CN112199359A - Data checking method and device, electronic equipment and storage medium - Google Patents

Data checking method and device, electronic equipment and storage medium Download PDF

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Publication number
CN112199359A
CN112199359A CN202010986396.7A CN202010986396A CN112199359A CN 112199359 A CN112199359 A CN 112199359A CN 202010986396 A CN202010986396 A CN 202010986396A CN 112199359 A CN112199359 A CN 112199359A
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data table
user
checking
data
current
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石勇
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China Construction Bank Corp
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China Construction Bank Corp
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G06F16/24564—Applying rules; Deductive queries
    • G06F16/24565—Triggers; Constraints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification

Abstract

The embodiment of the application provides a data checking method, a device, an electronic device and a storage medium, and the method comprises the steps of acquiring original data of financial assets in the current period related to the value reduction, counting and improvement management of the financial assets at a preset time point, classifying and summarizing the original data of the financial assets in the current period to obtain a classification data table of the financial assets in the current period with different dimensionalities, checking data in the classification data table of the financial assets in the current period according to a pre-configured checking rule, realizing the omnibearing and multi-dimensionality automatic checking of the financial asset data of a user, improving the checking efficiency and the checking accuracy of the financial asset data of the user, and ensuring the quality of the financial asset data of the user.

Description

Data checking method and device, electronic equipment and storage medium
Technical Field
The embodiment of the application relates to the technical field of data analysis, in particular to a data checking method and device, electronic equipment and a storage medium.
Background
In the financial industry, the assessment of various businesses cannot be separated from the analysis of data, particularly the financial asset data of a user, and the quality of the data is the key influencing the assessment effectiveness, so that the assessment of the integrity and the accuracy of the financial asset data and the logicality among the data through a certain assessment mechanism becomes one of the most effective means for ensuring the quality of the financial asset data.
In the prior art, a data checking department is arranged, and the data checking department extracts and checks related financial asset data according to financial asset data problems reported by related departments, and processes the financial asset data with problems according to checking results to ensure the quality of the financial asset data.
However, the prior art has the problem of low checking efficiency and accuracy.
Disclosure of Invention
The embodiment of the application provides a data checking method and device, electronic equipment and a storage medium, and aims to solve the problems of low checking efficiency and accuracy in the prior art.
In a first aspect, an embodiment of the present application provides a data checking method, including:
at a preset time point, acquiring the original data of the financial assets of the current period related to the financial asset value reduction, counting and submitting management, wherein the original data of the financial assets of the current period comprises user information and loan debt information;
classifying and summarizing the original data of the current-stage financial assets to obtain classification data tables of the current-stage financial assets with different dimensions;
checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking rule comprises at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
Optionally, the current financial asset classification data table comprises at least one of the following data tables: the system comprises a latest user name list data table in the current period, a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table.
Optionally, the current financial asset classification data table comprises at least one of a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user product credit balance data table and a user credit rating data table; the target checking mode comprises consistency checking and attribute filling rate checking; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
taking a target index field configured in the checking rule as an index, and performing consistency checking on data corresponding to the debt field in the financial asset classification data sheet of the current period;
and/or, taking the target index field configured in the check rule as an index, and performing attribute filling rate check on the data corresponding to the mandatory property field in the financial asset classification data table of the current period.
Optionally, the performing consistency check on the data corresponding to the debt field in the current financial asset classification data sheet includes:
checking whether the amount of the debt of each record in the current-period financial asset classification data table is consistent with the amount of the debt of the target comparison data table; if not, controlling the interface to alarm;
and/or checking whether the number of the debt items recorded in each date financial asset classification data sheet is consistent with the number of the debt items recorded in the target comparison data sheet; and if the two are not consistent, the control interface gives an alarm.
Optionally, the performing attribute fill rate checking on the data corresponding to the mandatory property field in the current financial asset classification data table includes:
determining whether the number of unfilled target mandatory fill property fields in each record of the current-stage financial asset classification data table reaches a first target threshold; and if the first target threshold value is reached, controlling an interface to alarm.
Optionally, the current financial asset classification data table comprises a user product credit balance data table, the user product credit balance data table comprising a product type number; the checking mode comprises coverage rate checking; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
and checking the coverage rate of the data in the credit balance data table of the user product according to the product type number and a second target threshold value.
Optionally, the performing coverage check on the data in the user product credit balance data table according to the product type number and a second target threshold includes:
determining the product type quantity contained in the user product credit balance data table according to the product type number, and determining whether the product type quantity reaches a second target threshold value; if not, the control interface gives an alarm.
Optionally, the current financial asset classification data table comprises a user loan five-level classification change data table, a user loan overdue data table and a user refinancing frequency data table; the checking rule comprises combined relation checking; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
and checking the combination relation of the product structure of each user according to the user loan five-level classification change data table, the user loan overdue data table and the user re-financing time data table, wherein the product structure is formed by products related to first overdue, re-financing or next-movement attention.
Optionally, the checking, according to the user loan five-level classification change data table, the user loan overdue data table, and the user refinancing times data table, a combination relationship of the product structure of each user includes:
obtaining products related to first overdue, re-financing or next-movement attention of each user, and obtaining the product structure of each user;
checking whether the loss condition and the reject ratio condition of each product in the product structure in a future period of time are matched with the loss condition and the reject ratio condition required by the target comparison data table; and if not, controlling the interface to alarm.
Optionally, the current financial asset classification data table includes a current latest user name list data table and other data tables, and the other data tables include at least one of the following data tables: a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
and checking the newly added data items in the other data tables by taking a target index field configured in the checking rule as an index according to the latest user name list data table in the current period.
Optionally, the checking, according to the current latest user name list data table, the newly added data items in the other data tables, includes:
judging whether the newly added data items in the other data tables are consistent with the information in the latest user name list data table in the current period; and if the two are not consistent, the control interface gives an alarm.
Optionally, the user debt credit balance total data table, the user risk exposure summary data table, the user loan five-level classification change data table, the user loan overdue data table, the user refinancing times data table, the user product credit balance data table, and the user credit rating data table all include a report period, a user number, and a debt item number.
Optionally, before the data in the current financial asset classification data table is checked according to a pre-configured check rule, the method further includes:
and configuring the operation of a worker on a rule import interface according to the checking rule, and configuring the checking rule.
In a second aspect, an embodiment of the present application provides a data checking apparatus, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the current-stage financial asset original data related to financial asset value reduction, counting and submitting management at a preset time point, and the current-stage financial asset original data comprises user information and loan debt information;
the processing module is used for classifying and summarizing the original data of the current-stage financial assets to obtain classification data tables of the current-stage financial assets with different dimensions; checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking rule comprises at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the data checking method according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the data checking method according to the first aspect is implemented.
According to the data checking method, the data checking device, the electronic equipment and the storage medium, the original data of the financial assets in the current period related to the financial asset value reduction, counting and submitting management are obtained at the preset time point, the original data of the financial assets in the current period comprise user information and loan and debt item information, the original data of the financial assets in the current period are classified and gathered, the classified data sheets of the financial assets in the current period with different dimensionalities are obtained, the data in the classified data sheets of the financial assets in the current period are checked according to the pre-configured checking rules, and the checking rules comprise at least one of the following parameters: the method comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
Drawings
Fig. 1 is a schematic flowchart of a data checking method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a data checking method according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of a data checking apparatus according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
In the prior art, a data checking department is arranged, and the data checking department extracts and checks related financial asset data according to financial asset data problems reported by the related department and processes the financial asset data with problems according to checking results to ensure the quality of the financial asset data.
The main ideas of the technical scheme are as follows: based on the problems in the prior art, the application provides a technical scheme of data checking, which comprises the steps of periodically acquiring original data including user information, loan item information and the like related to financial asset reduction, calculation, submission and management from an upstream data statistical system, preprocessing the original data through a classification idea to obtain a classification data sheet capable of reflecting the financial asset condition of a user from different dimensions, configuring checking rules configured by a checking rule configuration worker in a rule import interface according to the checking rules, performing consistency checking, attribute filling rate checking, coverage rate checking, combination relation checking and the like on the classification data sheets of different dimensions, actively finding the problem possibly existing in the classification data sheet and influencing the data quality, and integrating the classification data sheet of the current period with the classification data sheet when the problem does not exist in the classification data sheet of the current period, therefore, the financial asset data of the full life cycle of the user, which is high in quality and is counted by the classification data sheet, is obtained. Firstly, the technical scheme of the application carries out multi-dimensional and omnibearing automatic checking on data through a checking mechanism, so that the checking efficiency and the checking accuracy of the data are improved, secondly, potential hazards possibly existing in the data can be actively found and timely processed in a periodic and automatic checking mode, and the problems of service termination, complaints, difficulty in problem troubleshooting and the like caused by the occurrence of data quality problems are avoided to a certain extent.
Example one
Fig. 1 is a schematic flow chart of a data checking method provided in an embodiment of the present application, where the method of this embodiment may be executed by a data checking apparatus provided in the embodiment of the present application, and the apparatus may be implemented in a software and/or hardware manner, and may be integrated in an electronic device such as a server and an intelligent terminal. As shown in fig. 1, the data checking method of this embodiment includes:
s101, acquiring original data of the financial assets in the current period related to the financial asset value reduction and counting management at a preset time point.
In the step, through a mode of time periodic triggering, when a preset time comes, the original data of the financial assets in the current period related to the deduction, calculation and improvement management of the financial assets are automatically acquired, and therefore the follow-up data checking step can be smoothly carried out.
The financial asset original data of the current period refers to financial asset original data required by the current check, the financial asset original data is data related to financial asset value reduction, calculation and delivery management, the financial asset original data comprises user information and loan debt item information, the user information can be information such as user name, user number, user industry, user scale and user credit rating, and the loan debt item information can be information such as debt item number, debt item risk classification, credit balance and longest overdue days of origin.
The preset time point, that is, the preset trigger time point for executing the data checking method, may be specifically a certain time of a certain day of a certain month in a certain year, the preset time point is usually closely related to the statistical period, and the statistical period may be a month or a quarter, and is not limited herein.
For example, if the month is taken as the statistical period in this embodiment, the zero point of the first day of each month may be taken as a preset time point, so that the zero point system on the first day of each month automatically acquires the financial asset raw data of the previous month, that is, the financial asset raw data of this period.
And S102, carrying out classification and summary processing on the original data of the financial assets in the current period to obtain a classification data table of the financial assets in the current period with different dimensionalities.
In this step, after S101, for example, the classification and summarization processing may be performed on the current-stage financial asset original data according to the preset classification dimensionality, so as to obtain the current-stage financial asset classification data table with different dimensionalities.
In one possible implementation, the current financial asset classification data table includes one or more of the following data tables: the system comprises a latest user name list data table in the current period, a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table.
The latest user name list data table at the current period is used for storing list information of newly added users at the current period, and the latest user name list data table at the current period usually comprises fields of a report period, user names of the newly added users, user numbers, user scales, debt item numbers and the like.
The user debt item credit balance total data sheet is used for storing the detailed information of the debt item credit balance total from the user who opens an account in the current bank to the current bank, and the user debt item credit balance total data sheet generally comprises fields such as a report period, a user number, a debt item credit balance and the like.
And the user risk exposure summary data table is used for storing risk exposure summary information of all users who open accounts in the current bank till the current period, and fields such as the period, the user number, the debt item number, the risk exposure and the like are usually reported in the user risk exposure summary data table.
The user loan five-level classification change data table is used for storing loan risk level summary information of all users who make an account in the current bank in the current period, and the user loan five-level classification change data table generally comprises fields such as a report period, a user number, a debt item number, a loan risk level and a product type number corresponding to the migration interest under the loan risk level.
The user loan overdue data sheet is used for storing the summary information of the overdue days or the overdue months of loans of all users who open an account in the bank till the bank is due, and the user loan overdue data sheet generally comprises fields such as report term, user number, debt item number, loan overdue days/months and product type number corresponding to first overdue.
The user re-financing times data table is used for storing the summary information of all the users who open accounts in the same bank and the re-financing times till the current date, and the user re-financing times data table generally comprises fields such as report date, user number, debt item number, re-financing times, product type number corresponding to the re-financing and the like.
The user product credit balance data sheet is used for storing the summary information of product credit balances of users who open an account in the bank till the bank, and the user product credit balance data sheet generally comprises fields such as a report period, a user number, a debt item number, a product type number, a product credit balance and the like.
The user credit rating data table is used for storing the credit rating summary information of all users who open an account in the current bank till the current period, and the user credit rating data table usually comprises fields such as a report period, a user number, a debt item number, a user credit rating and the like.
Optionally, if the system supports the re-run mechanism, before performing S102 each time, all the run-out data in the current financial asset classification data table needs to be cleared, so as to ensure the accuracy of the logical relationship between the processed data.
S103, checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule.
In this step, after S102, for the current-stage financial asset classification data tables of different dimensions, pre-configured corresponding checking rules are respectively adopted for checking, in this embodiment, the checking rules include at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table.
The target check mode refers to one or more check modes for checking the classification data table of the financial assets in the specific current period. In this embodiment, the target check mode may include one or more of consistency check, attribute fill-rate check, coverage check and combinatorial relationship check according to different financial asset classification data tables of this period, and the target check modes of different financial asset classification data tables of this period may be the same or different.
The target index field is a field selected for uniquely determining a data record when checking a specific current-stage financial asset classification data table, and the target index field can be formed by combining one field or a plurality of fields in the current-stage financial asset classification data table. For a specific current-stage financial asset classification data table, the target index field is usually a default value, and the target index fields of different current-stage financial asset classification data tables may be the same or different. For example, for a user debt credit balance total data sheet, a user risk exposure summary data sheet, a user loan five-level classification change data sheet, a user loan overdue data sheet and a user credit rating data sheet, the target index fields thereof are generally defaulted to a report period, a user number and a debt number, and for a user product credit balance data sheet, the target index fields thereof are generally defaulted to a report period, a user number, a debt number and a product number.
The target filling-necessary property field is a field filled in a necessary section in a specific current-stage financial asset classification data table, the target filling-necessary property field is usually a field which is important for the specific current-stage financial asset classification data table, the target filling-necessary property field can also be formed by combining one field or a plurality of fields in the current-stage financial asset classification data table, and the target filling-necessary property fields of different current-stage financial asset classification data tables can be the same or different.
In the embodiment, the target threshold used for performing the attribute filling rate check is called a first target threshold, and the target threshold used for performing the coverage rate check is called a second target threshold. In this embodiment, the target threshold used for checking different current-stage financial asset classification data tables may be the same or different.
The target comparison data table refers to a data table for comparing data in the financial asset classification data table of the current period with data in the financial asset classification data table of the current period when checking the financial asset classification data table of the specific current period, and is used as a standard for judging whether the data in the financial asset classification data table of the current period meets conditions, such as a prepared fund calculation result data table, the original data of the financial asset of the current period, a past financial asset classification data table and the like. It will be appreciated that financial asset classification data tables for one dimension typically only employ data tables for the same dimension as the target collation data tables.
In one possible implementation manner, the current financial asset classification data table comprises at least one of a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user product credit balance data table and a user credit level data table; the target checking mode comprises consistency checking and attribute filling rate checking; checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking comprises the following steps:
taking a target index field configured in the checking rule as an index, and performing consistency checking on data corresponding to the debt field in the current-stage financial asset classification data sheet; and/or, taking a target index field configured in the checking rule as an index, and performing attribute filling rate checking on data corresponding to the mandatory filling property field in the financial asset classification data table of the current period.
In this implementation, the user debt credit balance total amount data table, the user risk exposure summary data table, the user loan five-level classification change data table, the user loan overdue data table, the user product credit balance data table, and the user credit rating data table are all applicable to consistency check and attribute fill rate check, specifically:
(1) data sheet for sum of debt and credit balances of users
a. Checking whether the amount of the debt items recorded in each item credit balance total amount data table of the user is consistent with the amount of the debt items recorded in the target comparison data table or not by taking the report period, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
b. Taking the report period, the client number and the debt number as target index fields, and checking whether the number of the debt items recorded in each item credit balance total data sheet of the user is consistent with the number of the debt items recorded in the target comparison data sheet; and if the two are not consistent, the control interface gives an alarm.
c. Determining whether the number of unfilled target mandatory fields in each record of the user debt credit balance total data table reaches a first target threshold; and if the first target threshold value is reached, controlling the interface to alarm.
(2) User risk exposure summary data sheet
a. Taking the report period, the client number and the debt number as target index fields, and verifying whether the debt amount recorded in each item of the user risk exposure summary data table is consistent with the debt amount recorded in the target comparison data table; and if the two are not consistent, the control interface gives an alarm.
b. Taking the report period, the client number and the debt number as target index fields, and verifying whether the number of the debt items recorded in each item of the user risk exposure summary data sheet is consistent with the number of the debt items recorded in the target comparison data sheet; and if the two are not consistent, the control interface gives an alarm.
c. Determining whether the number of unfilled target mandatory property fields in each record of the user risk exposure summary data table reaches a first target threshold; and if the first target threshold value is reached, controlling the interface to alarm.
(3) Five-level classification change data table for user loan
a. Checking whether the amount of the debt items recorded in each item of the five-level classification change data table of the user loan is consistent with the amount of the debt items recorded in the target comparison data table by taking the report period, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
b. Checking whether the number of the debt items recorded in each of the five-level classification change data table of the user loan is consistent with the number of the debt items recorded in the target comparison data table by taking the report period, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
c. Determining whether the quantity of unfilled target mandatory fill property fields in each record of a user loan five-level classification change data table reaches a first target threshold value; and if the first target threshold value is reached, controlling the interface to alarm.
(4) User loan overdue data table
a. Checking whether the amount of the debt items recorded in each item of the user loan overdue data table is consistent with the amount of the debt items recorded in the target comparison data table by taking the report term, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
b. Checking whether the number of the debt items recorded in each item of the user loan overdue data table is consistent with the number of the debt items recorded in the target comparison data table by taking the report term, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
c. Determining whether the quantity of unfilled target mandatory filling property fields in each record of the user loan overdue data table reaches a first target threshold value; and if the first target threshold value is reached, controlling the interface to alarm.
(5) User product credit balance data sheet
a. Checking whether the amount of the debt items recorded in each item of the credit balance data table of the user product is consistent with the amount of the debt items recorded in the target comparison data table by taking the report period, the client number, the debt item number and the product number as target index fields; and if the two are not consistent, the control interface gives an alarm.
b. Checking whether the number of the debt items recorded in each item of the credit balance data sheet of the user product is consistent with the number of the debt items recorded in the target comparison data sheet by taking the report period, the client number, the debt item number and the product number as target index fields; and if the two are not consistent, the control interface gives an alarm.
c. Determining whether the number of unfilled target mandatory fields in each record of the user product credit balance data table reaches a first target threshold; and if the first target threshold value is reached, controlling the interface to alarm.
(6) User credit rating data table
a. Checking whether the amount of the debt items recorded in each record of the credit level data table of the user is consistent with the amount of the debt items recorded in the target comparison data table by taking the report period, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
b. Checking whether the number of the debt items recorded in each item of the credit rating data sheet of the user is consistent with the number of the debt items recorded in the target comparison data sheet by taking the report period, the client number and the debt item number as target index fields; and if the two are not consistent, the control interface gives an alarm.
c. Determining whether the number of unfilled target mandatory fill property fields in each record of the user credit rating data table reaches a first target threshold; and if the first target threshold value is reached, controlling the interface to alarm.
In the checking process of the six tables, a and b are consistency checking, c is attribute filling rate checking, and one or more of a, b and c can be adopted for checking according to actual checking requirements.
In this implementation, when performing consistency check, the target comparison data table may be a deposit calculation result data table, and whether the debt amount data and/or the debt data amount of each record are consistent or not may be checked according to the data of the record in the deposit calculation result data table.
In the attribute filling rate check, the target filling-necessary property fields are required to be completely filled, but individual non-filling situations can be allowed according to scene requirements, so that the number of the target filling-necessary fields which are not filled in one record needs to be counted, and whether the number of the target filling-necessary fields which are not filled in is judged to be within an allowed range or not by comparing with a first target threshold value. In this implementation, for different financial asset classification data tables of this period, the first target threshold values when performing attribute filling rate check on the different financial asset classification data tables may be the same or different.
In the implementation mode, the consistency check and the attribute filling rate check are carried out on the data in the current-stage financial asset classification data table, so that the accuracy and the integrity of the data in each data table can be ensured, and the quality of the data in each data table can be improved.
In another possible implementation, the current financial asset classification data table comprises a user product credit balance data table, and the user product credit balance data table comprises a product type number; the checking mode comprises coverage rate checking; checking the data in the classification data table of the financial assets at the current stage according to a pre-configured checking rule, wherein the checking comprises the following steps: and checking the coverage rate of the data in the credit balance data table of the user product according to the product type number and the second target threshold value.
In this implementation, the user product credit balance data table is also suitable for coverage check, specifically:
determining the product type quantity contained in the credit balance data table of the user product according to the product type number, and determining whether the product type quantity reaches a second target threshold value; if not, the control interface gives an alarm.
In this implementation, there is a determined correspondence between the product type number and the product type, and the correspondence may be stored in a corresponding data table, so that the product type may be determined by the product type number, and further, when performing coverage rate checking, the number of product types included in the user product credit balance data table may be determined by mathematical statistics according to the correspondence between the product type number and the product type number, and by comparing the number of product types included in the user product credit balance data table with the second target threshold, it is determined whether the number of product types included in the user product credit balance data table satisfies an expectation.
It should be noted that, in this implementation, the coverage check is used to determine the situation of the product type concerned by the business department contained in the user product credit balance data table, so that the user product credit balance data table is guaranteed to contain most of the product types concerned by the business department by setting the second target threshold.
In the realization mode, the rationality of the data of the credit balance data table of the user product is ensured by checking the coverage rate of the credit balance data table of the user product, thereby being beneficial to improving the quality of the data in the credit balance data table of the user product.
In another possible implementation manner, the current-stage financial asset classification data table comprises a user loan five-stage classification change data table, a user loan overdue data table and a user refinancing frequency data table; the checking rule comprises combined relation checking; checking the data in the classification data table of the financial assets at the current stage according to a pre-configured checking rule, wherein the checking comprises the following steps: and checking the combination relation of the product structure of each user according to the user loan five-level classification change data table, the user loan overdue data table and the user refinancing times data table. Specifically, the method comprises the following steps:
obtaining products related to each user and related to first overdue, re-financing or next migration attention, obtaining a product structure of each user, and checking whether the loss condition and the reject ratio condition of each product in the product structure in a future period of time are matched with the loss condition and the reject ratio condition required by the target comparison data table; and if not, controlling the interface to alarm.
It can be understood that, in this implementation manner, if a certain user only has overdue, the obtained product structure is only composed of products that cause the first overdue of the user, if a certain user has overdue and has rewarded, the obtained product structure is only composed of products that cause the first overdue of the user and the rewarded of the user, if a certain user has overdue and has rewarded, and if a certain user has either overdue or has rewarded and has moved from normal to concerned with loan risk level, the obtained product structure is only composed of products that cause the first overdue of the user and the loan risk level to pay from normal to concerned with and products that cause the rewarded of the user.
In addition, in the implementation mode, whether the loss situation and the reject ratio situation of each product in the product structure in a future period of time are matched with the loss situation and the reject ratio situation required by the target comparison data table is checked, namely, the loss situation and the reject ratio situation of each product in the future period of time are respectively calculated, and whether the loss and the reject ratio of each product in the future period of time are within the range of the loss and the reject ratio required by the target comparison data table is judged.
In this implementation, the target comparison data table is used to store the loss and reject rate conditions that will allow each product to be used for a future period of time. Wherein the future period of time is illustratively one to five years into the future.
In the implementation mode, the product structure problem of the user can be found in time by checking the combination relation, so that the business department can conveniently take remedial measures in time, and the loss risk of the loan of the business department is reduced.
In yet another possible implementation, the current financial asset classification data table includes a current latest user name list data table and other data tables, and the other data tables include at least one of the following data tables: a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table; checking the data in the classification data table of the financial assets at the current stage according to a pre-configured checking rule, wherein the checking comprises the following steps: and checking the newly added data items in other data tables by taking a target index field configured in the checking rule as an index according to the latest user name list data table at the current period. Specifically, the method comprises the following steps:
and checking newly added data items in other data tables by taking a target index field configured in the checking rule as an index according to the latest user name single data table at the current period, so that the integrity of data in other data tables is ensured, and the quality of the data in each data table is favorably improved.
In this embodiment, by acquiring, at a preset time point, current-stage financial asset raw data related to financial asset value-reducing, accounting, and offering management, where the current-stage financial asset raw data includes user information and loan debt item information, the current-stage financial asset raw data is classified and summarized to obtain current-stage financial asset classification data tables of different dimensions, and data in the current-stage financial asset classification data tables is checked according to a pre-configured check rule, where the check rule includes at least one of the following parameters: the method comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
Example two
Fig. 2 is a schematic flow chart of a data checking method provided in the second embodiment of the present application, where the method of this embodiment may be executed by the data checking apparatus provided in the second embodiment of the present application, and the apparatus may be implemented in a software and/or hardware manner, and may be integrated in an electronic device such as a server and an intelligent terminal. As shown in fig. 2, the data checking method of the present embodiment includes:
s201, acquiring original data of the financial assets in the current period related to the financial asset value reduction and counting management at a preset time point.
In this step, when the preset time point is reached, the current financial asset original data related to the financial asset deduction and improvement management is automatically acquired from the upstream data system, wherein the current financial asset original data comprises user information and loan debt item information. The specific implementation manner of this step is similar to S201, and is not described here again.
S202, carrying out classification and summary processing on the original data of the financial assets in the current period to obtain a classification data table of the financial assets in the current period with different dimensionalities.
In this step, based on statistics of the original data of the financial assets in this period, the original data are classified according to preset classification dimensions to obtain a classification data table of the financial assets in this period with different dimensions, and a specific implementation manner of this step is similar to that of S102, and details are not repeated here.
And S203, configuring the checking rule according to the operation of the checking rule configuration personnel on the rule import interface.
The data checking operation system provided in this embodiment may provide a rule import interface to the outside, so that a checking rule configurator may perform operations such as input and selection of relevant parameters in a checking rule on the rule import interface, and accordingly, the data checking operation system performs configuration of the checking rule according to the operation of the checking rule configurator on the rule import interface.
The checking rule configuration personnel is personnel capable of enabling the data checking operation system to perform checking rule configuration, and the checking rule configuration personnel is usually a business manager or a department manager and the like. The operation of the rule configuration personnel on the rule import interface can be input, import, click and other interface operation modes.
It is understood that this step is performed before S204, and is not specifically limited to be performed before or after S201 or S202, or performed in parallel with S201 or S202.
And S204, checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule.
In this step, after S201-S203, for data in the current-stage financial asset classification data table with different dimensions, pre-configured corresponding checking rules are respectively adopted for checking, where the checking rules include at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for carrying out attribute filling rate checking, and the second target threshold is used for carrying out coverage rate checking. The specific implementation manner of this step is similar to S103, and is not described here again.
In the embodiment, the check rule configuration interface is provided for the check rule configuration personnel, and the check rule configuration is performed according to the operation of the check rule configuration personnel on the rule import interface, so that the flexibility of checking the financial asset data of the user is improved, and the application scene of checking the financial asset data of the user is widened.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a data checking apparatus provided in a third embodiment of the present application, and as shown in fig. 3, the data checking apparatus 10 in this embodiment includes:
an acquisition module 11 and a processing module 12.
The acquisition module 11 is configured to acquire, at a preset time point, current-period financial asset original data related to financial asset deduction, valuation and improvement management, where the current-period financial asset original data includes user information and loan debt information;
the processing module 12 is configured to perform classification and summary processing on the current-stage financial asset original data to obtain current-stage financial asset classification data tables with different dimensions; checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking rule comprises at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
Optionally, the current financial asset classification data table comprises at least one of the following data tables: the system comprises a latest user name list data table in the current period, a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table.
Optionally, the current financial asset classification data table comprises at least one of a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user product credit balance data table and a user credit rating data table; the target checking mode comprises consistency checking and attribute filling rate checking; the processing module 12 is specifically configured to:
taking a target index field configured in the checking rule as an index, and performing consistency checking on data corresponding to the debt field in the financial asset classification data sheet of the current period;
and/or, taking the target index field configured in the check rule as an index, and performing attribute filling rate check on the data corresponding to the mandatory property field in the financial asset classification data table of the current period.
Optionally, the processing module 12 is specifically configured to:
checking whether the amount of the debt of each record in the current-period financial asset classification data table is consistent with the amount of the debt of the target comparison data table; if not, controlling the interface to alarm;
and/or checking whether the number of the debt items recorded in each date financial asset classification data sheet is consistent with the number of the debt items recorded in the target comparison data sheet; and if the two are not consistent, the control interface gives an alarm.
Optionally, the processing module 12 is specifically configured to:
determining whether the number of unfilled target mandatory fill property fields in each record of the current-stage financial asset classification data table reaches a first target threshold; and if the first target threshold value is reached, controlling an interface to alarm.
Optionally, the current financial asset classification data table comprises a user product credit balance data table, the user product credit balance data table comprising a product type number; the checking mode comprises coverage rate checking; the processing module 12 is specifically configured to:
and checking the coverage rate of the data in the credit balance data table of the user product according to the product type number and a second target threshold value.
Optionally, the processing module 12 is specifically configured to:
determining the product type quantity contained in the user product credit balance data table according to the product type number, and determining whether the product type quantity reaches a second target threshold value; if not, the control interface gives an alarm.
Optionally, the current financial asset classification data table comprises a user loan five-level classification change data table, a user loan overdue data table and a user refinancing frequency data table; the checking rule comprises combined relation checking; the processing module 12 is specifically configured to:
and checking the combination relation of the product structure of each user according to the user loan five-level classification change data table, the user loan overdue data table and the user re-financing time data table, wherein the product structure is formed by products related to first overdue, re-financing or next-movement attention.
Optionally, the processing module 12 is specifically configured to:
obtaining products related to first overdue, re-financing or next-movement attention of each user, and obtaining the product structure of each user;
checking whether the loss condition and the reject ratio condition of each product in the product structure in a future period of time are matched with the loss condition and the reject ratio condition required by the target comparison data table; and if not, controlling the interface to alarm.
Optionally, the current financial asset classification data table includes a current latest user name list data table and other data tables, and the other data tables include at least one of the following data tables: a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table; the processing module 12 is specifically configured to:
and checking the newly added data items in the other data tables by taking a target index field configured in the checking rule as an index according to the latest user name list data table in the current period.
Optionally, the processing module 12 is specifically configured to:
judging whether the newly added data items in the other data tables are consistent with the information in the latest user name list data table in the current period; and if the two are not consistent, the control interface gives an alarm.
Optionally, the user debt credit balance total data table, the user risk exposure summary data table, the user loan five-level classification change data table, the user loan overdue data table, the user refinancing times data table, the user product credit balance data table, and the user credit rating data table all include a report period, a user number, and a debt item number.
Optionally, the processing module 12 is further configured to:
and configuring the operation of a worker on a rule import interface according to the checking rule, and configuring the checking rule.
The data checking device provided by the embodiment can execute the data checking method provided by the first embodiment or the second embodiment, and has corresponding functional modules and beneficial effects of the execution method. The implementation principle and technical effect of this embodiment are similar to those of the above method embodiments, and are not described in detail here.
Example four
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present disclosure, as shown in fig. 4, the electronic device 20 includes a memory 21, a processor 22, and a computer program stored in the memory and executable on the processor; the number of the processors 22 of the electronic device 20 may be one or more, and one processor 22 is taken as an example in fig. 4; the processor 22 and the memory 21 in the electronic device 20 may be connected by a bus or other means, and fig. 4 illustrates the connection by the bus as an example.
The memory 21 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the acquisition module 11 and the processing module 12 in the embodiment of the present application. The processor 22 executes various functional applications of the device/terminal/server and data processing by running software programs, instructions and modules stored in the memory 21, that is, implements the data checking method described above.
The memory 21 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 21 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 21 may further include memory located remotely from the processor 22, which may be connected to the device/terminal/server through a grid. Examples of such a mesh include, but are not limited to, the internet, an intranet, a local area network, a mobile communications network, and combinations thereof.
EXAMPLE five
A fifth embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a computer processor, is configured to perform a data checking method, the method including:
at a preset time point, acquiring the original data of the financial assets of the current period related to the financial asset value reduction, counting and submitting management, wherein the original data of the financial assets of the current period comprises user information and loan debt information;
classifying and summarizing the original data of the current-stage financial assets to obtain classification data tables of the current-stage financial assets with different dimensions;
checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking rule comprises at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
Of course, the computer program of the computer-readable storage medium provided in this embodiment of the present application is not limited to the method operations described above, and may also perform related operations in the data checking method provided in any embodiment of the present application.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a grid device) to execute the methods described in the embodiments of the present application.
It should be noted that, in the embodiment of the data checking apparatus, each included unit and each included module are only divided according to functional logic, but are not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the application.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present application and the technical principles employed. It will be understood by those skilled in the art that the present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the application. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims (16)

1. A method for data detection, comprising:
at a preset time point, acquiring the original data of the financial assets of the current period related to the financial asset value reduction, counting and submitting management, wherein the original data of the financial assets of the current period comprises user information and loan debt information;
classifying and summarizing the original data of the current-stage financial assets to obtain classification data tables of the current-stage financial assets with different dimensions;
checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking rule comprises at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
2. The method of claim 1, wherein the current financial asset classification data table comprises at least one of the following data tables: the system comprises a latest user name list data table in the current period, a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table.
3. The method of claim 2, wherein the current financial asset classification data table comprises at least one of a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user product credit balance data table, and a user credit rating data table; the target checking mode comprises consistency checking and attribute filling rate checking; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
taking a target index field configured in the checking rule as an index, and performing consistency checking on data corresponding to the debt field in the financial asset classification data sheet of the current period;
and/or, taking the target index field configured in the check rule as an index, and performing attribute filling rate check on the data corresponding to the mandatory property field in the financial asset classification data table of the current period.
4. The method according to claim 3, wherein the consistency check of the data corresponding to the debt field in the current financial asset classification data sheet comprises:
checking whether the amount of the debt of each record in the current-period financial asset classification data table is consistent with the amount of the debt of the target comparison data table; if not, controlling the interface to alarm;
and/or checking whether the number of the debt items recorded in each date financial asset classification data sheet is consistent with the number of the debt items recorded in the target comparison data sheet; and if the two are not consistent, the control interface gives an alarm.
5. The method according to claim 3, wherein the performing attribute fill rate checking on the data corresponding to the mandatory property field in the current financial asset classification data table comprises:
determining whether the number of unfilled target mandatory fill property fields in each record of the current-stage financial asset classification data table reaches a first target threshold; and if the first target threshold value is reached, controlling an interface to alarm.
6. The method of claim 2, wherein the current financial asset classification data table comprises a user product credit balance data table, the user product credit balance data table comprising a product type number; the checking mode comprises coverage rate checking; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
and checking the coverage rate of the data in the credit balance data table of the user product according to the product type number and a second target threshold value.
7. The method of claim 6 wherein said coverage checking data in said user product credit balance data table based on said product type number and a second target threshold comprises:
determining the product type quantity contained in the user product credit balance data table according to the product type number, and determining whether the product type quantity reaches a second target threshold value; if not, the control interface gives an alarm.
8. The method of claim 2, wherein the current financial asset classification data table comprises a user loan five-level classification change data table, a user loan overdue data table and a user refinancing number data table; the checking rule comprises combined relation checking; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
and checking the combination relation of the product structure of each user according to the user loan five-level classification change data table, the user loan overdue data table and the user re-financing time data table, wherein the product structure is formed by products related to first overdue, re-financing or next-movement attention.
9. The method according to claim 8, wherein the checking the product structure of each user according to the user loan five-level classification change data table, the user loan overdue data table and the user refinancing times data table comprises:
obtaining products related to first overdue, re-financing or next-movement attention of each user, and obtaining the product structure of each user;
checking whether the loss condition and the reject ratio condition of each product in the product structure in a future period of time are matched with the loss condition and the reject ratio condition required by the target comparison data table; and if not, controlling the interface to alarm.
10. The method of claim 2, wherein the current financial asset classification data table comprises a current latest user profile data table and other data tables, the other data tables comprising at least one of: a user debt credit balance total data table, a user risk exposure summary data table, a user loan five-level classification change data table, a user loan overdue data table, a user refinancing times data table, a user product credit balance data table and a user credit rating data table; the checking the data in the current-stage financial asset classification data table according to the pre-configured checking rule comprises the following steps:
and checking the newly added data items in the other data tables by taking a target index field configured in the checking rule as an index according to the latest user name list data table in the current period.
11. The method of claim 10, wherein the checking the newly added data item in the other data table according to the current latest user name list data table comprises:
judging whether the newly added data items in the other data tables are consistent with the information in the latest user name list data table in the current period; and if the two are not consistent, the control interface gives an alarm.
12. The method of claim 2, wherein said user debt credit balance total amount data table, said user risk exposure summary data table, said user loan five-level classification change data table, said user loan overdue data table, said user refinancing times data table, said user product credit balance data table, and said user credit rating data table each include a reporting period, a user number, and a debt number.
13. The method according to any one of claims 1-12, wherein before the data in the current financial asset classification data table is checked according to the pre-configured checking rules, the method further comprises:
and configuring the operation of a worker on a rule import interface according to the checking rule, and configuring the checking rule.
14. A data checking apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the current-stage financial asset original data related to financial asset value reduction, counting and submitting management at a preset time point, and the current-stage financial asset original data comprises user information and loan debt information;
the processing module is used for classifying and summarizing the original data of the current-stage financial assets to obtain classification data tables of the current-stage financial assets with different dimensions; checking the data in the current-stage financial asset classification data table according to a pre-configured checking rule, wherein the checking rule comprises at least one of the following parameters: the system comprises a target checking mode, a target index field, a target mandatory filling property field, a first target threshold, a second target threshold and a target comparison data table, wherein the first target threshold is used for checking the attribute filling rate, and the second target threshold is used for checking the coverage rate.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the data checking method according to any one of claims 1 to 13 when executing the program.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of data checking according to any one of claims 1 to 13.
CN202010986396.7A 2020-09-18 2020-09-18 Data checking method and device, electronic equipment and storage medium Pending CN112199359A (en)

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