Financial transaction data monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a financial transaction data monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of big data and cloud computing or other related fields, and the method comprises the steps: receiving transaction monitoring demands at least designating a transaction date range and a batch time range; multi-source financial data in a transaction date range is obtained from a pre-constructed data environment, the multi-source financial data at least comprises transaction data and valuation data, and the valuation data is used for indicating price information recognized by the market; analyzing the transaction data and the valuation data based on the batch time range to obtain an analysis result of the financial transaction recording the transaction price abnormality; and generating a financial transaction monitoring report based on the analysis result, and storing the financial transaction monitoring report in a pre-constructed data environment. Through the method and the device, the technical problem of inaccuracy caused by transaction monitoring delay due to the fact that a financial institution cannot perform timely and accurate anomaly detection and deviation evaluation on a large amount of multi-source transaction data in related technologies is solved.

Description

Technical Field

[0001] This invention relates to the fields of big data and cloud computing technology or other related fields. Specifically, it relates to a method and apparatus for monitoring financial transaction data, an electronic device, and a storage medium. Background Technology

[0002] With the accelerating pace of globalization and the booming development of the financial derivatives market, financial institutions in the current financial system face the challenge of a rapid expansion in the volume of transaction data and an increasingly diversified range of information sources. In particular, the complex derivatives market, such as foreign exchange options, generates a massive number and variety of transaction records daily. The complexity and sheer scale of this data present unprecedented challenges to the real-time processing, analysis, and anomaly detection of transaction data.

[0003] In related technologies, financial institutions face difficulties in timely and accurate anomaly detection and deviation assessment of the large volumes of transaction data from multiple systems. Traditional manual verification and decentralized system processing are not only inefficient but also prone to monitoring delays and data integration errors, leading to inaccurate transaction monitoring results. This can potentially mask underlying improper trading activities, increasing compliance risks for financial institutions. Faced with increasingly stringent regulatory requirements and rising market specialization in the foreign exchange market, existing technologies are no longer sufficient to meet the needs of financial institutions for risk management across the entire financial market process. This limitation is particularly pronounced when dealing with complex derivative products such as foreign exchange options, resulting in financial institutions' inability to conduct timely and accurate anomaly detection and deviation assessment of large volumes of multi-source transaction data, leading to transaction monitoring delays and ultimately, inaccuracies.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] The main objective of this application is to provide a method, device, electronic equipment, and storage medium for monitoring financial transaction data, so as to at least solve the technical problem in the related technology that financial institutions are unable to perform timely and accurate anomaly detection and deviation assessment on a large amount of multi-source transaction data, resulting in transaction monitoring delays and inaccuracies.

[0006] To achieve the above objectives, according to one aspect of this application, a method for monitoring financial transaction data is provided. The method includes: receiving a transaction monitoring request, wherein the transaction monitoring request specifies at least a transaction date range and a batch time range; acquiring multi-source financial data within the transaction date range from a pre-built data environment, wherein the multi-source financial data includes at least: transaction data and valuation data, the valuation data indicating market-recognized price information; analyzing the transaction data and the valuation data based on the batch time range to obtain analysis results, wherein the analysis results record financial transactions with abnormal transaction prices; generating a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and storing the financial transaction monitoring report in the pre-built data environment.

[0007] Furthermore, the transaction monitoring requirement also specifies transaction data filtering conditions for filtering the transaction data by field. The transaction data filtering conditions are used to filter the following fields: transaction status field, data source field, and counterparty field.

[0008] Furthermore, after acquiring multi-source financial data within the transaction date range, the method further includes: filtering the transaction data by field according to the transaction data filtering conditions to obtain the transaction data to be monitored.

[0009] Further, the step of analyzing the transaction data and valuation data based on the batch time range to obtain analysis results includes: injecting the transaction data and valuation data into a batch processing framework to obtain a batch processing task, wherein the batch processing framework is a pre-set task framework for comparing transaction data and valuation data of financial transactions one by one; setting the execution time point of the batch processing task based on the batch time range; executing the batch processing task when the execution time point is reached to obtain task results, wherein the task results at least record financial transactions with abnormal transaction prices obtained from the one-by-one comparison; and generating the analysis results based on the financial transactions recorded in the task results.

[0010] Further, the step of executing the batch processing task and obtaining the task result when the execution time point arrives includes: for each financial transaction, calculating the price deviation and price deviation percentage of the financial transaction based on the transaction data of the financial transaction and the valuation data corresponding to the transaction data; comparing the price deviation with a first preset threshold to obtain a first comparison result; comparing the price deviation percentage with a second preset threshold to obtain a second comparison result; if the first comparison result indicates that the price deviation is less than the first preset threshold and the second comparison result indicates that the price deviation percentage is less than the second preset threshold, determining that the transaction price of the financial transaction is normal; otherwise, determining that the transaction price of the financial transaction is abnormal.

[0011] Furthermore, the data environment construction steps include: obtaining a data access file, wherein the data access file at least records: the data type of the accessed data, the data source and file synchronization method corresponding to each data type; for each data type, setting a data transmission channel based on the file synchronization method and the data source; and connecting the data transmission channels corresponding to all data types to a data marketplace to obtain the pre-constructed data environment.

[0012] Furthermore, the data types include: transaction data, reference data, valuation data, and market data. The data source for the transaction data is the transaction details table, the data source for the reference data is the defined currency pair information table, the data source for the valuation data is the valuation details table, and the data source for the market data is the spot time interface file.

[0013] To achieve the above objectives, according to another aspect of this application, a financial transaction data monitoring device is also provided. The device includes: a receiving unit for receiving transaction monitoring requests, wherein the transaction monitoring requests specify at least a transaction date range and a batch time range; an acquisition unit for acquiring multi-source financial data within the transaction date range from a pre-built data environment, wherein the multi-source financial data includes at least transaction data and valuation data, the valuation data indicating market-recognized price information; an analysis unit for analyzing the transaction data and the valuation data based on the batch time range to obtain analysis results, wherein the analysis results record financial transactions with abnormal transaction prices; and a generation unit for generating a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and storing the financial transaction monitoring report in the pre-built data environment.

[0014] Furthermore, the transaction monitoring requirement also specifies transaction data filtering conditions for filtering the transaction data by field. The transaction data filtering conditions are used to filter the following fields: transaction status field, data source field, and counterparty field.

[0015] Furthermore, the financial transaction data monitoring device further includes a filtering unit, used to filter the transaction data by field according to the transaction data filtering conditions after acquiring multi-source financial data within the transaction date range, to obtain the transaction data to be monitored.

[0016] Further, the analysis unit includes: an injection module for injecting the transaction data and the valuation data into a batch processing framework to obtain a batch processing task, wherein the batch processing framework is a pre-set task framework for comparing transaction data and valuation data of financial transactions one by one; a first setting module for setting the execution time point of the batch processing task based on the batch time range; an execution module for executing the batch processing task when the execution time point is reached to obtain a task result, wherein the task result records at least the financial transactions with abnormal transaction prices obtained from the one-by-one comparison; and a generation module for generating the analysis result based on the financial transactions recorded in the task result.

[0017] Further, the execution module includes: a calculation submodule, used for calculating, for each financial transaction, the price deviation amount and price deviation percentage of the financial transaction based on the transaction data of the financial transaction and the valuation data corresponding to the transaction data; a first comparison submodule, used for comparing the price deviation amount with a first preset threshold to obtain a first comparison result; a second comparison submodule, used for comparing the price deviation percentage with a second preset threshold to obtain a second comparison result; and a determination submodule, used for determining that the transaction price of the financial transaction is normal if the first comparison result indicates that the price deviation amount is less than the first preset threshold and the second comparison result indicates that the price deviation percentage is less than the second preset threshold; otherwise, determining that the transaction price of the financial transaction is abnormal.

[0018] Furthermore, the monitoring device for financial transaction data further includes: a construction unit for constructing the data environment, the construction unit comprising: an acquisition module for acquiring a data access file, wherein the data access file at least records: the data type of the accessed data, the data source and file synchronization method corresponding to each data type; a second setting module for setting a data transmission channel for each data type based on the file synchronization method and the data source; and an access module for accessing the data transmission channels corresponding to all data types to a data marketplace to obtain the pre-constructed data environment.

[0019] Furthermore, the data types include: transaction data, reference data, valuation data, and market data. The data source for the transaction data is the transaction details table, the data source for the reference data is the defined currency pair information table, the data source for the valuation data is the valuation details table, and the data source for the market data is the spot time interface file.

[0020] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the financial transaction data monitoring method described in any one of the above claims.

[0021] To achieve the above objectives, according to another aspect of this application, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the financial transaction data monitoring method described in any one of the above claims.

[0022] To achieve the above objectives, according to another aspect of this application, a computer program product is also provided, including computer instructions, wherein when the computer instructions are executed by a processor, they implement the steps of the method for monitoring financial transaction data described in any one of the above claims.

[0023] This invention proposes a method for monitoring financial transaction data. First, a transaction monitoring request is received, specifying at least a transaction date range and a batch time range. Then, multi-source financial data within the transaction date range is acquired from a pre-built data environment. This multi-source financial data includes at least transaction data and valuation data, with the valuation data indicating generally accepted market prices. Next, the transaction data and valuation data are analyzed based on the batch time range to obtain analysis results. These results record financial transactions with abnormal prices. Finally, a financial transaction monitoring report is generated based on the financial transactions recorded in the analysis results and stored in the pre-built data environment.

[0024] This invention employs an integrated approach of big data processing and intelligent analysis. By establishing a unified data platform and implementing automated batch analysis, it achieves efficient and accurate monitoring of the trading prices of financial derivatives such as foreign exchange options. This enables real-time processing and risk assessment of massive amounts of multi-source financial data, overcoming the challenges faced by financial institutions in related technologies where timely and accurate anomaly detection and price deviation assessment are impossible when dealing with large amounts of trading data. It effectively solves the problems of trading monitoring delays and inaccurate results.

[0025] This invention intelligently responds to received transaction monitoring requests, automatically extracting and integrating all relevant financial data from a pre-built unified data environment within a specified transaction date range and batch time frame. It accurately matches transaction data with valuation data, and through batch processing, automates the monitoring and deviation calculation of each transaction price, quickly identifying financial transactions with abnormal prices. This provides financial institutions with a comprehensive and real-time view of transaction price monitoring. Finally, based on the analysis results, it generates detailed financial transaction monitoring reports, further helping financial institutions understand the specific circumstances of deviation transactions. The generation and storage of these reports are both completed on a unified data platform, ensuring information consistency and traceability, while also facilitating long-term data preservation and periodic auditing. This solves the technical problem in related technologies where financial institutions are unable to perform timely and accurate anomaly detection and deviation assessment on large amounts of multi-source transaction data, leading to transaction monitoring delays and inaccuracies. Attached Figure Description

[0026] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0027] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for monitoring financial transaction data is shown.

[0028] Figure 2 This is a flowchart of an optional method for monitoring financial transaction data according to an embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of an optional financial transaction data monitoring device according to an embodiment of the present invention;

[0030] Figure 4 This is a structural block diagram of an electronic device for performing a method for monitoring financial transaction data according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] To facilitate understanding of the present invention by those skilled in the art, some terms or nouns involved in the various embodiments of the present invention are explained below:

[0034] A data mart is a data storage and analysis environment specifically designed to support specific business units. Data marts extract, transform, and load (ETL) data from data warehouses to enable business units to access and analyze data within their specific domain more quickly and easily, thereby making more effective business decisions.

[0035] It should be noted that the financial transaction data monitoring method and device in this application can be used in the field of big data and cloud computing technology for vulnerability detection of software program code, and can also be used in any field other than big data and cloud computing technology for vulnerability detection of software program code. This application does not limit the application field of the financial transaction data monitoring method and device.

[0036] It should be noted that all relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) involved in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, processing, transmission, provision, disclosure, use, and handling of such data comply with the laws, regulations, and standards of the relevant regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse access. For example, this system has interfaces with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface, and the relevant information is obtained only after receiving consent from the aforementioned user or organization.

[0037] The information collection (e.g., user voice, video, and text collection) and analysis operations involved in this application have provided users with corresponding operation entry points during execution, allowing users to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0038] The following embodiments of the present invention can be applied to various systems / applications / devices that require data integration and real-time risk analysis, enabling comprehensive monitoring and rapid response to financial derivatives transactions. The present invention uses a unified data platform for efficient collection of multi-source data, and then employs automated batch processing technology to achieve accurate data comparison and deviation analysis, thereby better optimizing the risk management processes of financial institutions and improving the accuracy and timeliness of market monitoring.

[0039] The present invention will now be described in detail with reference to various embodiments.

[0040] Example 1

[0041] According to an embodiment of the present invention, an embodiment of a method for monitoring financial transaction data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0042] The financial transaction data monitoring method provided in Embodiment 1 of the present invention can be executed on a mobile terminal, computer terminal or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a method for monitoring financial transaction data is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0043] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0044] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the financial transaction data monitoring method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned financial transaction data monitoring method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0045] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0046] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0047] Under the above operating environment, the present invention provides, as follows: Figure 2The financial transaction data monitoring method shown is implemented by a product control and management system, which combines big data processing and cloud computing technology for monitoring financial derivatives transactions. In particular, it addresses the real-time detection and assessment of price deviations in complex financial products. Through automated batch data analysis and market value assessment data comparison, specifically through data screening, value calculation, deviation assessment, and report generation steps, financial institutions can achieve the goal of real-time control and compliance monitoring of transaction risks.

[0048] The embodiments of the present invention will now be described in detail with reference to each specific step.

[0049] Figure 2 This is a flowchart of an optional method for monitoring financial transaction data according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0050] Step S201: Receive transaction monitoring requests, wherein the transaction monitoring requests specify at least the transaction date range and the batch time range.

[0051] Specifically, transaction monitoring requirements refer to requests from financial institutions or other relevant entities aimed at continuously or periodically monitoring specific types or categories of financial transactions to assess their price, size, frequency, or comparison with other market reference data. Transaction monitoring requirements include, but are not limited to, the following parameters: the type of financial product to be monitored, the transaction time window, the frequency and time range of batch processing, and the threshold setting for price deviations, enabling the monitoring system to specifically analyze and detect transaction data.

[0052] The transaction date range refers to the time interval of the target transaction data being monitored, defined by a start date and an end date. By setting a transaction date range, you can focus your analysis on transactions within that period, ensuring the relevance and timeliness of the monitoring results to market conditions. For example, users can specify a transaction date range of one week, one month, or a specific date period to concentrate on analyzing recent trading behavior.

[0053] Batch time range refers to the time period during which the monitoring system processes and analyzes data, typically within a fixed time period after a trading day (e.g., the early morning of T+1 day). By setting a batch time range, transaction data can be automatically collected, integrated, and processed within a predetermined time window during off-peak trading periods, ensuring efficient data processing and rational allocation of system resources. Setting a batch time range also allows financial institutions to complete large-scale data analysis tasks without affecting daily operations.

[0054] It should be noted that accepting different monitoring requests is to better suit the different transaction types, monitoring frequencies, and alarm thresholds that different financial institutions want to focus on. Therefore, the monitoring system is designed to be highly configurable and intelligent, capable of automatically adjusting monitoring strategies and parameters according to user needs, adapting to diverse monitoring scenarios.

[0055] To improve monitoring efficiency, ensure the accuracy of analysis, and focus on key transactions, optionally, in the financial transaction data monitoring method provided in this embodiment of the invention, the transaction monitoring requirements also specify transaction data filtering conditions for filtering transaction data by field. The transaction data filtering conditions are used to filter the following fields: transaction status field, data source field, and counterparty field.

[0056] It should be noted that the transaction data filtering criteria are designed as a set of rules to determine which specific transaction data will be included in the monitoring and analysis scope. The filtering criteria can be very granular, covering key fields such as transaction status, data source, and counterparty, to ensure that the monitoring process can focus on the most relevant transactions, improve efficiency, and reduce unnecessary computational load.

[0057] The transaction status field records the status of a transaction at different stages, such as completed, pending confirmation, canceled, or modified. By filtering transaction status, you can selectively analyze transactions that have been completed or are pending confirmation, which helps to eliminate the impact of transactions that are still in progress or have been canceled on the analysis results.

[0058] The data source field identifies the system or source from which the transaction data was generated, such as the front-end trading system, agent trading records, or back-end settlement data. The filter data source field is used to exclude data from certain specific sources, such as client trading or internal test data, to ensure that the analysis results only reflect the actual market trading situation.

[0059] The counterparty field is used to identify the other party in a transaction, including their name, number, or other identifier. Filtering counterparties allows for focused analysis of transactions with specific counterparties, which is crucial for identifying unusual pricing or manipulation by particular counterparties.

[0060] In one optional implementation scenario, by implementing the above steps, only transactions with a status of "completed" or "pending confirmation" can be monitored, while transactions with a status of "cancelled" or "tested" can be excluded. This ensures that the analysis focuses on real market transactions and avoids misjudging invalid data.

[0061] If a financial institution uses multiple trading systems, some of which are used to process agency transactions or internal transfers, then by filtering the data source field, these non-market transaction data can be excluded, allowing the monitoring system to focus more on market transactions and reduce interference from non-market factors.

[0062] If a financial institution engages in frequent transactions with a particular counterparty, there may be a potential abnormal transaction pattern. Filtering by counterparty field allows for focused analysis of this counterparty's transactions, which helps in the early identification and intervention of potential risks.

[0063] Step S202: Obtain multi-source financial data within the transaction date range from a pre-built data environment. The multi-source financial data includes at least: transaction data and valuation data, with the valuation data used to indicate market-recognized price information.

[0064] Specifically, in this embodiment of the invention, the pre-built data environment refers to a unified platform integrating financial data from different systems and sources. It can employ a data warehouse or data lake architecture, extracting data in real-time or periodically from various business systems such as trading systems, valuation systems, and parameter management systems, and performing ETL (Extract, Transform, Load) operations to enable the data to be accessed and analyzed in a unified format. The pre-built data environment is crucial for financial data monitoring and analysis, addressing the problem of data silos and improving data consistency and availability.

[0065] Multi-source financial data refers to various types of data related to financial transactions collected from different business systems and market sources. This includes not only detailed transaction information (such as transaction time, transaction amount, and counterparty), but also market valuation data, reference data (such as currency pair information), and market trend data. Integrating multi-source financial data allows for analysis of transactions from multiple dimensions, improving the comprehensiveness and accuracy of monitoring.

[0066] In one embodiment, the multi-source financial data includes at least transaction data and valuation data. Transaction data refers to data recording financial transaction activities, typically including information such as the transaction date, time, product type, transaction amount, execution price, counterparty, and trader. For the monitoring system in this embodiment, transaction data is the basis for price deviation detection; by comparing it with valuation data, the fairness of the transaction price can be assessed.

[0067] Valuation data, reflecting generally accepted market prices, is provided by professional financial valuation systems or third-party services. For complex products such as financial derivatives, valuation data is calculated based on market conditions, historical data, and financial models, providing an external benchmark for the reasonableness of transaction prices. In the monitoring process of this invention, comparing valuation data with transaction data is a crucial step in identifying deviations from fair market prices.

[0068] To ensure that financial transaction data from different systems and platforms can be collected, stored, and analyzed uniformly, eliminating data silos and improving data utilization efficiency, optionally, in the financial transaction data monitoring method provided in this embodiment of the invention, the data environment construction step includes: obtaining a data access file, wherein the data access file at least records: the data type of the accessed data, the data source and file synchronization method corresponding to each data type; for each data type, setting up a data transmission channel based on the file synchronization method and data source; and connecting the data transmission channels corresponding to all data types to a data marketplace to obtain a pre-constructed data environment.

[0069] It's important to note that a data access file is a configuration file that details the types of data to be accessed, the source of each type of data, and the specific rules governing how this data is transferred from the source system to the data environment. The data access file is fundamental to building the data environment, ensuring the orderly and automated transmission of data.

[0070] In practical implementation scenarios, the data access file acts as a blueprint, guiding every step of data transmission and ensuring that no important data type or source is missed during the transfer of data from the source system to the data marketplace. For example, the data access file may include types such as transaction data, market data, reference data, and valuation data, as well as the corresponding source systems, such as front-end trading systems, market data providers, parameter management systems, and back-end valuation systems.

[0071] The data type of the accessed data defines the nature of the dataset, such as transaction data, valuation data, reference data, or market data; each data type typically contains specific fields and attributes to describe different aspects of financial transactions. The data source refers to the original system or platform from which the data was generated, such as a trading system, valuation system, or market data service. The file synchronization method specifies the data transmission mechanism from the source to the data environment, including full transmission, incremental transmission, or real-time streaming.

[0072] Optionally, in the financial transaction data monitoring method provided in this embodiment of the invention, the data types include: transaction data, reference data, valuation data, and market data. The data source of transaction data is the transaction details table, the data source of reference data is the defined currency pair information table, the data source of valuation data is the valuation details table, and the data source of market data is the spot time interface file.

[0073] It should be noted that transaction data contains all the key details of financial transactions, such as transaction time, trader identity, transaction amount, type of traded product, and transaction price. Transaction details, as the primary source of transaction data, provide a historical record of financial transactions and are crucial for identifying abnormal transactions and assessing their legitimacy.

[0074] Reference data typically contains static or low-frequency changing information, such as the definition of currency pairs and various parameter settings. The currency pair definition table is the source of this type of data, providing a fundamental reference for transaction data processing and market analysis, ensuring the consistency and accuracy of data processing.

[0075] Valuation data reflects the value of financial assets at different points in time and is crucial for monitoring deviations between transaction prices and market capitalization. The valuation details table provides valuation information for each transaction, including the valuation date, valuation amount, and valuation model, enabling the monitoring system to perform accurate price assessments of transactions.

[0076] Market data, including real-time market information, is crucial for assessing the fairness of transaction prices. The spot point-in-time interface file provides a real-time snapshot of market data, including exchange rates, interest rates, and other financial indicators, ensuring that the monitoring system can assess transaction prices based on the latest market conditions.

[0077] The technical advantage of setting up data transmission channels based on file synchronization methods and data sources is that it enables orderly and efficient data transmission, ensuring data real-time performance and accuracy. At the same time, the file synchronization strategy helps reduce bandwidth consumption during data transmission, lower storage costs, and improve data processing efficiency.

[0078] A data transmission channel refers to the logical or physical path connecting the data source and the data environment. Through the data transmission channel, data can be transmitted from the source system to the data marketplace or data warehouse according to preset rules and frequencies. The setup of the data transmission channel must consider data security, integrity, and transmission efficiency. After the data transmission channel is set up, it needs to be integrated with the data marketplace to form a unified data aggregation point.

[0079] A data mart is a specific architecture in a data environment used to store and manage data aggregated from multiple data sources. Designed to support rapid querying and analysis, a data mart is an important component of financial transaction data monitoring systems.

[0080] In another alternative embodiment, in addition to pre-building a data environment, Kafka or similar message queue technologies can be used to integrate real-time data streams. This allows transaction data and valuation data to be collected and sent to the monitoring system instantly, without waiting for batch processing by the data mart, thereby reducing data latency and improving monitoring speed.

[0081] Alternatively, instead of pre-building a data mart in a separate data environment, data virtualization technology can be used to read data directly from various source systems. This reduces the number of data copying steps, saves storage costs, and also reduces the latency of data updates.

[0082] Alternatively, data services provided by cloud platforms, such as cloud databases, cloud storage, and cloud data warehouses, can be used to store and process data in the cloud. These services offer advantages such as high scalability, low maintenance costs, and dynamic resource allocation, making them suitable for the financial sector, where data volumes are large and changes rapidly.

[0083] To ensure the targeting and efficiency of the monitoring process, a series of predefined filtering rules can be applied to locate and extract transaction data that needs to be further analyzed. Optionally, in the financial transaction data monitoring method provided in this embodiment of the invention, after obtaining multi-source financial data within the transaction date range, the method further includes: filtering the transaction data by field according to the transaction data filtering conditions to obtain the transaction data to be monitored.

[0084] It's important to note that filtering criteria can be based on multiple fields, such as transaction status, transaction type, counterparty, and transaction amount range, to precisely define the set of transactions to be monitored. Based on these criteria, transaction records that meet the filtering conditions are selectively extracted from the original transaction data. For example, all completed transactions can be filtered out, excluding incomplete or test transactions, ensuring that only actual transactions are monitored. After filtering by fields, a narrowed transaction dataset is obtained, which is the transaction data to be monitored.

[0085] For example, in a specific transaction monitoring scenario, the data mart has already collected transaction data for all options with a transaction date of T. Next, the monitoring system uses preset filtering criteria: the transaction status must be "completed" to exclude all transactions that are still in progress or have been canceled; the counterparty must not be on the list of high-risk clients to ensure the creditworthiness of the counterparty; and only transactions with a transaction amount greater than $1 million are selected.

[0086] After screening, the remaining transaction data was compared with valuation data to check the degree of price deviation. Ultimately, this process helped financial institutions identify several transactions with large amounts and significant price deviations, enabling them to take appropriate risk control measures and avoid potential economic losses and compliance risks.

[0087] Step S203: Analyze the transaction data and valuation data based on the batch time range to obtain the analysis results, in which the analysis results record financial transactions with abnormal transaction prices.

[0088] Specifically, the analysis results refer to a set of information extracted from financial transaction data and valuation data to assess whether a transaction price deviates from its fair market value. This typically includes transaction identification information (such as transaction number and transaction date), the actual transaction price, the fair market value, the amount of deviation, the percentage of deviation, and a conclusion regarding whether the transaction deviates. In this embodiment of the invention, the analysis results are the core output of the monitoring system, providing financial institutions with insights into transaction compliance and price fairness, helping them to promptly identify potential abnormal transactions and take appropriate risk management measures.

[0089] It should be noted that in financial transactions, the price of certain transactions may deviate significantly from the fair market value. This deviation may be due to operational errors, market manipulation, abnormal price fluctuations, or other internal or external factors. The monitoring system identifies these abnormally priced transactions by comparing the actual transaction price with the fair market price and applying preset deviation thresholds. Once a transaction is flagged as abnormally priced, financial institutions can conduct further review and investigation to determine its reasonableness and legality, preventing potential economic losses and legal risks.

[0090] Another point to note is that the monitoring system considers the unique characteristics of financial transactions when analyzing them, such as the difference between the transaction date and the valuation date, the discounting calculation of option premiums, and the acquisition and adjustment of zero-coupon rates. These details ensure the accuracy and reliability of the analysis results. Furthermore, it performs preliminary screening of transaction data based on preset rules, such as transaction status, data source, and counterparty selection criteria, thereby improving the efficiency and relevance of subsequent analysis.

[0091] To achieve regular, automated, and efficient analysis of large amounts of transaction and valuation data to identify potential anomalies in transaction prices, optionally, in the financial transaction data monitoring method provided in this embodiment of the invention, the step of analyzing transaction and valuation data based on a batch time range to obtain analysis results includes: injecting transaction and valuation data into a batch processing framework to obtain a batch processing task, wherein the batch processing framework is a pre-set task framework for comparing transaction and valuation data of financial transactions one by one; setting the execution time point of the batch processing task based on the batch time range; executing the batch processing task when the execution time point arrives to obtain task results, wherein the task results at least record financial transactions with abnormal transaction prices obtained from the one-by-one comparison; and generating analysis results based on the financial transactions recorded in the task results.

[0092] It should be noted that a batch processing framework is a pre-designed system architecture for handling large datasets. It can automate and sequentially process transaction and valuation data according to predefined rules, typically including steps such as data reading, data transformation, and data analysis.

[0093] Batch processing tasks are specific analytical operations based on defined rules, such as comparing transaction prices with fair value to identify anomalous transactions. Task execution occurs within a batch processing framework and can be customized for specific datasets and analytical needs. The execution time is the specific moment the system executes the batch processing task, typically at a specific time each day (e.g., at night) to avoid interfering with real-time trading systems. Task results are the output obtained after batch processing task execution, including financial transaction records identified as having anomalous transaction prices. Analysis results involve further summarizing, analyzing, and interpreting the task results to form highly readable and easily understood reports or alerts, providing a holistic view of anomalous transactions and helping decision-makers quickly understand the situation and take action.

[0094] Suppose a financial transaction monitoring system needs to identify transactions with abnormal prices in financial business. A batch processing-based monitoring method is used: At dawn on day T+1, all transaction and valuation data for foreign exchange options on day T are automatically retrieved from the data mart and injected into a pre-configured batch processing framework. The batch processing task is scheduled to execute at 4:00 AM daily, utilizing the downtime after the trading day ends for data processing. At 4:00 AM, the batch processing framework starts, comparing the option premium with the valuation result for each transaction, calculating the price deviation and percentage deviation. All transactions deviating from fair value are recorded in the task results. Based on the task results, a "Daily Transaction Price Monitoring Report" for the foreign exchange options business on day T is generated. The report details transactions deviating from fair value, including transaction number, counterparty, deviation amount, and deviation percentage, providing risk management personnel with a clear list of abnormal transactions.

[0095] Through the above steps, financial institutions can effectively identify and handle transactions with price deviations, which not only improves monitoring efficiency and accuracy, but also enables them to take timely measures to control risks and ensure the compliance and fairness of transactions.

[0096] Optionally, in the financial transaction data monitoring method provided in this embodiment of the invention, the step of executing a batch processing task and obtaining the task result when the execution time point arrives includes: for each financial transaction, calculating the price deviation amount and price deviation percentage of the financial transaction based on the transaction data of the financial transaction and the valuation data corresponding to the transaction data; comparing the price deviation amount with a first preset threshold to obtain a first comparison result; comparing the price deviation percentage with a second preset threshold to obtain a second comparison result; if the first comparison result indicates that the price deviation amount is less than the first preset threshold and the second comparison result indicates that the price deviation percentage is less than the second preset threshold, determining that the transaction price of the financial transaction is normal; otherwise, determining that the transaction price of the financial transaction is abnormal.

[0097] It should be noted that the price deviation in a financial transaction refers to the difference between the actual price of the transaction and the fair market price, used to quantify the difference between the transaction price and the market price; the percentage of price deviation in a financial transaction represents the proportion of the price deviation to the notional amount of the transaction, used to assess the degree of price deviation and more intuitively reflect the severity of the price deviation.

[0098] The first preset threshold (price deviation threshold) is a pre-defined absolute value standard used to determine whether the price deviation is within an acceptable range. Furthermore, the first comparison result compares the price deviation of a financial transaction with the first preset threshold, indicating whether the transaction's deviation is abnormal.

[0099] In contrast, the second preset threshold (price deviation percentage threshold) is a pre-set proportional standard used to determine whether the price deviation percentage is within a reasonable range. Furthermore, the second comparison result compares the price deviation percentage of the financial transaction with the second preset threshold, indicating whether the deviation percentage is abnormal.

[0100] When the price deviation of a financial transaction is less than a first preset threshold and the percentage deviation is less than a second preset threshold, the transaction is considered to be normal and without abnormal deviation. Conversely, if the price deviation exceeds the first preset threshold or the percentage deviation exceeds the second preset threshold, the transaction is considered to have price anomalies, which may involve operational errors, market manipulation, or other violations.

[0101] In one optional implementation scenario, assume a financial institution automatically collects and processes transaction and valuation data for foreign exchange options trading using a batch processing framework after the daily trading day ends. The specific steps are as follows: Processing of day T's transaction data begins at 4:00 AM on day T+1, at which point the data mart has already provided complete transaction information and valuation data from the IFAR system; for each foreign exchange options transaction, the deviation amount and percentage are calculated, involving the calculation of the discounted option premium and the determination of the relationship between the option premium settlement date and the trading day; a preset first threshold and second threshold are obtained to determine whether the deviation amount and percentage are within acceptable ranges, respectively. Here, it is assumed that the first threshold is $5,000 and the second threshold is 5%; if the deviation amount of a transaction is less than $5,000 and the deviation percentage is less than 5%, the transaction is marked as price-normal; otherwise, it is marked as price-abnormal and requires further review; a report is generated based on the results, including details of all price-abnormal transactions for verification by the risk control department, and alerts are automatically sent to the responsible traders and management to ensure timely response.

[0102] Through the above steps, financial institutions can not only effectively monitor transaction prices, but also achieve reasonable resource allocation and improve the efficiency of risk management and compliance.

[0103] Step S204: Generate a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and store the financial transaction monitoring report in a pre-built data environment.

[0104] Specifically, a financial transaction monitoring report is a document or dataset compiled based on the analysis of transaction and valuation data. It aims to summarize and present financial transaction information identified as price anomalies within a batch of timeframes. Monitoring reports typically include key transaction elements (such as transaction number, transaction date, product type, counterparty, and trader), detailed market valuation data, deviation calculations, deviation percentage analysis, and the determination of the deviating transaction based on this data. Sometimes, it may also include a preliminary analysis of the reasons for the deviation and recommended measures. The generation and storage of these reports facilitate a comprehensive review of the compliance and price reasonableness of transactions by financial institution management, compliance departments, and audit teams, and also represent a proactive response to internal risk control processes and external regulatory requirements.

[0105] Structured data presentation methods can be used when generating monitoring reports to ensure readability and traceability. For example, reports may categorize and sort data by trader, counterparty, specific time period, or degree of abnormal deviation, allowing users to quickly locate key information. Furthermore, reports are stored in standard data formats such as CSV (Comma Separated Values), XML (eXtensible Markup Language), or JSON (JavaScript Object Notation) for subsequent analysis, archiving, and data sharing. A pre-built data environment, i.e., a data mart or data warehouse, serves as the report storage destination, ensuring data security and consistency, and facilitating long-term data management and compliance audits.

[0106] Through the above steps S201 to S204, a transaction monitoring request can be received first, wherein the transaction monitoring request specifies at least the transaction date range and batch time range. Then, multi-source financial data within the transaction date range is obtained from the pre-built data environment. The multi-source financial data includes at least transaction data and valuation data. Valuation data is used to indicate the market's generally accepted price information. Then, the transaction data and valuation data are analyzed based on the batch time range to obtain analysis results. The analysis results record financial transactions with abnormal transaction prices. Finally, a financial transaction monitoring report is generated based on the financial transactions recorded in the analysis results and stored in the pre-built data environment.

[0107] In this embodiment of the invention, an integrated big data processing and intelligent analysis approach is adopted. By establishing a unified data platform and implementing automated batch analysis, the goal of efficient and accurate monitoring of the transaction prices of financial derivatives such as foreign exchange options is achieved. This enables real-time processing and risk assessment of massive amounts of multi-source financial data, overcoming the dilemma faced by financial institutions in related technologies where they cannot perform timely and accurate anomaly detection and price deviation assessment when dealing with large amounts of transaction data. It effectively solves the problems of transaction monitoring delays and inaccurate results.

[0108] This invention intelligently responds to received transaction monitoring requests, automatically extracting and integrating all relevant financial data from a pre-built unified data environment within a specified transaction date range and batch time frame. It accurately matches transaction data with valuation data, and through batch processing, automates the monitoring and deviation calculation of each transaction price, quickly identifying financial transactions with abnormal prices. This provides financial institutions with a comprehensive and real-time view of transaction price monitoring. Finally, based on the analysis results, a detailed financial transaction monitoring report is generated, further helping financial institutions understand the specific circumstances of deviation transactions. The generation and storage of the report are both completed on the unified data platform, ensuring information consistency and traceability, while also facilitating long-term data preservation and periodic auditing. This solves the technical problem in related technologies where financial institutions cannot perform timely and accurate anomaly detection and deviation assessment on large amounts of multi-source transaction data, leading to transaction monitoring delays and inaccuracies.

[0109] The invention will now be described in conjunction with another alternative embodiment.

[0110] Example 2

[0111] This invention also provides a financial transaction data monitoring device. It should be noted that the financial transaction data monitoring device of this invention includes multiple implementation units, which can be used to execute the financial transaction data monitoring method provided in the first embodiment above. Each implementation unit corresponds to each implementation step in the first embodiment above.

[0112] Figure 3 This is a schematic diagram of an optional financial transaction data monitoring device according to an embodiment of the present invention, such as... Figure 3 As shown, the device may include: a receiving unit 31, an acquisition unit 32, an analysis unit 33, and a generation unit 34.

[0113] The receiving unit 31 is used to receive transaction monitoring requests, wherein the transaction monitoring requests specify at least the transaction date range and the batch time range.

[0114] Acquisition unit 32 is used to acquire multi-source financial data within a transaction date range from a pre-built data environment, wherein the multi-source financial data includes at least: transaction data and valuation data, and the valuation data is used to indicate market-recognized price information.

[0115] Analysis unit 33 is used to analyze transaction data and valuation data based on a batch time range to obtain analysis results, including records of financial transactions with abnormal transaction prices.

[0116] The generation unit 34 is used to generate a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and to store the financial transaction monitoring report in a pre-built data environment.

[0117] The aforementioned financial transaction data monitoring device can first receive transaction monitoring requests through receiving unit 31, wherein the transaction monitoring requests specify at least the transaction date range and batch time range. Then, it can acquire multi-source financial data within the transaction date range from a pre-built data environment through acquisition unit 32. The multi-source financial data includes at least transaction data and valuation data, wherein the valuation data is used to indicate the market's generally accepted price information. Then, the analysis unit 33 analyzes the transaction data and valuation data based on the batch time range to obtain analysis results. The analysis results record financial transactions with abnormal transaction prices. Finally, the generation unit 34 generates a financial transaction monitoring report based on the financial transactions recorded in the analysis results and stores the financial transaction monitoring report in the pre-built data environment.

[0118] In this embodiment of the invention, an integrated big data processing and intelligent analysis approach is adopted. By establishing a unified data platform and implementing automated batch analysis, the goal of efficient and accurate monitoring of the transaction prices of financial derivatives such as foreign exchange options is achieved. This enables real-time processing and risk assessment of massive amounts of multi-source financial data, overcoming the dilemma faced by financial institutions in related technologies where they cannot perform timely and accurate anomaly detection and price deviation assessment when dealing with large amounts of transaction data. It effectively solves the problems of transaction monitoring delays and inaccurate results.

[0119] This invention intelligently responds to received transaction monitoring requests, automatically extracting and integrating all relevant financial data from a pre-built unified data environment within a specified transaction date range and batch time frame. It accurately matches transaction data with valuation data, and through batch processing, automates the monitoring and deviation calculation of each transaction price, quickly identifying financial transactions with abnormal prices. This provides financial institutions with a comprehensive and real-time view of transaction price monitoring. Finally, based on the analysis results, a detailed financial transaction monitoring report is generated, further helping financial institutions understand the specific circumstances of deviation transactions. The generation and storage of the report are both completed on the unified data platform, ensuring information consistency and traceability, while also facilitating long-term data preservation and periodic auditing. This solves the technical problem in related technologies where financial institutions cannot perform timely and accurate anomaly detection and deviation assessment on large amounts of multi-source transaction data, leading to transaction monitoring delays and inaccuracies.

[0120] Furthermore, the transaction monitoring requirements also specify transaction data filtering conditions for filtering transaction data by field. These conditions are used to filter the following fields: transaction status, data source, and counterparty.

[0121] Furthermore, the financial transaction data monitoring device also includes a filtering unit, which, after acquiring multi-source financial data within the transaction date range, filters the transaction data according to the transaction data filtering conditions to obtain the transaction data to be monitored.

[0122] Furthermore, the analysis unit includes: an injection module for injecting transaction data and valuation data into a batch processing framework to obtain a batch processing task, wherein the batch processing framework is a pre-set task framework for comparing transaction data and valuation data of financial transactions one by one; a first setting module for setting the execution time point of the batch processing task based on the batch time range; an execution module for executing the batch processing task when the execution time point is reached to obtain the task result, wherein the task result records at least the financial transactions with abnormal transaction prices obtained from the one-by-one comparison; and a generation module for generating analysis results based on the financial transactions recorded in the task result.

[0123] Furthermore, the execution module includes: a calculation submodule, used to calculate the price deviation and price deviation percentage of each financial transaction based on the transaction data and the corresponding valuation data; a first comparison submodule, used to compare the price deviation with a first preset threshold to obtain a first comparison result; a second comparison submodule, used to compare the price deviation percentage with a second preset threshold to obtain a second comparison result; and a determination submodule, used to determine that the transaction price of the financial transaction is normal if the first comparison result indicates that the price deviation is less than the first preset threshold and the second comparison result indicates that the price deviation percentage is less than the second preset threshold; otherwise, it determines that the transaction price of the financial transaction is abnormal.

[0124] Furthermore, the monitoring device for financial transaction data also includes: a construction unit for constructing a data environment, the construction unit comprising: an acquisition module for acquiring a data access file, wherein the data access file at least records: the data type of the accessed data, the data source and file synchronization method corresponding to each data type; a second setting module for setting a data transmission channel for each data type based on the file synchronization method and data source; and an access module for accessing the data transmission channels corresponding to all data types to the data marketplace to obtain the pre-constructed data environment.

[0125] Furthermore, the data types include: transaction data, reference data, valuation data, and market data. The data source for transaction data is the transaction details table, the data source for reference data is the defined currency pair information table, the data source for valuation data is the valuation details table, and the data source for market data is the spot time interface file.

[0126] It should be noted that the receiving unit 31, acquiring unit 32, analyzing unit 33, and generating unit 34 mentioned above correspond to steps S201 to S204 in Embodiment 1. The instances and application scenarios implemented by the above units and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules or units can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0127] The invention will now be described in conjunction with another alternative embodiment.

[0128] Example 3

[0129] The present invention can also provide an electronic device. Figure 4 This is a structural block diagram of an electronic device for implementing a method for monitoring financial transaction data according to an embodiment of the present invention, such as... Figure 4 As shown, the electronic device may include: one or more ( Figure 4 Only one of the following is shown: processor 402, memory 404, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0130] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the financial transaction data monitoring method and apparatus in this application embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned financial transaction data monitoring method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0131] The processor can invoke information and applications stored in memory via a transmission device to perform the following steps: receiving a transaction monitoring request, wherein the transaction monitoring request specifies at least a transaction date range and a batch time range; acquiring multi-source financial data within the transaction date range from a pre-built data environment, wherein the multi-source financial data includes at least: transaction data and valuation data, wherein the valuation data is used to indicate market-recognized price information; analyzing the transaction data and valuation data based on the batch time range to obtain analysis results, wherein the analysis results record financial transactions with abnormal transaction prices; generating a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and storing the financial transaction monitoring report in the pre-built data environment.

[0132] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: filter the transaction data by field according to the transaction data filtering conditions to obtain the transaction data to be monitored.

[0133] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: injecting transaction data and valuation data into a batch processing framework to obtain a batch processing task, wherein the batch processing framework is a pre-set task framework for comparing transaction data and valuation data of financial transactions one by one; setting the execution time point of the batch processing task based on the batch time range; executing the batch processing task when the execution time point arrives to obtain the task result, wherein the task result records at least the financial transactions with abnormal transaction prices obtained from the one-by-one comparison; generating analysis results based on the financial transactions recorded in the task result.

[0134] The processor can also invoke information and applications stored in the memory via a transmission device to perform the following steps: For each financial transaction, based on the transaction data and the corresponding valuation data, calculate the price deviation and price deviation percentage of the financial transaction; compare the price deviation with a first preset threshold to obtain a first comparison result; compare the price deviation percentage with a second preset threshold to obtain a second comparison result; if the first comparison result indicates that the price deviation is less than the first preset threshold and the second comparison result indicates that the price deviation percentage is less than the second preset threshold, determine that the transaction price of the financial transaction is normal; otherwise, determine that the transaction price of the financial transaction is abnormal.

[0135] The processor can also invoke information and applications stored in the memory via the transmission device to perform the following steps: obtain a data access file, wherein the data access file at least records: the data type of the accessed data, the data source and file synchronization method corresponding to each data type; for each data type, set up a data transmission channel based on the file synchronization method and data source; connect the data transmission channels corresponding to all data types to the data marketplace to obtain a pre-built data environment.

[0136] This invention provides a monitoring scheme for financial transaction data. By integrating big data processing and intelligent analysis, and establishing a unified data platform and implementing automated batch analysis, it achieves efficient and accurate monitoring of the prices of financial derivatives such as foreign exchange options. This enables real-time processing and risk assessment of massive amounts of multi-source financial data, overcoming the difficulties faced by financial institutions in timely and accurate anomaly detection and price deviation assessment when dealing with large volumes of transaction data. It effectively solves the problems of transaction monitoring delays and inaccurate results.

[0137] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0138] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0139] The invention will now be described in conjunction with another alternative embodiment.

[0140] Example 4

[0141] This invention also provides a computer-readable storage medium. Optionally, in this invention, the computer-readable storage medium can be used to store the program code executed by the financial transaction data monitoring method provided in Embodiment 1.

[0142] Optionally, in this embodiment of the invention, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0143] This invention also provides a computer program product, which, when executed on a data processing device, is suitable for performing the steps of a method for monitoring financial transaction data: receiving a transaction monitoring request, wherein the transaction monitoring request specifies at least a transaction date range and a batch time range; acquiring multi-source financial data within the transaction date range from a pre-built data environment, wherein the multi-source financial data includes at least: transaction data and valuation data, the valuation data being used to indicate market-recognized price information; analyzing the transaction data and valuation data based on the batch time range to obtain analysis results, wherein the analysis results record financial transactions with abnormal transaction prices; generating a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and storing the financial transaction monitoring report in the pre-built data environment.

[0144] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0145] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0150] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for monitoring financial transaction data, characterized in that, include: Receive transaction monitoring requests, wherein the transaction monitoring requests specify at least a transaction date range and a batch time range; The data is obtained from a pre-built data environment, which contains multi-source financial data within the specified transaction date range. The multi-source financial data includes at least transaction data and valuation data, wherein the valuation data is used to indicate market-recognized price information. The transaction data and valuation data are analyzed based on the batch time range to obtain analysis results, wherein the analysis results record financial transactions with abnormal transaction prices; A financial transaction monitoring report is generated based on the financial transactions recorded in the analysis results, and the financial transaction monitoring report is stored in the pre-built data environment.

2. The monitoring method according to claim 1, characterized in that, The transaction monitoring requirements also specify transaction data filtering conditions for filtering the transaction data by field. The transaction data filtering conditions are used to filter the following fields: transaction status field, data source field, and counterparty field.

3. The monitoring method according to claim 2, characterized in that, After acquiring multi-source financial data within the aforementioned transaction date range, the method further includes: Based on the transaction data filtering criteria, the transaction data is filtered by field to obtain the transaction data to be monitored.

4. The monitoring method according to claim 1, characterized in that, The steps for analyzing the transaction data and valuation data based on the batch time range to obtain analysis results include: The transaction data and the valuation data are injected into a batch processing framework to obtain a batch processing task. The batch processing framework is a pre-set task framework that compares the transaction data and valuation data of financial transactions one by one. The execution time point of the batch processing task is set based on the batch time range; When the specified execution time point arrives, the batch processing task is executed to obtain task results, wherein the task results record at least the financial transactions with abnormal transaction prices obtained by comparing each transaction individually. The analysis results are generated based on the financial transactions recorded in the task results.

5. The monitoring method according to claim 4, characterized in that, The steps of executing the batch processing task when the specified execution time point arrives and obtaining the task result include: For each financial transaction, based on the transaction data of the financial transaction and the valuation data corresponding to the transaction data, the price deviation amount and price deviation percentage of the financial transaction are calculated; The price deviation is compared with a first preset threshold to obtain a first comparison result; The price deviation percentage is compared with a second preset threshold to obtain a second comparison result; If the first comparison result indicates that the price deviation is less than the first preset threshold and the second comparison result indicates that the price deviation percentage is less than the second preset threshold, the transaction price of the financial transaction is determined to be normal; otherwise, the transaction price of the financial transaction is determined to be abnormal.

6. The monitoring method according to claim 1, characterized in that, The steps for constructing the data environment include: Obtain a data access file, wherein the data access file shall at least record: the data type of the accessed data, the data source and file synchronization method corresponding to each data type; For each data type, a data transmission channel is set based on the file synchronization method and the data source; Connect the data transmission channels corresponding to all the data types to the data marketplace to obtain the pre-built data environment.

7. The monitoring method according to claim 6, characterized in that, The data types include: transaction data, reference data, valuation data, and market data. The transaction data is sourced from the transaction details table, the reference data is sourced from the defined currency pair information table, the valuation data is sourced from the valuation details table, and the market data is sourced from the spot time interface file.

8. A monitoring device for financial transaction data, characterized in that, include: A receiving unit is configured to receive transaction monitoring requests, wherein the transaction monitoring requests specify at least a transaction date range and a batch time range; An acquisition unit is configured to acquire multi-source financial data within the transaction date range from a pre-built data environment, wherein the multi-source financial data includes at least: transaction data and valuation data, wherein the valuation data is used to indicate market-recognized price information; An analysis unit is used to analyze the transaction data and the valuation data based on the batch time range to obtain analysis results, wherein the analysis results record financial transactions with abnormal transaction prices; The generation unit is used to generate a financial transaction monitoring report based on the financial transactions recorded in the analysis results, and to store the financial transaction monitoring report in the pre-built data environment.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the financial transaction data monitoring method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method for monitoring financial transaction data as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, The method includes computer instructions, wherein when executed by a processor, the computer instructions implement the steps of the method for monitoring financial transaction data according to any one of claims 1 to 7.

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