Credit customer screening method, device and equipment, medium and product

Abstract

The invention discloses a point customer screening method, device and equipment, a medium and a product. The method comprises the steps of setting at least one to-be-screened customer in response to a selection operation of a user, and obtaining to-be-screened customer information of each to-be-screened customer; screening the clients to be screened according to the information of the clients to be screened and a preset screening rule to obtain a target client list, the screening rule comprising a client standard, a card type requirement, a total consumption threshold and a proportion threshold; and obtaining target customer information of each target customer in the target customer list, and sending the target customer information matched with each target customer to the user, so that the user formulates a point advance rule for the target customer based on the target customer information. According to the technical scheme of the invention, the method can achieve the screening of the point customers, and improves the screening efficiency and accuracy of the point customers.

Description

Technical Field

[0001] This invention relates to the field of financial technology, and in particular to a method, apparatus, equipment, medium and product for screening customers with points-based credits. Background Technology

[0002] In the digital transformation of the fintech and retail industries, points programs have become a core tool for companies to build customer loyalty systems. While traditional points systems can incentivize users by converting spending into points, they face significant technical bottlenecks in accurately identifying high-value customers and implementing differentiated operational strategies.

[0003] Existing points systems typically use single dimensions (such as spending amount or frequency) or static rules (such as "accumulated points > 1000 points") to filter customers, lacking dynamic analysis of customer behavior, preferences, and lifecycle. Current systems rely on batch processing mechanisms, failing to trigger immediate filtering and response based on real-time customer behavior (such as sudden large purchases or cross-channel activity). For example, a customer might reach the pre-paid points threshold through frequent spending during a certain period, but the system might fail to recognize this in time due to data delays, missing the optimal opportunity to guide the customer to upgrade their membership. Furthermore, existing filtering rules do not integrate data on customer attributes (such as age and occupation), consumption scenarios (such as online / offline), and points usage habits (such as preference for redeeming physical goods or services). For instance, younger customers prefer virtual benefits, but the filtering mechanism is not optimized accordingly; corporate and individual customers have significantly different needs, but the points policy is not tiered. This "lack of labeling" leads to a disconnect between filtering results and actual customer needs, resulting in a points conversion rate of less than 20%.

[0004] In summary, existing methods for screening customers using points systems suffer from both low screening efficiency and low screening accuracy. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, medium, and product for screening customers with points, which can solve the problems of low screening efficiency and low screening accuracy in existing methods for screening customers with points.

[0006] In a first aspect, embodiments of the present invention provide a method for screening loyalty program customers, the method comprising:

[0007] In response to the user's selection action, at least one customer to be filtered is set, and the customer information of each customer to be filtered is obtained;

[0008] Based on the information of each customer to be screened and the preset screening rules, the target customer list is obtained by screening each customer to be screened. The screening rules include: customer criteria, card type requirements, total consumption threshold and percentage threshold.

[0009] The target customer information of each target customer in the target customer list is acquired, and the target customer information respectively matched with each target customer is sent to the user, so that the user formulates the integral pre-withdrawal rule for the target customer based on the target customer information.

[0010] In a second aspect, an embodiment of the present application provides a screening device for integral customers, which comprises:

[0011] An information acquisition module is configured to set at least one to-be-screened customer in response to a selection operation of the user, and acquire to-be-screened customer information of each to-be-screened customer;

[0012] A list generation module is configured to perform a screening operation on each to-be-screened customer according to the to-be-screened customer information and a preset screening rule, to obtain a target customer list, wherein the screening rule comprises a customer standard, a card type requirement, a consumption total threshold, and a proportion threshold.

[0013] An information sending module is configured to acquire target customer information of each target customer in the target customer list, and send the target customer information respectively matched with each target customer to the user, so that the user formulates the integral pre-withdrawal rule for the target customer based on the target customer information.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, which comprises:

[0015] at least one processor; and

[0016] a memory in communication with the at least one processor; wherein

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the integral customer screening method according to any one of the embodiments of the present application.

[0018] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to execute the integral customer screening method according to any one of the embodiments of the present application.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product, which comprises a computer program, and the computer program is used to enable a processor to execute the integral customer screening method according to any one of the embodiments of the present application.

[0020] The technical scheme of the embodiment of the present application sets at least one to-be-screened customer in response to a selection operation of a user, acquires to-be-screened customer information of each to-be-screened customer, then performs a screening operation on each to-be-screened customer according to each to-be-screened customer information and a preset screening rule to obtain a target customer list, finally acquires target customer information of each target customer in the target customer list, and sends the target customer information matched with each target customer to the user, so that the user formulates an integral advance rule for the target customer based on the target customer information, thereby solving the problem of low screening efficiency and screening accuracy of the existing integral customer screening method, realizing screening of integral customers, and improving the screening efficiency and screening accuracy of integral customers.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is a flowchart of an integral customer screening method according to the first embodiment of the present application;

[0024] Figure 2 is a flowchart of an integral customer screening method according to the second embodiment of the present application;

[0025] Figure 3 is a structural schematic diagram of an integral customer screening device according to the third embodiment of the present application;

[0026] Figure 4 is a structural schematic diagram of an electronic device for implementing an integral customer screening method according to the present application. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and in the above description of the drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment one

[0030] Figure 1 A flowchart of a screening method for an integral customer is provided for the first embodiment of the present application. The present embodiment can be applied to the screening of integral customers in a customer group. The method can be executed by an integral customer screening device, which can be realized in the form of hardware and / or software. The integral customer screening device can be configured in a terminal or server with integral customer screening function.

[0031] As shown in Figure 1 , the method comprises:

[0032] S110, in response to the user's selection operation, set at least one customer to be screened, and obtain the customer information of each customer to be screened.

[0033] The customer information to be screened includes the registration date, customer number, customer name, card type, total annual consumption and total consumption of one type of the customer to be screened.

[0034] The selection operation includes but is not limited to circle selection, keyword search or condition combination screening operation.

[0035] Further, the registration date refers to the specific timestamp when the customer information is included in the integral advance screening process, which is used to record the timeliness of the data and the screening batch; the customer number is the unique identity given by the bank to the customer, which is associated with the customer's whole life cycle data; the customer name is the real name reserved by the customer in the bank, which is used for manual verification and accurate touch; the card type held refers to the bank cards held by the customer, including but not limited to savings cards, credit cards, joint cards, etc., and different card types correspond to different integral rules; the total annual consumption refers to the total amount of consumption transactions generated by the customer through all channels in a specified historical year, including online shopping, offline card swiping, transfer payment and other types of consumption scenarios, wherein the specified historical year is modified and set by the user according to the actual implementation, and this embodiment does not limit it; the total consumption of the first type of consumption is the total amount of consumption that can accumulate points, which is used to evaluate the matching degree of the customer's consumption structure and the integral policy.

[0036] In one specific implementation scenario of the embodiment, the to-be-screened customer information is encrypted and stored by using the block chain storage technology, specifically including: the registration date is automatically generated by the system and is chained and fixed, which is used to record the timeliness of the data and the screening batch, and prevent human tampering; the customer number is encrypted and indexed with the customer's whole life cycle data through a hash algorithm, which supports fast data retrieval in high-concurrency scenarios; the customer name is manually verified by combining OCR optical character recognition technology and biometric verification during the acquisition process, which improves the efficiency of customer identity verification; the card type held is automatically matched with the differential integral rule through the smart contract algorithm, and the card type information is synchronized with the bank core system in real time to ensure the consistency of rule execution; the total annual consumption is obtained by using distributed transaction processing technology to ensure the consistency of cross-channel data; the total consumption that can accumulate points extracted by the convolutional neural network after semantic analysis of the consumption details is used to evaluate the matching degree of the customer's consumption structure and the integral policy, and reduce the cost of manual rule maintenance.

[0037] S120, screening each to-be-screened customer according to each to-be-screened customer information and a preset screening rule to obtain a target customer list.

[0038] The screening rule includes: customer standard, card type requirement, consumption total threshold and proportion threshold.

[0039] It should be noted that the specific content of the screening rule can be dynamically generated by the developer through a machine learning model; further, the machine learning model is trained based on historical screening data, which is used to optimize the resource consumption and screening efficiency in the screening process.

[0040] S130, obtaining target customer information of each target customer in the target customer list, and sending the target customer information respectively matched with each target customer to the user, so that the user formulates the integral pre-withdrawal rule for the target customer based on the target customer information.

[0041] Optionally, after obtaining the target customer information of each target customer in the target customer list and sending the target customer information respectively matched with each target customer to the user, the method further comprises: in response to an auditing operation of the user on the target customer information, obtaining a target integral amount input by the user; generating a user agreement based on the target integral amount and a preset agreement template and sending the user agreement to the target customer, so that the target customer performs an agreement signing operation based on the user agreement; in response to the agreement signing operation of the user, performing an integral distribution operation based on the target integral amount to the target customer; continuously obtaining an integral consumption condition of the target customer within a preset integral update period, obtaining an integral pre-withdrawal amount deduction condition table matched with the target customer based on the integral consumption condition and the target customer information of the target customer and sending the integral pre-withdrawal amount deduction condition table to the user, so that the user updates the integral pre-withdrawal rule of the target customer based on the integral pre-withdrawal amount deduction condition table.

[0042] Optionally, in the embodiment, the user agreement can also be generated by a smart contract generation module, the smart contract generation module is implemented based on blockchain technology, and is used to ensure the security and non-tamperability of the agreement generation and signing process.

[0043] Specifically, in response to the user's review operation on the target customer information through the bank system terminal (such as PC credit management platform or mobile bank management background), the system captures the target credit amount input by the user in real time, and the target credit amount is a pre-withdrawal credit value determined by the user according to the historical consumption characteristics of the target customer and the business strategy. Further, the system automatically generates a personalized user agreement based on the target credit amount and a preset agreement template, wherein the agreement template includes standardized terms (such as pre-withdrawal credit validity period, consumption behavior requirements, default deduction rules) and dynamic fields (such as customer name, pre-withdrawal credit, agreement period). The generated agreement is sent to the designated terminal of the target customer through an encrypted channel, and the customer can complete the agreement signing operation through the electronic signature function, and the system synchronously records the signing time and electronic voucher. Further, when the customer completes the agreement signing, the system triggers the credit issuance mechanism and performs real-time account operation based on the target credit amount to the customer's credit account. Within the preset credit update period, the system continuously obtains the credit consumption data of the target customer through the data interface to generate a structured credit pre-withdrawal credit deduction table. The credit pre-withdrawal credit deduction table includes fields such as: the amount of pre-withdrawal credit issued, the amount of credit deducted, the amount of credit to be deducted, the amount of consumption corresponding to the amount of credit to be deducted, etc. The system pushes the credit pre-withdrawal credit deduction table to the user terminal for business personnel to analyze the progress of customer agreement completion.

[0044] In one specific implementation scenario of the embodiment, in response to the user initiating an audit operation through the bank system terminal, the system captures the target credit amount input by the user in real time through the WebSocket protocol. The amount value is encrypted by the front-end encryption component and forwarded to the distributed micro-service cluster through the gateway. In the micro-service architecture, the credit decision service automatically checks the reasonableness of the credit based on the customer 360-degree portrait analysis engine, combined with historical consumption characteristic data and real-time business strategy configuration, to form a final advance credit plan. The historical consumption characteristic data can be a consumption stability index matched with the target customer calculated by a random forest algorithm. Then, based on the target credit amount, the intelligent document generation engine is called to select the matching standard clauses from the protocol template library stored in the blockchain, and fill in the dynamic fields through natural language generation technology. The generated document embeds the customer identity identifier using digital watermark technology and is pushed to the customer's designated terminal through a pre-set encrypted channel. The customer signature adopts biometric recognition technology combined with timestamp service to ensure the validity of the signature. The signed agreement is automatically stored in the distributed file system, and the storage hash value is recorded on the alliance chain. When the customer completes the agreement signing, the system triggers the credit issuance process through the message queue. The credit engine performs the credit entry operation based on the distributed transaction framework, and avoids duplicate issuance through idempotency design. After the entry is successful, real-time data is synchronized to the data lake, and the credit balance data is updated through the cache warm-up mechanism. At the preset credit update period, the system starts the scheduled batch processing task, collects the credit consumption flow from each business system through the preset data extraction tool, and imports it into the distributed computing platform after data cleaning. Based on the user portrait label system and consumption behavior clustering model of the target user, the distributed computing platform generates a multi-dimensional credit advance credit deduction table. Finally, the system converts the credit advance credit deduction table into an interactive dashboard through the visualization engine, and the system automatically triggers the RPA (Robotic Process Automation) robot to perform the following operations: 1) generate a prompt message through NLP (Natural Language Processing) technology; 2) call the outbound system for voice reminders; 3) push marketing messages to the client. All operation records are stored in the time series database (InfluxDB) to support subsequent effect evaluation.

[0045] It should be noted that the preset credit update period is modified and set by the user according to the actual implementation, and the embodiment does not limit this.

[0046] On the basis of the above steps, in response to the user's protocol signing operation, after performing the point issuance operation to the target customer based on the target point amount, the method further comprises: detecting that a preset first update time point is reached, obtaining the current point balance of the target customer, and after judging that the current point balance is not zero, generating and sending the point information push matching the target customer based on the current point balance and the pre-configured robot flow to the target customer; detecting that a preset second update time point is reached, obtaining the current point balance of the target customer, and after judging that the current point balance is not zero, generating and sending the alarm information to the target customer.

[0047] The first update time point and the second update time point are modified and set by the user according to actual implementation, and the present embodiment does not limit this.

[0048] Specifically, when it is detected that the preset first update time point is reached, the system obtains the current point balance of the target customer in real time through a bank core system interface, and the current point balance is the remaining value after deducting the used part from the pre-borrowed point amount. If it is judged that the current point balance is not zero, the robot process automation (RPA) technology module is triggered, and the personalized point information push is generated according to the pre-configured template. The push content includes: the current point balance, the corresponding remaining consumption amount, the consumption scenario distribution corresponding to the used points, and the point use suggestion based on the historical consumption preference of the customer, etc. The push reaches the customer through channels such as mobile banking APP pop-up window, SMS or WeChat public number, etc. When it is detected that the preset second update time point is reached, the system obtains the current point balance of the target customer again. If the balance is still not zero, an alarm information is generated. The alarm information includes: the remaining point amount, the distance to the agreement expiration date, the uncompleted consumption amount, and the breach consequence prompt. The alarm is sent through strong reminder channels such as forced pop-up window and high-frequency SMS. The above mechanism realizes full-process automation through RPA technology, without human intervention, which not only improves the customer service efficiency, but also reduces the point idle rate and breach risk through phased reminders. For example, a certain customer receives the point use suggestion at the first update time point and adjusts the consumption behavior in time to complete the amount deduction; if there is no response, the customer is forced to be reminded at the second update time point, so as to avoid wasting points or breach due to negligence, thereby optimizing the customer point use experience and strengthening the guiding effect of the bank on the consumption behavior.

[0049] Optionally, when it is detected that the preset second update time point is reached, the system again acquires the current balance of the target customer. If the balance is still not zero, an alarm information is generated, including: when it is detected that the preset second update time point is reached, acquiring the current balance of the target customer through the risk early warning system, and after judging that the current balance is not zero, generating an alarm information and sending it to the target customer; wherein the risk early warning system is realized based on real-time data stream processing technology, and is used to improve the timeliness and accuracy of the integral monitoring.

[0050] Further, in response to the user's agreement signing operation, after performing the integral distribution operation based on the target integral quota to the target customer, it further includes: after detecting that a preset time period has elapsed, acquiring the integral consumption data of the target customer within the time period, and generating a consumption style table matched with the target customer based on the integral consumption data; inputting the consumption style table into a pre-trained expert system model to obtain a target customer portrait matched with the target customer; sending the consumption style table and the target customer portrait to the user, so that the user updates the integral pre-withdrawal rule of the target customer based on the consumption style table and the target customer portrait.

[0051] Wherein, the preset time period is modified and set by the user according to the actual implementation, and the present embodiment does not limit it.

[0052] Specifically, when detecting that a preset time period has elapsed, the system automatically captures the target customer's integral consumption data in the period through the bank transaction database. The data dimensions of the integral consumption data include: the total consumption amount of integral collection behavior, the total consumption amount of non-integral collection behavior, consumption frequency, consumption scene distribution, integral use progress, etc. For example, a certain customer from January to December 2025, the total integral consumption amount is 8000 yuan, the total non-integral consumption amount is 2000 yuan, and the integral deduction rate is 80%. Based on the above data, the system generates a standardized consumption style table. The consumption style table adds new evaluation dimensions of stickiness degree and flexibility degree on the basis of the above integral consumption data: the stickiness degree reflects the customer's compliance with the bank's integral policy, and is divided according to indicators such as integral deduction rate and agreement completion rate; the flexibility degree embodies the adaptability of the customer's consumption behavior to the integral rules, and is divided according to indicators such as consumption scene diversity and emerging business acceptance. It should be noted that those skilled in the art should understand that calculating the stickiness degree and flexibility degree evaluation dimensions of the customer according to the customer's historical consumption records is a mature prior art, and the calculation method thereof will not be described here. Subsequently, the system inputs the consumption style table into a pre-trained expert system model. The expert system model is constructed based on historical consumption data and business expert rules, and automatically matches customer consumption characteristics and a preset label system through technologies such as decision tree and rule engine, and outputs the target customer portrait. For example, the model determines a certain customer as a “high-priority customer” according to the rule of “high stickiness degree (duty rate ≥ 90%) and flexible flexibility (covering ≥ 4 types of consumption scenes)”; if the “stickiness degree is medium and the flexibility degree is not flexible”, it is determined as a “medium-priority customer”. Finally, the system sends the consumption style table and the target customer portrait in the form of a visual report (such as a dynamic chart, a data dashboard) to a user terminal (such as a customer manager work platform), and business personnel can adjust the integral advance rules based on this information: increasing the advance limit for high-priority customers (such as from 10,000 points to 15,000 points) or extending the agreement period; for low-stickiness customers, additional incentives (such as additional gift point deduction coupons) or tightened advance conditions (such as increasing the total consumption threshold). For example, a certain customer portrait shows “low stickiness and inflexible flexibility”, and the business personnel can push the “specified dining scene consumption enjoys double points” rule to guide them to increase integral consumption and improve agreement completion rate.

[0053] The technical scheme of the embodiment of the present application sets at least one to-be-screened customer in response to a selection operation of a user, acquires to-be-screened customer information of each to-be-screened customer, then performs a screening operation on each to-be-screened customer according to the to-be-screened customer information and a preset screening rule, obtains a target customer list, finally acquires target customer information of each target customer in the target customer list, and sends the target customer information matched with each target customer to the user, so that the user formulates an integral pre-withdrawal rule for the target customer based on the target customer information, realizes screening of integral customers, and improves the screening efficiency and screening accuracy of integral customers.

[0054] Embodiment two

[0055] Figure 2 A flowchart of a method for screening integral customers provided by the second embodiment of the present application is provided, and the present embodiment is refined based on the above-mentioned embodiment. In the present embodiment, the method for obtaining a target customer list according to to-be-screened customer information of each to-be-screened customer and a preset screening rule is refined.

[0056] As Figure 2 shown, the method comprises the following steps.

[0057] S210, at least one to-be-screened customer is set in response to a selection operation of a user, and to-be-screened customer information of each to-be-screened customer is acquired.

[0058] S220, a to-be-authenticated customer is acquired from the to-be-screened customers, and to-be-authenticated customer information of the to-be-authenticated customer is acquired.

[0059] S230, whether the customer state of the to-be-authenticated customer is normal is judged based on a customer standard in the screening rule according to the to-be-authenticated customer information of the to-be-authenticated customer.

[0060] Specifically, whether the customer state of the to-be-authenticated customer is normal is judged based on a customer standard in the screening rule according to the to-be-authenticated customer information of the to-be-authenticated customer, which comprises: inputting the to-be-authenticated customer information into a pre-trained real-time risk assessment model to obtain a customer state judgment result matched with the to-be-authenticated customer; if the customer state judgment result is normal, acquiring a periodical consumption record of the to-be-authenticated customer, and detecting the periodical consumption record through a pre-configured isolation forest algorithm to obtain a transaction state judgment result matched with the to-be-authenticated customer; and if the transaction state judgment result is normal, determining that the customer state of the to-be-authenticated customer is normal.

[0061] Exemplarily, on the basis of the above steps, first, the to-be-authenticated client information is input into a pre-trained real-time risk assessment model. The real-time risk assessment model is an intelligent analysis model constructed based on a machine learning algorithm. Through learning and training on a large amount of historical client data, the real-time risk assessment model can quickly analyze and assess the risk of newly input client information. Optionally, the real-time risk assessment model can be an XGBoost algorithm. By analyzing the registration date, client number, historical consumption record and other information of the client, the XGBoost algorithm can predict the possible risks of the client, so as to obtain a client state judgment result matched with the to-be-authenticated client, to judge whether the client state is abnormal. If the client state judgment result is normal, the periodic consumption record of the to-be-authenticated client is obtained. The periodic consumption record refers to all consumption transaction data of the client within a specific time period (such as a month, a quarter, etc.), including consumption time, consumption amount, consumption place, consumption type and other detailed information. The isolation forest algorithm is an unsupervised anomaly detection algorithm. It maps data samples into multiple random binary trees by constructing multiple random binary trees, and judges whether the samples are outliers according to the path length of the samples in the trees. The shorter the path of a sample, the greater the possibility of being judged as an outlier. For example, if a client suddenly has a large transaction that is much higher than the average historical consumption amount in a period, the transaction may be identified as an abnormal transaction through the calculation and analysis of the isolation forest algorithm, so as to obtain a transaction state judgment result matched with the to-be-authenticated client. If the transaction state judgment result is normal, that is, the consumption transaction behavior of the client in the period is in line with the normal mode and no abnormal transaction occurs, it is determined that the client state of the to-be-authenticated client is normal. Through such a multi-step and multi-algorithm combination, the client state can be more comprehensively and accurately judged, potential risk clients can be effectively identified, and business safety can be ensured.

[0062] S240, after judging that the client state of the to-be-authenticated client is normal, judging whether the to-be-authenticated client is a holding client according to the card type held by the to-be-authenticated client in the to-be-authenticated client information and the card type requirement in the screening standard.

[0063] Specifically, after judging that the customer state of the to-be-authenticated customer is normal, the holding card type in the to-be-authenticated customer information is read through a pre-configured memory calculation technology, and is matched with the card type requirement in the screening standard stored in the solid state disk. Through the use of an intelligent classification algorithm, the card type data is quickly classified and processed, and in the matching process, the memory occupation is reduced through a data compression technology, and the matching efficiency is improved. For example, if the screening rule is set as "the card type requirement is a credit card platinum card and above", the system quickly identifies the customer's holding card type through a pattern recognition algorithm, and determines that the customer is a "holding collection customer" (i.e. a customer holding a card type that can collect points) who meets the conditions; if only a savings card or a common gold card is held, the customer is excluded, and the determination result is asynchronously pushed to the related business system through a message queue to avoid system blocking caused by data transmission and to improve the smoothness of the overall business process.

[0064] S250, after judging that the to-be-authenticated customer is a holding collection customer, calculating the one-class consumption proportion of the to-be-authenticated customer based on the formula: one-class consumption proportion = total historical annual consumption amount ÷ one-class consumption amount, and judging whether the one-class consumption proportion of the to-be-authenticated customer is not less than the proportion threshold value in the screening rule.

[0065] Specifically, for the object determined as a holding collection customer, the system calculates its consumption structure index based on the formula "one-class consumption proportion = one-class consumption total amount ÷ historical annual consumption total amount x 100%". The one-class consumption total amount refers to the consumption amount that can collect points according to the point policy, and the historical annual consumption total amount refers to the total consumption of the customer in the past year. For example, the total consumption of customer B in the last year is 100,000 yuan, of which the one-class consumption total amount that can collect points is 80,000 yuan, and the one-class consumption proportion is 80%. The system compares the proportion with the proportion threshold value (such as the proportion threshold value set by a business expert = 60%) in the screening rule. If the proportion is greater than or equal to 60%, the consumption structure requirement for advance points is met, and the subsequent quota calculation link is entered; if the proportion is less than the threshold value, the customer is considered to have insufficient matching degree between the consumption behavior and the point policy, and is not included in the target customer list.

[0066] S260, if the one-class consumption proportion of the to-be-authenticated customer is not less than the proportion threshold value in the screening rule, judging whether the historical annual consumption total amount in the to-be-authenticated customer information is not less than the consumption total amount threshold value in the screening rule.

[0067] S270, if it is not less than, determining that the to-be-authenticated customer is a target customer, and returning to perform the operation of obtaining a to-be-authenticated customer from each to-be-screened customer and obtaining to-be-authenticated customer information of the to-be-authenticated customer until a termination condition is met.

[0068] The termination condition includes that the to-be-screened customer list is empty.

[0069] Specifically, if the total consumption amount meets the threshold requirement (e.g., 12000 yuan > 10000 yuan), the system automatically determines that the to-be-authenticated customer is a target customer, and triggers a loop screening mechanism: returns to perform the operation of "obtaining a to-be-authenticated customer from each to-be-screened customer, and obtaining to-be-authenticated customer information", until all to-be-screened customers complete the full-process verification. For example, the system processes customer B (total consumption amount 8000 yuan, not meeting the threshold), customer C (total consumption amount 15000 yuan, meeting the threshold), and so on, until there is no new to-be-authenticated customer.

[0070] S280, obtaining each target customer information of each target customer, and performing a list operation on each target customer information to obtain a target customer list.

[0071] On the basis of the above steps, when all target customers meeting the conditions pass through the multi-layer screening, the system starts a parallel data extraction task to extract the complete information of the customers from multiple data sources such as the integral advance amount deduction table and the customer basic information table, including the basic fields of registration date, customer number, customer name, and card type. In the data transmission process, a compression algorithm is used to compress the data volume to reduce network bandwidth consumption. After the data is loaded into the memory, columnar storage technology is used to optimize data layout, supporting efficient batch query operations. Finally, the system converts the processed data into standardized structured data through the data aggregation service in the micro-service architecture, and stores it in the distributed cache system for subsequent business processes to quickly call.

[0072] S290, obtaining target customer information of each target customer in the target customer list, and sending target customer information matched with each target customer to the user, so that the user formulates integral advance rules for the target customers based on the target customer information.

[0073] The technical scheme of the embodiment of the present application sets at least one to-be-screened customer in response to a selection operation of a user, and obtains to-be-screened customer information of each to-be-screened customer, then obtains a to-be-authenticated customer in each to-be-screened customer and obtains to-be-authenticated customer information of the to-be-authenticated customer, then judges whether the customer state of the to-be-authenticated customer is normal according to the registration date and the customer number in the to-be-authenticated customer information and based on the customer standard in the screening rule, and after judging that the customer state of the to-be-authenticated customer is normal, judges whether the to-be-authenticated customer is a holding customer according to the holding card type in the to-be-authenticated customer information and the card type requirement in the screening standard, then after judging that the to-be-authenticated customer is a holding customer, calculates the first-class consumption proportion of the to-be-authenticated customer based on the formula: first-class consumption proportion = total historical annual consumption amount ÷ first-class consumption amount, and judges whether the first-class consumption proportion of the to-be-authenticated customer is not less than the proportion threshold in the screening rule, if the first-class consumption proportion of the to-be-authenticated customer is not less than the proportion threshold in the screening rule, judges whether the total historical annual consumption amount in the to-be-authenticated customer information is not less than the consumption total threshold in the screening rule, if yes, determines that the to-be-authenticated customer is a target customer, returns to perform the operation of obtaining the to-be-authenticated customer in each to-be-screened customer and obtaining the to-be-authenticated customer information of the to-be-authenticated customer until a termination condition is met, finally obtains each target customer information of each target customer, and performs a listing operation on each target customer information to obtain a target customer list, and finally obtains the target customer information of each target customer in the target customer list and sends the target customer information matched with each target customer to the user, so that the user formulates an integral advance rule for the target customer based on the target customer information, realizes screening of the integral customer, and improves the screening efficiency and screening accuracy of the integral customer.

[0074] Embodiment three

[0075] Figure 3 A structure schematic diagram of an integral customer screening device provided by the embodiment three of the present application.

[0076] As shown in Figure 3 , the device comprises:

[0077] The information obtaining module 310 is configured to set at least one to-be-screened customer in response to a selection operation of a user, and obtain to-be-screened customer information of each to-be-screened customer;

[0078] The list generating module 320 is configured to perform a screening operation on each to-be-screened customer according to each to-be-screened customer information and a preset screening rule to obtain a target customer list, wherein the screening rule comprises: a customer standard, a card type requirement, a consumption total threshold and a proportion threshold;

[0079] The information sending module 330 is configured to acquire target customer information of each target customer in the target customer list, and send the target customer information matched with each target customer to the user, so that the user formulates the integral pre-withdrawal rule for the target customer based on the target customer information.

[0080] The technical scheme of the embodiment of the present application sets at least one to-be-screened customer in response to the selection operation of the user, acquires to-be-screened customer information of each to-be-screened customer, then performs a screening operation on each to-be-screened customer according to the to-be-screened customer information and a preset screening rule to obtain a target customer list, finally acquires target customer information of each target customer in the target customer list, and sends the target customer information matched with each target customer to the user, so that the user formulates the integral pre-withdrawal rule for the target customer based on the target customer information, thereby realizing screening of the integral customers and improving the screening efficiency and accuracy of the integral customers.

[0081] On the basis of the above embodiment, the list generation module 320 comprises:

[0082] The customer information acquisition unit is configured to acquire a to-be-authenticated customer in each to-be-screened customer, and acquire to-be-authenticated customer information of the to-be-authenticated customer;

[0083] The state judgment unit is configured to judge whether the customer state of the to-be-authenticated customer is normal based on the customer standard in the screening rule according to the to-be-authenticated customer information of the to-be-authenticated customer;

[0084] The card type judgment unit is configured to judge whether the to-be-authenticated customer is a holding customer according to the holding card type in the to-be-authenticated customer information and the card type requirement in the screening standard after judging that the customer state of the to-be-authenticated customer is normal;

[0085] The proportion calculation unit is configured to calculate a first-class consumption proportion of the to-be-authenticated customer based on the formula: first-class consumption proportion = total historical annual consumption amount ÷ first-class consumption amount, and judge whether the first-class consumption proportion of the to-be-authenticated customer is not less than a proportion threshold in the screening rule after judging that the to-be-authenticated customer is a holding customer;

[0086] The threshold judgment unit is configured to judge whether the total historical annual consumption amount in the to-be-authenticated customer information is not less than a total consumption threshold in the screening rule if the first-class consumption proportion of the to-be-authenticated customer is judged to be not less than the proportion threshold in the screening rule;

[0087] The return execution unit is configured to determine that the to-be-authenticated customer is a target customer if the total historical annual consumption amount is not less than the total consumption threshold, and return to execute the operation of acquiring the to-be-authenticated customer in each to-be-screened customer and acquiring the to-be-authenticated customer information of the to-be-authenticated customer until a termination condition is met.

[0088] a listing unit, configured to acquire target customer information of each target customer, and perform a listing operation on the target customer information to obtain a target customer list.

[0089] On the basis of the above-mentioned embodiments, the information sending module 330 is further configured to: after acquiring the target customer information of each target customer in the target customer list and sending the target customer information matched with each target customer to the user, in response to the user's auditing operation on the target customer information, acquire the target credit amount input by the user; generate a user agreement based on the target credit amount and a preset agreement template and send it to the target customer, so that the target customer performs an agreement signing operation based on the user agreement; in response to the user's agreement signing operation, perform a credit distribution operation to the target customer based on the target credit amount; continuously acquire the credit consumption of the target customer within a preset credit update period, and based on the credit consumption and the target customer information of the target customer, obtain an integral advance amount deduction situation table matched with the target customer and send it to the user, so that the user updates the credit advance rules of the target customer based on the integral advance amount deduction situation table.

[0090] On the basis of the above-mentioned embodiments, the information sending module 330 is further configured to: after performing a credit distribution operation to the target customer based on the target credit amount in response to the user's agreement signing operation, detect when the preset first update time point is reached, acquire the current credit balance of the target customer, and after determining that the current credit balance is not zero, generate credit information matched with the target customer based on the current credit balance and a pre-configured robot process automation and send it to the target customer; detect when the preset second update time point is reached, acquire the current credit balance of the target customer, and after determining that the current credit balance is not zero, generate an alarm information and send it to the target customer.

[0091] On the basis of the above-mentioned embodiments, the information sending module 330 is further configured to: after performing a credit distribution operation to the target customer based on the target credit amount in response to the user's agreement signing operation, detect when the preset first update time point is reached, acquire the current credit balance of the target customer, and after determining that the current credit balance is not zero, generate credit information matched with the target customer based on the current credit balance and a pre-configured robot process automation and send it to the target customer; detect when the preset second update time point is reached, acquire the current credit balance of the target customer, and after determining that the current credit balance is not zero, generate an alarm information and send it to the target customer.

[0092] On the basis of the above-mentioned embodiments, the state judging unit further comprises:

[0093] The risk assessment unit is configured to input the to-be-authenticated customer information into a pre-trained real-time risk assessment model to obtain a customer state judgment result matched with the to-be-authenticated customer.

[0094] The isolation forest unit is configured to, if the customer state judgment result is normal, acquire a periodic consumption record of the to-be-authenticated customer, and detect the periodic consumption record through a pre-configured isolation forest algorithm to obtain a transaction state judgment result matched with the to-be-authenticated customer.

[0095] The determination normal unit is configured to, if the transaction state judgment result is normal, determine that the customer state of the to-be-authenticated customer is normal.

[0096] The screening device for the loyalty customer provided in the embodiments of the present application can execute the screening method for the loyalty customer provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0097] Embodiment four

[0098] Figure 4 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0099] As shown in Figure 4 The electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which are communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0101] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as a method of screening loyalty customers.

[0102] Correspondingly, the method includes:

[0103] In response to a selected operation of a user, at least one to-be-screened customer is set, and to-be-screened customer information of each to-be-screened customer is obtained;

[0104] According to each to-be-screened customer information and a preset screening rule, a screening operation is performed on each to-be-screened customer to obtain a target customer list, and the screening rule includes: a customer standard, a card type requirement, a consumption total threshold, and a proportion threshold;

[0105] Target customer information of each target customer in the target customer list is obtained, and the target customer information respectively matched with each target customer is sent to the user, so that the user formulates a loyalty advance rule for the target customer based on the target customer information.

[0106] In some embodiments, a method of screening loyalty customers can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of a method of screening loyalty customers described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform a method of screening loyalty customers by any other appropriate means (for example, by means of firmware).

[0107] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0108] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0109] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0110] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0111] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0112] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0113] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present application can be achieved, and the present application is not limited herein.

Claims

1. A method for screening customers with points-based loyalty programs, characterized in that, include: In response to the user's selection action, at least one customer to be filtered is set, and the customer information of each customer to be filtered is obtained; Based on the information of each customer to be screened and the preset screening rules, the target customer list is obtained by screening each customer to be screened. The screening rules include: customer criteria, card type requirements, total consumption threshold and percentage threshold. Obtain the target customer information of each target customer in the target customer list, and send the target customer information that matches each target customer to the user so that the user can formulate the points prepayment rules for the target customer based on the target customer information.

2. The method according to claim 1, characterized in that, The information of the customers to be screened includes: the registration date, customer number, customer name, card type, total historical annual spending, and total spending in one category.

3. The method according to claim 1, characterized in that, Based on the information of each customer to be screened and the preset screening rules, the customers to be screened are screened to obtain a target customer list, including: Obtain the customers to be certified from each customer to be screened, and obtain the customer information of the customers to be certified. Based on the customer information of the customer to be certified, determine whether the customer status of the customer to be certified is normal according to the customer criteria in the filtering rules; After determining that the customer status of the customer to be certified is normal, the system determines whether the customer to be certified is a cardholder based on the card type held in the customer information and the card type requirements in the screening criteria. After determining that the customer to be certified is a purchasing customer, the consumption ratio of the customer to be certified is calculated based on the formula: Consumption ratio of Category I = Total consumption amount in historical years ÷ Consumption amount of Category I. Then, it is determined whether the consumption ratio of the customer to be certified is not less than the percentage threshold in the screening rules. If it is determined that the proportion of a certain type of consumption of the customer to be certified is not less than the proportion threshold in the filtering rules, then it is determined whether the total historical annual consumption in the information of the customer to be certified is not less than the total consumption threshold in the filtering rules. If it is not less than, then the customer to be certified is determined to be the target customer, and the operation of obtaining the customer to be certified from each customer to be screened and obtaining the customer information of the customer to be certified is performed until the termination condition is met. Obtain the target customer information for each target customer, and perform a list operation on the target customer information to obtain a target customer list.

4. The method according to claim 3, characterized in that, Based on the customer information of the customer to be certified and the customer criteria in the filtering rules, determine whether the customer status of the customer to be certified is normal, including: The customer information to be certified is input into a pre-trained real-time risk assessment model to obtain a customer status judgment result that matches the customer to be certified. If the customer status judgment result is normal, then the periodic consumption record of the customer to be certified is obtained, and the periodic consumption record is detected by the pre-configured isolated forest algorithm to obtain the transaction status judgment result matching the customer to be certified. If the transaction status assessment result is normal, then the customer status of the customer to be authenticated is determined to be normal.

5. The method according to claim 1, characterized in that, After obtaining the target customer information of each target customer in the target customer list and sending the target customer information matched with each target customer to the user, the method further includes: In response to the user's review of the target customer information, obtain the target points amount entered by the user; A user agreement is generated based on the target points amount and a preset agreement template and sent to the target customer so that the target customer can sign the agreement based on the user agreement. In response to the user's agreement signing operation, a points distribution operation is performed to the target customer based on the target points amount; Within a preset points update cycle, the points consumption information of the target customer is continuously acquired. Based on the points consumption information and the target customer information, a points pre-spending credit deduction table matching the target customer is obtained and sent to the user, so that the user can update the points pre-spending rules of the target customer based on the points pre-spending credit deduction table.

6. The method according to claim 5, characterized in that, In response to the user's agreement signing action, after performing the points distribution operation to the target customer based on the target points amount, the process further includes: When the preset first update time point is detected, the current points balance of the target customer is obtained, and after determining that the current points balance is not zero, points information matching the target customer is generated based on the pre-configured robotic process automation and the current points balance and pushed to the target customer. When the preset second update time point is detected, the current points balance of the target customer is obtained, and after determining that the current points balance is not zero, an alarm message is generated and sent to the target customer.

7. The method according to claim 5, characterized in that, In response to the user's agreement signing action, after performing the points distribution operation to the target customer based on the target points amount, the process further includes: After a preset time period has elapsed, the points consumption data of the target customer within the time period is obtained, and a consumption style table matching the target customer is generated based on the points consumption data. The consumption style table is input into a pre-trained expert system model to obtain a target customer profile that matches the target customer. The consumption style table and the target customer profile are sent to the user so that the user can update the points prepayment rules for the target customer based on the consumption style table and the target customer profile.

8. A screening device for points-based customers, characterized in that, include: The information acquisition module is used to respond to the user's selection operation to set at least one customer to be filtered, and to acquire the customer information of each customer to be filtered; The list generation module is used to filter each customer to be screened based on the information of each customer to be screened and preset filtering rules to obtain a target customer list. The filtering rules include: customer criteria, card type requirements, total consumption threshold and percentage threshold. The information sending module is used to obtain the target customer information of each target customer in the target customer list, and send the target customer information that matches each target customer to the user, so that the user can formulate the points prepayment rules for the target customer based on the target customer information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a customer screening method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements a method for screening points-based customers according to any one of claims 1-7.

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