Bill installment processing method and device, electronic equipment and storage medium

Abstract

The application discloses a bill installment processing method and device, electronic equipment and storage medium, and relates to the technical field of financial technology. The method comprises the following steps: obtaining the latest decision factor associated with the user after the user initiates a bill installment request on a client; wherein the latest decision factor comprises the latest risk level, overdue time length, credit investigation behavior and complaint data of the user; performing installment control processing on the bill installment request of the user based on the bill installment control rule configured in advance on the visual rule configuration page corresponding to the bill installment control scene and the latest decision factor associated with the user, to obtain an installment control result; and transmitting the installment control processing result to the client for visual display. The application is based on real-time dynamic decision factors, combined with flexible rules configured in a visual manner, to realize accurate and differentiated installment control; the rule configuration is visual and low in code, so that the business rules can be quickly iterated and flexibly adjusted.

Description

Technical Field

[0001] This invention relates to the field of financial technology, and in particular to a bill installment processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the rapid development of the consumer finance industry, installment billing has become one of the core products for financial institutions to expand their retail business and enhance customer loyalty.

[0003] Currently, traditional installment payment management primarily integrates installment payment rules into business systems using hard-coding. However, this approach has several drawbacks: when business scenarios (such as installment product types or risk thresholds) change, developers must modify the code and restart the service, resulting in long rule iteration cycles (typically several days to weeks), making it difficult to quickly respond to changes in the market environment or regulatory policies. Furthermore, traditional solutions rely heavily on static data from user applications (such as initial credit ratings) for installment payment management, failing to promptly identify changes in user risk during the loan process, which can easily lead to high-risk users violating regulations by applying for installment payments. Therefore, a new method for handling installment payments is urgently needed. Summary of the Invention

[0004] This invention provides a bill installment processing method, apparatus, electronic device, storage medium, and computer program product.

[0005] According to one aspect of the present invention, a bill installment processing method is provided, applied to an intelligent decision engine, comprising: When a user initiates a bill installment request on the client, the latest decision factors associated with the user are obtained; wherein, the latest decision factors include the user's latest risk level, overdue period, credit behavior and complaint data; Based on the bill installment management rules pre-configured on the visualization rule configuration page corresponding to the bill installment management scenario and the latest decision factors associated with the user, the user's bill installment request is processed for installment management to obtain the installment management result; wherein, the installment management result includes at least one of the following: installment access judgment result, prohibited installment period, and maximum installment amount; The phased management and control results are transmitted to the client for visualization.

[0006] According to another aspect of the present invention, a bill installment processing device is provided, configured in an intelligent decision engine, comprising: The data acquisition module is used to acquire the latest decision factors associated with the user after the user initiates a bill installment request on the client; wherein, the latest decision factors include the user's latest risk level, overdue period, credit behavior and complaint data; The decision module is used to process the user's bill installment request for installment control based on the bill installment control rules pre-configured on the visualization rule configuration page corresponding to the bill installment control scenario and the latest decision factors associated with the user, and to obtain the installment control result; the installment control result includes at least one of the following: installment access judgment result, prohibited installment period, and maximum installment amount; The feedback module is used to transmit the phased management and control results to the client for visualization.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the bill installment processing method of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the bill installment processing method of the present invention.

[0009] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps in the above-described method.

[0010] The technical solution of this invention constructs a fully automated processing chain for data acquisition, intelligent decision-making, and result display, significantly shortening the processing cycle of installment bill payment business and improving business processing efficiency and user experience. Based on real-time dynamic decision factors and combined with flexible rules that can be configured visually, it achieves precise and differentiated installment control, effectively identifying high-risk users and reducing the bad debt rate of financial institutions, while ensuring the reasonable installment needs of high-quality users. The rule configuration is visual and low-code, enabling rapid iteration and flexible adjustment of business rules without modifying core code, reducing technical maintenance costs, and allowing for rapid response to market changes and regulatory requirements.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a bill installment processing method provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating another bill installment processing method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a bill installment processing device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the bill installment processing method of this invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] Example 1 Figure 1 This is a flowchart of a bill installment processing method provided by an embodiment of the present invention. This embodiment can be applied to the scenario of enterprise attendance statistics. The method can be executed by a bill installment processing device, which can be implemented in hardware and / or software and can be configured in an electronic device.

[0016] like Figure 1 As shown, the bill installment processing methods include: S101. After a user initiates a bill installment request on the client, obtain the latest decision factors associated with the user.

[0017] The client refers to the terminal carrier used by the user, including electronic devices and corresponding applications that can initiate installment payment requests, such as mobile banking apps and tablet banking applications. An installment payment request is a business application submitted by the user through the client to split an outstanding bill into installments according to the agreed-upon number of periods. It includes core request data such as user identification (e.g., user ID), bill information, and installment intention. The latest decision factors refer to a real-time dynamic data set used to determine a user's installment eligibility and risk level. It is the core basis for installment management decisions and includes four categories: 1. Latest Risk Level: Risk assessment results generated based on the user's recent credit behavior (e.g., high / medium / low risk); 2. Overdue Duration: The cumulative number of days the user has not made a timely payment; 3. Credit Behavior: The user's records in the credit reporting system, including the number of credit inquiries and performance of other credit products; 4. Complaint Data: The user's recent complaint records and processing status regarding financial institutions' services.

[0018] In some embodiments, this step may be implemented as follows: the client captures the user's installment request, which carries the user's identity identifier, and sends the installment request to the intelligent decision engine through an installment application; wherein, the installment application refers to the application that processes the installment request; after receiving the request, the intelligent decision engine calls the real-time data interface of the user profile system based on the identity identifier information; the user profile system extracts the user's dynamic data from multiple data sources (credit system, credit reporting platform, complaint management system), and after real-time cleaning, deduplication, and integration, generates a set of decision factors containing the latest risk level, overdue duration, credit behavior, and complaint data; the latest decision factors are returned to the intelligent decision engine through an encrypted transmission channel and stored in a temporary cache for subsequent steps to call.

[0019] It should be noted that the latest decision factors collected are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, and necessary confidentiality measures have been taken. The data does not violate public order and good morals, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0020] The benefits of this step are: it breaks through the reliance on traditional static data, obtains real-time dynamic data of users, ensures the timeliness of decision-making basis, and solves the problem that historical data cannot reflect the current risk status of users; multi-dimensional decision factors comprehensively cover the user's credit status, avoid risk misjudgment caused by a single data dimension, and improve the accuracy of risk identification; the data collection process is completed automatically without human intervention, improving the efficiency of installment request processing and shortening user waiting time.

[0021] S102. Based on the bill installment management rules pre-configured on the visualization rule configuration page corresponding to the bill installment management scenario and the latest decision factors associated with the user, perform installment management processing on the user's bill installment request to obtain the installment management result.

[0022] The visual rule configuration page is a graphical interface provided by the intelligent decision engine management terminal. It allows administrators to configure control logic using low-code methods such as drag-and-drop, checkboxes, and threshold input, eliminating the need for coding. Installment payment control rules are based on pre-set decision logic according to business needs and risk control objectives, such as executable rules like "reject installments if overdue for ≥20 days" and "limit maximum installment amount for high-risk users." The installment control result is a decision conclusion output by the intelligent decision engine after matching rules and data, including at least one of the following: 1. Installment access judgment result: Determines whether the user is eligible for installments (approved / rejected); 2. Prohibited installment periods: The number of installments the user cannot apply for (e.g., 48 months, 60 months); 3. Maximum installment amount: The maximum installment amount the user can apply for.

[0023] In some embodiments, this step mainly involves matching the preset business control rules with the user's latest decision factors to output differentiated decision conclusions that include installment eligibility, installment period limits, and amount limits. Specifically, the intelligent decision engine reads the latest decision factors obtained in S101 from the cache; calls the pre-stored bill installment control rule set (the rules are configured and activated by the configuration personnel through the visual configuration page); the rule engine matches the decision factors with each control rule according to a preset priority order (e.g., determining whether the overdue period meets the "≥20 days" condition); if a rejection rule is matched (e.g., for high-risk users), a transaction rejection admission result is directly generated; if no rejection rule is matched, the prohibited installment period and maximum installment amount are calculated based on the rules; and the various decision results are integrated to form structured installment control result data (including result codes and explanatory information).

[0024] The benefits of this step are: visual configuration lowers the barrier to rule maintenance, allowing business personnel to adjust rules independently without relying on R&D personnel, thus solving the problem of long iteration cycles for traditional hard-coded rules; automated matching and calculation of rules and data enables millisecond-level decision response, improving the efficiency of phased business processing; and it supports differentiated control result output, avoiding a single decision mode of all approvals or all rejections, thus balancing risk control and user experience.

[0025] S103. The phased management and control results are transmitted to the client for visualization.

[0026] The phased management and control results refer to the structured decision conclusion data generated in step S102, which includes result type, constraints, explanatory information, etc., and is standardized data that can be directly used for client display. Visual display refers to the client transforming the standardized management and control results into a display format that users can intuitively understand, including text descriptions, option displays, pop-up prompts, etc.

[0027] In some embodiments, the intelligent decision engine encapsulates the installment management results according to the output parameter format configured in the access service to ensure compatibility with the client's data format; the results are returned to the installment application via an encrypted transmission protocol, and then the installment application performs adaptation processing based on the basic installment conditions (such as removing prohibited installments); the processed results are transmitted to the client, and the client presents them according to a preset display logic. For example, when access is rejected: the reason for rejection is displayed (such as "overdue days ≥ 20 days, installment cannot be processed temporarily"); when access is approved: the number of installments that can be processed (excluding prohibited installments), the maximum installment amount, and the corresponding handling fee information are displayed.

[0028] In this embodiment of the invention, a fully automated processing chain is constructed, encompassing data acquisition, intelligent decision-making, and result display. This significantly shortens the processing cycle for installment bill payment services, improving business efficiency and user experience. Based on real-time dynamic decision factors and combined with flexible rules that can be configured visually, precise and differentiated installment management is achieved. This effectively identifies high-risk users, reduces the bad debt rate of financial institutions, and ensures reasonable installment needs for high-quality users. The rule configuration is visual and low-code, enabling rapid iteration and flexible adjustment of business rules without modifying core code, reducing technical maintenance costs, and allowing for rapid response to market changes and regulatory requirements.

[0029] Example 2 Figure 2 A flowchart illustrating a bill installment processing method is provided as an embodiment of the present invention. See also... Figure 2 The method includes the following steps: S201. On the intelligent decision engine management terminal, complete the scene configuration, access service configuration, interface feature template configuration of external interface related information, and selection and marking of feature fields adapted to the scene in sequence.

[0030] In this embodiment, the bill installment management scenario is first configured based on the scenario name and scenario identifier entered by the configuration personnel in the scenario configuration page of the intelligent decision engine management terminal. The configuration personnel refer to business managers and risk control strategy personnel of financial institutions who have risk control needs for bill installment business and can perform configuration operations through the intelligent decision engine management terminal. The intelligent decision engine management terminal is the backend operation interface of the intelligent decision platform, providing configuration personnel with a visual, low-code configuration entry point, including multiple functional modules such as scenario configuration and access service configuration. Specifically, the configuration personnel log in to the intelligent decision engine management terminal with their account and password, select the scenario management module in the function menu, and click the "Add Scenario" button. In the input box of the scenario configuration page, they fill in the scenario name (which must reflect business attributes) and the scenario identifier (which must be unique and supports combinations of letters and numbers). The configuration personnel can choose whether to associate auxiliary information such as the person in charge of the scenario and the business description. After clicking "Save," the intelligent decision engine persists the scenario information to the metadata database and allocates an independent resource isolation space for the scenario to ensure that the configuration of other business scenarios (such as consumer loan management scenarios) does not interfere with each other.

[0031] Furthermore, in the aforementioned bill installment management scenario, based on the input parameter mapping and output parameter mapping entered by the configuration personnel in the access service configuration page of the management terminal, an access service configuration adapted to the bill installment management scenario is generated. The input parameter mapping refers to the mapping between the parameters passed to the intelligent decision engine (such as customer code, channel number) and the engine's internal parameter names when the installment application (including installment application and installment product application) calls the intelligent decision engine. The output parameter mapping is the mapping between the engine's internal decision result parameters (such as hit rule code, unprocessable installment number) and the parameter names required by the installment application when the intelligent decision engine returns results to the installment application. In practice, the configuration personnel access the created bill installment management scenario in the intelligent decision engine management terminal, select access service configuration in scenario association configuration, and click "Add Access Service"; configure input parameter mapping: in the input parameter configuration area, click "Add Input Parameter", and enter the parameter name and data type (such as string, number) passed from the external system, and select the corresponding internal engine parameter name (such as customer number, channel number); configure output parameter mapping: in the output parameter configuration area, click "Add Output Parameter", select the preset decision result parameters inside the engine, and enter the parameter name required by the installment application (such as hit rule code, number of installments that cannot be processed, maximum installment amount), and specify the output parameter data type (such as string, array, number); the configuration personnel can set parameter validation rules to avoid invalid parameter input; click "Save" to obtain the access service configuration. It can be understood that the access service configuration is a set of configurations used to standardize the data interaction between the installment application and the intelligent decision engine, including input parameter mapping and output parameter mapping, ensuring consistent data transmission format and field alignment.

[0032] Furthermore, based on the external interface type, interface link address, request method, interface input parameters, and interface output parameters entered by the configuration personnel in the interface feature template configuration page of the management terminal, an interface feature template is generated. Here, the external interface type refers to the interface type of the external data source, such as the interface type of the user profiling system; the interface link address refers to the access address of the user profiling system interface; the interface input parameters refer to the parameters required when calling the profiling system interface (such as customer ID); and the interface output parameters refer to all data fields returned by the profiling system interface. Specifically, in the bill installment control scenario of the intelligent decision engine management terminal, the configuration personnel select interface feature template management and click "Add Template"; then configure the core information in sequence: select the external interface type, enter the interface link address, and select the request method; configure the interface input parameters: click "Add Interface Input Parameter," and enter the input parameter name and data type required by the external interface; configure the interface output parameters: enter the names of all fields returned by the profiling system interface and specify the data type of each field.

[0033] Furthermore, in response to the configuration personnel selecting a portion of the interface output parameters as feature fields in the feature configuration page of the management terminal, the feature fields are marked as suitable for the bill installment management scenario. This step mainly involves filtering out the core fields related to the bill installment management rules from the interface output parameters configured in the previous step (excluding irrelevant fields, such as customer name, contact information, and other non-risk control related data). Specifically, in the feature management module of the intelligent decision engine management terminal, the configuration personnel select to add a new interface type feature and associate it with the interface feature template generated in the previous step; all output parameter fields configured in the interface feature template are automatically loaded, and the configuration personnel select a portion of the fields as feature fields by checking boxes; in the adaptation scenario drop-down box, the bill installment management scenario configured in the previous step is selected, and the selected feature fields are marked as scenario-specific (or multi-scenario universal, selected according to business needs); clicking save feature, the system writes the feature field information into the feature metadata table and establishes a relationship with the bill installment management scenario; when configuring rules subsequently, the configuration personnel can directly select the required data fields from the feature field list of this scenario without repeatedly connecting to external interfaces.

[0034] Based on the above configuration, when a user initiates a bill installment request on the client, the process of obtaining the latest decision factors associated with the user includes steps S202-S204: S202. When a user initiates a bill installment request on the client, the installment application is triggered to call the intelligent decision engine interface to obtain the user's installment management result in response to the bill installment request. The parameters of the bill installment request are verified by adapting the access service configuration to the bill installment management scenario.

[0035] The installment application, encompassing both installment application and installment product applications, serves as the intermediary business unit that receives client requests and connects with the intelligent decision engine. It is responsible for request forwarding, parameter processing, and result adaptation. The intelligent decision engine interface is a standardized call interface provided by the intelligent decision engine to receive management and control query requests from the installment application and return the installment management results.

[0036] In some embodiments, this step is specifically implemented as follows: After the user clicks to submit a bill installment request on the client, the client encapsulates the request parameters (user identification, installment product type, etc.) into a standardized format and sends them to the installment application (first triggering the installment application application); the installment application responds to the request, triggers the intelligent decision engine interface call through preset calling logic, and loads the pre-configured bill installment management scenario access service configuration; the installment application or intelligent decision engine performs parameter verification according to the rules of the access service configuration, mainly performing integrity verification, format verification, and legality verification; if the verification passes, the subsequent process continues; if the verification fails, the installment application directly returns an error message (such as abnormal request parameters, please retry) to the client, terminating the process.

[0037] In some embodiments, the installment application is also used to: query the user's basic installment conditions based on the user identity identifier in the bill installment request; wherein the basic installment conditions include at least the bill installment amount before control and the number of installment periods before control.

[0038] Understandably, this step can intercept invalid requests in advance (such as missing parameters or incorrect format), preventing invalid requests from entering the subsequent data query and rule matching stages, reducing the resource consumption of the intelligent decision engine and user profiling system, and improving the overall business processing efficiency.

[0039] S203. After the verification is successful, a data acquisition request is initiated to the user profiling system according to the configured interface feature template to obtain the user's profile data.

[0040] In some embodiments, after the bill installment request parameters pass verification, the intelligent decision engine automatically loads a pre-configured interface feature template (which is associated with the user profile system interface information). Based on the interface link address and request method defined in the interface feature template, the intelligent decision engine uses the user's core identity parameters as input parameters and encapsulates them into a request format that conforms to the template requirements. The intelligent decision engine initiates a data acquisition request to the user profile system through an encrypted transmission channel to ensure the security of data transmission. After receiving the request, the user profile system queries the user's necessary profile data based on the user's identity parameters (including loan reassessment risk level, maximum overdue days, credit inquiry records for the past six months, average monthly complaint count, etc.). The user profile system returns the profile data to the intelligent decision engine according to the output parameter format defined in the interface feature template. The intelligent decision engine temporarily stores the data in a temporary cache for use in subsequent steps.

[0041] Understandably, in this step, the interface feature template standardizes the calling process of the user profiling system, avoiding data acquisition failures due to inconsistent interface call parameters and formats, and ensuring the stability of data source access; it automatically initiates data requests based on preset templates without manual intervention, realizing automated and real-time data acquisition, supporting millisecond-level risk control decisions, and shortening user waiting time; the interface feature template supports flexible configuration, can quickly adapt to interface changes of different user profiling systems, and improve system scalability.

[0042] Furthermore, the process by which the user profiling system responds to a data retrieval request to obtain user profile data includes: Based on the user's identity identifier in the data acquisition request, the system queries the user's latest overdue period, credit behavior, and complaint data. Natural language processing (NLP) is used to clean the complaint data, and the cleaned data is mapped to a first risk assessment model to generate a user complaint risk score. Cleaning the complaint data may include deduplication, filtering invalid data, and extracting key information (such as complaint frequency and type). A second risk assessment model is used to determine the user's latest risk level based on the complaint risk score, overdue period, and credit behavior. The user's latest risk level, overdue period, credit behavior, and complaint data are then fed back to the intelligent decision engine.

[0043] S204. Based on the feature fields adapted to the bill installment management scenario, select the latest decision factor for the user from the user's profile data.

[0044] In some embodiments, after receiving the necessary profile data returned by the user profiling system, the intelligent decision engine loads pre-configured feature fields adapted to the bill installment management scenario (such as loan reassessment risk level, maximum overdue days, number of credit inquiries in the past 6 months, and average number of complaints per month). The intelligent decision engine then precisely filters and extracts features from the necessary profile data according to the definition of these feature fields. Specifically, it retains only feature field data related to bill installment risk control and removes all data unrelated to bill installment risk control (such as basic user information and non-risk control consumption data). The extracted feature field data undergoes validity verification (e.g., overdue days are non-negative integers, and risk level is within a preset range) to ensure data usability. The filtered feature field data is integrated into a structured set of the latest decision factors, specifying the value of each factor (e.g., loan reassessment risk level = 5, maximum overdue days = 20, average number of complaints per month = 3). This latest set of decision factors serves as input data for rule matching and is passed to the rule execution module of the intelligent decision engine for subsequent matching calculations of installment management rules.

[0045] This step can accurately filter core risk control data, eliminate irrelevant information, reduce the amount of data processing in subsequent rule matching stages, improve decision-making efficiency, and achieve millisecond-level rule calculation; it extracts data based on feature fields adapted to the scenario, ensuring that decision factors are highly consistent with bill installment management rules, avoiding risk misjudgment due to data dimension deviations, and improving the accuracy of risk control decisions; the extraction logic of decision factors is defined by feature field configuration, and the extracted data dimensions can be adjusted without modifying the code to adapt to the iterative needs of risk control rules.

[0046] In this embodiment of the invention, bill installment management rules can also be configured in advance as follows: First, in the bill installment management scenario, based on the strategy set information entered by the configuration personnel in the strategy set configuration page of the intelligent decision engine management terminal, a target strategy set adapted to the bill installment management scenario is constructed. Specifically, the configuration personnel log in to the intelligent decision engine management terminal, select the strategy set management module in the function menu, and click the "Add Strategy Set" button; on the strategy set configuration page, fill in the core information: strategy set name, strategy set code, and adapted scenario (select the created bill installment management scenario from the drop-down box to establish the binding relationship between the strategy set and the scenario); the configuration personnel click "Save," and the system persists the strategy set information to the metadata database.

[0047] Secondly, for the target strategy set, based on the strategy name and decision mode entered by the configuration personnel on the strategy configuration page of the management terminal, the corresponding target strategy is configured for the target strategy set. Specifically, the configuration personnel enter the differentiated management strategy set for installment bill customers created in the previous step on the management terminal and click the "Add Strategy" button; on the strategy configuration page, they fill in the core information (strategy name, decision mode, etc.), where the decision mode refers to the selection based on the strategy function, such as selecting the worst-case mode for the high-risk customer access interception strategy (if any rejection rule is hit, the transaction is rejected); selecting the weighted mode for the low-risk customer restriction strategy (comprehensively judging the number of periods and amount restrictions according to the rule priority); the configuration personnel can add strategy descriptions (such as implementing rejection control for customers with a risk level of 1-6 during loan reassessment and overdue for ≥20 days), and click "Save".

[0048] Finally, based on the rule name and handling suggestions entered by the configuration personnel in the rule configuration page of the management terminal, the corresponding bill installment control rules are constructed and stored for the target strategy. Specifically, the configuration personnel enter the target strategy (such as the high-risk customer access interception strategy), click the "Add Rule" button, and enter the rule configuration page; fill in the basic rule information: 1. Rule name: accurately describe the judgment logic (such as "Reject bill installment for loan reassessment levels 1-6", "Reject bill installment for overdue period ≥ 20 days"); 2. Configure rule judgment conditions: add conditions through the visual condition editor, and select the configured feature fields (such as "Loan reassessment risk level", "Maximum overdue days", "Monthly average number of complaints"); 3. Set condition logic: configure the operator and threshold directly for a single condition; multiple conditions can select AND / OR logic (such as "Overdue period ≥ 20 days and installment product type = bill installment"); 4. Configure handling suggestions: select the handling type: such as "Transaction rejection", "Limit installment period", "Limit maximum installment amount"; click save, and the system writes the rule information into the rule table and establishes the association between the target strategy and the bill installment control rules.

[0049] S205. Based on the bill installment management rules pre-configured on the visualization rule configuration page corresponding to the bill installment management scenario and the latest decision factors associated with the user, the user's bill installment request is processed for installment management to obtain the installment management result.

[0050] In some embodiments, step S205 includes: First, loading a target strategy set adapted to the bill installment management scenario, and traversing the bill installment management rules included in the target strategies under the target strategy set. Second, for each bill installment management rule traversed, determining whether the user's latest decision factors match the bill installment management rule; specifically, when traversing a single bill installment management rule, the intelligent decision engine reads the user's latest decision factors (including loan reassessment risk level, overdue duration, credit behavior, complaint data, etc.) from the temporary cache; parses the judgment conditions of the rule, and clarifies the feature fields, operators, and thresholds in the conditions (such as "maximum overdue days ≥ 20", "average number of complaints per month ≥ 3", "loan reassessment risk level ∈ [1,6]"); if the rule contains multiple The system performs multi-condition matching based on configured logical relationships (AND / OR): AND logic requires all conditions to be met (e.g., "overdue ≥ 20 days and installment product = bill installment") for a match to be considered successful; OR logic requires any one condition to be met (e.g., "loan reassessment level 1-6 or credit inquiry count ≥ 15 times") for a match to be considered successful. During the matching process, the data type and value range of the decision factor are automatically verified (e.g., overdue duration is a non-negative integer). If the data is abnormal, it is considered a failure. After each rule is matched, a match / failure status is marked for subsequent result tracking. Further, based on the latest decision factor-matched bill installment control rule, an initial installment control processing result is generated. Finally, based on the output parameter mapping of the access service, the initial installment control processing result is converted into the parameter format required by the installment application to obtain the final installment control result. Specifically, the intelligent decision engine loads the pre-configured access service output parameter mapping, clarifying the correspondence between the engine's internal parameters and the parameters required by the installment application; according to the output parameter mapping rules, it converts the fields in the initial control processing results one by one; for example, data type conversion: if the number of installments that cannot be processed internally in the engine is an array type [48,60], it is converted to the string type 48,60 supported by the installment application according to the mapping requirements; field name conversion: the standardized fields internally in the engine are converted to the field names agreed upon by the installment application; After the conversion is complete, the final installment management result is generated and returned to the installment application through an encrypted interface.

[0051] S206. The phased management and control results are transmitted to the client for visualization.

[0052] In some embodiments, if the installment control processing result is installment access rejection, the result of not being allowed to process installments is forwarded to the client for display through the installment application; or, if the installment control result includes installment access approval, prohibited installment period, and maximum installment amount, the prohibited installment period and maximum installment amount are sent to the installment application, so that the installment application generates candidate installment amounts and candidate installment periods after control based on the user's basic installment conditions, prohibited installment period, and maximum installment amount, and forwards them to the client for display.

[0053] For example, if the basic installment conditions allow for installment periods of 9, 12, 24, 36, 48, or 60 periods, and the decision engine determines that the prohibited installment periods are 48 and 60, then the installment periods returned by the installment application to the bank's client will exclude 48 and 60.

[0054] In this embodiment of the invention, a fully automated processing chain is constructed, encompassing data acquisition, intelligent decision-making, and result display. This significantly shortens the processing cycle for installment bill payment services, improving business efficiency and user experience. Based on real-time dynamic decision factors and combined with flexible rules that can be configured visually, precise and differentiated installment management is achieved. This effectively identifies high-risk users, reduces the bad debt rate of financial institutions, and ensures reasonable installment needs for high-quality users. The rule configuration is visual and low-code, enabling rapid iteration and flexible adjustment of business rules without modifying core code, reducing technical maintenance costs, and allowing for rapid response to market changes and regulatory requirements.

[0055] Example 3 Figure 3 This is a schematic diagram of a bill installment processing device provided in an embodiment of the present invention. This device is configured in an intelligent decision engine and can execute any of the bill installment processing methods of the present invention. Figure 3 As shown, the bill installment processing device includes: The data acquisition module 301 is used to acquire the latest decision factors associated with the user after the user initiates a bill installment request on the client; wherein, the latest decision factors include the user's latest risk level, overdue period, credit behavior and complaint data; Decision module 302 is used to perform installment control processing on the user's bill installment request based on the bill installment control rules pre-configured on the visualization rule configuration page corresponding to the bill installment control scenario and the latest decision factors associated with the user, and obtain the installment control result; the installment control result includes at least one of the following: installment access judgment result, prohibited installment period, and maximum installment amount; Feedback module 303 is used to transmit the phased management and control results to the client for visual display.

[0056] In some embodiments, the apparatus further includes a first configuration module for: Configure the bill installment control scenario based on the scenario name and scenario identifier entered by the configuration personnel on the scenario configuration page of the intelligent decision engine management terminal; In the bill installment management scenario, based on the input parameter mapping and output parameter mapping entered by the configuration personnel in the access service configuration page of the management terminal, an access service configuration adapted to the bill installment management scenario is generated. An interface feature template is generated based on the external interface type, interface link address, request method, interface input parameters, and interface output parameters entered by the configuration personnel in the interface feature template configuration page of the management terminal. In response to the configuration personnel selecting a portion of the output parameter fields of the interface as feature fields in the feature configuration page of the management terminal, the feature fields are marked as suitable for the bill installment management scenario.

[0057] In some embodiments, when a user initiates a bill installment request on the client, the data acquisition module 301 is specifically used to obtain the latest decision factors associated with the user, in order to: When a user initiates a bill installment request on the client, the installment application is triggered to call the intelligent decision engine interface to obtain the user's installment management result in response to the bill installment request. The parameters of the bill installment request are verified by adapting the access service configuration to the bill installment management scenario. After the verification is successful, a data acquisition request is initiated to the user profiling system according to the configured interface feature template in order to obtain the user's profile data; Based on the feature fields adapted to the bill installment management scenario, the latest decision factors for the user are selected from the user's profile data.

[0058] In some embodiments, the process by which the user profiling system obtains user profile data in response to a data acquisition request includes: Based on the user identity identifier in the data acquisition request, query the user's latest overdue period, the user's credit behavior, and the user's complaint data; The complaint data is cleaned using natural language processing technology, and the cleaned complaint data is mapped to the first risk assessment model to generate a user complaint risk score. The latest risk level of the user is determined using the second risk assessment model based on the user's complaint risk score, overdue period, and credit behavior. The user's latest risk level, overdue period, credit behavior, and complaint data are fed back to the intelligent decision engine.

[0059] In some embodiments, the apparatus further includes a second configuration module for: In the scenario of bill installment management, a target strategy set adapted to the scenario of bill installment management is constructed based on the strategy set information entered by the configuration personnel on the strategy set configuration page of the intelligent decision engine management terminal. For the target policy set, based on the policy name and decision mode entered by the configuration personnel on the policy configuration page of the management terminal, the corresponding target policy is configured for the target policy set; Based on the rule name and handling suggestions entered by the configuration personnel in the rule configuration page of the management terminal, the corresponding bill installment control rules are constructed and stored for the target policy.

[0060] In some embodiments, regarding the process of performing installment management on the user's bill installment request based on the bill installment management rules pre-configured on the visualization rule configuration page corresponding to the bill installment management scenario and the latest decision factors associated with the user, and obtaining the installment management result, the decision module 302 is specifically used for: Load the target strategy set that is compatible with the bill installment management scenario, and iterate through the bill installment management rules included in the target strategy under the target strategy set; For each bill installment control rule encountered during iteration, it is determined whether the user's latest decision factor matches the bill installment control rule. Based on the bill installment control rules matched by the latest decision factors, an initial installment control processing result is generated; Based on the output parameter mapping of the access service, the initial phase management processing result is converted into the parameter format required by the phase application to obtain the final phase management result.

[0061] In some embodiments, the installment application is also used for: Based on the user's identity identifier in the bill installment request, query the user's basic installment conditions; wherein, the basic installment conditions include at least the bill installment amount before control and the number of installment periods before control; Feedback module 303 is specifically used for: If the installment management result is installment access rejection, the result of not being allowed to process installments will be forwarded to the client for display through the installment application; or, If the installment control result includes installment access approval, prohibited installment periods, and maximum installment amount, the prohibited installment periods and maximum installment amount are sent to the installment application, so that the installment application generates candidate installment amounts and candidate installment periods after control based on the user's basic installment conditions, prohibited installment periods, and maximum installment amount, and forwards them to the client for display.

[0062] The bill installment processing device provided in the embodiments of the present invention can execute the bill installment processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0063] According to embodiments of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0064] Example 4 Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the invention described and / or claimed herein.

[0065] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory 12 or a random access memory 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 12 or loaded from storage unit 18 into the random access memory 13. The random access memory 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, read-only memory 12, and random access memory 13 are interconnected via a bus 14. An input / output interface 15 is also connected to the bus 14.

[0066] Multiple components in electronic device 10 are connected to input / output interface 15, including: input unit 16; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disks, optical disks, etc.; and communication unit 19, such as network interface cards, modems, wireless transceivers, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0067] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as performing bill installment processing methods.

[0068] In some embodiments, the bill installment processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory 12 and / or communication unit 19. When the computer program is loaded into random access memory 13 and executed by processor 11, one or more steps of the bill installment processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the bill installment processing method by any other suitable means (e.g., by means of firmware).

[0069] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), complex programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable billing installment processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device or liquid crystal display for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with a user; 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 sound input, voice input, or tactile input).

[0073] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet. The computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having client-server relationships with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service system, addressing the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0074] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0075] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for processing bill installment payments, characterized in that, Applications in intelligent decision engines include: When a user initiates a bill installment request on the client, the latest decision factors associated with the user are obtained; wherein, the latest decision factors include the user's latest risk level, overdue period, credit behavior and complaint data; Based on the bill installment management rules pre-configured on the visualization rule configuration page corresponding to the bill installment management scenario and the latest decision factors associated with the user, the user's bill installment request is processed for installment management to obtain the installment management result; wherein, the installment management result includes at least one of the following: installment access judgment result, prohibited installment period, and maximum installment amount; The phased management and control results are transmitted to the client for visualization.

2. The method according to claim 1, characterized in that, The method further includes: Configure the bill installment control scenario based on the scenario name and scenario identifier entered by the configuration personnel on the scenario configuration page of the intelligent decision engine management terminal; In the bill installment management scenario, based on the input parameter mapping and output parameter mapping entered by the configuration personnel in the access service configuration page of the management terminal, an access service configuration adapted to the bill installment management scenario is generated. An interface feature template is generated based on the external interface type, interface link address, request method, interface input parameters, and interface output parameters entered by the configuration personnel in the interface feature template configuration page of the management terminal. In response to the configuration personnel selecting a portion of the output parameter fields of the interface as feature fields in the feature configuration page of the management terminal, the feature fields are marked as suitable for the bill installment management scenario.

3. The method according to claim 2, characterized in that, When a user initiates a bill installment request on the client, the latest decision factors associated with the user are obtained, including: When a user initiates a bill installment request on the client, the installment application is triggered to call the intelligent decision engine interface to obtain the user's installment management result in response to the bill installment request. The parameters of the bill installment request are verified by adapting the access service configuration to the bill installment management scenario. After the verification is successful, a data acquisition request is initiated to the user profiling system according to the configured interface feature template in order to obtain the user's profile data; Based on the feature fields adapted to the bill installment management scenario, the latest decision factors for the user are selected from the user's profile data.

4. The method according to claim 3, characterized in that, The process by which the user profiling system responds to a data acquisition request and acquires user profile data includes: Based on the user identity identifier in the data acquisition request, query the user's latest overdue period, the user's credit behavior, and the user's complaint data; The complaint data is cleaned using natural language processing technology, and the cleaned complaint data is mapped to the first risk assessment model to generate a user complaint risk score. The latest risk level of the user is determined using the second risk assessment model based on the user's complaint risk score, overdue period, and credit behavior. The user's latest risk level, overdue period, credit behavior, and complaint data are fed back to the intelligent decision engine.

5. The method according to claim 2, characterized in that, The method further includes determining the bill installment control rules as follows: In the scenario of bill installment management, a target strategy set adapted to the scenario of bill installment management is constructed based on the strategy set information entered by the configuration personnel on the strategy set configuration page of the intelligent decision engine management terminal. For the target policy set, based on the policy name and decision mode entered by the configuration personnel on the policy configuration page of the management terminal, the corresponding target policy is configured for the target policy set; Based on the rule name and handling suggestions entered by the configuration personnel in the rule configuration page of the management terminal, the corresponding bill installment control rules are constructed and stored for the target policy.

6. The method according to claim 5, characterized in that, The bill installment management process, based on the pre-configured bill installment management rules on the visualization rule configuration page corresponding to the bill installment management scenario and the latest decision factors associated with the user, performs installment management on the user's bill installment request to obtain the installment management result, including: Load the target strategy set that is compatible with the bill installment management scenario, and iterate through the bill installment management rules included in the target strategy under the target strategy set; For each bill installment control rule encountered during iteration, it is determined whether the user's latest decision factor matches the bill installment control rule. Based on the bill installment control rules matched by the latest decision factors, an initial installment control processing result is generated; Based on the output parameter mapping of the access service, the initial phase management processing result is converted into the parameter format required by the phase application to obtain the final phase management result.

7. The method according to claim 3 or 6, characterized in that, The installment application is also used for: Based on the user's identity identifier in the bill installment request, query the user's basic installment conditions; wherein, the basic installment conditions include at least the bill installment amount before control and the number of installment periods before control; The phased management and control results are transmitted to the client for visualization, including: If the installment management result is installment access rejection, the result of not being allowed to process installments will be forwarded to the client for display through the installment application; or, If the installment control result includes installment access approval, prohibited installment periods, and maximum installment amount, the prohibited installment periods and maximum installment amount are sent to the installment application, so that the installment application generates candidate installment amounts and candidate installment periods after control based on the user's basic installment conditions, prohibited installment periods, and maximum installment amount, and forwards them to the client for display.

8. A bill installment processing device, characterized in that, Configured in the intelligent decision engine, including: The data acquisition module is used to acquire the latest decision factors associated with the user after the user initiates a bill installment request on the client; wherein, the latest decision factors include the user's latest risk level, overdue period, credit behavior and complaint data; The decision module is used to process the user's bill installment request for installment control based on the bill installment control rules pre-configured on the visualization rule configuration page corresponding to the bill installment control scenario and the latest decision factors associated with the user, and to obtain the installment control result; the installment control result includes at least one of the following: installment access judgment result, prohibited installment period, and maximum installment amount; The feedback module is used to transmit the phased management and control results to the client for visualization.

9. An electronic device, characterized in that, include: At least one processor; as well as 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 to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to perform the method of any one of claims 1-7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-7.

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