Risk adjustment term determination method and apparatus, electronic device, and storage medium

Abstract

The application provides a risk adjustment item determination method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a historical fixed value of a historical month uploaded in advance; determining an i-th statistical month and an i-1-th statistical month corresponding to the i-th statistical month; if the i-1-th statistical month belongs to the historical month, determining an i-th risk adjustment item of the i-th statistical month based on an i-th estimated risk cost cumulative value of the i-th statistical month and the historical fixed value; if the i-1-th statistical month does not belong to the historical month, determining an i-1-year risk version adopted by the i-1-th statistical month based on a current statistical time of the i-th statistical month; determining an i-1-th estimated risk cost of the i-1-th statistical month based on the i-1-year risk version; and determining the i-th risk adjustment item of the i-th statistical month based on the i-th estimated risk cost cumulative value, the i-1-th estimated risk cost and an i-1-th estimated risk cost cumulative value. The method can improve the determination accuracy of the risk adjustment item.

Description

Technical Field

[0001] This application relates to the field of data analysis and processing technology, and in particular to a method, apparatus, electronic device, and storage medium for determining risk adjustment items. Background Technology

[0002] Big data business refers to a broad range of business models that utilize massive, diverse, highly timely, and low-value-density big data as their core production factor. Through technologies such as big data collection, storage, cleaning, analysis, mining, and visualization, data is deeply processed and its value extracted to ultimately provide data-driven decision support and refined operational services for enterprise operations, industry services, and public decision-making. Taking business risk control as an example, big data analysis of historical risk data, such as determining business risk adjustment items based on historical averages, is used to make decisions regarding business risk reserves. However, traditional methods for calculating risk adjustment items cannot adapt to dynamic risk changes, leading to significant calculation errors. Summary of the Invention

[0003] This application provides a method, apparatus, electronic device, and storage medium for determining risk adjustment items, which can improve the accuracy of risk adjustment item determination. The technical solution is as follows: According to one aspect of this application, a method for determining a risk adjustment item is provided, the method further comprising: Obtain the historical fixed value of the pre-uploaded historical month, wherein the historical fixed value is determined by the historical risk estimate and historical risk adjustment value of the historical month; Determine the i-th statistical month and the (i-1)-th statistical month corresponding to the i-th statistical month, where the i-th statistical month is a future month of the historical month; If the (i-1)th statistical month belongs to the historical month, the i-th risk adjustment item for the i-th statistical month is determined based on the i-th estimated risk cost accumulation value of the i-th statistical month and the historical fixed value; If the (i-1)th statistical month does not belong to the historical month, the (i-1)th annualized risk version used in the (i-1)th statistical month is determined based on the current statistical time of the (i-1)th statistical month. Based on the (i-1)th annualized risk version, determine the (i-1)th estimated risk cost for the (i-1)th statistical month; Based on the cumulative value of the estimated risk cost of the i-th statistical month, the estimated risk cost of the (i-1)-th statistical month, and the cumulative value of the estimated risk cost of the (i-1)-th statistical month, the i-th risk adjustment item for the i-th statistical month is determined.

[0004] According to another aspect of this application, a risk adjustment item determination apparatus is provided, the apparatus further comprising: The acquisition module is used to acquire the historical fixed values ​​of the historical month that have been uploaded in advance. The historical fixed values ​​are determined by the historical risk estimate and historical risk adjustment value of the historical month. The first determining module is used to determine the i-th statistical month and the (i-1)-th statistical month corresponding to the i-th statistical month, wherein the i-th statistical month is a future month of the historical month; The second determining module is used to determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value of the i-th statistical month and the historical fixed value if the (i-1)-th statistical month belongs to the historical month. The third determining module is used to determine the (i-1)th annualized risk version adopted by the (i-1)th statistical month based on the current statistical time of the (i-1)th statistical month if the (i-1)th statistical month does not belong to the historical month. The fourth determining module is used to determine the estimated risk cost of the (i-1)th statistical month based on the (i-1)th annualized risk version. The fifth determining module is used to determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value for the i-th statistical month, the i-1 estimated risk cost for the (i-1)-th statistical month, and the i-1 estimated risk cost accumulation value for the (i-1)-th statistical month.

[0005] According to one aspect of this application, an electronic device is provided, comprising: a processor and a memory storing a program, the program including instructions that, when executed by the processor, cause the processor to perform the risk adjustment item determination method as described above.

[0006] According to another aspect of this application, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the risk adjustment item determination method as described above.

[0007] According to another aspect of this application, a computer program product is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned risk adjustment item determination method.

[0008] The beneficial effects of the technical solutions provided in this application include at least the following: Based on different statistical periods in the statistical month, the annualized risk version is dynamically determined to ensure that the latest annualized risk version is always used when calculating the estimated risk cost. This allows the risk adjustment item to adapt to the dynamic changes in the annualized risk version during calculation, improving the accuracy of the risk adjustment item determination. Furthermore, by simplifying the calculation formula of the risk adjustment item through reasoning, the calculation complexity of the risk adjustment item is reduced, thereby reducing the processing complexity of calculating large-scale risk adjustment items for equipment and improving the efficiency of determining the risk adjustment item. Attached Figure Description

[0009] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a risk adjustment item determination method according to an exemplary embodiment of this application is shown; Figure 2 This is a schematic diagram of a risk adjustment item determination device provided in an embodiment of this application; Figure 3 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of this application is shown. Detailed Implementation

[0010] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0011] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0012] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this application are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies. It should be noted that the modifications "a" and "a plurality" mentioned in this application are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated in the context, they should be understood as "one or more". The names of messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0013] The present invention will now be described with reference to the accompanying drawings. The technical solutions provided by the embodiments of this application will be explained in detail through specific examples and application scenarios.

[0014] In the field of financial risk management, enterprises and financial institutions typically use the following traditional methods to calculate risk adjustment items, such as bad debt provisions and credit risk provisions.

[0015] (1) Static estimation method: Risk reserve is calculated once based on a fixed formula or historical average value, without considering subsequent data updates.

[0016] (2) Simple difference adjustment method: The latest risk estimate is subtracted from the original estimate as the adjustment item, which usually relies on manual intervention.

[0017] (3) Manual experience correction method: Financial personnel regularly review risk data and manually adjust reserves. This method is common in traditional corporate financial systems, but it does not conform to the trend of modern automated risk control.

[0018] (4) Single-version risk model: The adjustment items are calculated using a single risk version (such as annual assessment), ignoring intermediate updates and lacking the ability to perform monthly / quarterly dynamic calibration.

[0019] The above-mentioned traditional methods have the following obvious limitations: (1) Static forecasting method cannot reflect market changes and risk dynamics, and is prone to insufficient reserves or over-provisioning. (2) Simple difference adjustment method does not consider the coordination of multiple versions of data, cannot handle the recursive correction of historical data, and has a large cumulative error. (3) Manual experience correction method relies on manual processing, is inefficient and highly subjective, is difficult to cope with large-scale data scenarios, and is difficult to audit and trace. (4) Risk models based on a single version have poor data timeliness and cannot capture risk changes (such as economic cycle fluctuations) in a timely manner.

[0020] Therefore, in response to the problems of the above-mentioned risk adjustment item determination methods being unable to dynamically respond to risk changes, having low adjustment efficiency, and being prone to errors, the embodiments of this application innovate by using a dynamic annualized risk version + risk adjustment value formula, which enables the calculation of risk adjustment values ​​to respond to changes in annualized risk in near real time, thereby improving the calculation efficiency and accuracy of risk adjustment values.

[0021] Please refer to Figure 1 The document illustrates a flowchart of a risk adjustment item determination method according to an exemplary embodiment of this application. The method is described using an example of its application to an electronic device. Figure 1 As shown, the method includes: Step 101: Obtain the historical fixed value of the pre-uploaded historical month. The historical fixed value is determined by the historical risk estimate and historical risk adjustment value of the historical month. Step 102: Determine the i-th statistical month and the (i-1)-th statistical month corresponding to the i-th statistical month, where the i-th statistical month is a future month of the historical month; Step 103: If the (i-1)th statistical month is a historical month, determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value and the historical fixed value for the i-th statistical month. Step 104: If the (i-1)th statistical month does not belong to a historical month, determine the (i-1)th annualized risk version used for the (i-1)th statistical month based on the current statistical time of the (i)th statistical month. Step 105: Based on the (i-1)th annualized risk version, determine the (i-1)th estimated risk cost for the (i-1)th statistical month; Step 106: Based on the cumulative value of the estimated risk cost of the i-th statistical month, the estimated risk cost of the i-1th statistical month, and the cumulative value of the estimated risk cost of the i-1th statistical month, determine the i-th risk adjustment item for the i-th statistical month.

[0022] The risk adjustment item is a reserve fund set aside in advance to address future business risks under the target business scenario. For example, if the target business scenario is credit risk, the risk adjustment item could be a bad debt reserve or a credit risk provision; if the target business scenario is e-commerce sales, the risk adjustment item could be a sales loss buffer to cope with losses caused by fluctuations in commodity prices; if the target business scenario is industrial production, the risk adjustment item could be a production reserve to cover liquidity reserves in case of supply chain disruptions. This embodiment does not limit the specific application scenarios of the risk adjustment item.

[0023] The risk adjustment item determination method provided in this application only requires uploading historical fixed values ​​for historical months, which can then be used to calculate risk adjustment items for several future months. In one possible implementation, at the start of risk adjustment item calculation, historical fixed values ​​for historical months are first uploaded. These historical fixed values ​​are determined by the historical risk estimates and historical risk adjustment values ​​(historical risk adjustment items) for that month. For example, the historical fixed value can be set to H. Subsequently, by applying the historical fixed value and relevant data from the current statistical month, risk adjustment items for each statistical month can be calculated on a rolling basis.

[0024] In this context, the i-th statistical month is the current statistical month, the (i-1)-th statistical month is the month preceding the current statistical month, and the i-th statistical month is a future month of the historical month. For example, the historical months are January to September of 2024, meaning that the risk adjustment item for each statistical month is calculated starting from October 2024 using the risk adjustment item method described in this application. The i-th statistical month could be October 2024, November 2024, December 2024, or January 2025. If the i-th statistical month is October 2024, then the (i-1)-th statistical month is September 2024 (a historical month). If the i-th statistical month is December 2024, then the (i-1)-th statistical month is November 2024 (not a historical month). By assigning different month values ​​to i, the risk adjustment item for each statistical month after the historical month can be calculated cyclically.

[0025] For example, taking the uploaded fixed values ​​as January 2024 to September 2024 (historical months), and starting from October 2024, with the MTD month (current statistical month) as December 2024, the relationship between the annualized risk version number, annualized risk version, and risk adjustment can be shown in Table 1 (null indicates an empty value): Table 1

[0026] Analysis of Table 1 reveals that the calculation of risk adjustment items mainly involves several parameters: annualized risk version, cumulative estimated risk cost, and estimated risk cost. The relationship between the annualized risk version, cumulative estimated risk cost, and estimated risk cost for each month is shown in Table 2. Table 2

[0027] Combining Tables 1 and 2, the calculation formulas and simplified formulas for the risk adjustment items for each month can be derived, as shown in Table 3: Table 3

[0028] As shown in Table 3, the simplified calculation formula for the risk adjustment indicator involves two calculation principles: (1) The risk adjustment indicator for October 2024 is determined by the cumulative value of the estimated risk cost in October and the historical fixed value uploaded in advance; (2) The risk adjustment indicator for subsequent statistical months is determined by the cumulative value of the estimated risk cost in the current statistical month, the cumulative value of the estimated risk cost in the previous statistical month, and the estimated risk cost in the previous statistical month.

[0029] Combining Tables 1, 2, and 3, the actual risk adjustment item calculation process is divided into two cases. In one possible implementation, after determining the i-th statistical month and the (i-1)-th statistical month, it can be determined whether the (i-1)-th statistical month belongs to a historical month. If it belongs to a historical month, that is, the case where the i-th statistical month in Table 3 is October 2024, the i-th risk adjustment item for the i-th statistical month is determined based on the i-th estimated risk cost cumulative value (a1) and the historical fixed value (H) for the i-th statistical month.

[0030] In another possible implementation, if the (i-1)th statistical month is not a historical month, corresponding to November 2024, December 2024, and January 2025 in Table 3, the i-th risk adjustment item for the i-th statistical month is determined based on the cumulative value of the i-th estimated risk cost of the i-th statistical month, the i-1th estimated risk cost of the (i-1)th statistical month, and the cumulative value of the i-1th estimated risk cost of the (i-1)th statistical month.

[0031] For example, taking November 2024 as the i-th statistical month, the i-th risk adjustment item of the i-th statistical month is determined by the estimated cumulative risk cost (b1) of November, the estimated risk cost (a) of October, and the estimated cumulative risk cost (a1) of October.

[0032] The cumulative estimated risk cost for the i-th statistical month is determined by the sum of the estimated risk costs from the start month of the historical statistics to the (i-1)-th statistical month. For example, if the i-th statistical month is December 2024, the cumulative estimated risk cost for the i-th statistical month is determined by the sum of the estimated risk costs from the start month of the historical statistics (January 2024) to November 2024.

[0033] By analyzing the calculation logic of the above risk adjustment item, the main parameter involved is the estimated risk cost for each statistical month. In other words, the accuracy of the estimated risk cost determines the accuracy of the risk adjustment item. The estimated risk cost is calculated as loan amount × annualized risk × risk duration / 12. In actual statistical work, the annualized risk version for the statistical month generally includes version A and version B. That is, the annualized risk version may change with the statistical period. If a fixed annualized risk version is used, the calculation of the risk adjustment item will obviously not be able to adapt to the dynamic changes in annualized risk, resulting in a large error in the risk adjustment item.

[0034] To enable the risk adjustment item to utilize the dynamic changes in annualized risk, the calculation of the i-th risk adjustment item first determines the i-1 annualized risk version used in the (i-1)-th statistical month based on the current statistical time of the i-th statistical month. Then, based on the i-1 annualized risk version, the i-1-th estimated risk cost of the (i-1)-th statistical month is determined. Finally, based on the cumulative value of the i-th estimated risk cost of the i-th statistical month, the i-1-th estimated risk cost of the (i-1)-th statistical month, and the cumulative value of the i-1-th estimated risk cost of the (i-1)-th statistical month, the i-th risk adjustment item of the i-th statistical month is determined.

[0035] Based on Table 1, taking December as the current statistical month as an example, there will be two annualized risk versions, A and B, each month. For example, version A will be uploaded on December 6th, and version B will be uploaded on December 22nd. This will divide December into three time periods: 1st-6th, 6th-22nd, and 22nd-30th. During the period from 1st to 6th, since no annualized risk version has been uploaded yet, the corresponding number of annualized risk versions is 0. Since an annualized risk version was uploaded on the 6th, the corresponding number of annualized risk versions during the period from 6th to 22nd is 1 (12A). Since an annualized risk version was also uploaded on the 22nd, the corresponding number of annualized risk versions during the period from 22nd to 30th is 2 (12A+12B).

[0036] Considering that the annualized risk version for the previous month is determined by the latest annualized risk version for the following month, taking December as the current statistical month, during the period from December 1st to 6th, since the annualized risk version for December has not yet been uploaded, the annualized risk version for November will still use the latest annualized risk version for November (11B). During the period from December 6th to 22nd, since the latest annualized risk version for December has changed to 12A, the corresponding latest annualized risk version for November will also be 12A. During the period from December 22nd to 30th, since the latest annualized risk version for December has changed to 12B, the corresponding latest annualized risk version for November will also be updated to 12B.

[0037] Based on the above examples, the process of determining the annualized risk version for the (i-1)th statistical month may include: if the current statistical time of the i-th statistical month is the first time period, the B version of the annualized risk version for the (i-1)th statistical month is determined as the i-1th annualized risk version used in the i-1th statistical month; if the current statistical time of the i-th statistical month is the second time period, the A version of the annualized risk version for the i-th statistical month is determined as the i-1th annualized risk version used in the i-1th statistical month; if the current statistical time of the i-th statistical month is the third time period, the B version of the annualized risk version for the i-th statistical month is determined as the i-1th annualized risk version used in the i-1th statistical month.

[0038] The first, second, and third time periods are determined by the first time point of the first submission of the annualized risk version and the second time point of the second submission of the annualized risk version in the i-th statistical month. That is, the time period from the beginning of the i-th statistical month to the first time point is the first time period, the time period from the first time point to the second time point is the second time period, and the time period from the second time point to the end of the i-th statistical month is the third time period.

[0039] For example, taking December as the i-th statistical month, version A will be uploaded on the 6th of December (the first time point), and version B will be uploaded on the 22nd of December (the second time point). Accordingly, December will be divided into the first time period from the 1st to the 6th, the second time period from the 6th to the 22nd, and the third time period from the 22nd to the 30th.

[0040] In one possible implementation, after determining the (i-1)th annualized risk version for the (i-1)th statistical month, the (i-1)th estimated risk cost for the (i-1)th statistical month can be determined according to the formula for calculating the estimated risk cost. Specifically, based on the loan amount, the (i-1)th annualized risk version, and the risk duration for the (i-1)th statistical month, the (i-1)th estimated risk cost for the (i-1)th statistical month is determined as (loan amount × (i-1)th annualized risk version * risk duration / 12).

[0041] After calculating the (i-1)th estimated risk cost, the i-th risk adjustment item for the i-th statistical month can be calculated according to the simplified formula shown in Table 3, categorized by scenario. Specifically, if the (i-1)th statistical month is not a historical month, the formula for calculating the i-th risk adjustment item is: i-th risk adjustment item = c1 - (b + b1); where c1 represents the cumulative value of the i-th estimated risk cost, b represents the (i-1)th estimated risk cost for the (i-1)th statistical month, and b1 represents the cumulative value of the (i-1)th estimated risk cost for the (i-1)th statistical month.

[0042] Optionally, if the (i-1)th statistical month is a historical month, the calculation formula for the i-th risk adjustment term is: i-th risk adjustment term = a1 - H; where a1 represents the cumulative value of the i-th estimated risk cost in the i-th statistical month, and H represents the historical fixed value.

[0043] In summary, the embodiments of this application provide a method for determining risk adjustment items: the annualized risk version is dynamically determined according to different statistical periods of the statistical month to ensure that the latest annualized risk version is always used when calculating the estimated risk cost, so that the risk adjustment item can adapt to the dynamic changes of the annualized risk version during calculation, thereby improving the accuracy of the risk adjustment item calculation; moreover, by simplifying the calculation formula of the risk adjustment item through reasoning, the calculation complexity of the risk adjustment item is reduced, thereby reducing the processing complexity of the equipment in calculating large-scale risk adjustment items and improving the efficiency of determining the risk adjustment item.

[0044] Based on the above embodiments, the technical logic of the risk adjustment item involved in this application is as follows: The table in question is: Multi-version Annualized Risk - Estimated Risk Cost Table; it includes the annualized risk version, statistical month, and estimated risk cost (a, b, c, etc. are calculated by loan amount × annualized risk * risk duration / 12). (1) Data before 2024: Estimated risk cost: Latest version data of annualized risk Risk Adjustment: null (2) Financial uploads from January 2024 to September 2024 (3) After October 2024 Step 1: First calculate the summary values ​​(a1, b1, c1, etc.) after 2202410. Step 2: Calculate the risk adjustment item Historical Month: The annualized number of risks is 2 (this value will remain fixed and will not change). Risk adjustment item: Calculated according to the formula in Table 3. Estimated risk cost: The annualized risk version is the estimated risk cost of version B one month prior to the statistical month. Current month and last month: (1) Number of annualized risk versions for the current month: 0, 1: The risk adjustment item is null, and the estimated risk cost uses the latest version of the annualized risk data. 2: Risk Adjustment Item: Calculated according to the formula in Table 3, estimated risk cost: Take version B data (which will remain unchanged thereafter).

[0045] This application provides a risk adjustment calculation mechanism based on multiple versions: A / B dual-version data fusion: Two annualized risk versions (Version A and Version B) are used for preliminary estimation and final calibration each month, respectively, to ensure data accuracy; Data fallback mechanism: Risk estimation costs are always calculated using the latest version of data for the current month (e.g., Version B), avoiding errors caused by outdated historical data; Cross-version data dependency management: A mapping relationship between adjustment items and data versions is established through version tags (e.g., 2024-11B), supporting retrospective queries.

[0046] Furthermore, the progressive calculation formula for multi-version risk data is automatically optimized through elimination: through mathematical derivation, fixed values ​​(such as H) and duplicate calculation terms are eliminated, simplifying the original complex formula into a lightweight expression that only relies on the latest version of the data. Computational complexity is optimized: reduced from O(n²) of traditional methods to O(n), making it suitable for efficient calculation of large-scale financial data. The calculation process is auditable: the complete formula logic before simplification is preserved, ensuring that the calculation process can be reconstructed during auditing.

[0047] Please refer to Figure 2 This is a schematic diagram of a risk adjustment item determination device provided in an embodiment of this application. For example, as shown... Figure 2 As shown, the device 200 includes: The acquisition module 201 is used to acquire the historical fixed value of the historical month that has been uploaded in advance. The historical fixed value is determined by the historical risk estimate and historical risk adjustment value of the historical month. The first determining module 202 is used to determine the i-th statistical month and the (i-1)-th statistical month corresponding to the i-th statistical month, wherein the i-th statistical month is a future month of the historical month; The second determining module 203 is used to determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value of the i-th statistical month and the historical fixed value if the (i-1)-th statistical month belongs to the historical month. The third determining module 204 is used to determine the (i-1)th annualized risk version adopted by the (i-1)th statistical month based on the current statistical time of the (i-1)th statistical month if the (i-1)th statistical month does not belong to the historical month. The fourth determining module 205 is used to determine the estimated risk cost of the (i-1)th statistical month based on the (i-1)th annualized risk version; The fifth determining module 206 is used to determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value for the i-th statistical month, the i-1 estimated risk cost for the (i-1)-th statistical month, and the i-1 estimated risk cost accumulation value for the (i-1)-th statistical month.

[0048] Optionally, the third determining module 204 is further configured to: If the current statistical time of the i-th statistical month is the first time period, the B version of the annualized risk version of the (i-1)-th statistical month is determined as the (i-1)-th annualized risk version adopted in the (i-1)-th statistical month. If the current statistical time of the i-th statistical month is the second time period, the A version of the annualized risk version of the i-th statistical month is determined as the i-1 annualized risk version used in the (i-1)-th statistical month; If the current statistical time of the i-th statistical month is the third time period, the B version of the annualized risk version of the i-th statistical month is determined as the i-1 annualized risk version used in the (i-1)-th statistical month.

[0049] Optionally, the fourth determining module 205 is further configured to: Based on the loan amount in the (i-1)th statistical month, the annualized risk version in the (i-1)th statistical month, and the risk duration, the estimated risk cost in the (i-1)th statistical month is determined.

[0050] Optionally, if the (i-1)th statistical month does not belong to the historical month, the calculation formula for the i-th risk adjustment term is: The i-th risk adjustment term = c1 - (b + b1) Where c1 represents the cumulative value of the estimated risk cost for the i-th month, b represents the estimated risk cost for the (i-1)-th month of the (i-1)-th month, and b1 represents the cumulative value of the estimated risk cost for the (i-1)-th month of the (i-1)-th month.

[0051] Optionally, if the (i-1)th statistical month belongs to the historical month, the calculation formula for the i-th risk adjustment term is: Risk adjustment term i = a1 - H Where a1 represents the cumulative value of the estimated risk cost for the i-th statistical month, and H represents the historical fixed value.

[0052] Optionally, the cumulative value of the estimated risk cost for the i-th statistical month is determined by the sum of the estimated risk costs from the statistical start month of the historical month to the (i-1)-th statistical month.

[0053] In summary, the embodiments of this application provide a method for determining risk adjustment items: the annualized risk version is dynamically determined according to different statistical periods of the statistical month to ensure that the latest annualized risk version is always used when calculating the estimated risk cost, so that the risk adjustment item can adapt to the dynamic changes of the annualized risk version during calculation, thereby improving the accuracy of the risk adjustment item calculation; moreover, by simplifying the calculation formula of the risk adjustment item through reasoning, the calculation complexity of the risk adjustment item is reduced, thereby reducing the processing complexity of the equipment in calculating large-scale risk adjustment items and improving the efficiency of determining the risk adjustment item.

[0054] An exemplary embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program, when executed by the at least one processor, causing the electronic device to perform a risk adjustment item determination method according to an embodiment of this application.

[0055] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a risk adjustment item determination method according to an embodiment of this application.

[0056] An exemplary embodiment of this application also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a risk adjustment item determination method according to an embodiment of this application.

[0057] refer to Figure 3 The present invention describes a structural block diagram of an electronic device 300 that can serve as a server or client of this application, which is an example of a hardware device that can be applied to various aspects of this application. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the application described and / or claimed herein.

[0058] like Figure 3As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0059] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, output unit 307, storage unit 308, and communication unit 309. Input unit 306 can be any type of device capable of inputting information to electronic device 300. Input unit 306 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 307 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 308 may include, but is not limited to, disk and optical disk. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0060] Optionally, the electronic device 300 also includes a single-channel EEG signal acquisition module (not shown in the figure). This module is used to acquire EEG signals and transmit them to the signal processor of the electronic device 300 for EEG signal processing.

[0061] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above. For example, in some embodiments, the methods shown in the above embodiments can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. In some embodiments, the computing unit 301 can be configured to perform the methods shown in the above embodiments by any other suitable means (e.g., by means of firmware).

[0062] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may 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.

[0063] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. 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 (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0064] As used in this application, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0065] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the 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).

[0066] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user 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., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0067] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A method for determining risk adjustment items, characterized in that, The method includes: Obtain the historical fixed value of the pre-uploaded historical month, wherein the historical fixed value is determined by the historical risk estimate and historical risk adjustment value of the historical month; Determine the i-th statistical month and the (i-1)-th statistical month corresponding to the i-th statistical month, where the i-th statistical month is a future month of the historical month; If the (i-1)th statistical month belongs to the historical month, the i-th risk adjustment item for the i-th statistical month is determined based on the i-th estimated risk cost accumulation value of the i-th statistical month and the historical fixed value; If the (i-1)th statistical month does not belong to the historical month, the (i-1)th annualized risk version used in the (i-1)th statistical month is determined based on the current statistical time of the (i-1)th statistical month. Based on the (i-1)th annualized risk version, determine the (i-1)th estimated risk cost for the (i-1)th statistical month; Based on the cumulative value of the estimated risk cost of the i-th statistical month, the estimated risk cost of the (i-1)-th statistical month, and the cumulative value of the estimated risk cost of the (i-1)-th statistical month, the i-th risk adjustment item for the i-th statistical month is determined.

2. The method according to claim 1, characterized in that, The step of determining the (i-1)th annualized risk version used in the (i-1)th statistical month based on the current statistical time of the i-th statistical month includes: If the current statistical time of the i-th statistical month is the first time period, the B version of the annualized risk version of the (i-1)-th statistical month is determined as the (i-1)-th annualized risk version adopted in the (i-1)-th statistical month. If the current statistical time of the i-th statistical month is the second time period, the A version of the annualized risk version of the i-th statistical month is determined as the i-1 annualized risk version used in the (i-1)-th statistical month; If the current statistical time of the i-th statistical month is the third time period, the B version of the annualized risk version of the i-th statistical month is determined as the i-1 annualized risk version used in the (i-1)-th statistical month.

3. The method according to claim 1, characterized in that, The determination of the estimated risk cost for the (i-1)th statistical month based on the (i-1)th annualized risk version includes: Based on the loan amount in the (i-1)th statistical month, the annualized risk version in the (i-1)th statistical month, and the risk duration, the estimated risk cost in the (i-1)th statistical month is determined.

4. The method according to claim 1, characterized in that, If the (i-1)th statistical month does not belong to the historical month, the calculation formula for the i-th risk adjustment item is: The i-th risk adjustment term = c1 - (b + b1) Where c1 represents the cumulative value of the estimated risk cost of the i-th statistical month, b represents the estimated risk cost of the (i-1)-th statistical month, and b1 represents the cumulative value of the estimated risk cost of the (i-1)-th statistical month.

5. The method according to claim 1, characterized in that, If the (i-1)th statistical month belongs to the historical month, the calculation formula for the i-th risk adjustment term is: Risk adjustment term i = a1 - H Where a1 represents the cumulative value of the estimated risk cost for the i-th statistical month, and H represents the historical fixed value.

6. The method according to claim 1, characterized in that, The cumulative value of the estimated risk cost for the i-th statistical month is determined by the sum of the estimated risk costs from the statistical start month of the historical month to the (i-1)-th statistical month.

7. A risk adjustment item determination device, characterized in that, The device further includes: The acquisition module is used to acquire the historical fixed values ​​of the historical month that have been uploaded in advance. The historical fixed values ​​are determined by the historical risk estimate and historical risk adjustment value of the historical month. The first determining module is used to determine the i-th statistical month and the (i-1)-th statistical month corresponding to the i-th statistical month, wherein the i-th statistical month is a future month of the historical month; The second determining module is used to determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value of the i-th statistical month and the historical fixed value if the (i-1)-th statistical month belongs to the historical month. The third determining module is used to determine the (i-1)th annualized risk version adopted by the (i-1)th statistical month based on the current statistical time of the (i-1)th statistical month if the (i-1)th statistical month does not belong to the historical month. The fourth determining module is used to determine the estimated risk cost of the (i-1)th statistical month based on the (i-1)th annualized risk version. The fifth determining module is used to determine the i-th risk adjustment item for the i-th statistical month based on the i-th estimated risk cost accumulation value for the i-th statistical month, the i-1 estimated risk cost for the (i-1)-th statistical month, and the i-1 estimated risk cost accumulation value for the (i-1)-th statistical month.

8. The apparatus according to claim 7, characterized in that, The third determining module is further configured to: If the current statistical time of the i-th statistical month is the first time period, the B version of the annualized risk version of the (i-1)-th statistical month is determined as the (i-1)-th annualized risk version adopted in the (i-1)-th statistical month. If the current statistical time of the i-th statistical month is the second time period, the A version of the annualized risk version of the i-th statistical month is determined as the i-1 annualized risk version used in the (i-1)-th statistical month; If the current statistical time of the i-th statistical month is the third time period, the B version of the annualized risk version of the i-th statistical month is determined as the i-1 annualized risk version used in the (i-1)-th statistical month.

9. An electronic device, comprising: processor; as well as Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the risk adjustment item determination method according to any one of claims 1-6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the risk adjustment item determination method according to any one of claims 1-6.

Images