Method, device, equipment and computer storage medium for generating risk list data

Abstract

The present invention discloses a method for generating risk list data. The method for generating risk list data comprises: upon receiving a risk list data generation request, obtaining new data and existing risk list detail data according to the risk list data generation request; analyzing and processing the new data to obtain incremental risk list detail data, and clearing the existing risk list detail data to clear expired data; merging the cleared existing risk list detail data and incremental risk list detail data to obtain risk list summary data. The present invention also discloses a risk list data generation device, equipment and computer storage medium. The present invention can realize intelligent generation of risk list summary data, thereby facilitating banks and other financial institutions to directly query the credit risk data of enterprises or individuals based on the risk list summary data, thereby improving risk assessment efficiency. In addition, the present invention can also be combined with blockchain technology.

Description

Technical Field

[0001] The present invention relates to the technical field of financial technology (Fintech), and in particular to a method, device, equipment and computer storage medium for generating risk list data. Background Art

[0002] In recent years, with the development of computer technology, more and more technologies (big data, distributed, blockchain, artificial intelligence, etc.) are applied in the financial field, and the traditional financial industry is gradually transforming to financial technology (Fintech). At present, financial credit has gradually become an important foundation for the development of Internet finance. In a sense, the modern market economy has actually become a "credit economy". In the entire credit process, credit risk analysis is required to determine relevant operations. For example, before lending, banks and other financial institutions often assess the credit risk of enterprises or individuals to determine whether to lend to them; after lending, credit risk assessment is often required to monitor risks and timely discover and resolve risks. However, currently, credit risk analysis is usually performed for individual individuals or enterprises. When credit risk analysis is required, data is collected and analyzed manually or by computer, and the risk assessment time is inevitably long, resulting in low risk assessment efficiency. Therefore, there is an urgent need for a method that can intelligently generate risk list summary data, summarize and regularly update the risk data of many enterprises and users, so that banks and other financial institutions can directly query the credit risk data of each enterprise or individual based on the risk list summary data. Summary of the invention

[0003] The main purpose of the present invention is to provide a risk list data generation method, device, equipment and computer storage medium, aiming to realize the intelligent generation of risk list summary data, thereby facilitating banks and other financial institutions to query the credit risk data of enterprises or individuals, and improving the efficiency of risk assessment.

[0004] To achieve the above object, the present invention provides a method for generating risk list data, the method comprising:

[0005] Upon receiving a risk list data generation request, acquiring new data and existing risk list detail data according to the risk list data generation request;

[0006] Analyze and process the newly added data to obtain incremental risk list detail data, and clear the existing risk list detail data to clear outdated data;

[0007] The cleared stock risk list detail data and the incremental risk list detail data are merged to obtain risk list summary data.

[0008] Optionally, the step of analyzing and processing the newly added data to obtain detailed data of the incremental risk list includes:

[0009] Classifying the newly added data, and detecting whether the newly added data in each category meets the preset risk conditions, to obtain a first detection result;

[0010] Determine the risk category and risk level of the newly added data according to the first detection result, and determine the data validity period according to the risk category and the risk level;

[0011] Incremental risk list detail data is generated based on the user identity information, the risk category, the risk level and the data validity period in the newly added data.

[0012] Optionally, after the step of determining the risk category and risk level of the newly added data according to the first detection result, the method further includes:

[0013] Detecting whether the newly added data meets the preset account opening prohibition conditions and the preset loan prohibition conditions according to the risk category and the risk level, obtaining a second detection result, and generating a corresponding label according to the second detection result;

[0014] The step of generating incremental risk list detailed data according to the user identity information, the risk category, the risk level and the data validity period in the newly added data comprises:

[0015] Incremental risk list detail data is generated based on the user identity information, the risk category, the risk level, the data validity period and the label in the newly added data.

[0016] Optionally, the step of merging the cleared existing risk list detailed data and the incremental risk list detailed data to obtain risk list summary data includes:

[0017] Detect whether there is mergeable data between the cleared existing risk list detailed data and the incremental risk list detailed data, and obtain a third detection result;

[0018] According to the third detection result, the cleared existing risk list detail data and the incremental risk list detail data are merged using a corresponding merging rule to obtain risk list detail data;

[0019] The data corresponding to the same user in the risk list detailed data are spliced ​​according to a preset format to generate risk list summary data.

[0020] Optionally, the risk list detailed data and the risk list summary data are stored in a preset database.

[0021] Optionally, when receiving the risk list data generation request, before the step of acquiring the newly added data and the stock risk list detailed data according to the risk list data generation request, the step further includes:

[0022] Upon receiving a data import instruction, determining a data source and a data storage format thereof according to the data import instruction;

[0023] A data import tool corresponding to the data storage format is called to import the data of the data source into the preset database to obtain the incremental data.

[0024] Optionally, before the step of analyzing and processing the newly added data to obtain the incremental risk list detailed data, the step further includes:

[0025] Using a preset encryption algorithm to perform identity encryption processing on the user identity information in the newly added data;

[0026] The step of analyzing and processing the newly added data to obtain the incremental risk list detailed data includes:

[0027] Analyze and process the newly added data that has been identity encrypted to obtain detailed data on the incremental risk list.

[0028] Optionally, the risk list data generation method further includes:

[0029] When receiving a risk data query request, obtaining target query user identity information according to the risk data query request;

[0030] Using the preset encryption algorithm to perform identity encryption processing on the target query user identity information to obtain encrypted identity information;

[0031] Detecting whether the encrypted identity information exists in the risk list summary data;

[0032] If the encrypted identity information exists in the risk list summary data, the risk data corresponding to the encrypted identity information is obtained and sent to the corresponding query end.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides a risk list data generating device, the risk list data generating device comprising:

[0034] A data acquisition module, configured to, upon receiving a risk list data generation request, acquire new data and stock risk list detail data according to the risk list data generation request;

[0035] A data processing module is used to analyze and process the newly added data to obtain the incremental risk list detailed data, and to clear the existing risk list detailed data to clear outdated data;

[0036] The data generation module is used to merge the cleared stock risk list detail data and the incremental risk list detail data to obtain risk list summary data.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also provides a risk list data generating device, which includes: a memory, a processor, and a risk list data generating program stored on the memory and executable on the processor, and the risk list data generating program, when executed by the processor, implements the steps of the risk list data generating method described above.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer storage medium, on which a risk list data generation program is stored, and when the risk list data generation program is executed by a processor, the steps of the risk list data generation method as described above are implemented.

[0039] The present invention provides a risk list data generation method, device, equipment and computer storage medium. When a risk list data generation request is received, newly added data and stock risk list detail data are obtained according to the risk list data generation request; the newly added data is analyzed and processed to obtain incremental risk list detail data, and the stock risk list detail data is cleared to clear expired data; after clearing the stock risk list detail data, the cleared stock risk list detail data and incremental risk list detail data are merged to obtain risk list summary data. The present invention batch processes data, then identifies and classifies and grades the data, and then intelligently generates risk list summary data, which can realize the aggregation and regular update of risk data of many enterprises and users, so that banks and other financial institutions can directly query the credit risk data of each enterprise or individual according to the risk list summary data, and improve the risk assessment efficiency of financial institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention;

[0041] Figure 2 This is a flow chart of a first embodiment of a method for generating risk list data according to the present invention;

[0042] Figure 3 This is a functional module diagram of the first embodiment of the risk list data generation device of the present invention.

[0043] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0045] Reference Figure 1 , Figure 1 The figure is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0046] The risk list data generating device in the embodiment of the present invention may be a smart phone, or a terminal device such as a PC (Personal Computer), a tablet computer, or a portable computer.

[0047] like Figure 1 As shown, the risk list data generating device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0048] Those skilled in the art will understand that Figure 1 The structure of the risk list data generating device shown in the figure does not constitute a limitation on the risk list data generating device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0049] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a risk list data generation program.

[0050] exist Figure 1In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and perform data communication with the backend server; the user interface 1003 is mainly used to connect to the client and perform data communication with the client; and the processor 1001 can be used to call the risk list data generation program stored in the memory 1005 and perform the following operations:

[0051] Upon receiving a risk list data generation request, acquiring new data and existing risk list detail data according to the risk list data generation request;

[0052] Analyze and process the newly added data to obtain incremental risk list detail data, and clear the existing risk list detail data to clear outdated data;

[0053] The cleared stock risk list detail data and the incremental risk list detail data are merged to obtain risk list summary data.

[0054] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0055] Classifying the newly added data, and detecting whether the newly added data in each category meets the preset risk conditions, to obtain a first detection result;

[0056] Determine the risk category and risk level of the newly added data according to the first detection result, and determine the data validity period according to the risk category and the risk level;

[0057] Incremental risk list detail data is generated based on the user identity information, the risk category, the risk level and the data validity period in the newly added data.

[0058] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0059] Detecting whether the newly added data meets the preset account opening prohibition conditions and the preset loan prohibition conditions according to the risk category and the risk level, obtaining a second detection result, and generating a corresponding label according to the second detection result;

[0060] Incremental risk list detail data is generated based on the user identity information, the risk category, the risk level, the data validity period and the label in the newly added data.

[0061] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0062] Detect whether there is mergeable data between the cleared existing risk list detailed data and the incremental risk list detailed data, and obtain a third detection result;

[0063] According to the third detection result, the cleared existing risk list detail data and the incremental risk list detail data are merged using a corresponding merging rule to obtain risk list detail data;

[0064] The data corresponding to the same user in the risk list detailed data are spliced ​​according to a preset format to generate risk list summary data.

[0065] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0066] The risk list detailed data and the risk list summary data are stored in a preset database.

[0067] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0068] Upon receiving a data import instruction, determining a data source and a data storage format thereof according to the data import instruction;

[0069] A data import tool corresponding to the data storage format is called to import the data of the data source into the preset database to obtain the incremental data.

[0070] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0071] Using a preset encryption algorithm to perform identity encryption processing on the user identity information in the newly added data;

[0072] Analyze and process the newly added data that has been identity encrypted to obtain detailed data on the incremental risk list.

[0073] Further, the processor 1001 may call the risk list data generation program stored in the memory 1005, and further perform the following operations:

[0074] When receiving a risk data query request, obtaining target query user identity information according to the risk data query request;

[0075] Using the preset encryption algorithm to perform identity encryption processing on the target query user identity information to obtain encrypted identity information;

[0076] Detecting whether the encrypted identity information exists in the risk list summary data;

[0077] If the encrypted identity information exists in the risk list summary data, the risk data corresponding to the encrypted identity information is obtained and sent to the corresponding query end.

[0078] Based on the above hardware structure, various embodiments of the risk list data generation method of the present invention are proposed.

[0079] The invention provides a method for generating risk list data.

[0080] Reference Figure 2 , Figure 2 This is a flow chart of the first embodiment of the method for generating risk list data of the present invention.

[0081] In this embodiment, the risk list data generation method includes:

[0082] Step S10, when receiving a risk list data generation request, acquiring new data and existing risk list detail data according to the risk list data generation request;

[0083] Before the above step S10, the risk list data generation method may further include the following steps:

[0084] Step A, upon receiving a data import instruction, determining a data source and a data storage format thereof according to the data import instruction;

[0085] Step B: calling a data import tool corresponding to the data storage format to import the data of the data source into the preset database to obtain the incremental data.

[0086] The risk list data generation method of this embodiment is implemented by a risk list data generation device, which is described by taking a server as an example. In this embodiment, when the server receives a data import instruction, it determines the data source and the data storage form of the data source according to the data import instruction. Among them, the data import instruction can be sent regularly. For example, when setting T day to process the data of T-1 day, and then updating and summarizing the risk list summary data, it can be set to automatically send the data import instruction at a fixed time every day to import the data of the previous day. It can be understood that data from multiple data sources can be used to generate risk list summary data to improve the accuracy and comprehensiveness of risk list summary data. For example, taking the credit risk assessment scenario of a bank in the financial industry for loans as an example, its data source may include internal data sources, external data sources and other channels, wherein the internal data source may include data from various departments such as loans, security, compliance, such as overdue loan data, write-off data, loan blacklists and whitelists, etc., and the external data source may include the credit report of the People's Bank of China, the court announcement, execution documents and judgments of the court, etc., which are only used as examples here and are not used as limitations of the present invention.

[0087] After determining the data source and its data storage form, call the data import tool corresponding to each data storage form to import the data of each data source into the preset database to obtain incremental data. Optionally, the preset database can be a Hive data warehouse. Different input import tools are required for importing different data storage forms. For example, when the preset database is a Hive data warehouse; for data stored in the Hive data warehouse, the Hive SQL (a SQL-like query language used for Hive) import tool can be used to import; for data stored in the relational database MySQL, use the Sqoop tool (a tool for transferring data between Hive and traditional databases (MySQL, etc.)) to import; the third type is for data stored in file form, imported through the Hive SQL command.

[0088] When the server receives the risk list data generation request, it obtains the newly added data and the detailed data of the existing risk list according to the risk list data generation request. Among them, the risk list data generation request can also be sent by the system regularly, or it can be automatically triggered after the data import is completed. The newly added data is the data newly imported into the preset database, that is, the data from each data source on T-1 day in the above example. The detailed data of the existing risk list refers to the data processed from each data source before T-1 day, which may include but is not limited to user identity information, risk category, risk level, data validity period and label and other risk detailed data.

[0089] Step S20, analyzing and processing the newly added data to obtain incremental risk list detailed data, and clearing the existing risk list detailed data to clear outdated data;

[0090] After acquiring the newly added data and the detailed data of the existing risk list, the newly added data is analyzed and processed to obtain the incremental risk list detailed data, and the detailed data of the existing risk list is cleared to remove expired data. When clearing the detailed data of the existing risk list, the clearing can be performed based on the data validity period and the current date in the detailed data of the existing risk list. If the current date has exceeded the data validity period, the data corresponding to the data validity period will be deleted, wherein the data validity period is determined according to the risk category and risk level corresponding to the data. Through the clearing process, expired data can be cleared in time to avoid the expired data affecting the accuracy of the detailed data of the risk list.

[0091] In addition, the step of "analyzing and processing the newly added data to obtain detailed data of the incremental risk list" includes:

[0092] Step a1, classifying the newly added data, and detecting whether the newly added data in each category meets the preset risk conditions, and obtaining a first detection result;

[0093] In this embodiment, the newly added data is first classified, and the newly added data in each category is tested to see whether they meet the preset risk conditions, and the first test result is obtained. Among them, the classification method can be classified according to the data source and business scenario. For example, in the above example, the data source includes loans, security, compliance, PBOC credit, courts and other channels. The same data source includes different business scenario types, such as loans include micro-credit and non-micro-credit, and PBOC credit can be subdivided into credit card and / or loan overdue, administrative penalties, compulsory execution, etc. Correspondingly, it can be divided into loan micro-credit, loan non-micro-credit, PBOC credit credit card and / or loan overdue, PBOC credit administrative penalties, PBOC credit compulsory execution and other categories. When setting the preset risk conditions, the corresponding preset risk conditions can be set according to each category, and the risk level corresponding to each preset risk condition can be set. For example, for overdue loans, the levels can be divided into different intervals according to the number of overdue days. Overdue for A days or more (including endpoint A) is level 1, overdue for AB days (including endpoint B, excluding endpoint A) is level 2, overdue for BC days (including endpoint C, excluding endpoint B) is level 3, and so on; it can also be graded according to the number of overdue times, for example, M overdue times within N months is level 1, and Y overdue times within X months is level 2. Of course, this is only for example purposes and is not intended to limit the present invention. The specific classification method and the method for setting the preset risk conditions can be set according to actual needs.

[0094] Step a2, determining the risk category and risk level of the newly added data according to the first detection result, and determining the data validity period according to the risk category and risk level;

[0095] Step a3, generating incremental risk list detailed data according to the user identity information, the risk category, the risk level and the data validity period in the newly added data.

[0096] Then, the risk category and risk level of the newly added data are determined based on the first detection result, and the data validity period is determined based on the risk category and risk level, and then the incremental risk list detailed data is generated based on the user identity information, risk category, risk level and data validity period in the newly added data.

[0097] Furthermore, after the step of “determining the risk category and risk level of the newly added data according to the first detection result”, the following steps may also be included:

[0098] Step a4, detecting whether the newly added data meets the preset account opening prohibition conditions and the preset loan prohibition conditions according to the risk category and the risk level, obtaining a second detection result, and generating a corresponding label according to the second detection result;

[0099] According to the regulatory requirements of the China Banking Regulatory Commission, users with certain risks are not allowed to hold meetings or take out loans. Therefore, after determining the risk category and risk level of the newly added data, it is possible to detect whether there is data that is not allowed to open an account or take out a loan, and mark it for subsequent review and analysis by banks and other financial companies. Specifically, it is possible to detect whether the newly added data meets the preset conditions for not opening an account and the preset conditions for not taking out a loan based on the risk category and risk level, obtain a second detection result, and generate a corresponding label based on the second detection result. Among them, the preset conditions for not opening an account and the preset conditions for not taking out a loan can be set according to the regulatory requirements of the China Banking Regulatory Commission. If it is detected that a certain newly added data meets the preset conditions for not opening an account and / or the preset conditions for not taking out a loan, a corresponding label for not opening an account and / or a label for not taking out a loan is generated. It is understandable that in a specific embodiment, other types of labels can also be set, not limited to labels for not opening an account and labels for not taking out a loan.

[0100] At this time, step a3 includes: generating incremental risk list detailed data according to the user identity information, the risk category, the risk level, the data validity period and the label in the newly added data.

[0101] Then, based on the user identity information, risk category, risk level, data validity period and label in the newly added data, the incremental risk list detailed data is generated. Of course, in a specific embodiment, the data indicators included in the incremental risk list detailed data are not limited to the user identity information, risk category, risk level, data validity period and label listed above, but may also include the occurrence time, duration, amount, etc. The data indicators included in the incremental risk list detailed data can be set according to actual needs, and are not specifically limited here.

[0102] Step S30, merging the cleared stock risk list detail data and the incremental risk list detail data to obtain risk list summary data.

[0103] After obtaining the incremental risk list detailed data and clearing the existing risk list detailed data, the cleared existing risk list detailed data and the incremental risk list detailed data are merged to obtain the risk list summary data. Specifically, step S30 includes:

[0104] Step b1, detecting whether there is mergeable data between the cleared existing risk list detailed data and the incremental risk list detailed data, and obtaining a third detection result;

[0105] Step b2, merging the cleared existing risk list detailed data and the incremental risk list detailed data using a corresponding merging rule according to the third detection result to obtain risk list detailed data;

[0106] First, detect whether there is mergeable data between the cleared stock risk list detailed data and the incremental risk list detailed data to obtain a third detection result, and then merge the cleared stock risk list detailed data and the incremental risk list detailed data according to the third detection result using the corresponding merge rule to obtain the risk list detailed data. For the convenience of explanation, the cleared stock risk list detailed data is referred to as stock data, and the incremental risk list detailed data is referred to as incremental data, wherein the definition of mergeable data can be data of the same risk category and risk level of the same user between the stock data and the incremental data, or data of multiple different risk levels of the same risk category of the same user between the stock data and the incremental data. If there is mergeable data between the stock data and the incremental data, first merge the mergeable data between the stock data and the incremental data to obtain the updated stock data, and then merge the non-mergeable data in the incremental data except the mergeable data with the updated stock data to further update the stock data, that is, to obtain the risk list detailed data. If there is no data that can be merged between the stock data and the incremental data, the stock data and the incremental data are directly merged to obtain the risk list detailed data. For example, if there is data of the same risk category and the same risk level for the same user between the stock data and the incremental data, the stock data is saved, and the data validity period of the mergeable data in the stock data is updated to the data validity period of the mergeable data in the corresponding incremental data to obtain the stock data after preliminary merger, and then the other data in the incremental data except the mergeable data is merged into the above-mentioned stock data after preliminary merger to obtain the risk list detailed data. For another example, if there are multiple data of different risk levels of the same risk category for the same user between the stock data and the incremental data, the data corresponding to the most serious level of the risk category is retained, and the mergeable data in the incremental data is preliminarily merged into the stock data based on this standard to obtain the preliminarily merged stock data, and then the other data in the incremental data except the mergeable data is merged into the above-mentioned preliminarily merged stock data to obtain the risk list detailed data.

[0107] Step b3, splicing the data corresponding to the same user in the risk list detailed data according to a preset format to generate risk list summary data.

[0108] After obtaining the detailed data of the risk list, the data corresponding to the same user in the detailed data of the risk list is spliced ​​according to the preset format to generate the summary data of the risk list. By splicing the data of the same user, the detailed data of the same user can be summarized into one, which is convenient for subsequent inquiries by banks and other financial institutions. Among them, the preset format can be set according to actual needs and is not specifically limited here. The summary data of the risk list can be used for risk identification and prevention in the pre-loan credit process of credit business, monitoring and quota management during the loan, and risk identification and control in the post-loan principal and interest collection process. Banks and other financial institutions will be cautious about lending or refusing loans, or increase loan interest rates if they find that the enterprise or individual has a risk of default through the summary data of the risk list.

[0109] Furthermore, the risk list data generation method also includes: storing the risk list detailed data and the risk list summary data in a preset database.

[0110] After obtaining the risk list detailed data and the risk list summary data, the risk list detailed data and the risk list summary data can be stored in a preset database, which can be a Hive data warehouse.

[0111] In this embodiment, the task scheduler is used to periodically perform offline batch processing of big data, thereby identifying and classifying risk data, and finally intelligently generating risk list summary data, which can realize the aggregation and regular update of risk data of many enterprises and users, so that banks and other financial institutions can directly query the credit risk data of each enterprise or individual based on the risk list summary data, thereby improving the risk assessment efficiency of financial institutions. It should be noted that the above examples are all explained by taking the Hive computing framework as an example, and the data is processed by Hive SQL. In specific embodiments, other offline processing architectures can also be selected to process and analyze the data, such as Spark (a fast distributed computing framework based on big data), and correspondingly, Spark SQL (a Spark framework-based SQL-like query language for structured data processing and Spark data execution) is used to implement data processing.

[0112] It should also be noted that the entire processing flow can be automatically executed by the WTSS scheduler (a batch workflow task scheduler developed in the industry) according to the scheduling plan every day, and the data of day T-1 is processed on day T to regularly update the risk list summary data. Of course, the WTSS scheduling system can also be replaced with other task scheduling systems that support big data clusters, such as Apache Azkaban (Apache Azkaban is a batch workflow task scheduler), Apache Oozie (Apache Oozie is a workflow scheduling engine for the Hadoop platform), etc. In addition, the specific execution time of the data can be set according to actual needs.

[0113] The embodiment of the present invention provides a method for generating risk list data. When a risk list data generation request is received, newly added data and existing risk list detail data are obtained according to the risk list data generation request; the newly added data is analyzed and processed to obtain incremental risk list detail data, and the existing risk list detail data is cleared to clear expired data; after clearing the existing risk list detail data, the cleared existing risk list detail data and the incremental risk list detail data are merged to obtain risk list summary data. The embodiment of the present invention batch processes the data, and then identifies and classifies and grades the data, and then intelligently generates risk list summary data, which can realize the aggregation and regular update of risk data of many enterprises and users, so that banks and other financial institutions can directly query the credit risk data of each enterprise or individual according to the risk list summary data, which can improve the risk assessment efficiency of financial institutions.

[0114] Further, based on Figure 2 The first embodiment shown provides a second embodiment of the risk list data generating method of the present invention.

[0115] In this embodiment, to ensure data security, before step S20, the risk list data generation method further includes:

[0116] Step C, using a preset encryption algorithm to perform identity encryption processing on the user identity information in the newly added data;

[0117] In this embodiment, in order to ensure the security of data, sensitive information (such as ID number, name, mobile phone number, bank card number, customer number, enterprise organization code, etc.) can be encrypted. In this embodiment, the user identity information is selected for identity encryption. Considering the uniqueness of the ID number and organization code, for individuals, the user identity information includes at least the ID number, and may also include one or more of other identity information such as name, bank card number, etc., which can be selected according to the specific situation and is not limited here; for enterprises, the user identity information includes at least the organization code, and may also include some other enterprise information. Specifically, after obtaining the newly added data and the detailed data of the existing risk list, the server first uses a preset encryption algorithm to encrypt the user identity information in the newly added data. Among them, the preset encryption algorithm can be salted encryption through the SHA256 algorithm (Secure Hash Algorithm 256). Of course, it can be understood that the SHA256 algorithm can also be replaced by other encryption algorithms, such as MD5 (Message Digest Algorithm), SHA224 (Secure Hash Algorithm 224), SHA384 (Secure Hash Algorithm 384), SHA512 (Secure Hash Algorithm 512), etc. The specific encryption process is consistent with the existing encryption process and will not be repeated here.

[0118] Of course, it is understandable that in a specific embodiment, the identity encryption processing process can be executed while the data is imported, that is, when the data is imported, the user identity information in the imported data is directly encrypted, so that the data imported into the preset database has been processed by identity encryption.

[0119] At this time, step S20 includes: analyzing and processing the newly added data after identity encryption to obtain detailed data of the incremental risk list.

[0120] After the user identity information in the newly added data is encrypted, the newly added data that has been encrypted is analyzed and processed to obtain the incremental risk list detail data, and then the subsequent steps are executed. The execution process can refer to the first embodiment above and will not be repeated here.

[0121] In addition, it should be noted that, in a specific embodiment, data desensitization can also be used to desensitize sensitive information in the newly added data to ensure data security.

[0122] In this embodiment, the security of the data can be improved by performing identity encryption processing on the user identity information in the newly added data.

[0123] Furthermore, based on the above-mentioned first and second embodiments, a third embodiment of the method for generating risk list data of the present invention is proposed.

[0124] In this embodiment, the risk list data generation method further includes:

[0125] Step D, upon receiving a risk data query request, obtaining target query user identity information according to the risk data query request;

[0126] In this embodiment, when a bank or other financial institution needs to query the credit risk data of a certain enterprise or individual, it can initiate a corresponding risk data query request. At this time, when the server receives the risk data query request, it obtains the target query user identity information according to the data risk query request. It should be noted that the user here can represent an enterprise or an individual.

[0127] Step E, using the preset encryption algorithm to perform identity encryption processing on the target query user identity information to obtain encrypted identity information;

[0128] Step F, detecting whether the encrypted identity information exists in the risk list summary data;

[0129] After obtaining the target query user identity information, a preset encryption algorithm is used to perform identity encryption processing on the target query user identity information to obtain encrypted identity information, and then, it is detected whether the encrypted identity information exists in the risk list summary data.

[0130] Step G: If the encrypted identity information exists in the risk list summary data, the risk data corresponding to the encrypted identity information is obtained and sent to the corresponding query end.

[0131] If the encrypted identity information exists in the risk list summary data, the risk data corresponding to the encrypted identity information is obtained and sent to the query end that initiated the risk data query request. The risk data may include but is not limited to risk category, risk level, data validity period, and label.

[0132] If the encrypted identity information does not exist in the risk list summary data, a corresponding prompt message is generated and sent to the query end to inform the inquirer that there is no risk data of the target query user.

[0133] In addition, it should be noted that in the process of generating risk list summary data, if the user identity information is not encrypted, after obtaining the target query user identity information based on the received risk data query request, it is directly detected whether the target query user identity information exists in the risk list summary data, and then the subsequent steps are executed.

[0134] In this embodiment, when a risk data query request is received, the corresponding risk data is queried in the risk list summary data based on the target query user identity information, which can facilitate banks and other financial institutions to quickly query and obtain the risk data of each enterprise or individual based on the risk list summary data, and then make corresponding judgments and operations based on the risk data in a timely manner.

[0135] In addition, it should be noted that due to the limited data processing capabilities of each financial institution, the risk list summary data obtained may not be comprehensive. In this regard, each financial institution can share the risk list summary data based on blockchain technology. Specifically, each financial institution can obtain authorization through the access mechanism, and then join the blockchain sharing platform as a different node of the blockchain, and then store the clear list summary data generated by each in the corresponding block. When a bank or other financial institution needs to query the credit risk data of a certain enterprise or individual, it can initiate a corresponding risk data query request, and then broadcast the data query request to each blockchain node through the blockchain to obtain the risk data of the target query user.

[0136] The present invention also provides a risk list data generating device.

[0137] Reference Figure 3 , Figure 3 This is a functional module diagram of the first embodiment of the risk list data generation device of the present invention.

[0138] like Figure 3 As shown, the risk list data generating device includes:

[0139] The data acquisition module 10 is used to acquire the newly added data and the detailed data of the existing risk list according to the risk list data generation request when receiving the risk list data generation request;

[0140] The data processing module 20 is used to analyze and process the newly added data to obtain the incremental risk list detailed data, and to clear the existing risk list detailed data to clear outdated data;

[0141] The data generation module 30 is used to merge the cleared stock risk list detail data and the incremental risk list detail data to obtain risk list summary data.

[0142] Furthermore, the data processing module 20 includes:

[0143] A first detection unit is used to classify the newly added data and detect whether the newly added data in each category meets the preset risk conditions to obtain a first detection result;

[0144] an information determination unit, configured to determine a risk category and a risk level of the newly added data according to the first detection result, and determine a data validity period according to the risk category and the risk level;

[0145] The first generating unit is used to generate incremental risk list detailed data according to the user identity information, the risk category, the risk level and the data validity period in the newly added data.

[0146] Furthermore, the data processing module 20 also includes:

[0147] a label generating unit, configured to detect whether the newly added data meets the preset account opening prohibition condition and the preset loan prohibition condition according to the risk category and the risk level, obtain a second detection result, and generate a corresponding label according to the second detection result;

[0148] The first generating unit is specifically used to generate incremental risk list detailed data according to the user identity information, the risk category, the risk level, the data validity period and the label in the newly added data.

[0149] Furthermore, the data generation module 30 includes:

[0150] The second detection unit is used to detect whether there is mergeable data between the cleared existing risk list detailed data and the incremental risk list detailed data, and obtain a third detection result;

[0151] a data merging unit, configured to merge the cleared existing risk list detail data and the incremental risk list detail data using a corresponding merging rule according to the third detection result to obtain risk list detail data;

[0152] The second generating unit is used to splice the data corresponding to the same user in the risk list detailed data according to a preset format to generate risk list summary data.

[0153] Furthermore, the risk list data generating device further includes:

[0154] The data storage module is used to store the risk list detailed data and the risk list summary data in a preset database.

[0155] Furthermore, the risk list data generating device further includes:

[0156] An information determination module is used to determine a data source and a data storage form thereof according to the data import instruction when receiving the data import instruction;

[0157] The data import module is used to call a data import tool corresponding to the data storage form to import the data of the data source into the preset database to obtain the incremental data.

[0158] Furthermore, the risk list data generating device further includes:

[0159] A first encryption module, used to perform identity encryption processing on the user identity information in the newly added data using a preset encryption algorithm;

[0160] The data processing unit 20 is specifically used to analyze and process the newly added data after identity encryption processing to obtain the incremental risk list detailed data.

[0161] Furthermore, the risk list data generating device further includes:

[0162] An information acquisition module, configured to acquire target query user identity information according to the risk data query request upon receiving the risk data query request;

[0163] A second encryption module is used to perform identity encryption processing on the target query user identity information using the preset encryption algorithm to obtain encrypted identity information;

[0164] An information detection module, used to detect whether the encrypted identity information exists in the risk list summary data;

[0165] The data sending module is used to obtain the risk data corresponding to the encrypted identity information if the encrypted identity information exists in the risk list summary data, and send it to the corresponding query end.

[0166] Among them, the functional implementation of each module in the above-mentioned risk list data generation device corresponds to the various steps in the above-mentioned risk list data generation method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0167] The present invention also provides a computer storage medium, on which a risk list data generation program is stored. When the risk list data generation program is executed by a processor, the steps of the risk list data generation method described in any of the above embodiments are implemented.

[0168] The specific embodiments of the computer storage medium of the present invention are basically the same as the embodiments of the risk list data generation method described above, and will not be described in detail here.

[0169] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0170] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0171] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0172] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for generating risk list data, characterized in that: The risk list data generation method comprises: Upon receiving a risk list data generation request, acquiring new data and existing risk list detail data according to the risk list data generation request; Analyze and process the newly added data to obtain incremental risk list detailed data, and clear the existing risk list detailed data to clear expired data; wherein, the step of analyzing and processing the newly added data to obtain incremental risk list detailed data includes: classifying the newly added data, and detecting whether the newly added data in each category meets the preset risk conditions to obtain a first detection result; determining the risk category and risk level of the newly added data according to the first detection result, and determining the data validity period according to the risk category and the risk level; generating incremental risk list detailed data according to the user identity information in the newly added data, the risk category, the risk level and the data validity period; The cleared stock risk list detail data and the incremental risk list detail data are merged to obtain risk list summary data.

2. The method for generating risk list data according to claim 1, characterized in that: After the step of determining the risk category and risk level of the newly added data according to the first detection result, the method further includes: Detecting whether the newly added data meets the preset account opening prohibition conditions and loan prohibition conditions according to the risk category and the risk level, obtaining a second detection result, and generating a corresponding label according to the second detection result; The step of generating incremental risk list detailed data according to the user identity information, the risk category, the risk level and the data validity period in the newly added data comprises: Incremental risk list detail data is generated based on the user identity information, the risk category, the risk level, the data validity period and the label in the newly added data.

3. The method for generating risk list data according to claim 1, characterized in that: The step of merging the cleared existing risk list detailed data and the incremental risk list detailed data to obtain risk list summary data includes: Detect whether there is mergeable data between the cleared existing risk list detailed data and the incremental risk list detailed data, and obtain a third detection result; According to the third detection result, the cleared existing risk list detail data and the incremental risk list detail data are merged using a corresponding merging rule to obtain risk list detail data; The data corresponding to the same user in the risk list detailed data are spliced ​​according to a preset format to generate risk list summary data.

4. The method for generating risk list data according to claim 3, characterized in that: The risk list data generation method further includes: The risk list detailed data and the risk list summary data are stored in a preset database.

5. The method for generating risk list data according to claim 4, characterized in that: Before the step of acquiring the newly added data and the stock risk list detailed data according to the risk list data generation request upon receiving the risk list data generation request, the method further includes: Upon receiving a data import instruction, determining a data source and a data storage format thereof according to the data import instruction; A data import tool corresponding to the data storage format is called to import the data from the data source into the preset database to obtain the newly added data.

6. The method for generating risk list data according to any one of claims 1 to 5, characterized in that: Before the step of analyzing and processing the newly added data to obtain the incremental risk list detailed data, the method further includes: Using a preset encryption algorithm to perform identity encryption processing on the user identity information in the newly added data; The step of analyzing and processing the newly added data to obtain the incremental risk list detailed data includes: Analyze and process the newly added data that has been identity encrypted to obtain detailed data on the incremental risk list.

7. The method for generating risk list data according to claim 6, characterized in that: The risk list data generation method further includes: When receiving a risk data query request, obtaining target query user identity information according to the risk data query request; Using the preset encryption algorithm to perform identity encryption processing on the target query user identity information to obtain encrypted identity information; Detecting whether the encrypted identity information exists in the risk list summary data; If the encrypted identity information exists in the risk list summary data, the risk data corresponding to the encrypted identity information is obtained and sent to the corresponding query end.

8. A risk list data generating device, characterized in that: The risk list data generating device comprises: A data acquisition module, configured to, upon receiving a risk list data generation request, acquire new data and stock risk list detail data according to the risk list data generation request; A data processing module is used to analyze and process the newly added data to obtain incremental risk list detailed data, and to clear the existing risk list detailed data to clear expired data; the data processing module is specifically used to classify the newly added data, and detect whether the newly added data in each category meets the preset risk conditions to obtain a first detection result; determine the risk category and risk level of the newly added data according to the first detection result, and determine the data validity period according to the risk category and the risk level; generate incremental risk list detailed data according to the user identity information in the newly added data, the risk category, the risk level and the data validity period; The data generation module is used to merge the cleared stock risk list detail data and the incremental risk list detail data to obtain risk list summary data.

9. A risk list data generation device, characterized in that: The risk list data generating device comprises: a memory, a processor and a risk list data generating program stored in the memory and executable on the processor. When the risk list data generating program is executed by the processor, the steps of the risk list data generating method as described in any one of claims 1 to 7 are implemented.

10. A computer storage medium, characterized in that: The computer storage medium stores a risk list data generation program, and when the risk list data generation program is executed by the processor, the steps of the risk list data generation method according to any one of claims 1 to 7 are implemented.

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