Customer information approval method, device, electronic device and medium

Abstract

Provided are a customer information approval method, apparatus, electronic device, and medium, which can be applied in the fields of big data technology and artificial intelligence technology. The method comprises: obtaining information of a customer to be approved; calculating a target single customer value for the customer to be approved based on the information of the customer to be approved; inputting the target single customer value into a customer value model to perform customer value classification and output a customer value rating; in response to the customer value rating being high, inputting the information of the customer to be approved and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate of customer information approval and output a pass rate prediction result; and approving the customer to be approved based on the pass rate prediction result and outputting an approval result.

Description

Technical Field

[0001] The present invention relates to the fields of big data technology and artificial intelligence technology, and more specifically to a customer information approval method, device, electronic device and medium. Background Art

[0002] In the banking industry, high-value users are a customer segment that banks require special attention. These customers typically possess higher creditworthiness, more stable repayment capacity, and greater potential financial needs. Therefore, banks require special attention and service for high-value users' business applications. Currently, user value models used to assess customer value tend to use single-dimensional metrics, such as spending amount or transaction frequency, and omit the customer's lifetime value. Meanwhile, existing bank approval probability models roughly calculate the probability of credit card repayment using a set threshold and further use this probability to determine the approval outcome. However, due to the low quality of customer behavior and transaction data, the long time span, and bias introduced by model selection, user value measurements are prone to errors. Furthermore, the high-dimensionality and large volume of customer data lead to high computational complexity and the risk of mutual exclusion during the credit approval process.

[0003] Therefore, in the current banking system's approval process, it is easy for the user value of new customers to be unclearly defined, and for customers who should have been approved to be mistakenly rejected by the system, thus affecting the customer experience. Summary of the Invention

[0004] In view of the above problems, according to the first aspect of the present invention, a customer information approval method is provided, which includes: obtaining information of customers to be approved, and calculating the target single customer value of the customers to be approved based on the information of the customers to be approved; inputting the target single customer value into a customer value model to perform customer value classification, and outputting a customer value level; in response to the customer value level being high, inputting the information of the customers to be approved and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate of customer information approval, and outputting a pass rate prediction result; and approving the customers to be approved based on the pass rate prediction result, and outputting an approval result.

[0005] According to some exemplary embodiments, the pass rate prediction model is trained by the LightGBM model; the information of the customer to be approved and the target single customer value are input into the pre-trained pass rate prediction model to predict the pass rate of the customer information approval, and the pass rate prediction result is output, which specifically includes: preprocessing and feature engineering the information of the customer to be approved and the target single customer value to obtain a discretized customer information feature histogram; inputting the customer information feature histogram into N decision trees, each of the N decision trees contains at least one leaf node, and obtaining the prediction results of the N decision trees based on the leaf nodes, wherein N is a positive integer; and based on the prediction results of the N decision trees, using voting or majority voting, selecting the prediction result with the largest number of occurrences as the pass rate prediction result.

[0006] According to some exemplary embodiments, the information of the customer to be approved and the target single customer value are preprocessed and feature engineered, specifically including: data cleaning of the information of the customer to be approved and the target single customer value, feature selection based on expert experience, and obtaining preliminary customer features; using a mutually exclusive bundling algorithm to bundle the high-dimensional sparse features in the preliminary customer features to obtain customer bundled features; and using a histogram algorithm to construct a discretized feature histogram of the customer bundled features to obtain a customer information feature histogram.

[0007] According to some exemplary embodiments, the training of the pass rate prediction model by the LightGBM model specifically includes: generating a decision tree using a Leaf-wise algorithm, wherein the Leaf-wise algorithm is used to recursively process the splitting of the leaf nodes and the depth of the nodes in the N decision trees until a stopping condition is met.

[0008] According to some exemplary embodiments, the training of the pass rate prediction model by the LightGBM model specifically includes: using the new customer personal credit card approval record table and the customer credit card issuance record table to screen new customer applications; based on the customer number in the new customer application, fully associating the customer portrait assets; and establishing a training set for model training based on the content of the full information association.

[0009] According to some exemplary embodiments, the use of the new customer personal credit card approval record table and the customer credit card issuance record table to screen new customer applications specifically includes: based on the customer number in the new customer personal credit card approval record table, corresponding to the customer credit card issuance record table, determining whether the same customer association can be established; if the same customer association fails to be established, using the approval record in the new customer personal credit card approval record table corresponding to the customer number as the new customer application; and if the same customer association is successfully established, obtaining the first credit card issuance date corresponding to the customer number from the customer credit card issuance record table, and using the approval record corresponding to the customer number that is no later than the first credit card issuance date as the new customer application.

[0010] According to some exemplary embodiments, calculating the target single-customer value of the pending customer based on the information of the pending customer specifically includes: calculating the single-customer full life cycle revenue and single-customer acquisition cost based on the information of the pending customer; calculating the difference between the single-customer full life cycle revenue and single-customer acquisition cost to obtain a first difference; and using the first difference as the target single-customer value.

[0011] According to some exemplary embodiments, the calculation of the life cycle revenue of a single customer specifically includes: obtaining the daily revenue of a single customer and the daily maintenance cost of a single customer, calculating the difference between the daily revenue of a single customer and the daily maintenance cost of a single customer, and obtaining a second difference; obtaining a customer retention curve, and calculating the retention time of a single customer using the area between the customer retention curve and the coordinate axis; and calculating the product of the second difference and the retention time of the single customer to obtain the life cycle revenue of a single customer.

[0012] According to some exemplary embodiments, calculating the single customer acquisition cost specifically includes: obtaining the total customer acquisition cost and the number of new customers in a target time period; and calculating the quotient of the total customer acquisition cost and the number of new customers to obtain the single customer acquisition cost.

[0013] According to some exemplary embodiments, the customer value model is an RFM model; inputting the target single customer value into the customer value model for customer value classification and outputting a customer value grade specifically includes: calculating the RFM score of the customer to be approved based on the target single customer value; and based on a preset threshold, classifying the RFM score of the customer to be approved and outputting a customer value grade.

[0014] According to a second aspect of the present invention, a customer information approval device is proposed, which includes: a target single customer value calculation module, which is used to obtain information of customers to be approved, and calculate the target single customer value of the customers to be approved based on the information of the customers to be approved; a customer value level acquisition module, which is used to input the target single customer value into a customer value model to perform customer value classification, and output a customer value level; an inference result generation module, which is used to take the speech semantic information as input, use a pre-built hierarchical and sub-hyperordinate inference model to perform relational inference in the speech semantic information, and generate an inference result, wherein the inference result is used to represent the content understanding of the speech semantic information; a pass rate prediction result output module, which is used to input the information of the customers to be approved and the target single customer value into a pre-trained pass rate prediction model to perform pass rate prediction of customer information approval in response to the customer value level being high, and output a pass rate prediction result; and an approval result output module, which is used to approve the customers to be approved based on the pass rate prediction result, and output an approval result.

[0015] According to some exemplary embodiments, the target single-customer value calculation module may include a calculation module, a first difference acquisition unit, and a target single-customer value acquisition unit.

[0016] According to some exemplary embodiments, the calculation module may be configured to calculate the lifetime revenue of a single customer and the cost of acquiring a single customer based on the information of the customer to be approved.

[0017] According to some exemplary embodiments, the first difference obtaining unit may be configured to calculate the difference between the single-customer lifetime revenue and the single-customer acquisition cost to obtain a first difference.

[0018] According to some exemplary embodiments, the target single-customer value obtaining unit may be configured to use the first difference as the target single-customer value.

[0019] According to some exemplary embodiments, the calculation module may include a single-customer full-lifecycle revenue calculation module and a single-customer acquisition cost calculation module.

[0020] According to some exemplary embodiments, the single-customer full-lifecycle revenue calculation module may include a second difference calculation unit, a single-customer retention time calculation unit, and a single-customer full-lifecycle revenue calculation unit.

[0021] According to some exemplary embodiments, the second difference calculation unit may be configured to obtain a single-customer daily revenue and a single-customer daily maintenance cost, calculate a difference between the single-customer daily revenue and the single-customer daily maintenance cost, and obtain a second difference.

[0022] According to some exemplary embodiments, the single-customer retention duration calculation unit may be used to obtain a customer retention curve, and calculate the single-customer retention duration using the area between the customer retention curve and a coordinate axis.

[0023] According to some exemplary embodiments, the single-customer full-lifecycle revenue calculation unit may be configured to calculate the product of the second difference and the single-customer retention duration to obtain the single-customer full-lifecycle revenue.

[0024] According to some exemplary embodiments, the single customer acquisition cost calculation module may include a total customer acquisition cost and new customer quantity acquisition unit and a single customer acquisition cost acquisition unit.

[0025] According to some exemplary embodiments, the total customer acquisition cost and new customer number acquisition unit may be used to acquire the total customer acquisition cost and the number of new customers within a target time period.

[0026] According to some exemplary embodiments, the single customer acquisition cost obtaining unit may be configured to calculate a quotient of the total customer acquisition cost and the number of new customers to obtain the single customer acquisition cost.

[0027] According to some exemplary embodiments, the customer value level acquisition module may include an RFM score calculation unit and a customer value level acquisition unit.

[0028] According to some exemplary embodiments, the RFM score calculation unit may be configured to calculate the RFM score of the customer to be approved based on the target single customer value.

[0029] According to some exemplary embodiments, the customer value rating acquisition unit may be configured to perform customer value classification on the RFM scores of the customers to be approved based on a preset threshold value, and output a customer value rating.

[0030] According to some exemplary embodiments, the pass rate prediction result output module may include a preprocessing and feature engineering module, a prediction unit, and a prediction result output unit.

[0031] According to some exemplary embodiments, the preprocessing and feature engineering module may be used to perform preprocessing and feature engineering on the information of the pending customers and the target single customer value to obtain a discretized customer information feature histogram.

[0032] According to some exemplary embodiments, the prediction unit can be used to input the customer information feature histogram into N decision trees, each of the N decision trees includes at least one leaf node, and obtain prediction results of the N decision trees based on the leaf nodes, where N is a positive integer.

[0033] According to some exemplary embodiments, the prediction result output unit may be configured to select the prediction result with the greatest number of occurrences as the pass rate prediction result based on the prediction results of the N decision trees by voting or majority vote.

[0034] According to some exemplary embodiments, the preprocessing and feature engineering module may further include a feature selection unit, a feature bundling unit, and a feature histogram construction unit.

[0035] According to some exemplary embodiments, the feature selection unit may be used to perform data cleaning on the information of the customer to be approved and the target single customer value, perform feature selection based on expert experience, and obtain preliminary customer features.

[0036] According to some exemplary embodiments, the feature bundling unit may be configured to utilize a mutually exclusive bundling algorithm to perform bundling processing on high-dimensional sparse features in the customer preliminary features to obtain customer bundled features.

[0037] According to some exemplary embodiments, the feature histogram construction unit may be configured to construct a discretized feature histogram from the customer bundling features using a histogram algorithm to obtain a customer information feature histogram.

[0038] According to some exemplary embodiments, the client information approval device may further include a training set establishment module.

[0039] According to some exemplary embodiments, the training set building module may include a new customer application screening module, an association unit, and a training set building unit.

[0040] According to some exemplary embodiments, the new customer application screening module may be used to screen new customer applications using a new customer personal credit card approval record table and a customer credit card issuance record table.

[0041] According to some exemplary embodiments, the association unit may be configured to perform full information association with a customer profile asset based on a customer number in the new customer application.

[0042] According to some exemplary embodiments, the training set establishing unit may be configured to establish a training set for model training based on the content of the full information association.

[0043] According to some exemplary embodiments, the new customer application screening module may include a judgment unit, a first processing unit, and a second processing unit.

[0044] According to some exemplary embodiments, the judgment unit may be configured to judge whether a same customer association can be established based on a customer number in the new customer personal credit card approval record table corresponding to the customer credit card issuance record table.

[0045] According to some exemplary embodiments, the first processing unit may be configured to use the approval record in the new customer personal credit card approval record table corresponding to the customer number as the new customer application if the same customer association establishment fails.

[0046] According to some exemplary embodiments, the second processing unit can be used to obtain the first credit card issuance date corresponding to the customer number from the customer credit card issuance record table if the same customer association is successfully established, and use the approval record corresponding to the customer number that is no later than the first credit card issuance date as the new customer application.

[0047] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the method as described above.

[0048] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method described above.

[0049] According to a fifth aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method as described above is implemented.

[0050] One or more of the above-mentioned embodiments have the following advantages or beneficial effects: According to the customer information approval method provided by the present invention, by calculating the target single customer value of each customer to be approved, the calculation results of the customer value model can be optimized, thereby obtaining more accurate customer value classification results, which is helpful for the accurate screening of high-value users, and enables the computer to allocate resources more targetedly, thereby improving the calculation speed, and then improving the prediction efficiency of the pass rate prediction model, reducing computing time and resource consumption; at the same time, by screening high-value users and predicting the pass rate, it is ensured that high-value customers obtain approval results faster, thereby improving customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0052] Figure 1 The application scenario diagram of the customer information approval method, apparatus, device, and medium according to an embodiment of the present invention is schematically shown.

[0053] Figure 2 The flowchart of the customer information approval method according to an embodiment of the present invention is schematically shown.

[0054] Figure 3 The flowchart of the method for calculating the target single customer value of a customer to be approved according to an embodiment of the present invention is schematically shown.

[0055] Figure 4 The flowchart of the method for calculating the full life cycle revenue of a single customer according to an embodiment of the present invention is schematically shown.

[0056] Figure 5 The flowchart of the method for calculating the single customer acquisition cost according to an embodiment of the present invention is schematically shown.

[0057] Figure 6 The flowchart of the method for classifying customer value according to an embodiment of the present invention is schematically shown.

[0058] Figure 7 The figure schematically shows a flowchart of a method for outputting a pass rate prediction result through a LightGBM model according to an embodiment of the present invention.

[0059] Figure 8 The flowchart of the method of preprocessing and feature engineering according to an embodiment of the present invention is schematically shown.

[0060] Figure 9 The flowchart of the method for establishing a training set for model training according to an embodiment of the present invention is schematically shown.

[0061] Figure 10 The flowchart of the method for screening new customer applications according to an embodiment of the present invention is schematically shown.

[0062] Figure 11 The structure block diagram of the customer information approval device according to an embodiment of the present invention is schematically shown.

[0063] Figure 12 The block diagram schematically shows an electronic device suitable for the customer information approval method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the detailed description that follows, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention. However, it is apparent that one or more embodiments may be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0065] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the presence of the features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0066] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0067] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0068] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0069] First, the technical terms described in this article are explained and described as follows.

[0070] LightGBM (Light Gradient Boosting Machine) is a machine learning framework based on gradient boosting trees. It is a powerful ensemble learning algorithm for processing structured data, such as tabular data. LightGBM efficiently and accurately builds multiple decision trees and integrates them into a more powerful prediction model. It has high performance and the ability to handle large-scale data.

[0071] Decision tree: A tree-based machine learning model used for classification and regression tasks. In classification tasks, a decision tree gradually partitions data into different categories based on input features. In regression tasks, a decision tree predicts continuous values. Each node in the decision tree represents a feature, each branch represents the value of a feature, and the leaf nodes represent the final prediction result.

[0072] Mutually exclusive bundling algorithm: High-dimensional data is often sparse. In a sparse feature space, many features are mutually exclusive (i.e., they cannot take non-zero values ​​at the same time), so mutually exclusive features can be safely bundled. Furthermore, a bias can be added to ensure the feasibility of feature bundling: for example, if two features A and B are bound together, and the original value of feature A is in the range [0, 10), and the original value of feature B is in the range [0, 20), a bias constant of 10 can be added to the value of feature B, changing its value range to [10, 30). After binding, the value range of features A and B is now [0, 30), thus achieving the fusion of features A and B.

[0073] Histogram algorithm: Divide the continuous values ​​of the feature into discrete bins, and use the bins to construct the feature histogram during the training process. The histogram of a leaf can be obtained by subtracting the histogram of its parent node from the histogram of its sibling node. By traversing the k buckets of the histogram, the optimal branching point is approximately found, which greatly reduces the amount of computation and is particularly effective when processing high-dimensional sparse data.

[0074] Leaf-wise (leaf node splitting) algorithm: A splitting strategy for building decision trees, usually used in the gradient boosting tree algorithm. With the same number of splits, it can reduce more errors and obtain better accuracy.

[0075] Gradient-based One-Side Sampling (GOSS): In a gradient boosted decision tree, data samples initially have no initial weights. When calculating information gain, samples with different gradients have different importance. According to the definition of information gain, larger gradients contribute more to the information gain. Therefore, when calculating information gain during training, the lightGBM model retains samples with large gradients and randomly samples samples with small gradients (to maintain the original data distribution). This approach provides more accurate evaluation than using random sampling with a uniform sampling ratio.

[0076] As part of the financial sector, the banking industry has a vast customer base, including high-value users. These customers contribute significantly to a bank's business and profits, often possessing higher creditworthiness and greater financial needs. Therefore, banks require specialized care and services for these customers. However, the information data associated with high-value users often exhibits the following characteristics: high dimensionality, large scale, and complexity. This means banks must process large amounts of complex data, significantly increasing computational costs. Furthermore, the high dimensionality of the data can easily lead to mutually exclusive features, hindering modeling and decision-making.

[0077] Currently, the models used by banks in the customer approval process may lead to false rejections in some cases, which has a negative impact on the customer experience.

[0078] In addition, the current definition of user value tends to use single-dimensional indicators, such as customer transaction amount or transaction frequency, which ignores other factors affecting customers, such as lifetime revenue and acquisition cost, leading to deviations in the evaluation of user value.

[0079] Based on this, an embodiment of the present invention provides a customer information approval method, characterized in that the method includes: obtaining information of a customer to be approved, calculating a target single customer value of the customer to be approved based on the information of the customer to be approved; inputting the target single customer value into a customer value model to perform customer value classification and output a customer value grade; in response to the customer value grade being high, inputting the information of the customer to be approved and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate of customer information approval and output a pass rate prediction result; and approving the customer to be approved based on the pass rate prediction result and outputting an approval result. According to the customer information approval method provided by the present invention, through customer value classification, a computer can more accurately distinguish users of different value grades, which means that the computer can allocate resources more specifically, thereby improving computing speed, thereby improving the prediction efficiency of the pass rate prediction model and reducing computing time and resource consumption; at the same time, by screening high-value users and predicting the pass rate, it is ensured that high-value customers obtain approval results more quickly, thereby improving customer satisfaction.

[0080] It should be noted that the customer information approval method, apparatus, device, and medium provided by the present invention can be used in the fields of big data technology and artificial intelligence technology, as well as in the financial field, and can also be used in various fields other than big data technology, artificial intelligence technology, and finance. The application fields of the customer information approval method, apparatus, device, and medium provided by the embodiments of the present invention are not limited.

[0081] In the technical solution of the present invention, the user information involved (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0082] Figure 1 The application scenario diagram of the customer information approval method, apparatus, device, and medium according to an embodiment of the present invention is schematically shown.

[0083] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0084] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0085] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0086] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0087] It should be noted that the customer information approval method provided in the embodiments of the present invention can generally be executed by the server 105. Accordingly, the customer information approval device provided in the embodiments of the present invention can generally be located in the server 105. The customer information approval method provided in the embodiments of the present invention can also be executed by a server or server cluster that is different from the server 105 and that is capable of communicating with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the customer information approval device provided in the embodiments of the present invention can also be located in a server or server cluster that is different from the server 105 and that is capable of communicating with the terminal devices 101, 102, 103 and / or the server 105.

[0088] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0089] Figure 2 The flowchart of the customer information approval method according to an embodiment of the present invention is schematically shown.

[0090] like Figure 2 As shown, the customer information approval method 200 of this embodiment may include operations S210 to S240.

[0091] In operation S210 , information of customers to be approved is obtained, and based on the information of the customers to be approved, the target single customer value of the customers to be approved is calculated.

[0092] In an embodiment of the present invention, it is necessary to collect relevant information about the customer to be approved. This information may include the customer's personal information (such as name, age, and gender), financial status (such as income, expenses, and liabilities), credit history, transaction history, expenses, income, and fees. This information is typically stored in a bank's database or system.

[0093] In the embodiment of the present invention, in order to further help the bank understand the long-term value of each customer and thus optimize the evaluation result of customer value, an operation of calculating the target single customer value of customers to be approved is also provided.

[0094] Figure 3 The flowchart of the method for calculating the target single customer value of a customer to be approved according to an embodiment of the present invention is schematically shown.

[0095] like Figure 3 As shown, the method for calculating the target single customer value of customers to be approved in this embodiment may include operations S310 to S330, and operations S310 to S330 may at least partially execute operation S210.

[0096] In operation S310 , based on the information of the customer to be approved, the lifecycle revenue of a single customer and the cost of acquiring a single customer are calculated.

[0097] Figure 4 The flowchart of the method for calculating the full life cycle revenue of a single customer according to an embodiment of the present invention is schematically shown.

[0098] like Figure 4 As shown, the method for calculating the lifetime revenue of a single customer in this embodiment may include operations S410 to S430 , and operations S410 to S430 may at least partially execute operation S310 .

[0099] In operation S410 , a single-customer daily revenue and a single-customer daily maintenance cost are obtained, and a difference between the single-customer daily revenue and the single-customer daily maintenance cost is calculated to obtain a second difference.

[0100] In embodiments of the present invention, the daily revenue per customer and the daily maintenance cost per customer refer to the daily profit a bank earns from each customer and the costs incurred to maintain that customer. Records of the daily revenue per customer and the daily maintenance cost per customer can be obtained from a bank system data source. Calculating the difference between the daily revenue and the daily maintenance cost, i.e., the daily net revenue, can help the bank understand the profitability of each customer.

[0101] In operation S420 , a customer retention curve is obtained, and the single customer retention duration is calculated using the area between the customer retention curve and the coordinate axis.

[0102] In an embodiment of the present invention, the customer retention curve shows the customer retention status over a period of time. For example, the customer retention curve can be a time-related curve that shows the customer retention status from the time of acquisition.

[0103] In an embodiment of the present invention, the retention status of customers over a period of time can be obtained, which can be at daily, weekly or monthly time intervals. The retention data can record the customer's activity at different time points, the situation of continuing to use products or services, etc.; using the obtained retention data, a customer retention curve can be drawn, with the horizontal axis representing time (usually in days or months) and the vertical axis representing the retention rate (or the number of retained people), and the shape of the curve reflects the customer's retention status at different time points; calculating the retention time of a single customer involves measuring the area between the customer retention curve and the coordinate axis, which represents the total retention time of the customer since acquisition, and then, by dividing by the total retention rate (or the total number of customers), the average retention time can be obtained.

[0104] In an embodiment of the present invention, the single customer retention time can help banks understand customer retention status and retention time, thereby helping banks determine which customers are more likely to maintain a long-term cooperative relationship with the enterprise, so as to formulate better customer relationship management strategies, including customer maintenance, customized services and promotional activities.

[0105] In operation S430, the product of the second difference and the single-customer retention time is calculated to obtain the single-customer full-lifecycle revenue.

[0106] In this embodiment of the present invention, single-customer lifetime revenue helps assess the value contribution of customer retention to the bank. If the second difference is positive, it indicates an increase in the customer's daily net revenue and a longer retention period, which has a positive impact on the bank's long-term profit potential.

[0107] Figure 5 The flowchart of the method for calculating the single customer acquisition cost according to an embodiment of the present invention is schematically shown.

[0108] like Figure 5As shown, the method for calculating the single customer acquisition cost in this embodiment may include operations S510 to S520, and operations S510 to S520 may at least partially execute operation S310.

[0109] In operation S510 , the total customer acquisition cost and the number of new customers within a target time period are obtained.

[0110] In an embodiment of the present invention, customer acquisition cost refers to the cost invested by a bank in acquiring new customers within a certain period of time, including advertising expenses, marketing expenses, sales expenses, etc.; the number of new customers can be obtained from channels such as new user registrations and the number of products or services purchased by new customers.

[0111] In operation S520 , the quotient of the total customer acquisition cost and the number of new customers is calculated to obtain the single customer acquisition cost.

[0112] In embodiments of the present invention, the cost per customer acquisition (CAC) helps banks make decisions, such as determining customer acquisition budgets, selecting customer acquisition channels, and improving customer acquisition strategies. By monitoring changes in CAC, banks can evaluate the effectiveness of customer acquisition activities and optimize them based on cost-effectiveness.

[0113] Return to reference Figure 3 In operation S320, the difference between the single-customer lifetime revenue and the single-customer acquisition cost is calculated to obtain a first difference.

[0114] In operation S330 , the first difference is used as the target single customer value.

[0115] In the embodiment of the present invention, customer value classification based on target single customer value allows banks to classify and analyze customers to be approved more finely.

[0116] Return to reference Figure 2 In operation S220, the target single customer value is input into the customer value model to perform customer value classification and output a customer value level.

[0117] In an embodiment of the present invention, the customer value model is an RFM model.

[0118] Figure 6 The flowchart of the method for classifying customer value according to an embodiment of the present invention is schematically shown.

[0119] like Figure 6 As shown, the method for classifying customer value in this embodiment may include operations S610 to S620 , and operations S610 to S620 may at least partially execute operation S220 .

[0120] In operation S610 , the RFM score of the customer to be approved is calculated based on the target single customer value.

[0121] In an embodiment of the present invention, the RFM score generally includes the following aspects: a Recency score, which is measured based on the time of the most recent interaction or purchase behavior of the customer to be approved. The more recently the customer interacted with the bank, the higher the Recency score, indicating that the customer is more active; a Frequency score, which indicates the number of interactions or purchases made by the customer within a certain period of time. Customers who interact frequently receive a higher Frequency score, indicating that they are highly engaged; and a Monetary score, which represents the total transaction amount or income of the customer within a certain period of time. Customers with high transaction amounts receive a higher Monetary score, indicating that they contribute more to the bank.

[0122] In embodiments of the present invention, the target customer value per customer can be used to adjust the weights of the RFM score. For example, if the customer value per customer is positive, the weights of Recency, Frequency, and Monetary can be increased to emphasize high-value customers. If the customer value per customer is negative, the weights can be adjusted accordingly to focus on reducing customer acquisition costs and increasing customer value. Furthermore, based on the specific value of the customer value per customer, weighting and scoring rules can be defined to ensure that different customer value levels have different impacts on the RFM score.

[0123] In operation S620 , based on a preset threshold, the RFM scores of the customers to be approved are classified into customer value categories, and a customer value level is output.

[0124] In an embodiment of the present invention, customers to be approved may be classified into high-value, medium-value, and low-value based on a preset threshold and a calculated RFM score.

[0125] Return to reference Figure 2 In operation S230, in response to the customer value level being high, the information of the customer to be approved and the target single customer value are input into a pre-trained pass rate prediction model to predict the pass rate of customer information approval, and a pass rate prediction result is output.

[0126] In an embodiment of the present invention, the pass rate prediction model is trained by a LightGBM model.

[0127] In an embodiment of the present invention, a Leaf-wise algorithm is used to generate a decision tree, wherein the Leaf-wise algorithm is used to recursively process the splitting of the leaf nodes and the depth of the nodes in the decision tree until a stopping condition is met.

[0128] Specifically, the Leaf-Wise algorithm begins at the root node of the tree. First, the root node is split into two child nodes to minimize impurity. Next, unlike other algorithms, the Leaf-Wise algorithm does not generate the entire decision tree hierarchy at once. Instead, it selects a leaf node and attempts to split it. The leaf node is selected based on a pre-defined strategy, typically determined by the current tree depth, impurity before the split, and other factors. The selected leaf node is split, resulting in two new leaf nodes. The splitting process is similar to the root node splitting process, selecting the optimal features and split points to minimize impurity. This process continues until a stopping condition is met.

[0129] In an embodiment of the present invention, the stopping condition may be the maximum depth of the tree, the number of samples in a leaf node is less than a preset threshold, the splitting no longer significantly reduces the impurity, etc.

[0130] In an embodiment of the present invention, gradient-based unilateral sampling can also be used for sampling and weighted training to improve the effect of model training.

[0131] In an embodiment of the present invention, since the number of samples of different categories in the information of customers awaiting approval varies greatly, the model tends to predict categories with a larger number during training. Therefore, gradient-based unilateral sampling and weighted training can be used to solve this problem so that the model training can better identify these categories.

[0132] In an embodiment of the present invention, the number of samples in the minority class can be increased by unilateral sampling. Specifically, this can be achieved by undersampling (reducing the number of samples in the majority class) or oversampling (increasing the number of samples in the minority class), which helps balance the weights of different classes and reduce overfitting of the model to the majority class.

[0133] In an embodiment of the present invention, in addition to sampling, different weights can be assigned to samples of different categories during the training process. A lower weight is assigned to the majority category (usually the negative category), while a higher weight is assigned to the minority category (usually the positive category). These weights play a role in the model's loss function, affecting the update of model parameters and making the model pay more attention to important or rare categories. This helps balance the importance of different categories and improves the model's generalization ability.

[0134] In an embodiment of the present invention, sampling and weighted training can reduce the risk of overfitting. By introducing more minority category samples or increasing their weights, the model can more easily capture the characteristics of these categories, reducing the risk of overfitting.

[0135] Figure 7The figure schematically shows a flowchart of a method for outputting a pass rate prediction result through a LightGBM model according to an embodiment of the present invention.

[0136] like Figure 7 As shown, the method of outputting a pass rate prediction result through the LightGBM model in this embodiment may include operations S710 to S730, and operations S710 to S730 may at least partially execute operation S230.

[0137] In operation S710 , preprocessing and feature engineering are performed on the information of the customer to be approved and the target single customer value to obtain a discretized customer information feature histogram.

[0138] Figure 8 The flowchart of the method of preprocessing and feature engineering according to an embodiment of the present invention is schematically shown.

[0139] like Figure 8 As shown, the preprocessing and feature engineering method of this embodiment may include operations S810 to S830, and operations S810 to S830 may at least partially execute operation S710.

[0140] In operation S810 , data cleaning is performed on the information of the customer to be approved and the target single customer value, and feature selection is performed based on expert experience to obtain preliminary customer features.

[0141] In an embodiment of the present invention, data cleaning may include removing duplicate values, processing missing values, processing outliers, etc. to ensure the quality and accuracy of the data.

[0142] In an embodiment of the present invention, based on expert experience, the most relevant and valuable features can be selected from the original data for subsequent modeling and analysis. Expert experience can help determine which features are important for evaluating the target single customer value of a customer.

[0143] In operation S820 , a mutually exclusive bundling algorithm is used to bundle the high-dimensional sparse features in the preliminary customer features to obtain customer bundled features.

[0144] In an embodiment of the present invention, to address the issues of large, redundant, and mutually exclusive samples of customer information awaiting approval, a mutually exclusive bundling algorithm can be further employed to reduce dimensionality and improve data interpretability. This mutually exclusive feature bundling algorithm is used to process high-dimensional, sparse features within preliminary customer features. These features, in other words, have a large number of possible values, but a limited number of samples for each value. Such features can lead to the curse of dimensionality during modeling, and the number of samples with different values ​​is unbalanced.

[0145] In operation S830, a histogram algorithm is used to construct a discretized feature histogram of the customer bundling features to obtain a customer information feature histogram.

[0146] In embodiments of the present invention, the bundled features can also be constructed into a discretized feature histogram, which converts continuous numerical features into a set of discrete intervals or bins for easier modeling and analysis. The customer information feature histogram can be used to capture the distribution of customer information, with each bin representing the number of samples or other statistical information within a specific range.

[0147] Return to reference Figure 7 In operation S720, the customer information feature histogram is input into N decision trees, each of the N decision trees includes at least one leaf node, and prediction results of the N decision trees are obtained based on the leaf nodes, where N is a positive integer.

[0148] In operation S730 , based on the prediction results of the N decision trees, a prediction result with the largest number of occurrences is selected as a pass rate prediction result by voting or majority vote.

[0149] In addition, in order to avoid noise caused by irrelevant information, sample screening of the training set can be performed during the training process and the sample screening strategy can be refined.

[0150] Figure 9 The flowchart of the method for establishing a training set for model training according to an embodiment of the present invention is schematically shown.

[0151] like Figure 9 As shown, the method for establishing a training set for model training in this embodiment may include operations S910 to S930.

[0152] In operation S910, the new customer application is screened using the new customer personal credit card approval record table and the customer credit card issuance record table.

[0153] In an embodiment of the present invention, new customer personal credit card approval records and customer credit card issuance records that have submitted applications and completed approval within a certain period of time and do not contain the aforementioned warning signs can be selected as sample base tables.

[0154] Figure 10 The flowchart of the method for screening new customer applications according to an embodiment of the present invention is schematically shown.

[0155] like Figure 10 As shown, the method for screening new customer applications in this embodiment may include operations S1010 to S1030.

[0156] In operation S1010, based on the customer number in the new customer personal credit card approval record table and corresponding to the customer credit card issuance record table, it is determined whether the same customer association can be established.

[0157] In an embodiment of the present invention, the credit card approval record and card issuance record of the same customer can be identified by the customer number.

[0158] In operation S1020, if the same customer association establishment fails, the approval record in the new customer personal credit card approval record table corresponding to the customer number is used as the new customer application.

[0159] In an embodiment of the present invention, if it is determined in operation S1010 that the same customer association cannot be established, it means that the customer has not obtained the first credit card, so all applications of the customer can be determined as new customer applications.

[0160] In operation S1030, if the same customer association is successfully established, the first credit card issuance date corresponding to the customer number is obtained from the customer credit card issuance record table, and the approval record corresponding to the customer number that is no later than the first credit card issuance date is used as the new customer application.

[0161] In an embodiment of the present invention, if it is determined in operation S1010 that the same customer association can be established, it means that the customer has obtained a credit card, so all applications before the customer obtains the first credit card can be determined as new customer applications.

[0162] Return to reference Figure 9 In operation S920, full information association is performed with the customer portrait asset based on the customer number in the new customer application.

[0163] In the embodiment of the present invention, the customer profile generally includes the customer's basic information, historical transaction records, financial status, etc. Through full information association, the complete information of the new customer can be obtained for subsequent model training and analysis.

[0164] In an embodiment of the present invention, the customer number applied for by the new customer can be used to fully associate the customer portrait assets, obtain the data of the corresponding customer portrait assets within the selected time period, and together with the approval records, form a complete sample set.

[0165] In operation S930 , a training set for model training is established based on the content of the full information association.

[0166] Return to reference Figure 2 In operation S240, the client to be approved is approved based on the pass rate prediction result, and the approval result is output.

[0167] In an embodiment of the present invention, a threshold can be selected before approval is made to determine which customers' applications will be approved. The threshold is typically selected based on business needs and risk management strategies. If the predicted approval rate for a customer is greater than or equal to the set threshold, the customer's credit card application will be approved; otherwise, it will be rejected.

[0168] In an embodiment of the present invention, the approval result can be "approved" or "rejected." If the customer's application is approved, the bank can continue to process the application and issue the credit card; if the customer's application is rejected, the bank needs to send a rejection letter to the customer and explain the reason for the rejection.

[0169] According to the customer information approval method provided by the present invention, by classifying customer value, the computer can more accurately distinguish users of different value levels and allocate resources more targetedly, thereby improving computing speed, thereby improving the prediction efficiency of the approval rate prediction model and reducing computing time and resource consumption. At the same time, by screening high-value users and predicting the approval rate, high-value customers can obtain approval results more quickly, thereby improving customer satisfaction. Specifically, it brings the following beneficial effects:

[0170] 1. By calculating the target customer value for each pending customer, the RFM model's calculation results can be optimized, resulting in more accurate customer value classification results. This helps accurately screen high-value users and improves customer satisfaction and loyalty.

[0171] 2. Using the RFM model to classify customers can better understand their consumption behavior and values, helping banks to develop personalized strategies for different customer groups, thereby improving marketing and product sales effectiveness.

[0172] 3. The Leaf-Wise algorithm can more efficiently generate decision trees, reducing the number of node splits during training and effectively improving training speed on large-scale data sets. As a result, banks can more quickly update models and respond to customer information approval needs, improving efficiency.

[0173] 4. Leaf-wise algorithms also help reduce the memory footprint of the model, thereby reducing memory pressure and enabling banks to run the model on smaller hardware configurations;

[0174] 5. In bank credit card approval scenarios, customer information may contain a large number of sparse features. The mutually exclusive bundling algorithm helps process high-dimensional and sparse features by bundling related features together, thereby reducing model complexity and improving training and prediction efficiency.

[0175] 6. The histogram algorithm is used to construct a discretized feature histogram after the mutually exclusive bundling algorithm. This method allows for parallel computing and effectively utilizes the advantages of multi-core processors, thereby further improving the training speed of the model.

[0176] 7. Gradient-based unilateral sampling and weighted training are beneficial for processing imbalanced datasets such as customer information. They can ensure that the model better learns information from minority categories, improve the model's training effectiveness and generalization performance. In bank credit card approval scenarios, they can help accurately identify high-risk scenarios and reduce credit risk.

[0177] It should be noted that the customer information approval method provided by the present invention is applied to the credit approval of bank customers, which is only a preferred embodiment and is not intended to limit the specific application of the present invention. That is, the customer information approval method provided in the embodiment of the present invention can also be applied to scenarios such as approval of other physical enterprises.

[0178] Based on the above customer information approval method, the present invention also provides a customer information approval device. Figure 11 The device is described in detail.

[0179] Figure 11 The structure block diagram of the customer information approval device according to an embodiment of the present invention is schematically shown.

[0180] like Figure 11 As shown, the customer information approval device 1100 according to this embodiment includes a target single customer value calculation module 1110 , a customer value level acquisition module 1120 , a pass rate prediction result output module 1130 and an approval result output module 1140 .

[0181] The target single customer value calculation module 1110 can be used to obtain information about pending customers and calculate the target single customer value of the pending customers based on the information. In one embodiment, the target single customer value calculation module 1110 can be used to perform operation S210 described above, which will not be repeated here.

[0182] The customer value level acquisition module 1120 can be used to input the target single customer value into the customer value model to perform customer value classification and output a customer value level. In one embodiment, the customer value level acquisition module 1120 can be used to perform the operation S220 described above, which will not be repeated here.

[0183] The pass rate prediction result output module 1130 can be configured to, in response to the customer value rating being high, input the pending customer information and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate for customer information approval and output a pass rate prediction result. In one embodiment, the pass rate prediction result output module 1130 can be configured to perform operation S230 described above, which will not be further described here.

[0184] The approval result output module 1140 can be used to approve the client to be approved based on the pass rate prediction result and output the approval result. In one embodiment, the approval result output module 1140 can be used to perform the operation S240 described above, which will not be repeated here.

[0185] According to an embodiment of the present invention, the target single customer value calculation module 1110 may include a calculation module, a first difference acquisition unit and a target single customer value acquisition unit.

[0186] The calculation module can be used to calculate the lifecycle revenue and acquisition cost of a single customer based on the information of the pending customer. In one embodiment, the calculation module can be used to perform the operation S310 described above, which will not be repeated here.

[0187] The first difference obtaining unit may be used to calculate the difference between the single customer lifetime revenue and the single customer acquisition cost to obtain a first difference. In one embodiment, the first difference obtaining unit may be used to perform the operation S320 described above, which will not be repeated here.

[0188] The target single customer value acquisition unit may be configured to use the first difference as the target single customer value. In one embodiment, the target single customer value acquisition unit may be configured to perform the operation S330 described above, which will not be described in detail here.

[0189] According to an embodiment of the present invention, the calculation module may include a single-customer full-lifecycle revenue calculation module and a single-customer acquisition cost calculation module.

[0190] According to an embodiment of the present invention, the single-customer full-lifecycle revenue calculation module may include a second difference calculation unit, a single-customer retention time calculation unit and a single-customer full-lifecycle revenue calculation unit.

[0191] The second difference calculation unit can be used to obtain the single-customer daily revenue and the single-customer daily maintenance cost, and calculate the difference between the single-customer daily revenue and the single-customer daily maintenance cost to obtain a second difference. In one embodiment, the second difference calculation unit can be used to perform operation S410 described above, which will not be repeated here.

[0192] The single-customer retention duration calculation unit can be used to obtain a customer retention curve and calculate the single-customer retention duration using the area between the customer retention curve and the coordinate axis. In one embodiment, the single-customer retention duration calculation unit can be used to perform operation S420 described above, which will not be repeated here.

[0193] The single-customer lifetime revenue calculation unit can be used to calculate the product of the second difference and the single-customer retention time to obtain the single-customer lifetime revenue. In one embodiment, the single-customer lifetime revenue calculation unit can be used to perform operation S430 described above, which is not repeated here.

[0194] According to an embodiment of the present invention, the single customer acquisition cost calculation module may include a total customer acquisition cost and new customer quantity acquisition unit and a single customer acquisition cost acquisition unit.

[0195] The total customer acquisition cost and new customer number acquisition unit can be used to obtain the total customer acquisition cost and the number of new customers in the target time period. In one embodiment, the total customer acquisition cost and new customer number acquisition unit can be used to perform the operation S510 described above, which will not be repeated here.

[0196] The single customer acquisition cost acquisition unit can be used to calculate the quotient of the total customer acquisition cost and the number of new customers to obtain the single customer acquisition cost. In one embodiment, the single customer acquisition cost acquisition unit can be used to perform the operation S520 described above, which will not be repeated here.

[0197] According to an embodiment of the present invention, the customer value level acquisition module 1120 may include an RFM score calculation unit and a customer value level acquisition unit.

[0198] The RFM score calculation unit may be configured to calculate the RFM score of the pending customer based on the target single customer value. In one embodiment, the RFM score calculation unit may be configured to perform the operation S610 described above, which will not be described in detail herein.

[0199] The customer value level acquisition unit may be configured to classify the RFM scores of the pending customers into customer value categories based on a preset threshold value and output a customer value level. In one embodiment, the customer value level acquisition unit may be configured to perform operation S620 described above, which will not be described in detail herein.

[0200] According to an embodiment of the present invention, the pass rate prediction result output module 1130 may include a preprocessing and feature engineering module, a prediction unit, and a prediction result output unit.

[0201] The preprocessing and feature engineering module can be used to preprocess and feature engineer the information of the pending customers and the target single customer value to obtain a discretized customer information feature histogram. In one embodiment, the preprocessing and feature engineering module can be used to perform operation S710 described above, which will not be repeated here.

[0202] The prediction unit may be configured to input the customer information feature histogram into N decision trees, each of which includes at least one leaf node, and obtain prediction results from the N decision trees based on the leaf nodes, where N is a positive integer. In one embodiment, the prediction unit may be configured to perform operation S720 described above, which will not be further described here.

[0203] The prediction result output unit can be used to select the prediction result with the largest number of occurrences as the pass rate prediction result based on the prediction results of the N decision trees by voting or majority vote. In one embodiment, the prediction result output unit can be used to perform operation S730 described above, which will not be repeated here.

[0204] According to an embodiment of the present invention, the preprocessing and feature engineering module may further include a feature selection unit, a feature bundling unit and a feature histogram construction unit.

[0205] The feature selection unit can be used to clean the information of the pending customer and the target single customer value, perform feature selection based on expert experience, and obtain preliminary customer features. In one embodiment, the feature selection unit can be used to perform operation S810 described above, which will not be repeated here.

[0206] The feature bundling unit may be configured to use a mutually exclusive bundling algorithm to perform bundling processing on the high-dimensional sparse features in the customer preliminary features to obtain customer bundled features. In one embodiment, the feature bundling unit may be configured to perform operation S820 described above, which will not be described in detail here.

[0207] The feature histogram construction unit may be configured to construct a discretized feature histogram from the customer bundling features using a histogram algorithm to obtain a customer information feature histogram. In one embodiment, the feature histogram construction unit may be configured to perform operation S830 described above, which will not be described in detail herein.

[0208] According to an embodiment of the present invention, the customer information approval device 1100 may further include a training set establishment module.

[0209] According to an embodiment of the present invention, the training set establishment module may include a new customer application screening module, an association unit and a training set establishment unit.

[0210] The new customer application screening module can be used to screen new customer applications using the new customer personal credit card approval record table and the customer credit card issuance record table. In one embodiment, the new customer application screening module can be used to perform the operation S910 described above, which will not be repeated here.

[0211] The association unit may be used to associate the customer ID in the new customer application with the customer profile asset. In one embodiment, the association unit may be used to perform operation S920 described above, which will not be repeated here.

[0212] The training set establishment unit may be used to establish a training set for model training based on the content of the full information association. In one embodiment, the training set establishment unit may be used to perform the operation S930 described above, which will not be repeated here.

[0213] According to an embodiment of the present invention, the new customer application screening module may include a judgment unit, a first processing unit and a second processing unit.

[0214] The determination unit may be configured to determine whether a same customer association can be established based on the customer number in the new customer personal credit card approval record table and the customer credit card issuance record table. In one embodiment, the determination unit may be configured to perform operation S1010 described above, which will not be repeated here.

[0215] The first processing unit may be configured to, if the same customer association establishment fails, use the approval record in the new customer personal credit card approval record table corresponding to the customer number as the new customer application. In one embodiment, the first processing unit may be configured to perform operation S1020 described above, which will not be repeated here.

[0216] If the same-customer association is successfully established, the second processing unit may be configured to retrieve the first credit card issuance date corresponding to the customer number from the customer credit card issuance record table, and use the approval record corresponding to the customer number that is no later than the first credit card issuance date as the new customer application. In one embodiment, the second processing unit may be configured to perform operation S1030 described above, which will not be further described here.

[0217] According to an embodiment of the present invention, any multiple modules among the target single customer value calculation module 1110, the customer value level acquisition module 1120, the pass rate prediction result output module 1130, and the approval result output module 1140 can be combined into a single module for implementation, or any one of them can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in a single module. According to an embodiment of the present disclosure, at least one of the target single customer value calculation module 1110, the customer value level acquisition module 1120, the pass rate prediction result output module 1130, and the approval result output module 1140 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware through any other reasonable method of integrating or packaging circuits, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the target single customer value calculation module 1110, the customer value level acquisition module 1120, the pass rate prediction result output module 1130 and the approval result output module 1140 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0218] Figure 12 The block diagram schematically shows an electronic device suitable for the customer information approval method according to an embodiment of the present invention.

[0219] like Figure 12 As shown, the electronic device 1200 according to an embodiment of the present invention includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1202 or a program loaded from a storage portion 1208 into a random access memory (RAM) 1203. The processor 1201 may, for example, include a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1201 may also include an onboard memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0220] RAM 1203 stores various programs and data required for the operation of electronic device 1200. Processor 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. Processor 1201 executes the programs in ROM 1202 and / or RAM 1203 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the programs may also be stored in one or more memories other than ROM 1202 and RAM 1203. Processor 1201 may also execute the programs stored in the one or more memories to perform various operations according to the method flow of the embodiment of the present invention.

[0221] According to an embodiment of the present invention, electronic device 1200 may further include an input / output (I / O) interface 1205, which is also connected to bus 1204. Electronic device 1200 may further include one or more of the following components connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 1208 including a hard disk; and a communication section 1209 including a network interface card such as a LAN card or modem. Communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 1210 as needed, so that computer programs read from the removable media can be installed into storage section 1208 as needed.

[0222] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0223] According to an embodiment of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, a computer-readable storage medium may include the ROM 1202 and / or RAM 1203 described above and / or one or more memories other than ROM 1202 and RAM 1203.

[0224] The embodiments of the present invention further include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to cause the computer system to implement the method provided by the embodiments of the present invention.

[0225] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when executed by the processor 1201. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0226] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 1209, and / or installed from the removable medium 1211. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0227] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1209 and / or installed from the removable medium 1211. When the computer program is executed by the processor 1201, the above-described functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0228] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, Python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect via the Internet).

[0229] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0230] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A customer information approval method, characterized in that: The method comprises: Obtaining information of customers to be approved, and calculating the target single customer value of the customers to be approved based on the information of the customers to be approved; Inputting the target single customer value into the customer value model to classify the customer value and output the customer value level; In response to the customer value level being high, inputting the information of the customer to be approved and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate of customer information approval, and outputting a pass rate prediction result; and Approving the customers to be approved based on the pass rate prediction result and outputting the approval result; Calculating the target single customer value of the customer to be approved based on the information of the customer to be approved specifically includes: Based on the information of the pending customers, calculate the lifetime revenue and acquisition cost of a single customer; Calculating the difference between the single-customer lifetime revenue and the single-customer acquisition cost to obtain a first difference; and Using the first difference as the target single customer value; The calculation of the lifetime revenue of a single customer specifically includes: Obtain the daily revenue of a single customer and the daily maintenance cost of a single customer, calculate the difference between the daily revenue of a single customer and the daily maintenance cost of a single customer, and obtain a second difference: Obtaining a customer retention curve, and calculating the single customer retention duration using the area between the customer retention curve and the coordinate axis; and Calculate the product of the second difference and the single customer retention time to obtain the single customer lifetime revenue; Calculate the cost of acquiring a single customer, including: Obtain the total customer acquisition cost and number of new customers during the target time period; and The quotient of the total customer acquisition cost and the number of new customers is calculated to obtain the single customer acquisition cost.

2. The method according to claim 1, characterized in that The pass rate prediction model is trained by the LightGBM model; The step of inputting the information of the customer to be approved and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate of customer information approval and outputting the pass rate prediction result specifically includes: Preprocessing and feature engineering the information of the pending customers and the target single customer value to obtain a discretized customer information feature histogram; Inputting the customer information feature histogram into N decision trees, each of the N decision trees including at least one leaf node, and obtaining prediction results of the N decision trees based on the leaf nodes, where N is a positive integer; and Based on the prediction results of the N decision trees, the prediction result with the largest number of occurrences is selected as the pass rate prediction result by voting or majority vote.

3. The method according to claim 2, characterized in that The preprocessing and feature engineering of the information of the pending customers and the target single customer value to obtain a discretized customer information feature histogram specifically includes: Perform data cleaning on the information of the pending customers and the target customer value, perform feature selection based on expert experience, and obtain preliminary customer features; Using a mutually exclusive bundling algorithm, bundling high-dimensional sparse features in the customer preliminary features to obtain customer bundled features; and A histogram algorithm is used to construct a discretized feature histogram of the customer bundling features to obtain a customer information feature histogram.

4. The method according to claim 2 or 3, characterized in that The pass rate prediction model is trained by the LightGBM model, specifically including: A leaf-wise algorithm is used to generate a decision tree, wherein the leaf-wise algorithm is used to recursively process the splitting of the leaf nodes and the depth of the nodes in the N decision trees until a stopping condition is met.

5. The method according to claim 4, characterized in that The pass rate prediction model is trained by the LightGBM model, specifically including: Use the new customer personal credit card approval record form and customer credit card issuance record form to screen new customer applications; Based on the customer number in the new customer application, fully associate the customer profile asset; and Based on the content of the full information association, a training set for model training is established.

6. The method according to claim 5, characterized in that The use of the new customer personal credit card approval record form and the customer credit card issuance record form to screen new customer applications specifically includes: Based on the customer number in the new customer's personal credit card approval record table, corresponding to the customer credit card issuance record table, determining whether a same customer association can be established; If the same customer association establishment fails, the approval record in the new customer personal credit card approval record table corresponding to the customer number is used as the new customer application; and If the same customer association is successfully established, the first credit card issuance date corresponding to the customer number is obtained from the customer credit card issuance record table, and the approval record corresponding to the customer number that is no later than the first credit card issuance date is used as the new customer application.

7. The method according to claim 1, characterized in that The customer value model is an RFM model; Inputting the target single customer value into the customer value model to perform customer value classification and output the customer value level specifically includes: Calculating the RFM score of the customer to be approved based on the target single customer value; and Based on a preset threshold, the RFM scores of the customers to be approved are classified into customer value categories, and a customer value level is output.

8. A customer information approval device, used to execute the method according to any one of claims 1 to 7, characterized in that: The device comprises: A target single customer value calculation module is used to: obtain information of customers to be approved, and calculate the target single customer value of the customers to be approved based on the information of the customers to be approved; A customer value level acquisition module is used to: input the target single customer value into the customer value model to perform customer value classification and output the customer value level; A pass rate prediction result output module is configured to: in response to the customer value level being high, input the information of the customer to be approved and the target single customer value into a pre-trained pass rate prediction model to predict the pass rate of customer information approval, and output a pass rate prediction result; and The approval result output module is used to: approve the customers to be approved based on the pass rate prediction result and output the approval result.

9. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to perform the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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