target user; the product recommendation neural network model is trained by a training data set including a plurality of training financing demand data and training credit data and corresponding training financing products; According to a preset evaluation-product mapping relationship, determining a target financing product group corresponding to the credit evaluation parameter from a plurality of preset financing product groups; A target financing product that is most suitable for the target user is determined according to the target financing product group and the plurality of recommended financing products.

9. A financing data processing device based on product matching, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the financing data processing method based on product matching as described in any one of claims 1-7.

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