A method and system for intelligent judgment and self-checking of transaction speculation behavior

Abstract

This invention discloses an intelligent judgment and traceability method and system for speculative trading behavior, belonging to the field of financial derivatives regulatory technology. It collects original heterogeneous business data through standardized data interfaces, cleans and formats this data, replacing manual cross-departmental integration of heterogeneous data, significantly improving judgment efficiency. It uses a pre-built regulatory indicator system for quantitative calculation and automatically matches it through a compliance rule base, eliminating reliance on human supervisor experience and ensuring the uniformity and accuracy of judgment standards, reducing the probability of misjudgments or omissions. By constructing a complete data chain, it solves the problem of data silos, enabling collaborative analysis and judgment of multi-source heterogeneous data. It also provides visualization and retrospective query functions, allowing for rapid tracing of the judgment source. The regulatory analysis indicator system and compliance judgment rule base can be configured and adjusted in real time according to regulatory requirements, facilitating application and promotion.

Description

Technical Field

[0001] This invention belongs to the field of financial derivatives regulatory technology, specifically relating to a method and system for intelligent judgment and traceability of speculative trading behavior. Background Technology

[0002] With the increasing frequency of cross-border trade and investment and financing activities by central state-owned enterprises (SOEs), financial derivatives have become an important tool for enterprises to hedge market risks such as exchange rates and interest rates. However, some enterprises still engage in speculative trading using financial derivatives, which violates national regulatory requirements and internal compliance management regulations, and may trigger significant financial risks and jeopardize the security of state-owned assets.

[0003] Currently, the verification of speculative behavior in financial derivatives transactions by central state-owned enterprises mainly relies on manual auditing. This requires the collaboration of business personnel, financial personnel, and compliance personnel to manually collect a large amount of scattered data such as cross-border contracts, customs declarations, and transaction contracts, and then check and judge them one by one against compliance standards. However, this model has the following shortcomings, such as (1) low efficiency: manual verification involves multiple departments and various types of heterogeneous data. It is difficult and time-consuming to integrate the data. A single batch of verification often takes several weeks, which cannot meet the needs of central state-owned enterprises for routine and high-frequency self-assessment and self-inspection; (2) insufficient accuracy: the judgment of speculative behavior relies on human subjective experience, lacks uniformity, and is prone to misjudgment or omission; (3) lack of systematic support: the existing ERP (Enterprise Resource Planning) system is not fully functional. In the Planning (Enterprise Resource Planning) system, the financial derivatives module and the supply chain management, financial management and risk control management modules have data silos. There is no special regulatory indicator system for the verification of speculative behavior, and it is impossible to achieve automated verification and intelligent judgment of speculative behavior; (4) Weak traceability: In the process of manual verification, the relationship between the judgment basis and the original data is easily lost. In subsequent verification, rectification and regulatory reporting, it is difficult to quickly trace the source of the judgment, which increases the difficulty of compliance evidence; (5) Poor compliance adaptability: In the existing technology, there is no targeted matching of the regulatory policy requirements of central enterprises and their own business characteristics. There is no standardized self-inspection logic and report output template, which makes it difficult to efficiently meet the internal compliance management and external regulatory reporting needs.

[0004] Therefore, how to provide an effective technical solution to address the problems of low efficiency, insufficient accuracy, lack of systematic support, weak traceability, and poor compliance adaptability in existing technologies has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent judgment and traceability of speculative trading behavior, in order to solve the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for intelligent determination and tracing of speculative trading behavior, including: Acquire raw, heterogeneous business data from the ERP system using standardized data interfaces; The original heterogeneous business data is cleaned and formatted to obtain the processed original heterogeneous business data. Based on a unique business identifier, the original heterogeneous business data after processing related to the same derivative transaction are associated and mapped to build a complete data chain; Based on the calculation rules in the pre-built regulatory analysis indicator system, the standardized related data in the complete data chain are quantitatively calculated to obtain the quantitative results of each core regulatory indicator. Based on a pre-built compliance judgment rule base, the quantitative results of each core regulatory indicator are matched with the speculation judgment threshold of the corresponding core regulatory indicator to obtain the indicator items that trigger the speculation judgment standard. The number of indicator items that trigger the speculation judgment criteria is used to classify the speculation risk level, and the judgment results are visualized. Based on the judgment results, the relevant original business heterogeneous data can be traced back level by level.

[0007] In one possible design, the regulatory analysis indicator system includes at least one of the following: business background indicators, transaction operation indicators, risk management indicators, and profit-oriented indicators.

[0008] In one possible design, the business background category indicators include at least the real exposure correspondence rate and the exposure certificate authenticity rate. The real exposure correspondence rate is used to measure whether the derivatives transaction is supported by real business exposure, and the exposure certificate authenticity rate is used to verify the authenticity of the exposure certificate. The trading operation indicators include at least the maturity mismatch rate, the overdue amount ratio, the number of high-frequency trades, the direction switching frequency, and the leverage over-limit rate. The maturity mismatch rate is used to assess the degree of matching between the contract maturity and the risk exposure period. The overdue amount ratio is used to assess the degree of matching between the trading amount and the risk exposure amount. The number of high-frequency trades, the direction switching frequency, and the leverage over-limit rate are used to identify abnormal trading behavior. The risk management indicators include at least the risk control system coverage rate, approval compliance rate, and authorization scope compliance rate. The risk control system coverage rate is used to assess whether the transaction is included in comprehensive risk management, the approval compliance rate is used to assess the approval compliance rate of the transaction, and the authorization scope compliance rate is used to assess the authorization compliance. The aforementioned revenue-oriented indicators include at least the proportion of independent profit and loss accounting, the revenue-oriented assessment indicator, and the excess return pursuit indicator. The proportion of independent profit and loss accounting is used to identify whether the profit and loss accounting deviates from the hedging accounting principle. The revenue-oriented assessment indicator is used to verify whether the enterprise's performance assessment indicators include derivatives trading income as a core assessment item. The excess return pursuit indicator is used to verify whether there are any decision records of actively giving up hedging opportunities or deliberately pursuing excess returns from exchange rate or interest rate fluctuations.

[0009] In one possible design, the original heterogeneous business data is cleaned and formatted to obtain processed original heterogeneous business data, including: The original heterogeneous business data is processed for duplicate and missing values ​​to remove duplicate values ​​and fill in missing values, resulting in the completed original heterogeneous business data. The authenticity of the completed original heterogeneous business data is verified based on the preset data verification rules, and the original heterogeneous business data with authenticity greater than the preset authenticity threshold is extracted. Based on preset formatting rules, the original heterogeneous business data with authenticity greater than the preset authenticity threshold is formatted to obtain the processed original heterogeneous business data.

[0010] In one possible design, the original heterogeneous business data with an authenticity greater than a preset authenticity threshold is formatted based on preset formatting rules, including at least one of the following: Convert the amounts in different currencies in the original heterogeneous business data with authenticity greater than a preset authenticity threshold into the base currency; Convert dates in different formats from the original heterogeneous business data whose authenticity exceeds a preset authenticity threshold into a standard format; Text information of original heterogeneous business data with authenticity greater than a preset authenticity threshold is uniformly encoded based on a preset encoding format.

[0011] In one possible design, the determination result includes high-risk speculative transactions, medium-risk speculative transactions, and compliant transactions; the determination of the speculative risk level based on the number of indicator items triggering the speculative determination criteria, to obtain the determination result, includes: If the number of indicators that trigger the speculative judgment criteria is greater than or equal to the first threshold, it is judged as a high-risk speculative transaction. If the number of indicators that trigger the speculative judgment criteria is less than the first threshold and greater than or equal to the second threshold, it is judged as a medium-risk speculative transaction. If the number of indicators that trigger the speculation criteria reaches the third threshold, the transaction is judged to be compliant.

[0012] In one possible design, after obtaining the judgment result, the following steps are also included: marking the original heterogeneous business data located in the complete data chain corresponding to the indicator item that triggers the speculation judgment criterion in the judgment result.

[0013] Secondly, this invention provides an intelligent system for determining and tracing speculative trading behavior, comprising: The acquisition module is used to acquire raw, heterogeneous business data collected from the ERP system via a standardized data interface. The processing module is used to perform data cleaning and formatting on the original heterogeneous business data to obtain the processed original heterogeneous business data. The module is used to associate and map the processed original heterogeneous business data related to the same derivative transaction based on a unique business identifier, so as to build a complete data chain. The calculation module is used to perform quantitative calculations on standardized related data in the complete data chain according to the calculation rules in the pre-built regulatory analysis indicator system, so as to obtain the quantitative results of each core regulatory indicator. The matching module is used to match the quantitative results of each core regulatory indicator with the speculation judgment threshold of the corresponding core regulatory indicator based on the pre-built compliance judgment rule base, so as to obtain the indicator items that trigger the speculation judgment standard. The judgment module is used to classify the speculative risk level based on the number of indicator items that trigger the speculative judgment criteria, obtain the judgment result, and visualize the judgment result so that the related original business heterogeneous data can be traced back step by step according to the judgment result.

[0014] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the intelligent judgment and traceability method for trading speculation behavior as described in the first aspect above.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the intelligent judgment and traceability method for trading speculation as described in the first aspect above.

[0016] Fifthly, the present invention provides a computer program product containing instructions that, when the instructions are executed on a computer, cause the computer to perform the intelligent judgment and traceability method for trading speculation as described in the first aspect above.

[0017] The beneficial effects of this invention are as follows: This invention discloses an intelligent judgment and traceability method and system for trading speculation. It acquires original heterogeneous business data from an ERP system via a standardized data interface, performs data cleaning and formatting on this data, and maps the processed original heterogeneous business data related to the same derivative transaction based on a unique business identifier to construct a complete data chain. According to the calculation rules in a pre-built regulatory analysis indicator system, it quantifies the standardized associated data in the complete data chain to obtain the quantification results of each core regulatory indicator. Based on a pre-built compliance judgment rule library, it matches the quantification results of each core regulatory indicator with the corresponding core regulatory indicator's speculation judgment threshold to obtain the indicator items that trigger the speculation judgment standard. It then classifies the number of indicator items that trigger the speculation judgment standard to determine the speculation risk level, obtains the judgment results, and visualizes the judgment results to allow for step-by-step backtracking to the associated original heterogeneous business data. This invention collects raw heterogeneous business data through a standardized data interface, cleans and formats the data, replacing manual cross-departmental integration of heterogeneous data, significantly improving judgment efficiency. It uses a pre-built regulatory indicator system for quantitative calculation and automatically matches data through a compliance rule base, eliminating reliance on human supervisor experience and ensuring the uniformity and accuracy of judgment standards, reducing the probability of misjudgments or omissions. By constructing a complete data chain, it solves the problem of data silos, enabling collaborative analysis and judgment of multi-source heterogeneous data. It also provides visualization and retrospective query functions, allowing for rapid tracing of the judgment source. The regulatory analysis indicator system and compliance judgment rule base can be configured and adjusted in real time according to regulatory requirements, facilitating application and promotion. Attached Figure Description

[0018] Figure 1 A flowchart of the intelligent judgment and traceability method for trading speculation behavior provided in this embodiment of the invention; Figure 2 A block diagram of the intelligent judgment and traceability system for trading speculation provided in this embodiment of the invention; Figure 3 A structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0020] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0021] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0022] Example: like Figure 1 As shown, the first aspect of this embodiment provides a method for intelligent determination and tracing of speculative trading behavior, which can be executed, but is not limited to, by a computer device or virtual machine with certain computing resources, such as a personal computer or smartphone, or by a virtual machine; the method for intelligent determination and tracing of speculative trading behavior includes, but is not limited to, the following steps: S1. Obtain raw, heterogeneous business data from the ERP system using standardized data interfaces; It should be noted that the ERP system includes, but is not limited to, supply chain modules, finance modules, risk control modules, approval modules, authorization management modules, derivatives modules, and human resource management modules; the original heterogeneous business data includes, but is not limited to, cross-border contracts, customs declarations, foreign exchange receipts and payments vouchers, derivatives trading contracts, financial accounting vouchers, approval forms, authorization documents, risk control rules, and performance appraisal schemes. In specific implementation, the collection method can be set to real-time collection or scheduled collection. Scheduled collection can be exemplified by batch collection of original heterogeneous business data in the early morning of each day.

[0023] S2. Perform data cleaning and formatting on the original heterogeneous business data to obtain the processed original heterogeneous business data; Specifically, in step S2, the original heterogeneous business data is cleaned and formatted to obtain processed original heterogeneous business data, including: S21. Perform duplicate value processing and missing value processing on the original heterogeneous business data to remove duplicate values ​​and fill in missing values, so as to obtain the completed original heterogeneous business data. S22. Based on preset data verification rules, perform authenticity verification on the completed original heterogeneous business data, and extract the original heterogeneous business data whose authenticity is greater than the preset authenticity threshold; S23. Based on preset formatting rules, format the original heterogeneous business data whose authenticity is greater than the preset authenticity threshold to obtain the processed original heterogeneous business data.

[0024] In practice, the original heterogeneous business data contains scanned copies of contracts in different formats, monetary data in different calibers, and date data in different formats. The preset data verification rules can be exemplified by verifying the authenticity of invoices by connecting to an invoice verification system, thereby determining the authenticity of the original heterogeneous business data. By performing duplicate value processing, missing value processing, authenticity verification, and formatting on the original heterogeneous business data, the dimensional differences between different data can be eliminated, facilitating subsequent data processing.

[0025] Furthermore, based on preset formatting rules, the original heterogeneous business data with an authenticity greater than a preset authenticity threshold is formatted, including at least one of the following: S23.1. Convert the amounts of different currencies in the original heterogeneous business data with authenticity greater than the preset authenticity threshold into the base currency; S23.2. Convert dates in different formats from the original heterogeneous business data whose authenticity exceeds a preset authenticity threshold into a standard format; S23.3. Based on a preset encoding format, uniformly encode the text information of the original heterogeneous business data whose authenticity exceeds a preset authenticity threshold.

[0026] In this embodiment, the base currency can be RMB, and the standard date format can be "YYYY-MM-DD" to facilitate comparison of different heterogeneous original business data.

[0027] S3. Based on a unique business identifier, perform association mapping on the processed original heterogeneous business data related to the same derivative transaction to construct a complete data chain; It should be noted that unique business identifiers include, but are not limited to, transaction number, order number, and / or contract number.

[0028] In practice, transaction numbers, business order numbers, and / or contract numbers are used to map derivatives transaction data, business data, risk control data, approval data, and financial data together to form a complete data chain of "business-transaction-risk control-finance-approval," providing a foundation for subsequent indicator calculation, intelligent judgment, and retrospective query.

[0029] S4. Based on the calculation rules in the pre-built regulatory analysis indicator system, perform quantitative calculations on the standardized related data in the complete data chain to obtain the quantitative results of each core regulatory indicator; It should be noted that the quantitative results of each core regulatory indicator include calculation results or judgment indicators; the regulatory analysis indicator system includes at least one of the following: business background indicators, trading operation indicators, risk management indicators, and profit-oriented indicators; the business background indicators include at least the actual exposure correspondence rate and the exposure certificate authenticity rate. The actual exposure correspondence rate is used to measure whether derivative transactions are supported by actual business exposure, and the exposure certificate authenticity rate is used to verify the authenticity of exposure certificates; the trading operation indicators include at least the maturity matching deviation rate, the amount overdue ratio, the number of high-frequency trades, the direction switching frequency, and the leverage over-limit rate. The maturity matching deviation rate is used to assess the degree of matching between the contract term and the risk exposure cycle, the amount overdue ratio is used to assess the degree of matching between the transaction amount and the risk exposure amount, the number of high-frequency trades, the direction switching frequency, and the leverage over-limit rate. Frequency of turnover and leverage exceeding regulations are used to identify abnormal trading behavior; the risk management indicators include at least the risk control system coverage rate, approval compliance rate, and authorization scope compliance rate. The risk control system coverage rate is used to assess whether the transaction is included in comprehensive risk management, the approval compliance rate is used to assess the approval compliance rate of the transaction, and the authorization scope compliance rate is used to assess the authorization compliance; the profit-oriented indicators include at least the independent profit and loss accounting ratio, profit-oriented assessment indicators, and excess return pursuit indicators. The independent profit and loss accounting ratio is used to identify whether profit and loss accounting deviates from hedging accounting principles. The profit-oriented assessment indicators are used to verify whether derivatives trading profits are used as a core assessment item in the enterprise's performance assessment indicators. The excess return pursuit indicators are used to verify whether there are records of decisions to actively give up hedging opportunities or deliberately pursue excess returns from exchange rate or interest rate fluctuations.

[0030] The calculation rule for the true exposure correspondence ratio in the regulatory analysis indicator system constructed in this embodiment is (the ratio of the number of derivative transactions without corresponding real business to the total number of transactions). 100%, with data sourced from the supply chain module, investment and financing module, and derivatives module; the calculation rule for the authenticity rate of open-ended certificates is (number of transactions corresponding to false / tampered open-ended certificates ÷ total number of transactions). 100%, with data sourced from the supply chain, finance, and internal audit modules; For the maturity mismatch rate, the calculation rule is (contract term - exposure duration) ÷ exposure duration, only calculating positive deviations, i.e., cases where the contract term is longer than the exposure duration, with data sourced from the derivatives and risk exposure management modules; For the excess amount ratio, the calculation rule is (derivatives transaction amount - actual exposure amount) ÷ actual exposure amount, with data sourced from the derivatives and risk exposure management modules; For the number of high-frequency trades, the calculation rule is the total number of opening and closing positions for the same trading instrument within a month, with data sourced from the derivatives module; For the direction switching frequency, the calculation rule is the number of times the trading direction (buy / sell) for the same trading instrument is switched within a month, with data sourced from the derivatives module; For the leverage over-limit rate, the calculation rule is (number of trades exceeding regulatory / internal leverage ratios ÷ total number of trades) 100%, with data sourced from the derivatives and risk control modules; the risk control system coverage rate is calculated as (number of derivatives transactions included in comprehensive risk management ÷ total number of transactions). 100%, with data sourced from the risk control and derivatives modules; the approval compliance rate is calculated as (number of transactions that completed the full internal approval process ÷ total number of transactions). 100%, with data sourced from the approval and derivatives modules; the calculation rule for the authorized scope compliance rate is (number of transactions by the counterparty / product within the authorized scope ÷ total number of transactions). 100%, with data sourced from the authorization management module and derivatives module; the calculation rule for the proportion of independent profit and loss accounting is (number of derivative transactions separately accounted for as "investment income" ÷ total number of transactions). 100%, with data sourced from the finance and derivatives modules; for the profit-oriented assessment indicator, the calculation rule is to verify whether the enterprise's performance assessment indicators include derivatives trading profits as a core assessment item, with data sourced from the human resources and strategic management modules; for the excess return pursuit indicator, the calculation rule is to verify whether there are any decision records of actively giving up hedging opportunities and deliberately pursuing excess returns from exchange rate or interest rate fluctuations, with data sourced from the decision management and derivatives modules.

[0031] S5. Based on the pre-built compliance judgment rule base, the quantitative results of each core regulatory indicator are matched with the speculation judgment threshold of the corresponding core regulatory indicator to obtain the indicator items that trigger the speculation judgment standard. It should be noted that the speculation threshold for the true exposure correspondence rate in the compliance judgment rule base is 100%; the speculation threshold for the exposure certificate authenticity rate is >0%, indicating the existence of fictitious contracts, forged invoices, and / or altered customs declarations; the speculation threshold for the maturity matching deviation rate is >0 without reasonable justification, such as a 3-month exposure corresponding to a contract of more than 1 year without providing compliance explanation materials; the speculation threshold for the excess amount ratio is >100% without reasonable hedging basis, such as without providing compliance demonstration materials for excess hedging; the speculation threshold for the number of high-frequency transactions is ≥3 transactions of the same underlying asset per month without hedging risk needs, such as without being associated with the corresponding exposure business; the speculation threshold for the direction switching frequency is ≥2 direction switchings per month without reasonable hedging needs, such as without the switching basis being associated with changes in exposure; the speculation threshold for the leverage excess rate is >0%, indicating that the leverage ratio exceeds the national standard. The following are considered speculative thresholds for different types of transactions: leverage limits required by regulators or approved internally; a speculation threshold of <100% for risk control system coverage, indicating transactions not included in risk control management, without unlimited control or stop-loss mechanisms; a speculation threshold of <100% for approval compliance rate, indicating transactions bypassing approval processes, falsifying approval materials, or having approval authority inconsistent with regulations; a speculation threshold of <100% for authorized scope compliance rate, indicating transactions with unauthorized counterparties or trading in unauthorized instruments; a speculation threshold of >0% for the proportion of independent profit and loss accounting, indicating that the profit and loss is not consolidated and offset with the corresponding exposure business profit and loss, but is separately accounted for as investment income; a speculation threshold of "yes" for profit-oriented performance indicators, indicating that the core of performance evaluation is transaction profit, rather than risk hedging effect; and a speculation threshold of "yes" for excess return pursuit indicators, indicating that there are clear decision-making records proving that the purpose of the transaction was to deliberately pursue excess returns.

[0032] S6. Classify the number of indicator items that trigger the speculation judgment criteria to determine the level of speculation risk, obtain the judgment results, and visualize the judgment results so that the original heterogeneous business data can be traced back level by level according to the judgment results.

[0033] It should be noted that after obtaining the judgment results, the results can be visualized in the form of dashboards or statistical reports, such as the total number of speculative transactions, the proportion of high / medium risk speculative transactions, the distribution of violations across various indicators, and the distribution of speculative risks across different trading instruments. This allows users to quickly understand the overall compliance status. Furthermore, the dashboards or statistical reports support users querying the verification details of individual transactions by combining multiple conditions such as transaction date, trading instrument, risk level, and / or transaction number. This includes the calculation results of various regulatory indicators for the transaction, the triggered speculative judgment criteria, and / or the basis for risk level determination. When users click on abnormal transaction details, they can... The complete data chain directly jumps to the corresponding original heterogeneous business data (such as scanned copies of cross-border trade contracts, derivatives trading contracts, electronic versions of approval forms, or financial accounting vouchers), realizing full-link traceability of "judgment results - indicator data - original vouchers", clarifying the source of judgment and improving the efficiency of compliance evidence collection; furthermore, it supports the automatic generation of standardized self-assessment and self-inspection reports that meet the internal compliance management and external regulatory requirements of central enterprises. The report content includes the scope of verification, explanation of the regulatory indicator system, statistics of verification results, a list of high / medium risk transactions, and rectification suggestions, and can be directly exported as Word or PDF format for internal compliance review or regulatory reporting.

[0034] The determination results include high-risk speculative transactions, medium-risk speculative transactions, and compliant transactions.

[0035] Specifically, the number of indicators that trigger the speculation criteria is used to classify the level of speculative risk, and the judgment results include: S61. If the number of indicator items that trigger the speculative judgment criteria is greater than or equal to the first threshold, it is judged as a high-risk speculative transaction; S62. If the number of indicators that trigger the speculative judgment criteria is less than the first threshold and greater than or equal to the second threshold, it is judged as a medium-risk speculative transaction; S63. If the number of indicators that trigger the speculation criteria is the third threshold, then the transaction is judged to be compliant.

[0036] It should be noted that the third threshold is less than the second threshold, and the second threshold is less than the first threshold.

[0037] Furthermore, after obtaining the judgment result, it also includes marking the original heterogeneous business data located in the complete data chain corresponding to the indicator items that trigger the speculation judgment criteria in the judgment result.

[0038] In practice, for transactions that trigger speculation, abnormal data in the corresponding original business heterogeneous data will be automatically marked. Abnormal data, such as false contracts, overdue contracts, unapproved records and / or independent profit and loss accounting vouchers, will provide accurate clues for subsequent backtracking queries.

[0039] like Figure 2 As shown, the second aspect of this embodiment provides an intelligent judgment and traceability system for trading speculation behavior, including: The acquisition module is used to acquire raw, heterogeneous business data collected from the ERP system via a standardized data interface. The processing module is used to perform data cleaning and formatting on the original heterogeneous business data to obtain the processed original heterogeneous business data. The module is used to associate and map the processed original heterogeneous business data related to the same derivative transaction based on a unique business identifier, so as to build a complete data chain. The calculation module is used to perform quantitative calculations on standardized related data in the complete data chain according to the calculation rules in the pre-built regulatory analysis indicator system, so as to obtain the quantitative results of each core regulatory indicator. The matching module is used to match the quantitative results of each core regulatory indicator with the speculation judgment threshold of the corresponding core regulatory indicator based on the pre-built compliance judgment rule base, so as to obtain the indicator items that trigger the speculation judgment standard. The judgment module is used to classify the speculative risk level based on the number of indicator items that trigger the speculative judgment criteria, obtain the judgment result, and visualize the judgment result so that the related original business heterogeneous data can be traced back step by step according to the judgment result.

[0040] The working process, working details and technical effects of the intelligent judgment and traceability system for trading speculative behavior provided in the second aspect of this embodiment can be found in the intelligent judgment and traceability method for trading speculative behavior described in the first aspect, and will not be repeated here.

[0041] like Figure 3As shown, the third aspect of this embodiment provides a computer device, including a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver sends and receives messages, and the processor reads the computer program and executes the intelligent judgment and traceability method for speculative trading behavior as described in the first aspect. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0042] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be found in the intelligent judgment and traceability method for trading speculation behavior described in the first aspect, and will not be repeated here.

[0043] The fourth aspect of this embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the intelligent judgment and traceability method for trading speculation behavior as described in the first aspect is performed. The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, computer-readable storage media such as floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0044] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the intelligent judgment and traceability method for trading speculation behavior as described in the first aspect, and will not be repeated here.

[0045] The fifth aspect of this embodiment provides a computer program product, including a computer program or instructions, which, when executed by a computer, are used to implement the intelligent judgment and traceability method for trading speculation behavior as described in the first aspect.

[0046] The working process, working details and technical effects of the aforementioned computer program product provided in this embodiment can be found in the intelligent judgment and traceability method for trading speculation behavior as described in the first aspect, and will not be repeated here.

[0047] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent judgment and traceability of speculative trading behavior, characterized in that, include: Acquire raw, heterogeneous business data from the ERP system using standardized data interfaces; The original heterogeneous business data is cleaned and formatted to obtain the processed original heterogeneous business data. Based on a unique business identifier, the original heterogeneous business data after processing related to the same derivative transaction are associated and mapped to build a complete data chain; Based on the calculation rules in the pre-built regulatory analysis indicator system, the standardized related data in the complete data chain are quantitatively calculated to obtain the quantitative results of each core regulatory indicator. Based on a pre-built compliance judgment rule base, the quantitative results of each core regulatory indicator are matched with the speculation judgment threshold of the corresponding core regulatory indicator to obtain the indicator items that trigger the speculation judgment standard. The number of indicator items that trigger the speculation judgment criteria is used to classify the speculation risk level, and the judgment results are visualized. Based on the judgment results, the relevant original business heterogeneous data can be traced back level by level.

2. The intelligent judgment and tracing method for speculative trading behavior according to claim 1, characterized in that, The regulatory analysis indicator system includes at least one of the following: business background indicators, transaction operation indicators, risk management indicators, and profit-oriented indicators.

3. The intelligent judgment and traceability method for trading speculation behavior according to claim 2, characterized in that, The business background indicators include at least the actual exposure correspondence rate and the exposure certificate authenticity rate. The actual exposure correspondence rate is used to measure whether derivative transactions are supported by actual business exposure, and the exposure certificate authenticity rate is used to verify the authenticity of exposure certificates. The trading operation indicators include at least the maturity mismatch rate, the overdue amount ratio, the number of high-frequency trades, the direction switching frequency, and the leverage over-limit rate. The maturity mismatch rate is used to assess the degree of matching between the contract maturity and the risk exposure period. The overdue amount ratio is used to assess the degree of matching between the trading amount and the risk exposure amount. The number of high-frequency trades, the direction switching frequency, and the leverage over-limit rate are used to identify abnormal trading behavior. The risk management indicators include at least the risk control system coverage rate, approval compliance rate, and authorization scope compliance rate. The risk control system coverage rate is used to assess whether the transaction is included in comprehensive risk management, the approval compliance rate is used to assess the approval compliance rate of the transaction, and the authorization scope compliance rate is used to assess the authorization compliance. The aforementioned revenue-oriented indicators include at least the proportion of independent profit and loss accounting, the revenue-oriented assessment indicator, and the excess return pursuit indicator. The proportion of independent profit and loss accounting is used to identify whether the profit and loss accounting deviates from the hedging accounting principle. The revenue-oriented assessment indicator is used to verify whether the enterprise's performance assessment indicators include derivatives trading income as a core assessment item. The excess return pursuit indicator is used to verify whether there are any decision records of actively giving up hedging opportunities or deliberately pursuing excess returns from exchange rate or interest rate fluctuations.

4. The intelligent judgment and traceability method for trading speculation behavior according to claim 1, characterized in that, The original heterogeneous business data is cleaned and formatted to obtain processed original heterogeneous business data, including: The original heterogeneous business data is processed for duplicate and missing values ​​to remove duplicate values ​​and fill in missing values, resulting in the completed original heterogeneous business data. The authenticity of the completed original heterogeneous business data is verified based on the preset data verification rules, and the original heterogeneous business data with authenticity greater than the preset authenticity threshold is extracted. Based on preset formatting rules, the original heterogeneous business data with authenticity greater than the preset authenticity threshold is formatted to obtain the processed original heterogeneous business data.

5. The intelligent judgment and traceability method for trading speculation behavior according to claim 4, characterized in that, Based on preset formatting rules, the original heterogeneous business data with an authenticity greater than a preset authenticity threshold is formatted, including at least one of the following: Convert the amounts in different currencies in the original heterogeneous business data with authenticity greater than a preset authenticity threshold into the base currency; Convert dates in different formats from the original heterogeneous business data whose authenticity exceeds a preset authenticity threshold into a standard format; Text information of original heterogeneous business data with authenticity greater than a preset authenticity threshold is uniformly encoded based on a preset encoding format.

6. The intelligent judgment and traceability method for trading speculation behavior according to claim 1, characterized in that, The determination results include high-risk speculative transactions, medium-risk speculative transactions, and compliant transactions; The determination of the speculative risk level based on the number of indicator items triggering the speculative judgment criteria, and the resulting judgment, includes: If the number of indicators that trigger the speculative judgment criteria is greater than or equal to the first threshold, it is judged as a high-risk speculative transaction. If the number of indicators that trigger the speculative judgment criteria is less than the first threshold and greater than or equal to the second threshold, it is judged as a medium-risk speculative transaction. If the number of indicators that trigger the speculation criteria reaches the third threshold, the transaction is judged to be compliant.

7. The intelligent judgment and tracing method for speculative trading behavior according to claim 1, characterized in that, After obtaining the judgment result, the process also includes: marking the original heterogeneous business data located in the complete data chain corresponding to the indicator items that trigger the speculation judgment criteria in the judgment result.

8. A smart system for determining and tracing speculative trading behavior, used to implement the method described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire raw, heterogeneous business data collected from the ERP system via a standardized data interface. The processing module is used to perform data cleaning and formatting on the original heterogeneous business data to obtain the processed original heterogeneous business data. The module is used to associate and map the processed original heterogeneous business data related to the same derivative transaction based on a unique business identifier, so as to build a complete data chain. The calculation module is used to perform quantitative calculations on standardized related data in the complete data chain according to the calculation rules in the pre-built regulatory analysis indicator system, so as to obtain the quantitative results of each core regulatory indicator. The matching module is used to match the quantitative results of each core regulatory indicator with the speculation judgment threshold of the corresponding core regulatory indicator based on the pre-built compliance judgment rule base, so as to obtain the indicator items that trigger the speculation judgment standard. The judgment module is used to classify the speculative risk level based on the number of indicator items that trigger the speculative judgment criteria, obtain the judgment result, and visualize the judgment result so that the related original business heterogeneous data can be traced back step by step according to the judgment result.

9. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the intelligent judgment and traceability method for trading speculation behavior as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the intelligent judgment and traceability method for trading speculation behavior as described in any one of claims 1 to 7.

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