Method, device and equipment for reducing invalid deduction

Abstract

The embodiment of the invention relates to the technical field of finance, in particular to a method, device and equipment for reducing invalid deduction. According to the scheme, the historical deduction rule and the priority mechanism of the deduction rule are obtained, the existing deduction rule is generated according to the priority weight, the priority weight is distributed to each rule through the rule priority mechanism, and it is ensured that the high-priority rule is executed preferentially; the method comprises the following steps of: acquiring transaction data in a transaction execution process, automatically analyzing a transaction result, generating a new deduction rule, and dynamically optimizing the new deduction rule according to an execution effect to generate a real-time rule base. By intercepting the invalid deduction request, the probability of invalid deduction is reduced, the situation that the customer cannot repay due to the fact that the number of failures exceeds the limit is reduced, and the customer repayment experience is optimized on the premise that the transaction success rate is guaranteed.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of financial technology, and more particularly to a method, device, and apparatus for reducing invalid deductions. Background Art

[0002] In financial payment scenarios, debit repayment is a primary business use case. However, debit repayment failures frequently occur due to insufficient customer account balances, excessive number of insufficient balances, bank system failures, network delays, and other reasons. When the number of failed debit repayments exceeds a certain limit, the customer may be unable to make further repayments, resulting in overdue fees and damaged credit records. For financial institutions, frequent debit repayment failures not only increase the difficulty of fund recovery but also increase operating costs and management risks.

[0003] In existing technologies, financial institutions typically use fixed debit strategies, lacking the ability to intelligently analyze and dynamically adjust customer repayment history. This results in a large number of invalid debit requests, wasting system resources and reducing customer repayment success rates and user experience.

[0004] Therefore, there is an urgent need in the prior art for a method for formulating an intelligent deduction prohibition strategy, which can dynamically formulate and optimize the deduction prohibition strategy, intercept invalid deduction requests, and thus reduce the probability of invalid deductions. Summary of the Invention

[0005] To address the problem of customer repayment failures caused by excessive invalid deductions in the prior art, the embodiments of this specification provide a method, device, and apparatus for reducing invalid deductions. By combining real-time data analysis with an adaptive reverse inference mechanism, a deduction prohibition policy is dynamically formulated and optimized to intercept invalid deduction requests, thereby reducing the probability of invalid deductions and improving the customer's repayment success rate and user experience.

[0006] The specific technical solutions of the embodiments of this specification are as follows:

[0007] On the one hand, embodiments of this specification provide a method for reducing invalid deductions, including:

[0008] Obtaining historical deduction rules and a priority mechanism for the historical deduction rules, generating a priority weight for each of the historical deduction rules according to the priority mechanism, and generating an existing deduction rule according to the priority weight;

[0009] Process the transaction according to the existing deduction rules, and obtain the transaction information and transaction results after the processing is completed;

[0010] Reversely derive the transaction information of the transaction with a failed transaction result to generate a new deduction rule;

[0011] The execution effect of the new deduction rule is monitored in real time, and the new deduction rule is dynamically optimized according to the execution effect to generate a real-time rule library.

[0012] Furthermore, the mechanism for obtaining historical deduction rules and the priority of the historical deduction rules further includes:

[0013] Obtain historical deduction rules that have taken effect, and construct an inverted index structure of the historical deduction rules based on key dimensions of the historical deduction rules;

[0014] The priority mechanism of the historical deduction rules is obtained according to the inverted index structure of the historical deduction rules.

[0015] Furthermore, the priority mechanism of obtaining the historical deduction rules according to the inverted index structure of the historical deduction rules further includes:

[0016] The inverted index structure includes channel type, effective time period, and user credit rating;

[0017] Assign a basic weight coefficient to the channel type, effective time period, and user credit rating, and dynamically adjust the weight according to the number of hits of the historical deduction rule in the inverted index;

[0018] Each time a hit record is added, the weight coefficient of the corresponding inverted index structure is increased;

[0019] A priority mechanism of the historical deduction rule is obtained according to the weight coefficient of the inverted index structure.

[0020] Furthermore, reverse deduction based on the transaction information of the transaction with a failed transaction result further includes:

[0021] Extract the failure reason from the transaction information of failed transactions and map it to a unified failure code;

[0022] According to the unified failure code, the unified failure code is traversed in the existing deduction rules.

[0023] If there is no deduction rule that matches the unified failure code, the failure cause is analyzed and a new deduction rule is generated.

[0024] Furthermore, traversing the unified failure code in the existing deduction rules further includes:

[0025] If there is a deduction rule that matches the unified failure code, the failure cause is analyzed and a corresponding deduction rule adjustment instruction is generated.

[0026] Furthermore, the failure reasons include: user repayment status data, user account real-time balance data and historical deduction record data.

[0027] Furthermore, analyzing the failure reason and generating a corresponding deduction rule adjustment instruction further includes:

[0028] If a user account fails continuously within a short period of time, a new deduction rule will be generated for the user account and a prohibition deduction time will be set.

[0029] If the frequency of use of an existing deduction rule increases, the priority weight of the corresponding deduction rule will be increased;

[0030] If an existing deduction rule fails to execute multiple times within the preset period, the priority weight of the corresponding deduction rule will be reduced.

[0031] On the other hand, an embodiment of this specification further provides a device for reducing invalid deductions, the device comprising:

[0032] a historical rule acquisition unit, configured to acquire historical deduction rules and a priority mechanism for the historical deduction rules, generate a priority weight for each of the historical deduction rules according to the priority mechanism, and generate an existing deduction rule according to the priority weight;

[0033] A transaction information acquisition unit, configured to process a transaction according to existing deduction rules and obtain transaction information and transaction results after the transaction is completed;

[0034] A new rule generating unit, configured to reversely deduce the transaction information of the transaction with a failed transaction result to generate a new deduction rule;

[0035] The implementation rule updating unit is used to monitor the execution effect of the new deduction rule in real time, and dynamically optimize the new deduction rule according to the execution effect to generate a real-time rule library.

[0036] On the other hand, an embodiment of this specification further provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor implements the above method when executing the computer program.

[0037] On the other hand, an embodiment of this specification further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0038] Finally, an embodiment of this specification also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above method is implemented.

[0039] Using the embodiments of this specification, a method for assessing the credit risk of a counterparty is provided. This solution obtains historical deduction rules and their priority mechanism, generates a priority weight for each of the historical deduction rules according to the priority mechanism, generates existing deduction rules according to the priority weight, and assigns a priority weight to each rule through the rule priority mechanism to ensure that high-priority rules are executed first; then, transactions are processed according to the existing deduction rules, and transaction data sets are obtained during the transaction execution process. After the processing is completed, transaction information and transaction results are obtained, so as to automatically analyze the transaction results, reversely deduce the reasons for failure, and reversely deduce the transaction information of the transactions with failed transaction results to generate new deduction rules; while generating new deduction rules, the execution effect of the new deduction rules is monitored in real time, and the new deduction rules are dynamically optimized according to the execution effect to generate a real-time rule library. The embodiments of this specification reduce the probability of invalid deductions by intercepting invalid deduction requests, reduce the situation where customers are unable to repay due to excessive failure times, realize intelligent interception of invalid deduction requests and dynamic tuning of policy parameters, effectively reduce the bad debt risk of financial institutions and improve capital recovery efficiency, and at the same time, based on the adaptive reverse strategy of the exponential backoff algorithm and the credit rating adaptation mechanism, optimize the customer repayment experience while ensuring the transaction success rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 FIG2 is a schematic diagram of an implementation system of a method for reducing invalid deductions in an embodiment of this specification;

[0042] Figure 2 Schematic diagram of the process of reducing invalid deductions in the embodiment of this specification;

[0043] Figure 3 FIG2 is a flow chart of obtaining historical deduction rules and a priority mechanism of the historical deduction rules in an embodiment of this specification;

[0044] Figure 4 FIG2 is a flow chart of a mechanism for obtaining the priority of the historical deduction rules according to the inverted index structure of the historical deduction rules in an embodiment of this specification;

[0045] Figure 5FIG2 is a flow chart of reverse deduction based on the transaction information of a transaction with a failed transaction result in an embodiment of this specification;

[0046] Figure 6 The figure shows a schematic diagram of the structure of analyzing the failure cause and generating the corresponding deduction rule adjustment instruction in the embodiment of this specification;

[0047] Figure 7 FIG2 is a schematic diagram showing the specific structure of the device for reducing invalid deductions according to the present embodiment;

[0048] Figure 8 The figure shows a schematic diagram of the structure of a computer device in an embodiment of this specification.

[0049]

Description of the accompanying drawings

[0050] 101. Platform side;

[0051] 102. Terminal;

[0052] 701. Historical rule acquisition unit;

[0053] 702. Transaction information acquisition unit;

[0054] 703. New rule generation unit;

[0055] 704. Implementation rule update unit;

[0056] 802. Computer equipment;

[0057] 804. Processing equipment;

[0058] 806. Storage resources;

[0059] 808, drive system;

[0060] 810, input / output module;

[0061] 812. Input devices;

[0062] 814. Output device;

[0063] 816. Presentation equipment;

[0064] 818. Graphical User Interface;

[0065] 820, network interface;

[0066] 822, communication link;

[0067] 824. Communication bus. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the embodiments of this specification.

[0069] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of this specification and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0070] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments of this specification comply with the relevant provisions of national laws and regulations.

[0071] It should be noted that in the embodiments of this specification, some existing industry solutions such as software, components, and models may be mentioned. They should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0072] like Figure 1 The system diagram of a method for reducing invalid deductions according to an embodiment of the present invention is shown, which may include a platform end 101 and a terminal 102. A communication connection is established between the platform end 101 and the terminal 102 to enable data interaction.

[0073] The platform 101 can obtain historical deduction rules from the business system and send them to the terminal 102. Based on the usage records of the historical deduction rules, the platform determines the priority mechanism, thereby generating a priority weight for each historical deduction rule and generating the current deduction rules. The platform then updates the deduction rules in real time based on the transaction processing status of the existing deduction rules, generating a real-time rule library that is updated in real time. The terminal 102 can simulate different network environments, where the network environment includes various factors such as IP address, network status, and connection method.

[0074] In the embodiments of this specification, the platform end 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms. At the same time, through universal cross-platform code, using modules in the standard Python library, universal configurable parameters such as input and output paths, data dates, etc. Configured dependency management ensures that the same version of the system for reducing invalid deduction methods can be installed on all platform ends 101. Full cross-platform testing, sufficient testing on environments such as Windows and Linux, to ensure normal operation of multiple platforms. Standardized data interface, forming a fixed input and output data interface format and content, to facilitate docking with the database. Support for containerized deployment ensures the consistency and portability of the device in different environments.

[0075] In addition, it should be noted that Figure 1 What is shown is only one application environment provided by the embodiment of this specification. In actual application, other application environments may also be included, and this specification does not limit them.

[0076] In view of the problems existing in the prior art, the embodiments of this specification provide a method for reducing invalid deductions. In the embodiments of this specification, Figure 2 The figure shows a flow chart of the method for reducing invalid deductions in the embodiment of this specification. The process of reducing invalid deductions is described in this figure. The order of steps listed in the embodiment is only one way of executing the steps among many steps, and does not represent the only execution order. When the actual system or device product is executed, it can be executed in the order or in parallel according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 2 As shown, the method may include:

[0077] Step 201: Obtain historical deduction rules and a priority mechanism for the historical deduction rules, generate a priority weight for each historical deduction rule according to the priority mechanism, and generate an existing deduction rule according to the priority weight;

[0078] Step 202: Process the transaction according to existing deduction rules, and obtain transaction information and transaction results after the transaction is completed;

[0079] Step 203: reversely derive the transaction information of the transaction with a failed transaction result to generate a new deduction rule;

[0080] Step 204: monitor the execution effect of the new deduction rule in real time, and dynamically optimize the new deduction rule according to the execution effect to generate a real-time rule library.

[0081] Using the embodiments of this specification, a method for assessing the credit risk of a counterparty is provided. This solution obtains historical deduction rules and their priority mechanism, generates a priority weight for each of the historical deduction rules according to the priority mechanism, generates existing deduction rules according to the priority weight, and assigns a priority weight to each rule through the rule priority mechanism to ensure that high-priority rules are executed first; then, transactions are processed according to the existing deduction rules, and transaction data sets are obtained during the transaction execution process. After the processing is completed, transaction information and transaction results are obtained, so as to automatically analyze the transaction results, reversely deduce the reasons for failure, and reversely deduce the transaction information of the transactions with failed transaction results to generate new deduction rules; while generating new deduction rules, the execution effect of the new deduction rules is monitored in real time, and the new deduction rules are dynamically optimized according to the execution effect to generate a real-time rule library. The embodiments of this specification reduce the probability of invalid deductions by intercepting invalid deduction requests, reduce the situation where customers are unable to repay due to excessive failure times, realize intelligent interception of invalid deduction requests and dynamic tuning of policy parameters, effectively reduce the bad debt risk of financial institutions and improve capital recovery efficiency, and at the same time, based on the adaptive reverse strategy of the exponential backoff algorithm and the credit rating adaptation mechanism, optimize the customer repayment experience while ensuring the transaction success rate.

[0082] This specification uses hash tables or inverted index technology to index historical deduction rules according to key dimensions (such as bank cards, channels, unified codes, etc.) to achieve fast rule search; through the priority mechanism of historical deduction rules, each rule is assigned a priority weight to ensure that high-priority rules are executed first. After the existing deduction rules are generated, each time a transaction is completed, the system automatically analyzes the transaction results, reversely deduces the cause of the failure, and dynamically generates or adjusts the prohibition of deduction rules based on the unified code mapped to the current error code. For example, if a bank card fails continuously in a short period of time, the system will automatically generate a prohibition of deduction rule for the bank card and set a prohibition of deduction time; at the same time, the system will monitor the execution effect of the new deduction rule in real time, dynamically optimize the rule parameters (such as prohibition of deduction time, failure number threshold, etc.) according to actual data, and continuously optimize the rule base through feedback from rule execution data to improve the accuracy and timeliness of the strategy. It solves the problem of customer repayment failure caused by too many invalid deductions in the existing technology. At the same time, combined with real-time data analysis and adaptive reverse inference mechanism, we dynamically formulate and optimize the prohibition of deduction strategies and intercept invalid deduction requests, thereby reducing the probability of invalid deductions and improving customers' repayment success rate and user experience.

[0083] According to the embodiment of this specification, in order to obtain the historical deduction rules and the priority mechanism of the historical deduction rules, the following is further included: Figure 3 As shown, further comprising,

[0084] Step 301: Obtain historical deduction rules that have taken effect, and construct an inverted index structure of the historical deduction rules based on key dimensions of the historical deduction rules;

[0085] Step 302: Obtain the priority mechanism of the historical deduction rules according to the inverted index structure of the historical deduction rules.

[0086] Specifically, a rule storage structure is constructed based on a hash table, wherein the hash key is generated by a combination of the card number, channel number and user credit rating associated with the rule, and the hash value is the rule set under the corresponding dimension; an inverted index structure is simultaneously established, and a multi-dimensional mapping relationship is established with the failure unified code, time range and channel status as index keys; a dynamic priority weight value is assigned to each rule, and the weight calculation is based on the historical execution success rate of the rule, the channel stability score and the rule effective time attenuation coefficient; when a deduction request is received, the candidate rule set that matches the current transaction scenario is quickly screened through the inverted index, and the highest priority rule is selected for execution based on the weight value sorting. The hash table and inverted index realize real-time data synchronization update through shared memory.

[0087] According to the embodiment of this specification, in order to obtain the priority mechanism, the inverted index structure includes channel type, effective time period, and user credit level;

[0088] like Figure 4 As shown, the priority mechanism of obtaining the historical deduction rules according to the inverted index structure of the historical deduction rules further includes:

[0089] Step 401: assigning a basic weight coefficient to the channel type, effective time period, and user credit rating, and dynamically adjusting the weight according to the number of hits of the historical deduction rule in the inverted index;

[0090] Step 402: Every time a hit record is added, the weight coefficient of the corresponding inverted index structure is increased;

[0091] Step 403: Obtain the priority mechanism of the historical deduction rule according to the weight coefficient of the inverted index structure.

[0092] Specifically, the process of formulating an intelligent strategy for prohibiting deductions includes: building a strategy decision-making system based on seven core dimensions: user bank card, repayment initiation source, payment action type, unified failure code for prohibiting deductions, restriction rules, prohibition time and prohibition channels. First, the transaction log parsing module is used to extract customer historical order failure data, and the original error information is converted into a standardized failure code according to the preset failure code mapping table; then a multi-dimensional feature association analysis is performed, and cross-statistics are conducted on the bank card accounts, transaction initiation channels and failure time periods that appear frequently in failed orders to identify abnormal transaction patterns; an initial prohibition strategy set is generated based on the analysis results, and the specific rules include: when it is detected that a specific bank card has triggered a set number of unified failure codes through a specified source channel in a single day, a control instruction containing a prohibited deduction channel, time window and failure number threshold is automatically generated; finally, the rule execution effect is tracked in real time through the strategy monitoring module, and the prohibition time coefficient and failure number judgment standard are dynamically adjusted based on the actual interception data and the failure rate change trend, forming a cyclically optimized strategy management closed loop.

[0093] In the embodiment of this specification, in order to obtain a new deduction rule, such as Figure 5 As shown, reverse deduction based on the transaction information of the transaction with a failed transaction result further includes:

[0094] Step 501: extract the failure reason from the transaction information of the failed transaction result and map it to a unified failure code;

[0095] Step 502: Based on the unified failure code, traverse the unified failure code in the existing deduction rules.

[0096] Step 503: If there is no deduction rule that matches the unified failure code, analyze the failure cause and generate a new deduction rule.

[0097] Step 504: If there is a deduction rule that matches the unified failure code, analyze the failure cause and generate a corresponding deduction rule adjustment instruction.

[0098] Specifically, after each transaction is completed, the system automatically analyzes the transaction results, captures the original error information of the failed transaction in real time, converts it into a standardized unified failure code based on the preset failure code mapping table (such as "B001" for insufficient balance, "C005" for channel timeout), extracts the failure cause and maps it to a unified failure code. Subsequently, based on the cause of failure, a deduction prohibition rule is dynamically generated or adjusted. For example, a composite hash key is generated based on the failure code, the card number involved, and the channel number, and an exact matching query is performed in the rule library stored in the hash table. If no valid rule is retrieved, the transaction spatiotemporal feature data (including the failure time distribution density, the continuous failure frequency, and the channel response delay rate) is analyzed to automatically generate dynamic rules containing prohibited channels, effective time periods, and trigger thresholds (for example, when a bank card triggers the "B001" failure code three times within 10 minutes through a mobile channel, a temporary policy of "prohibiting the channel for 30 minutes" is generated). If a matching rule exists, the rule execution deviation is evaluated (such as if the actual number of failures exceeds the existing threshold by 20% or the failure period deviates from the preset time window), and the prohibition duration is dynamically adjusted or the channel restriction range is expanded. The newly generated rules are synchronized with the policy library at the millisecond level through hash table key value updates and inverted index reconstruction (with failure code, time granularity, and channel status as index dimensions), and finally a weighted priority sorting mechanism is used to ensure that high-timeliness rules are triggered first in subsequent transaction interceptions.

[0099] In the embodiment of this specification, in order to analyze the failure reason and generate the corresponding deduction rule adjustment instruction, the transaction information includes: user repayment status data, user account real-time balance data and historical deduction record data;

[0100] Analyze the failure reasons and generate corresponding deduction rule adjustment instructions, such as Figure 6 As shown, further comprising,

[0101] Step 601: If a user account fails to process payments continuously within a short period of time, a new deduction rule is generated for the user account and a prohibition period for deduction is set;

[0102] Step 602: If the usage frequency of an existing deduction rule increases, the priority weight of the corresponding deduction rule is increased;

[0103] Step 603: If the existing deduction rule fails to execute multiple times within the preset period, the priority weight of the corresponding deduction rule is reduced.

[0104] For example, the specific implementation process of the deduction rule includes: the system first collects the customer's historical order failure data, maps the original error information into a standardized unified failure code for prohibiting deductions, and generates an initial restriction strategy by analyzing the bank card accounts, transaction initiation channels and failure time distributions that are frequently associated with failed orders; when the user initiates a repayment request, the system performs multi-dimensional interception verification in sequence according to priority: first verify whether the repayment bank card exists in the prohibited list, then check whether the initiation source is restricted, identify whether the payment action type is compliant, associate the unified failure code of the previous transaction to determine whether it hits the interception condition, and statistically analyze the failure code of the previous transaction. The number of times the same failure code is triggered in the current channel within the set time window is used to ultimately verify whether the target channel is in a prohibited service state; at the same time, the rule execution indicators are monitored in real time. When the rule interception success rate is lower than 60%, the prohibition time is automatically extended to 1.5 times the original value or the failure threshold is relaxed by 1-2 times. Inefficient rules that have not been triggered for 7 consecutive days are eliminated at dawn every day and core policy parameters are optimized. The credit grading mechanism is used to flexibly adjust the threshold for high-value customers, forming a closed-loop management of policy generation, execution interception, and dynamic tuning, while ensuring the timeliness of the rule base and improving the accuracy of invalid deduction interception to more than 90%.

[0105] As an example of this specification, you can also refer to Figure 7 Shown is a schematic diagram of the specific structure of the device for reducing invalid deductions in this embodiment.

[0106] A historical rule acquisition unit 701 is configured to acquire historical deduction rules and a priority mechanism for the historical deduction rules, generate a priority weight for each historical deduction rule according to the priority mechanism, and generate an existing deduction rule according to the priority weight;

[0107] The transaction information acquisition unit 702 is used to process the transaction according to the existing deduction rules and obtain the transaction information and transaction results after the processing is completed;

[0108] A new rule generating unit 703 is configured to reversely deduce the transaction information of the transaction with a failed transaction result to generate a new deduction rule;

[0109] The implementation rule updating unit 704 is used to monitor the execution effect of the new deduction rule in real time, and dynamically optimize the new deduction rule according to the execution effect to generate a real-time rule library.

[0110] Since the principle of solving the problem by the above device is similar to that of the above method, the implementation of the above system can refer to the implementation of the above method, and the repeated parts will not be repeated.

[0111] like Figure 8 The figure shows a schematic diagram of the structure of a computer device according to an embodiment of the present specification. The computer device according to the embodiment of the present specification can run the method according to the embodiment of the present specification.

[0112] Computer device 802 may include one or more processing devices 804, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 802 may also include any storage resources 806 for storing any type of information, such as code, settings, data, and the like. For example, and without limitation, storage resources 806 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, and the like. More generally, any storage resource may use any technology to store information.

[0113] Further, any storage resource may provide volatile or non-volatile retention of information.

[0114] Furthermore, any storage resource may represent a fixed or removable component of the computer device 802. In one embodiment, when the processing device 804 executes the associated instructions stored in any storage resource or combination of storage resources, the computer device 802 may perform any operation of the associated instructions. The computer device 802 also includes one or more drive systems 808, such as a hard disk drive system, an optical disk drive system, etc., for interacting with any storage resource.

[0115] The computer device 802 may also include an input / output module 810 (I / O) for receiving various inputs (via input devices 812) and for providing various outputs (via output devices 814). A specific output mechanism may include a presentation device 816 and an associated graphical user interface 818 (GUI). In other embodiments, the input / output module 810 (I / O), input devices 812, and output devices 814 may not be included, and the computer device 802 may simply be a computer device in a network. The computer device 802 may also include one or more network interfaces 820 for exchanging data with other devices via one or more communication links 822. One or more communication buses 824 couple the components described above together.

[0116] The communication link 822 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 822 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0117] The embodiments of this specification also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0118] The embodiments of this specification also provide a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to execute the above method.

[0119] It should be understood that in the various embodiments of the present specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present specification.

[0120] It should also be understood that in the embodiments of this specification, the term "and / or" is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent three situations: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, in the embodiments of this specification, the character " / " generally indicates that the associated objects are in an "or" relationship.

[0121] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of this specification can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of this specification.

[0122] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0123] In the several embodiments provided in the embodiments of this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0124] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0125] In addition, the functional units in each embodiment of the present specification may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0126] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of this specification is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the embodiment of this specification. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0127] The embodiments of this specification use specific embodiments to illustrate the principles and implementation methods of the embodiments of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of this specification. At the same time, for those skilled in the art, based on the ideas of the embodiments of this specification, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of this specification.

Claims

1. A method for reducing invalid deductions, characterized in that: The method comprises, Obtaining historical deduction rules and a priority mechanism for the historical deduction rules, generating a priority weight for each of the historical deduction rules according to the priority mechanism, and generating an existing deduction rule according to the priority weight; Process the transaction according to the existing deduction rules, and obtain the transaction information and transaction results after the processing is completed; Reversely derive the transaction information of the transaction with a failed transaction result to generate a new deduction rule; The execution effect of the new deduction rule is monitored in real time, and the new deduction rule is dynamically optimized according to the execution effect to generate a real-time rule library.

2. The method for reducing invalid deductions according to claim 1, characterized in that: The mechanism for obtaining historical deduction rules and the priority of the historical deduction rules further includes: Obtain historical deduction rules that have taken effect, and construct an inverted index structure of the historical deduction rules based on key dimensions of the historical deduction rules; The priority mechanism of the historical deduction rules is obtained according to the inverted index structure of the historical deduction rules.

3. The method for reducing invalid deductions according to claim 2, characterized in that: The priority mechanism of obtaining the historical deduction rules according to the inverted index structure of the historical deduction rules further includes: The inverted index structure includes channel type, effective time period, and user credit rating; Assign a basic weight coefficient to the channel type, effective time period, and user credit rating, and dynamically adjust the weight according to the number of hits of the historical deduction rule in the inverted index; Each time a hit record is added, the weight coefficient of the corresponding inverted index structure is increased; A priority mechanism of the historical deduction rule is obtained according to the weight coefficient of the inverted index structure.

4. The method for reducing invalid deductions according to claim 2, characterized in that: The reverse deduction based on the transaction information of the transaction whose transaction result is a failed transaction further includes: Extract the failure reason from the transaction information of failed transactions and map it to a unified failure code; According to the unified failure code, the unified failure code is traversed in the existing deduction rules. If there is no deduction rule that matches the unified failure code, the failure cause is analyzed and a new deduction rule is generated.

5. The method for reducing invalid deductions according to claim 4, characterized in that: Traversing the unified failure code in the existing deduction rules further includes: If there is a deduction rule that matches the unified failure code, the failure cause is analyzed and a corresponding deduction rule adjustment instruction is generated.

6. The method for reducing invalid deductions according to claim 5, characterized in that: The reasons for failure include: user repayment status data, user account real-time balance data and historical deduction record data.

7. The method for reducing invalid deductions according to claim 6, characterized in that: Analyzing the failure cause and generating corresponding deduction rule adjustment instructions further includes: If a user account fails to pay continuously within a short period of time, a new deduction rule will be generated for the user account and a prohibition period for deduction will be set; If the frequency of use of an existing deduction rule increases, the priority weight of the corresponding deduction rule will be increased; If an existing deduction rule fails to execute multiple times within the preset period, the priority weight of the corresponding deduction rule will be reduced.

8. A device for reducing invalid deductions, characterized in that: The device comprises: a historical rule acquisition unit, configured to acquire historical deduction rules and a priority mechanism for the historical deduction rules, generate a priority weight for each of the historical deduction rules according to the priority mechanism, and generate an existing deduction rule according to the priority weight; A transaction information acquisition unit, configured to process a transaction according to existing deduction rules and obtain transaction information and transaction results after the transaction is completed; A new rule generating unit, configured to reversely deduce the transaction information of the transaction with a failed transaction result to generate a new deduction rule; The real-time rule updating unit is used to monitor the execution effect of the new deduction rule in real time, and dynamically optimize the new deduction rule according to the execution effect to generate a real-time rule library.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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