CN111383094A - Product service full-chain driving method, equipment and readable storage medium - Google Patents
Product service full-chain driving method, equipment and readable storage medium Download PDFInfo
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Abstract
The application discloses a product service full-chain driving method, equipment and a readable storage medium, wherein the product service full-chain driving method comprises the following steps: receiving client related data, inputting the client related data into a preset public data model and a preset federal model respectively to determine initial qualification grade information, constructing a client portrait corresponding to the client related data, matching a product message corresponding to the initial qualification grade information based on the client portrait, recommending the product message to a client corresponding to the client related data, and providing a target product corresponding to the product message to a target client corresponding to the client through a preset intelligent service module if client intention information fed back by the client based on the product message is received. The technical problems of low product service efficiency and high cost are solved.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a product service full-chain driving method, device, and readable storage medium.
Background
With the continuous development of financial technologies, especially internet technology and finance, more and more technologies (such as distributed, Blockchain, artificial intelligence and the like) are applied to the financial field, but the financial industry also puts higher requirements on the technologies, such as higher requirements on the distribution of backlog of the financial industry.
With the application of artificial intelligence in the financial field becoming more and more extensive, currently, before providing financial products to enterprises, financial providers usually provide financial product services to customers through traditional artificial service systems, but the traditional artificial service systems are difficult to provide pure online, high-efficiency and high-quality standardized services to customers, so that the financial product homogeneity is serious, and in a complex and tedious customer service process, customers are easy to run off, so that the financial product service efficiency is low, and the financial providers are usually difficult to acquire comprehensive enterprise information, so that the financial providers are large in loan risk and low in loan income, so that the financial product service cost is high, and therefore, the technical problems of low product service efficiency and high cost exist in the prior art.
Disclosure of Invention
The application mainly aims to provide a product service full-chain driving method, product service full-chain driving equipment and a readable storage medium, and aims to solve the technical problems of low product service efficiency and high cost in the prior art.
In order to achieve the above object, the present application provides a product service full-chain driving method, where the product service full-chain driving method is applied to a product service full-chain driving device, and the product service full-chain driving method includes:
receiving client related data, and respectively inputting the client related data into a preset public data model and a preset federal model to determine initial qualification grade information;
constructing a client portrait corresponding to the client related data, and matching a product message corresponding to the initial qualification grade information based on the client portrait so as to recommend the product message to a client corresponding to the client related data;
and if the client intention information fed back by the client based on the product message is received, providing a target product corresponding to the product message to a target client corresponding to the client through a preset intelligent service module.
Optionally, the step of inputting the customer-related data into a preset public data model and a preset federal model respectively to determine the initial qualification level information includes:
inputting the customer related data into the public data model to score a target customer corresponding to the customer related data to obtain a first target score;
inputting the customer-related data into the federal model to score the target customer to obtain a second target score;
determining the initial qualification level information based on the first target score and the second target score.
Optionally, the step of determining the initial qualification level information based on the first target score and the second target score comprises:
respectively acquiring a first target weight corresponding to the first target score and a second target weight corresponding to the second target score;
calculating a wind control score for the target customer based on the first target score, the first target weight, the second target score, and the second target weight;
determining the initial qualification grade information based on the wind control score.
Optionally, the preset intelligent service module comprises an AI (Artificial Intelligence) intelligent customer service module, an identity verification module, a material verification module and an operation loan module, the customer intention information comprises communication information, the target product comprises a target financial product,
if the client intention information fed back by the client based on the product message is received, the step of providing the target product corresponding to the product message to the target client corresponding to the client through a preset intelligent service module comprises the following steps:
receiving the communication information fed back by the client, and performing service consultation service on the client through the AI intelligent customer service module based on the communication information to obtain a service consultation result;
based on the service consultation result, performing identity verification on the client through the identity verification module to obtain an identity verification result;
based on the identity verification result, identifying the business related data uploaded by the client through the data verification module to obtain a data verification result, and recording the data verification result into a deposit reference factor;
providing, by the operational payout module, the target financial product to the target customer based on the payout reference factor.
Optionally, the identity verification module comprises an AI electric nuclear robot, the identity verification result comprises a client intention confirmation result and enterprise basic information,
the step of performing identity verification on the client through the identity verification module based on the service consultation result to obtain an identity verification result comprises the following steps:
based on the service consultation result, performing voice interaction with the target client through the AI electric nuclear robot to obtain a result of confirming the intention of the client;
and acquiring the enterprise basic information through the voice interaction based on the client intention confirmation result.
Optionally, the identity verification module comprises an AI quality inspection robot,
the step of voice interaction with the target client through the AI electric nuclear robot based on the business consultation result comprises the following steps:
acquiring voice interaction records corresponding to the voice interaction, and performing quality inspection on the voice interaction records through the AI quality inspection robot to obtain quality inspection results;
and comparing the quality inspection result with a preset quality inspection standard, generating a detailed quality inspection report corresponding to the quality inspection result, and recording the detailed quality inspection report into a deposit reference factor.
Optionally, the identity verification module comprises an AI wind-controlled dialogue robot,
the step of performing identity verification on the client through the identity verification module based on the service consultation result to obtain an identity verification result comprises the following steps:
obtaining client communication information through the AI wind control dialogue robot, and matching a question-answer suggestion corresponding to the client communication information;
and acquiring client risk information through the AI wind control dialogue robot based on the question and answer suggestions and the client communication information, and recording the client risk information into a deposit reference factor.
Based on the customer representation, matching the product message corresponding to the initial qualification level information includes:
inputting the client portrait into a preset portrait analysis model to analyze the client portrait and obtain a client portrait analysis result;
matching the product message based on the customer representation analysis results and the initial qualification level information.
This application still provides a product service full chain drive arrangement, product service full chain drive arrangement is virtual device, just product service full chain drive arrangement is applied to product service full chain drive device, product service full chain drive arrangement includes:
the determining module is used for receiving the client related data and inputting the client related data into a preset public data model and a preset federal model respectively so as to determine initial qualification grade information;
the recommending module is used for constructing a client portrait corresponding to the client related data, and matching a product message corresponding to the initial qualification grade information based on the client portrait so as to recommend the product message to a client corresponding to the client related data;
and the service module is used for providing a target product corresponding to the product message to a target client corresponding to the client through a preset intelligent service module if the client intention information fed back by the client based on the product message is received.
Optionally, the determining module includes:
the first scoring unit is used for inputting the client related data into the public data model so as to score a target client corresponding to the client related data to obtain a first target score;
the first scoring unit is used for inputting the client related data into the federal model so as to score the target client and obtain a second target score;
a determining unit configured to determine the initial qualification grade information based on the first target score and the second target score.
Optionally, the determining unit includes:
a first obtaining subunit, configured to separately obtain a first target weight corresponding to the first target score and a second target weight corresponding to the second target score;
a calculating subunit configured to calculate a wind control score for the target customer based on the first target score, the first target weight, the second target score, and the second target weight;
a determining subunit, configured to determine the initial qualification grade information based on the wind control score.
Optionally, the service module includes:
the business consultation unit is used for receiving the communication information fed back by the client, and carrying out business consultation service on the client through the AI intelligent customer service module based on the communication information to obtain a business consultation result;
the identity verification unit is used for verifying the identity of the client through the identity verification module based on the service consultation result to obtain an identity verification result;
the data auditing unit is used for identifying the business related data uploaded by the client through the data auditing module based on the identity verification result to obtain a data auditing result, and counting the data auditing result into a deposit reference factor;
and the deposit unit is used for providing the target financial product to the target customer through the operation deposit module based on the deposit reference factor.
Optionally, the identity verification unit includes:
the voice interaction subunit is used for performing voice interaction with the target client through the AI electric nuclear robot based on the service consultation result so as to obtain a result of confirming the intention of the client;
and the second acquisition subunit is used for acquiring the basic enterprise information through the voice interaction based on the client intention confirmation result.
Optionally, the identity verification unit further comprises:
the quality inspection subunit is used for acquiring the voice interaction record corresponding to the voice interaction, and performing quality inspection on the voice interaction record through the AI quality inspection robot to obtain a quality inspection result;
and the generation subunit is used for comparing the quality inspection result with a preset quality inspection standard, generating a detailed quality inspection report corresponding to the quality inspection result, and recording the detailed quality inspection report into a deposit reference factor.
Optionally, the service module includes:
the first matching unit is used for acquiring client communication information through the AI wind control conversation robot and matching question and answer suggestions corresponding to the client communication information;
and the acquisition unit is used for acquiring the risk information of the client through the AI wind control dialogue robot based on the question-answer suggestions and the client communication information and recording the risk information of the client into a deposit reference factor.
Optionally, the recommendation module includes:
the analysis unit is used for inputting the client portrait into a preset portrait analysis model so as to analyze the client portrait and obtain a client portrait analysis result;
a second matching unit for matching the product message based on the customer representation analysis result and the initial qualification level information.
The application also provides a product service full-chain drive device, the product service full-chain drive device is entity equipment, the product service full-chain drive device includes: a memory, a processor and a program of the product service full chain drive method stored on the memory and executable on the processor, which when executed by the processor, may implement the steps of the product service full chain drive method as described above.
The present application also provides a readable storage medium having a program for implementing the product service full-chain driving method stored thereon, where the program for implementing the product service full-chain driving method implements the steps of the product service full-chain driving method as described above when being executed by a processor.
This application is through receiving customer associated data, and will customer associated data imports respectively and predetermines public data model and predetermines federal model to confirm initial qualification grade information, and then founds the customer that customer associated data corresponds portrays, and based on customer portrays, matches the product message that initial qualification grade information corresponds, with the product message recommends extremely the corresponding customer end of customer associated data, and then if receive the customer end is based on the customer intention information of product message feedback, then through predetermineeing intelligent service module to the target customer that the customer end corresponds provides the target product that the product message corresponds. That is, the present application processes the client-related data through a preset public data model and a preset federal model respectively to determine initial qualification grade information, wherein the preset public data model is obtained based on public data training, and the federal model is obtained by combining private data training of multi-party financial institutions, so that the present application achieves the purpose of predicting qualification grade information of a target client based on a wider and more comprehensive data model, thereby improving the wind control capability of the financial institutions, reducing the risk of product service, improving the income of financial product service, that is, reducing the cost of product service, and further, the present application provides a better message recommendation service for the target client by recommending product messages to the target client individually based on client figures, thereby improving the conversion efficiency of the target client, the product service cost is further reduced, the product service efficiency is improved, the intelligent, automatic and full-chain-driven preset intelligent service module is further provided, high-quality and pure-line product service can be provided for the client, the product service flow and the product service period are further simplified, the phenomenon that the client runs off due to the fact that the product service flow is too complicated and long is avoided, the product service efficiency is further improved, and therefore the technical problems of low product service efficiency and high cost are solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flowchart illustrating a first embodiment of a product service full-chain driving method according to the present application;
FIG. 2 is a schematic diagram of image data in the product service full chain driving method of the present application;
FIG. 3 is a flowchart illustrating a second embodiment of a product service full-chain driving method according to the present application;
fig. 4 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In a first embodiment of the product service full-chain driving method of the present application, referring to fig. 1, the product service full-chain driving method includes:
step S10, receiving client-related data, and inputting the client-related data into a preset public data model and a preset federal model respectively to determine initial qualification grade information;
in this embodiment, it should be noted that the client-related data includes enterprise operation data, business data, tax data, satellite remote sensing data, ESG (environmental, Social responsiveness, corporate goovernance, environmental, Social, and corporate governance) other data, and the like, and the client-related data includes public type data and private type data, where the public type data is data directly obtained from a public data source, the public data source includes a public website, a public forum, and the like, the private type data is data that is owned by each financial institution and cannot be disclosed to other financial institutions, for example, client label data, client image data, and the like that are owned by each financial institution, the preset public data model is a machine learning model obtained based on training sample privacy corresponding to the public type data, and the preset federal model is federal to train a training sample held by each other financial institution in association with each of the type data Learning the obtained machine learning model, wherein the federal learning includes horizontal federal learning and vertical federal learning.
Receiving client-related data, inputting the client-related data into a preset public data model and a preset federation model respectively to determine initial qualification level information, specifically, receiving public type data and privacy type data, inputting the public type data into the preset public data model to predict a first qualification level of a target client corresponding to the client-related data based on the public type data, inputting the privacy type data into a preset federation model to predict a second qualification level of the target client, further, obtaining a first weight corresponding to the preset public data model and a second weight corresponding to the preset federation model, and obtaining the initial qualification level information based on the first qualification level, the first weight, the second qualification level and the first weight, wherein the initial qualification level information includes a payment amount, a, For example, if the initial qualification level information is a loan amount, the first loan amount corresponding to the first qualification level is 10 ten thousand, the first weight is 0.6, the second loan amount corresponding to the second qualification level is 5 ten thousand, and the first weight is 0.4, the loan amount is 8 ten thousand.
Wherein the step of inputting the customer-related data into a preset public data model and a preset federal model, respectively, to determine initial qualification level information includes:
step S11, inputting the customer related data into the public data model to score a target customer corresponding to the customer related data to obtain a first target score;
in this embodiment, it should be noted that a preset comparison relationship table between the client score and the loan amount is stored in a preset local database, for example, if the client score is 1 to 3, the loan amount corresponding to 1 is less than or equal to 10 ten thousand, the loan amount corresponding to 2 is less than or equal to 100 ten thousand, the loan amount corresponding to 3 is less than or equal to 10000 ten thousand, and the client related data is the public type data and the privacy type data related to the target client.
Inputting the customer related data into the public data model to score a target customer corresponding to the customer related data to obtain a first target score, specifically, inputting public type data in the customer related data into the public data model to process the public type data based on a data processing layer in the public data model, and outputting the first target score, wherein the data processing layer comprises a convolution layer, a pooling layer, a full connection layer and the like.
Step S12, inputting the relevant data of the customer into the federal model to score the target customer to obtain a second target score;
in this embodiment, the client-related data is input into the federated model to score the target client, so as to obtain a second target score, and specifically, the privacy type data in the client-related data is input into the federated model to process the public type data based on a data processing layer in the federated model, so as to output the second target score.
Step S13, determining the initial qualification grade information based on the first target score and the second target score.
In this embodiment, the initial qualification grade information is determined based on the first target score and the second target score, specifically, a wind control score corresponding to both the first target score and the second target score is calculated, and the initial qualification grade information corresponding to the wind control score is queried in a preset relation comparison table, where the preset relation comparison table is a comparison relation table between the customer score and the initial qualification grade information.
Wherein the step of determining the initial qualification level information based on the first target score and the second target score comprises:
step S131, respectively obtaining a first target weight corresponding to the first target score and a second target weight corresponding to the second target score;
in this embodiment, it should be noted that the first target weight corresponds to the common data model, that is, after the common data model is trained, the first target weight is set by a user, and the second target weight corresponds to the federal model, that is, after the federal model is trained, the second target weight is set by the user.
Step S132, calculating a wind control score of the target customer based on the first target score, the first target weight, the second target score and the second target weight;
in this embodiment, a wind control score of the target customer is calculated based on the first target score, the first target weight, the second target score and the second target weight, specifically, a first product between the first target score and the first target weight is calculated, a second product between the second target score and the second target weight is calculated, and further, a sum of the first product and the second product is calculated to obtain the wind control score.
Step S133, determining the initial qualification grade information based on the wind control score.
In this embodiment, the initial qualification grade information is determined based on the wind control score, specifically, a preset relationship comparison table is obtained, and the initial qualification grade information is queried in the preset relationship comparison table based on the wind control score.
Step S20, constructing a customer portrait corresponding to the customer related data, and matching a product message corresponding to the initial qualification grade information based on the customer portrait so as to recommend the product message to a client corresponding to the customer related data;
in this embodiment, the customer representation is the representation data, which includes features and codes, wherein the features include customer consumption records, customer loan records, etc., and the codes include character strings, numbers, letters, etc., as shown in fig. 2, which is a schematic diagram of the representation data, wherein "marketed", "market value 100 to 1000 ten thousand", "good business condition", "annual profit 10 to 100 ten thousand" and "bad loan record" are the features, and "0", "2", "5", "10" and "65532" are all the codes.
And constructing a client portrait corresponding to the client related data, matching a product message corresponding to the initial qualification grade information based on the client portrait, recommending the product message to a client corresponding to the client related data, specifically, constructing the client portrait based on the client related data, inputting the client portrait into a preset portrait analysis model, outputting a portrait analysis result, matching a product message corresponding to the portrait analysis result in a preset advertisement storage database based on the portrait analysis result, and recommending the product message to the client corresponding to the client related data.
Wherein the step of matching the product message corresponding to the initial qualification level information based on the customer representation comprises:
step S21, inputting the customer portrait into a preset portrait analysis model to analyze the customer portrait and obtain a customer portrait analysis result;
in this embodiment, it should be noted that the preset portrait analysis model is a machine learning model trained in advance.
And inputting the client portrait into a preset portrait analysis model to analyze the client portrait and obtain a client portrait analysis result, and specifically, inputting the client portrait into the preset portrait analysis model to perform data processing on data in the client portrait through a data processing layer of the preset portrait analysis model so as to analyze the client portrait and obtain a client analysis result.
Step S22, matching the product message based on the customer representation analysis result and the initial qualification level information.
In this embodiment, the customer portrait analysis result includes customer repayment capability, customer reputation value, and the like.
Matching the product message based on the customer portrait analysis result and the initial qualification level information, specifically, matching the product message in a preset product information storage database based on the customer portrait analysis result and the initial qualification level information, for example, assuming that the initial qualification level information is a loan amount, the loan amount is 10 to 100 ten thousand, the customer portrait analysis result includes a customer repayment ability and a customer reputation value, and the customer short-term repayment ability is weaker, but the customer reputation value is better, further since the customer reputation value of the target customer is better, the loan amount corresponding to the product message that can be recommended to the target customer can be set to 100 ten thousand, and since the short-term repayment ability of the target customer is weaker, the repayment installment can be set to a longer 24, and the repayment time per installment is 1 month, and recommending corresponding product information to the target client.
Step S30, if the client will information fed back by the client based on the product message is received, providing a target product corresponding to the product message to a target client corresponding to the client through a preset intelligent service module.
In this embodiment, if client intention information fed back by the client based on the product message is received, a target product corresponding to the product message is provided to a target client corresponding to the client through a preset intelligent service module, specifically, if client intention information fed back by the client is received, it is indicated that the target client has an intention to acquire the target product, and further, based on communication information in the client intention information, identity information of the target client is automatically verified through the preset intelligent service module, a target product is automatically determined, and further, the target product is provided to the target client.
According to the method, client related data are received and are respectively input into a preset public data model and a preset federal model to determine initial qualification grade information, so that a client portrait corresponding to the client related data is constructed, a product message corresponding to the initial qualification grade information is matched based on the client portrait, so that the product message is recommended to a client corresponding to the client related data, and if client intention information fed back by the client based on the product message is received, a target product corresponding to the product message is provided to a target client corresponding to the client through a preset intelligent service module. That is, in the present embodiment, the client-related data is processed through the preset public data model and the preset federal model respectively to determine the initial qualification grade information, wherein the preset public data model is obtained based on public data training, and the federal model is obtained by combining private data training of multiple financial institutions, so that the present application achieves the purpose of predicting the qualification grade information of the target client based on a wider and more comprehensive data model, thereby improving the wind control capability of the financial institutions, reducing the risk of product service, improving the profit of financial product service, that is, reducing the cost of product service, further, the present embodiment provides a better message recommendation service for the target client by recommending product messages to the target client individually based on the client image, and further improving the conversion efficiency of the target client, the product service cost is further reduced, the product service efficiency is improved, the intelligent, automatic and full-chain-driven preset intelligent service module is further provided, high-quality and pure-line product service can be provided for the client, the product service flow and the product service period are further simplified, the phenomenon that the client runs off due to the fact that the product service flow is too complicated and long is avoided, the product service efficiency is further improved, and therefore the technical problems of low product service efficiency and high cost are solved.
Further, referring to fig. 3, in another embodiment of the product service full-chain driving method according to the first embodiment of the present application, the preset intelligent service module includes an AI intelligent customer service module, an identity verification module, a data verification module, and an operation loan module, the customer intention information includes communication information, the target product includes a target financial product,
if the client intention information fed back by the client based on the product message is received, the step of providing the target product corresponding to the product message to the target client corresponding to the client through a preset intelligent service module comprises the following steps:
step S31, receiving the communication information fed back by the client, and based on the communication information, performing service consultation service on the client through the AI intelligent customer service module to obtain a service consultation result;
in this embodiment, it should be noted that the communication information includes voice information, text information, and the like, that is, the AI intelligent customer service module may communicate with the client through voice telephone, text input, and the like, and during the communication process, the AI intelligent customer service module determines whether the client has a financial product application qualification, populates an application process to the client, and collects basic information of the client, and the service consultation result includes a determination of whether the client has the application qualification, basic information of the client, and the like, where the client having the application qualification means that the client meets a condition for applying for a financial product.
Receiving the communication information fed back by the client, and based on the communication information, performing business consultation service on the client through the AI intelligent customer service module to obtain a business consultation result, specifically, receiving the communication information fed back by the client, searching a business answer corresponding to the communication information in a preset intelligent customer service database through the AI intelligent customer service module to perform the business consultation service, recording information in the process of the business consultation service, and obtaining the business consultation result, wherein the preset intelligent customer service database can perform editing and self-learning through a preset AI management platform, and additionally, the preset AI management platform can perform operations such as operation report statistics and visualization of financial products, monitoring of operation conditions of financial products, and the like.
Step S32, based on the service consultation result, the identity verification module is used for verifying the identity of the client to obtain an identity verification result;
in this embodiment, it should be noted that the identity verification includes confirming the will of the client and confirming the basic enterprise information corresponding to the client, and the identity verification result includes a confirmation result of the will of the client and a confirmation result of the basic enterprise information corresponding to the client.
And based on the service consultation result, performing identity verification on the client through the identity verification module to obtain an identity verification result, specifically, based on the basic data of the client in the service consultation result, confirming whether the willingness of the client to apply for financial products is the willingness of the client through the identity verification module, and collecting the basic information of an enterprise corresponding to the client, namely, obtaining the identity verification result.
Wherein the identity verification module comprises an AI electric nuclear robot, the identity verification result comprises a client intention confirmation result and enterprise basic information,
the step of performing identity verification on the client through the identity verification module based on the service consultation result to obtain an identity verification result comprises the following steps:
step S321, based on the service consultation result, performing voice interaction with the target client through the AI electric nuclear robot to obtain a result of confirming the intention of the client;
in this embodiment, it should be noted that the customer intention confirmation result refers to a result of the customer determining whether the intention of the customer to apply for the financial product is a personal intention.
Based on the service consultation result, the AI electric nuclear robot performs voice interaction with the target client to obtain the client intention confirmation result, specifically, based on the service consultation result, the AI electric nuclear robot automatically performs voice interaction with the target client to obtain the client intention confirmation result, namely, in the voice interaction process, the voice interaction process is completely performed by the AI electric nuclear robot and the client without manual intervention.
Wherein, in step S331, the identity verification module comprises an AI quality inspection robot,
the step of voice interaction with the target client through the AI electric nuclear robot based on the business consultation result comprises the following steps:
step B10, acquiring voice interaction records corresponding to the voice interaction, and performing quality inspection on the voice interaction records through the AI quality inspection robot to obtain quality inspection results;
in this embodiment, it should be noted that the AI electric nuclear robot includes a voice recognition function and a quality inspection function.
The voice interaction recording corresponding to the voice interaction is obtained, the AI quality inspection robot performs quality inspection on the voice interaction recording to obtain a quality inspection result, specifically, the voice interaction recording corresponding to the voice interaction is obtained, voice information in the voice interaction recording is identified through the voice identification function, multi-dimensional quality inspection is performed on the voice information to obtain a quality inspection result, wherein the multi-dimensional quality inspection comprises compliance speech inspection, service attitude inspection, service proficiency inspection and the like.
And step B20, comparing the quality inspection result with a preset quality inspection standard, generating a detailed quality inspection report corresponding to the quality inspection result, and recording the detailed quality inspection report into a deposit reference factor.
In this embodiment, the quality inspection result is compared with a preset quality inspection standard, a detailed quality inspection report corresponding to the quality inspection result is generated, and the detailed quality inspection report is counted into a deposit reference factor, specifically, the quality inspection result is compared with a preset quality inspection standard, the quality inspection result is scored, a detailed quality inspection report corresponding to the quality inspection result is generated, and the detailed quality inspection report is counted into a deposit reference factor, for example, if the preset quality inspection standard includes a compliance technical standard, a service attitude standard, a service proficiency standard, and the like, and each preset quality inspection standard corresponds to a weight, if the compliance technical standard in the quality inspection result is 80 minutes, the corresponding weight is 30 minutes, the service attitude is 70 minutes, the corresponding weight is 40%, the service proficiency is 90 minutes, and the corresponding weight is 30%, the generated detailed quality inspection report includes the scoring of each quality inspection standard, and the scoring of each quality inspection standard, And corresponding to total points, deduction errors and the like, the total points are divided into 79 points, and the 79 points are counted into the deposit factors.
Step S312, based on the client intention confirmation result, the enterprise basic information is obtained through the voice interaction.
In this embodiment, the enterprise basic information is obtained through the voice interaction based on the result of the confirmation of the client's will, and specifically, after confirming that the financial product application will of the client is the will of the client, the client is asked and answered through the voice interaction to obtain the enterprise basic information.
Wherein the identity verification module comprises an AI wind control dialogue robot,
the step of performing identity verification on the client through the identity verification module based on the service consultation result to obtain an identity verification result comprises the following steps:
step A10, obtaining client communication information through the AI wind control dialogue robot, and matching the question and answer suggestions corresponding to the client communication information;
in this embodiment, it should be noted that the customer communication information includes a question asked by the customer.
The method comprises the steps of obtaining client communication information through the AI wind control conversation robot, matching question and answer suggestions corresponding to the client communication information, specifically, obtaining the client question through the AI wind control conversation robot, and further inquiring the question and answer suggestions corresponding to the client question and answer in a preset question and answer suggestion database, namely, matching the question and answer suggestions corresponding to the client communication information.
And A20, acquiring client risk information through the AI wind control dialogue robot based on the question and answer suggestions and the client communication information, and recording the client risk information into a deposit reference factor.
In this embodiment, it should be noted that the AI wind control dialog robot is used in the process of the business consultation service or the voice interaction process corresponding to the AI electric nuclear robot, and valuable interaction information, such as a question posed by a client during voice interaction or a question posed to the client during the business consultation service, can be found out in the voice interaction or the business consultation service process, so as to quantitatively evaluate the interaction information.
Acquiring client risk information through the AI wind control dialogue robot based on the question and answer suggestion and the client communication information, and recording the client risk information into a deposit reference factor, specifically, rating the client through the AI wind control dialogue robot based on the question and answer suggestion and the client communication information, that is, judging whether the client is a first-level client or a second-level client, and acquiring a client rating level, and further acquiring the client risk information through the rating level, for example, if the client is classified into 1 to 4 levels, the 1 level indicates that the client is a high-quality client, the client risk information is a 0-level risk level, the 2 level indicates that the client is a better client, the client risk information is a 1-level risk level, the 3 level indicates that the client is a common client, the client risk information is a 2-level risk level, and 4, the client risk information is in a 3-level risk level if the client is a poor-quality client, wherein the higher the risk level is, the higher the risk of providing the financial products applied by the client to the client is.
Step S33, based on the identity verification result, the data verification module identifies the business-related data uploaded by the client to obtain a data verification result, and the data verification result is added into a deposit reference factor;
in this embodiment, it should be noted that the business-related data includes text document data, image document data, certificate photo data, and the like, and the loan reference factor is used to evaluate the amount of the financial product provided to the customer.
Based on the identity verification result, the data auditing module identifies the business related data uploaded by the client to obtain a data auditing result, and the data auditing result is added into a deposit reference factor, after confirming that the willingness of the client to apply for the financial products is the willingness of the client, receiving the business related data sent by the client, and performs character recognition on the service-related data through a preset character recognition model to obtain a character recognition result, compares the character recognition result with a preset character recognition standard or stores the character recognition result in a preset storage database to obtain a character recognition result, that is, a data audit result is obtained and the data audit result is included in a deposit reference factor, the preset character recognition model comprises an image recognition model, a target detection model and other trained machine learning models.
And step S34, providing the target financial product to the target customer through the operation loan module based on the loan reference factor.
In this embodiment, the target financial product is provided to the target customer through the operation loan module based on the loan reference factor, specifically, when the data audit result is that the audit is passed, the financial product that the customer can apply is checked through the operation loan module based on the loan reference factor to obtain the amount limit that the customer can apply, and then the target customer can perform face recognition detection, and if the face recognition detection passes, the financial product corresponding to the amount limit is provided to the target customer, where the other various types of data include online query data, collected data when communicating with the customer telephone, enterprise operation data, and the like.
In this embodiment, the communication information sent by the client is received, the AI intelligent customer service module performs business consultation service on the client based on the communication information, a business consultation result is obtained, the identity verification module performs identity verification on the client based on the business consultation result, an identity verification result is obtained, the data verification module identifies business-related data uploaded by the client based on the identity verification result, a data verification result is obtained, the data verification result is included in a payment reference factor, and further, based on the payment reference factor, the payment module is operated to provide corresponding target financial products for the target client. That is, the communication information of the client is received at first, the business consultation service of the client is performed through the AI intelligent customer service module based on the communication information, the business consultation result is obtained, the identity of the client is verified through the identity verification module based on the business consultation result, the identity verification result is obtained, the identification of the business related data uploaded by the client is performed through the data verification module based on the identity verification result, the data verification result is obtained, the data verification result is counted into the reimbursement reference factor, and the corresponding target financial product is provided for the target client through the operation of the reimbursement module based on the reimbursement reference factor. That is, this application provides an intelligent, automatic, full chain drive's intelligent service system, and then through will predetermine intelligent service module and divide into four modules such as AI intelligent customer service module, identity verification module, data audit module and operation put money module and provide high-quality, the online financial product service of pure to the customer, and then simplified financial product service's flow and cycle, avoided because financial product service flow is too complicated and lengthy and lead to the condition emergence that the customer runs off, improved financial product service's efficiency, reduced financial product service's cost of labor, so, for solving the technical problem that product service efficiency is low and with high costs among the prior art and laying a foundation.
Referring to fig. 4, fig. 4 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
As shown in fig. 4, the product-service full-chain driving apparatus may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used for realizing connection communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Optionally, the product service full-chain driving device may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).
Those skilled in the art will appreciate that the product-service full-chain drive apparatus configuration shown in FIG. 4 does not constitute a limitation of product-service full-chain drive apparatus, and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 4, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, and a product service full chain driver. An operating system is a program that manages and controls the hardware and software resources of a product services full-chain driver, supporting the operation of the product services full-chain driver as well as other software and/or programs. The network communication module is used for realizing communication among the components in the memory 1005 and communication with other hardware and software in the product service full-chain driving system.
In the product-service full-chain driver apparatus shown in fig. 4, the processor 1001 is configured to execute a product-service full-chain driver stored in the memory 1005, and implement the steps of the product-service full-chain driver method described in any one of the above.
The specific implementation of the product service full-chain driving device of the present application is substantially the same as each embodiment of the product service full-chain driving method, and is not described herein again.
The embodiment of the present application further provides a product service full-chain driving device, the product service full-chain driving device is applied to a product service full-chain driving device, the product service full-chain driving device includes:
the determining module is used for receiving the client related data and inputting the client related data into a preset public data model and a preset federal model respectively so as to determine initial qualification grade information;
the recommending module is used for constructing a client portrait corresponding to the client related data, and matching a product message corresponding to the initial qualification grade information based on the client portrait so as to recommend the product message to a client corresponding to the client related data;
and the service module is used for providing a target product corresponding to the product message to a target client corresponding to the client through a preset intelligent service module if the client intention information fed back by the client based on the product message is received.
Optionally, the determining module includes:
the first scoring unit is used for inputting the client related data into the public data model so as to score a target client corresponding to the client related data to obtain a first target score;
the first scoring unit is used for inputting the client related data into the federal model so as to score the target client and obtain a second target score;
a determining unit configured to determine the initial qualification grade information based on the first target score and the second target score.
Optionally, the determining unit includes:
a first obtaining subunit, configured to separately obtain a first target weight corresponding to the first target score and a second target weight corresponding to the second target score;
a calculating subunit configured to calculate a wind control score for the target customer based on the first target score, the first target weight, the second target score, and the second target weight;
a determining subunit, configured to determine the initial qualification grade information based on the wind control score.
Optionally, the service module includes:
the business consultation unit is used for receiving the communication information fed back by the client, and carrying out business consultation service on the client through the AI intelligent customer service module based on the communication information to obtain a business consultation result;
the identity verification unit is used for verifying the identity of the client through the identity verification module based on the service consultation result to obtain an identity verification result;
the data auditing unit is used for identifying the business related data uploaded by the client through the data auditing module based on the identity verification result to obtain a data auditing result, and counting the data auditing result into a deposit reference factor;
and the deposit unit is used for providing the target financial product to the target customer through the operation deposit module based on the deposit reference factor.
Optionally, the identity verification unit includes:
the voice interaction subunit is used for performing voice interaction with the target client through the AI electric nuclear robot based on the service consultation result so as to obtain a result of confirming the intention of the client;
and the second acquisition subunit is used for acquiring the basic enterprise information through the voice interaction based on the client intention confirmation result.
Optionally, the identity verification unit further comprises:
the quality inspection subunit is used for acquiring the voice interaction record corresponding to the voice interaction, and performing quality inspection on the voice interaction record through the AI quality inspection robot to obtain a quality inspection result;
and the generation subunit is used for comparing the quality inspection result with a preset quality inspection standard, generating a detailed quality inspection report corresponding to the quality inspection result, and recording the detailed quality inspection report into a deposit reference factor.
Optionally, the service module includes:
the first matching unit is used for acquiring client communication information through the AI wind control conversation robot and matching question and answer suggestions corresponding to the client communication information;
and the acquisition unit is used for acquiring the risk information of the client through the AI wind control dialogue robot based on the question-answer suggestions and the client communication information and recording the risk information of the client into a deposit reference factor.
Optionally, the recommendation module includes:
the analysis unit is used for inputting the client portrait into a preset portrait analysis model so as to analyze the client portrait and obtain a client portrait analysis result;
a second matching unit for matching the product message based on the customer representation analysis result and the initial qualification level information.
The specific implementation of the product service full-chain driving apparatus of the present application is substantially the same as the embodiments of the product service full-chain driving method, and is not described herein again.
The embodiment of the present application provides a readable storage medium, and the readable storage medium stores one or more programs, which can be further executed by one or more processors for implementing the steps of the product service full chain driving method described in any one of the above.
The specific implementation of the medium of the present application is substantially the same as that of each embodiment of the product service full-chain driving method, and is not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A product service full-chain driving method is characterized by comprising the following steps:
receiving client related data, and respectively inputting the client related data into a preset public data model and a preset federal model to determine initial qualification grade information;
constructing a client portrait corresponding to the client related data, and matching a product message corresponding to the initial qualification grade information based on the client portrait so as to recommend the product message to a client corresponding to the client related data;
and if the client intention information fed back by the client based on the product message is received, providing a target product corresponding to the product message to a target client corresponding to the client through a preset intelligent service module.
2. The product-service full-chain driven method according to claim 1, wherein the step of inputting the customer-related data into a preset common data model and a preset federal model, respectively, to determine initial qualification grade information comprises:
inputting the customer related data into the public data model to score a target customer corresponding to the customer related data to obtain a first target score;
inputting the customer-related data into the federal model to score the target customer to obtain a second target score;
determining the initial qualification level information based on the first target score and the second target score.
3. The product-service full-chain driven method of claim 2, wherein the step of determining the initial qualification level information based on the first target score and the second target score comprises:
respectively acquiring a first target weight corresponding to the first target score and a second target weight corresponding to the second target score;
calculating a wind control score for the target customer based on the first target score, the first target weight, the second target score, and the second target weight;
determining the initial qualification grade information based on the wind control score.
4. The product service full-chain driving method according to claim 1, wherein the preset intelligent service module comprises an AI intelligent customer service module, an identity verification module, a data verification module and an operation loan module, the customer intention information comprises communication information, the target product comprises a target financial product,
if the client intention information fed back by the client based on the product message is received, the step of providing the target product corresponding to the product message to the target client corresponding to the client through a preset intelligent service module comprises the following steps:
receiving the communication information fed back by the client, and performing service consultation service on the client through the AI intelligent customer service module based on the communication information to obtain a service consultation result;
based on the service consultation result, performing identity verification on the client through the identity verification module to obtain an identity verification result;
based on the identity verification result, identifying the business related data uploaded by the client through the data verification module to obtain a data verification result, and recording the data verification result into a deposit reference factor;
providing, by the operational payout module, the target financial product to the target customer based on the payout reference factor.
5. The product service full-chain driving method according to claim 4, wherein the identity verification module comprises an AI electric nuclear robot, the identity verification result comprises a client intention confirmation result and basic enterprise information,
the step of performing identity verification on the client through the identity verification module based on the service consultation result to obtain an identity verification result comprises the following steps:
based on the service consultation result, performing voice interaction with the target client through the AI electric nuclear robot to obtain a result of confirming the intention of the client;
and acquiring the enterprise basic information through the voice interaction based on the client intention confirmation result.
6. The product service full-chain driving method of claim 5, wherein the identity verification module comprises an AI quality inspection robot,
the step of voice interaction with the target client through the AI electric nuclear robot based on the business consultation result comprises the following steps:
acquiring voice interaction records corresponding to the voice interaction, and performing quality inspection on the voice interaction records through the AI quality inspection robot to obtain quality inspection results;
and comparing the quality inspection result with a preset quality inspection standard, generating a detailed quality inspection report corresponding to the quality inspection result, and recording the detailed quality inspection report into a deposit reference factor.
7. The product-service full-chain driven method of claim 4, wherein the identity verification module comprises an AI wind-controlled dialogue robot,
the step of performing identity verification on the client through the identity verification module based on the service consultation result to obtain an identity verification result comprises the following steps:
obtaining client communication information through the AI wind control dialogue robot, and matching a question-answer suggestion corresponding to the client communication information;
and acquiring client risk information through the AI wind control dialogue robot based on the question and answer suggestions and the client communication information, and recording the client risk information into a deposit reference factor.
8. The product service full chain driving method of claim 1, wherein the step of matching product messages corresponding to the initial qualification level information based on the customer representation comprises:
inputting the client portrait into a preset portrait analysis model to analyze the client portrait and obtain a client portrait analysis result;
matching the product message based on the customer representation analysis results and the initial qualification level information.
9. A product-service full-chain drive apparatus, comprising: a memory, a processor, and a program stored on the memory for implementing the product services full chain drive method,
the memory is used for storing a program for realizing the product service full-chain driving method;
the processor is configured to execute a program implementing the product service full chain driving method to implement the steps of the product service full chain driving method according to any one of claims 1 to 8.
10. A readable storage medium having stored thereon a program for implementing a product service full chain driving method, the program being executed by a processor to implement the steps of the product service full chain driving method according to any one of claims 1 to 8.
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