Electric charge income and expenditure risk early warning method and device

Abstract

The invention provides an electric charge income and expenditure risk early warning method and device. The method comprises the following steps: acquiring electric charge income and expenditure data information; respectively inputting the electric charge income and expenditure data information into a preset electric charge channel fund risk assessment model and an electric charge fund anti-money laundering risk assessment model, and outputting a scoring result; and carrying out risk early warning analysis according to the scoring result, and outputting a risk early warning suggestion. According to the method and the device, the electric charge channel fund risk is monitored in all links, the electric charge channel fund risk assessment model and the anti-money laundering prevention and control model are constructed, the potential electric charge revenue and expenditure risk is pre-warned, the operation risk of covering real arrearage is completely eradicated, the economic loss of an enterprise is avoided, the risk in the electric charge revenue process is reduced, and the electric charge management and control efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of risk warning for electricity fee recovery, and in particular to a method and device for warning of electricity fee revenue and expenditure risks. Background Art

[0002] With the development of power business, electricity fee accounting, as a core component of electricity fee management, will directly affect the normal operation of the power supply department's power marketing business, and will also have a vital impact and significance on the sustainable development of power companies. In recent years, power grid companies have established a relatively complete electricity fee fund risk management system, including electricity fee recovery assessment system, electricity fee prepayment system, electricity fee bad debt management system, etc., to ensure the security and stability of electricity fee funds.

[0003] Traditional electricity bill accounting mainly relies on manual accounting, which involves manually checking whether the electricity bill is generated incorrectly, whether there are any abnormal situations, whether the user information has changed, etc. The accounting personnel manually calculate and then input the electricity bill information into the system. For the accounting personnel, the whole process takes a lot of time and is cumbersome. In addition, new financial risk management issues continue to emerge in the electricity bill accounting process, which is mainly manifested in the weak monitoring of some links of electricity bill financial risk. Full-link monitoring has not been achieved in the charging link, recovery link, account arrival link, and refund link, resulting in the inability to accurately and completely grasp the overall situation of the current electricity bill financial risk, and unable to provide data support for subsequent electricity bill financial management. It is difficult to analyze and warn electricity users who have defaulted on electricity bills, and to formulate corresponding measures, resulting in a low management level of power companies. Summary of the invention

[0004] Multiple aspects of the present application provide a method and device for early warning of electricity fee revenue and expenditure risks, which monitor and analyze each abnormal business link in real time, reduce the risk of the electricity fee collection process, and improve the efficiency of electricity fee management and control.

[0005] In order to achieve the above technical effects, one aspect of the present application provides an electricity fee revenue and expenditure risk warning method, comprising:

[0006] Obtain electricity bill revenue and expenditure data information;

[0007] Input the electricity fee revenue and expenditure data information into a preset electricity fee channel fund risk assessment model and an electricity fee fund anti-money laundering risk assessment model respectively, and output a scoring result;

[0008] A risk warning analysis is performed based on the scoring results, and risk warning suggestions are output.

[0009] According to a preferred embodiment of the present invention, the obtaining of electricity fee income and expenditure data information further comprises:

[0010] Obtain user information and user payment information, wherein the user information includes low-voltage user information and high-voltage user information, and the user payment information includes user account information, payment name, payment channel and transaction flow.

[0011] According to a preferred embodiment of the present invention, the electricity fee revenue and expenditure data information is respectively input into a preset electricity fee channel fund risk assessment model and an electricity fee fund anti-money laundering risk assessment model, and outputting the scoring results further includes:

[0012] Construct risk assessment models for electricity bill channel funds and anti-money laundering risk assessment models for electricity bill funds;

[0013] Input the electricity fee revenue and expenditure data information into the electricity fee channel fund risk assessment model, and output the risk score of the corresponding channel fund;

[0014] The electricity bill revenue and expenditure data information is input into the electricity bill fund anti-money laundering risk assessment model, and the money laundering behavior probability score is output.

[0015] According to a preferred embodiment of the present invention, the construction of the electricity fee channel fund risk assessment model and the electricity fee fund anti-money laundering risk assessment model further includes:

[0016] Set corresponding model indicators and scoring rules according to different business scenarios;

[0017] The electricity bill revenue and expenditure data of the previous six months are used as training samples to train the model.

[0018] According to a preferred embodiment of the present invention, the setting of corresponding model indicators and scoring rules according to different business scenarios further includes:

[0019] Indicators are set for the electricity fee channel fund risk assessment model, including: reversal of payment, expected electricity fee, overdue electricity fee, and overdue refund, and different weights are set for the indicators using the hierarchical analysis method, and each indicator is clustered using the clustering algorithm to divide the threshold and the corresponding score;

[0020] The indicators of the anti-money laundering risk assessment model for electricity bill funds include: risk scenarios, suspicious transactions and abnormal behaviors.

[0021] According to a preferred embodiment of the present invention, performing risk warning analysis according to the scoring results and outputting risk warning suggestions further includes:

[0022] Conduct risk trend analysis on the scoring results and rank the risk status of each link in electricity fee recovery;

[0023] Divide risk levels according to risk scores, and set corresponding risk warning suggestions and risk management strategies for different risk levels.

[0024] According to a preferred embodiment of the present invention, the method further comprises:

[0025] Update the model training samples regularly and use the updated training samples to update the model parameters.

[0026] Another aspect of the present application provides an electricity fee income and expenditure risk warning device, comprising:

[0027] Data acquisition module: used to obtain electricity bill revenue and expenditure data information;

[0028] Data processing module: used to input the electricity fee revenue and expenditure data information into the preset electricity fee channel fund risk assessment model and the electricity fee fund anti-money laundering risk assessment model, and output the scoring results;

[0029] Warning analysis module: used to perform risk warning analysis based on the scoring results and output risk warning suggestions.

[0030] Another aspect of the present application provides an electricity fee income and expenditure risk warning device, the device comprising:

[0031] at least one processor; and

[0032] a memory communicatively connected to the at least one processor; wherein,

[0033] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described above.

[0034] In another aspect of the present application, a computer-readable storage medium is provided, on which computer program instructions are stored. The computer program instructions can be executed by a processor to implement the above method.

[0035] In the solution provided in the embodiment of the present application, by monitoring all aspects of the financial risks of the electricity bill channel, an electricity bill channel financial risk assessment model and an anti-money laundering prevention and control model are constructed, and early warnings are issued for potential electricity bill revenue and expenditure risks, thereby eliminating the operating risks of concealing real arrears, avoiding economic losses for enterprises, reducing the risks in the electricity bill collection process, and improving the efficiency of electricity bill management and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0038] Figure 1 A schematic diagram of a flow chart of a method for early warning of electricity fee income and expenditure risk provided in one embodiment of the present application;

[0039] Figure 2 A schematic diagram of the structure of an electricity fee income and expenditure risk warning device provided in one embodiment of the present application;

[0040] Figure 3 The present invention is a schematic diagram of the structure of a device suitable for implementing the solution in the embodiment of the present application.

[0041] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPU), input / output interface, network interface and memory.

[0044] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0045] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer program instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0046] In actual scenarios, the execution subject of the method can be a user device, or a device formed by integrating a user device and a network device through a network, or an application running on the above device. The user device includes but is not limited to various terminal devices such as computers, mobile phones, tablet computers, smart watches, and bracelets. The network device includes but is not limited to network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets, which can be used to implement some processing functions when setting an alarm. Here, the cloud is composed of a large number of hosts or network servers based on cloud computing, where cloud computing is a type of distributed computing, a virtual computer composed of a group of loosely coupled computer sets.

[0047] As a large enterprise, the power grid company is involved in a large amount of capital flow and transactions. During the capital flow process, it involves many different types of payment channels and process links. In view of the urgent need for the construction of electricity bill capital risk business, comprehensive measures need to be taken, including establishing a capital risk prevention and control model for electricity bill channels, strengthening the electricity bill capital risk early warning mechanism, and building an anti-money laundering prevention and control model for electricity bill capital. This can effectively reduce the electricity bill capital risk and ensure the stable operation and development of power companies.

[0048] By building a risk assessment model for electricity bill channels and an anti-money laundering prevention and control model for electricity bills, we can strengthen the risk monitoring of electricity bill channels and reduce electricity bill losses, including arrears and missed payments due to poor management and human factors, avoid economic losses for enterprises, realize centralized management and unified allocation of electricity bill funds, improve the efficiency of fund use, and reduce the financial costs of enterprises. Through anti-money laundering risk prevention and control, we can avoid economic losses for power grid companies.

[0049] Figure 1 A schematic diagram of a method for early warning of electricity bill revenue and expenditure risk provided in an embodiment of the present application is shown in FIG. Figure 1As shown, the method includes:

[0050] S101, obtaining electricity bill income and expenditure data information.

[0051] Specifically, the user information of the user who pays the electricity bill and the user's payment information are obtained, wherein the user information includes low-voltage user information and high-voltage user information, specifically including low-voltage residential information, low-voltage non-residential information and high-voltage user information; the user's payment information includes user account information, payment name, payment channel, transaction flow and payment frequency, etc.

[0052] We sorted out the internal data of the power grid system and the external data of the relevant banking system, collected lists of risk accounts of financial institutions, designed business scenarios in combination with channel funding risks, and carried out data preparation, including data preparation for the following business scenarios: salesperson corrections, untimely payment, cancellation of account A with account B, irregular reduction or exemption of liquidated damages, untimely payment, average collection time, overdue electricity bills, arrears, unsettled electricity bills, untimely confirmation of receipt, cross-period confirmation, untimely refund, etc.

[0053] S102, inputting the electricity fee revenue and expenditure data information into a preset electricity fee channel fund risk assessment model and an electricity fee fund anti-money laundering risk assessment model respectively, and outputting a scoring result.

[0054] Specifically, after obtaining relevant information, the data information is first pre-processed. The transaction data of the electricity bill fund account is cleaned, processed, and converted. For some data that does not meet business needs, that is, missing data such as payment accounts and payment names, the data needs to be cleaned to exclude incomplete, abnormal, and duplicate data. The scope of data processing includes: customer information, full payment channel information, refund information, breach of contract electricity information, customer service data, and operation data. The customer payment name, payment account, transaction flow summary information, nature of funds, and other data governance processing of the access fund data are processed as needed.

[0055] Based on the abnormal monitoring model isolation forest model, the detection data has outliers, and the nearest neighbor algorithm and Lagrange interpolation method are used to process the abnormal data and missing data.

[0056] After data preprocessing, electricity bill funds are monitored from both internal and external directions. An internal risk assessment model for electricity bill channel funds is built to monitor and analyze all aspects of abnormal business situations in the flow of electricity bill funds, realize real-time monitoring, analysis and judgment of each abnormal business link, assist electricity bill administrators to timely grasp the risk issues in electricity bill management, evaluate and rate the fund risk management status of each city, reduce the risk of electricity bill collection process, improve electricity bill control efficiency, and enhance the core competitiveness of power supply companies; externally, an anti-money laundering risk assessment model for electricity bill funds is built to prevent criminals from using the power grid company's business to launder money, improve the security of electricity bill funds, and increase customer satisfaction.

[0057] In the process of constructing the risk assessment model of electricity bill channel funds and the anti-money laundering risk assessment model of electricity bill funds, corresponding model indicators and scoring rules are set according to different business scenarios. The model indicators of the risk assessment model of electricity bill channel funds include reversal of payment, expected electricity bills, overdue electricity bills, overdue refunds, etc. The model indicators of the anti-money laundering risk assessment model of electricity bill funds can be combined with the severity level of abnormal payment in user channels, the number of occurrences, the frequency of occurrence, marketing refunds and bill returns, bill payments, pre-deposits and other electricity consumption behaviors, sort out the anti-money laundering risk rules and collect external risk events, and abstractly form a continuously iterative risk feature library, including risk scenarios, suspicious transactions and abnormal behaviors, etc., and build models with big data algorithms such as random forests, XGBOOST, and neural networks.

[0058] The hierarchical analysis method is used to set different weights for the indicators, and the clustering algorithm is used to cluster each indicator to divide the thresholds and corresponding scores.

[0059] The electricity bill revenue and expenditure data of the previous six months are used as training samples to train the model. The training samples can specifically include: risk case information provided by external relevant institutions, abnormal payment account data of electricity bill payment channels, and abnormal payment behavior case data of other industries.

[0060] Training samples can be classified according to multiple dimensions of customers such as high-voltage users, low-voltage non-residents, low-voltage residents, etc. Starting from full-dimensional payment scenarios such as collection, collection on behalf of others, withholding, collection, and active transfer, risks can be identified based on indicators such as abnormal customer payment amounts, abnormal payment frequencies, abnormal payment times, abnormal number of payment accounts, abnormal number of payment account numbers, and abnormal real-name information of payment accounts.

[0061] The scenarios, features and indicators of the model are shown in the following table:

[0062]

[0063]

[0064] After constructing the electricity bill channel fund risk assessment model and the electricity bill fund anti-money laundering risk assessment model, the electricity bill revenue and expenditure data information is input into the electricity bill channel fund risk assessment model, and the risk score of the corresponding channel fund is output; the electricity bill revenue and expenditure data information is input into the electricity bill fund anti-money laundering risk assessment model, and the money laundering behavior probability score is output.

[0065] S103: Perform risk warning analysis based on the scoring results and output risk warning suggestions.

[0066] Specifically, a risk trend analysis is conducted on the scoring results, including an overview of electricity bill risks, electricity bill risk analysis, analysis of key risks in this period, electricity bill risk trend analysis, and historical electricity bill risk index trends. The risk situations of each link in electricity bill recovery are ranked, and risk levels are divided according to the risk scores. For example, the risk levels can be ranked from high to low as extremely high, high, medium, and low, and corresponding colors are assigned to them: red code, orange code, yellow code, and blue code. Graphic and image technology is used to visualize and display the results, and the visualization output results are used to compile data analysis reports and other results.

[0067] Corresponding risk warning suggestions and risk management strategies are set for different risk levels. For example, for users with extremely high risks, their accounts can be restricted from paying electricity bills or their payment amounts or payment times can be restricted.

[0068] Preferably, the model training samples are updated regularly, and the model parameters are updated using the updated training samples to stabilize the accuracy of the model.

[0069] In the method provided by the embodiment of the present invention, by monitoring all aspects of the financial risk of the electricity bill channel, an electricity bill channel financial risk assessment model and an anti-money laundering prevention and control model are constructed, and early warnings are issued for potential electricity bill revenue and expenditure risks, thereby eliminating the operating risks of concealing real arrears, avoiding economic losses for enterprises, reducing the risks in the electricity bill collection process, and improving the efficiency of electricity bill management and control.

[0070] Figure 2 A schematic diagram of a structure of an electricity fee income and expenditure risk warning device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system comprises:

[0071] The data acquisition module 11 is used to acquire electricity fee income and expenditure data information.

[0072] The data processing module 22 is used to input the electricity fee revenue and expenditure data information into a preset electricity fee channel fund risk assessment model and an electricity fee fund anti-money laundering risk assessment model, and output a scoring result.

[0073] The early warning analysis module 33 is used to perform risk early warning analysis according to the scoring results and output risk early warning suggestions.

[0074] The above device can execute the electricity fee income and expenditure risk warning method in the above embodiment, wherein the data acquisition module 11 executes step S101; the data processing module 22 executes step S102; and the warning analysis module 33 executes step S103.

[0075] Based on the same inventive concept, an electronic device is also provided in an embodiment of the present application, and the method corresponding to the electronic device may be the electricity fee income and expenditure risk warning method in the aforementioned embodiment, and its principle of solving the problem is similar to that of the method. The electronic device provided in an embodiment of the present application includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the methods and / or technical solutions of the aforementioned multiple embodiments of the present application.

[0076] The electronic device may be a user device, or a device formed by integrating a user device and a network device through a network, or may be an application running on the above device. The user device includes but is not limited to various terminal devices such as computers, mobile phones, tablet computers, smart watches, and bracelets. The network device includes but is not limited to network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets, which can be used to implement some processing functions when setting an alarm. Here, the cloud is composed of a large number of hosts or network servers based on cloud computing, where cloud computing is a type of distributed computing, a virtual computer composed of a group of loosely coupled computer sets.

[0077] Figure 3 The structure of a device suitable for implementing the method and / or technical solution in the embodiment of the present application is shown, and the device 1200 includes a central processing unit (CPU, Central Processing Unit) 1201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM, Read Only Memory) 1202 or the program loaded from the storage part 1208 to the random access memory (RAM, Random Access Memory) 1203. In RAM1203, various programs and data required for system operation are also stored. CPU 1201, ROM 1202 and RAM 1203 are connected to each other through bus 1204. Input / output (I / O, Input / Output) interface 1205 is also connected to bus 1204.

[0078] The following components are connected to the I / O interface 1205: an input section 1206 including a keyboard, a mouse, a touch screen, a microphone, an infrared sensor, etc.; an output section 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), an LED display, an OLED display, etc., and a speaker, etc.; a storage section 1208 including one or more computer-readable media such as a hard disk, an optical disk, a magnetic disk, a semiconductor memory, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1209 performs communication processing via a network such as the Internet.

[0079] In particular, the methods and / or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. When the computer program is executed by the central processing unit (CPU) 1201, the above functions defined in the method of the present application are executed.

[0080] Another embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of the present application described above.

[0081] Specifically, the present embodiment may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device.

[0082] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than a computer readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0083] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0084] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0085] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0086] Those skilled in the art can 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.

[0087] In the several embodiments provided in the present application, 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or page components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that 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, which can be electrical, mechanical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0089] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

[0091] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for early warning of electricity bill revenue and expenditure risk, characterized in that: include: Obtain electricity bill revenue and expenditure data information; Input the electricity fee revenue and expenditure data information into a preset electricity fee channel fund risk assessment model and an electricity fee fund anti-money laundering risk assessment model respectively, and output a scoring result; A risk warning analysis is performed based on the scoring results, and risk warning suggestions are output.

2. The electricity fee revenue and expenditure risk early warning method according to claim 1, characterized in that: The obtaining of electricity fee revenue and expenditure data information further includes: Obtain user information and user payment information, wherein the user information includes low-voltage user information and high-voltage user information, and the user payment information includes user account information, payment name, payment channel and transaction flow.

3. The electricity fee revenue and expenditure risk early warning method according to claim 1, characterized in that: The step of inputting the electricity fee revenue and expenditure data information into a preset electricity fee channel fund risk assessment model and an electricity fee fund anti-money laundering risk assessment model, and outputting the scoring results further includes: Construct risk assessment models for electricity bill channel funds and anti-money laundering risk assessment models for electricity bill funds; Input the electricity fee revenue and expenditure data information into the electricity fee channel fund risk assessment model, and output the risk score of the corresponding channel fund; The electricity bill revenue and expenditure data information is input into the electricity bill fund anti-money laundering risk assessment model, and the money laundering behavior probability score is output.

4. The electricity fee revenue and expenditure risk early warning method according to claim 3 is characterized in that: The construction of the electricity fee channel fund risk assessment model and the electricity fee fund anti-money laundering risk assessment model further includes: Set corresponding model indicators and scoring rules according to different business scenarios; The electricity bill revenue and expenditure data of the previous six months are used as training samples to train the model.

5. The electricity fee revenue and expenditure risk early warning method according to claim 4 is characterized in that: The setting of corresponding model indicators and scoring rules according to different business scenarios further includes: Indicators are set for the electricity fee channel fund risk assessment model, including: reversal of payment, expected electricity fee, overdue electricity fee, and overdue refund, and different weights are set for the indicators using the hierarchical analysis method, and each indicator is clustered using the clustering algorithm to divide the threshold and the corresponding score; The indicators of the anti-money laundering risk assessment model for electricity bill funds include: risk scenarios, suspicious transactions and abnormal behaviors.

6. The electricity fee revenue and expenditure risk early warning method according to claim 1, characterized in that: The performing risk warning analysis according to the scoring results and outputting risk warning suggestions further includes: Conduct risk trend analysis on the scoring results and rank the risk status of each link in electricity fee recovery; Divide risk levels according to risk scores, and set corresponding risk warning suggestions and risk management strategies for different risk levels.

7. The electricity fee revenue and expenditure risk early warning method according to claim 1, characterized in that: The method further comprises: Update the model training samples regularly and use the updated training samples to update the model parameters.

8. An electricity fee income and expenditure risk warning device, characterized in that: include: Data acquisition module: used to obtain electricity bill revenue and expenditure data information; Data processing module: used to input the electricity fee revenue and expenditure data information into the preset electricity fee channel fund risk assessment model and the electricity fee fund anti-money laundering risk assessment model, and output the scoring results; Warning analysis module: used to perform risk warning analysis based on the scoring results and output risk warning suggestions.

9. An electricity fee income and expenditure risk warning device, the device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method according to any one of claims 1 to 7.

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