CN111143339B - Method, device, equipment and storage medium for distributing service resources - Google Patents
Method, device, equipment and storage medium for distributing service resources Download PDFInfo
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Abstract
The invention discloses a method, a device, a setting and a storage medium for distributing business resources, which are characterized in that original data related to a resource application party is obtained, wherein the original data comprises one or more of bank data, credit investigation data, tax data, industrial and commercial data and judicial data; processing the original data, and calculating index data according to the processed original data; obtaining the score of the resource applicant according to the index data; carrying out rating mapping based on the scores of the resource applicants to obtain rating results of the resource applicants; calculating a resource allocation result based on the rating result of the resource applicant, and outputting the resource allocation result to a resource allocation formula so that the resource allocator allocates resources based on the resource allocation result; the obtained resource allocation result is more comprehensive and objective, the accuracy is higher, the decision period is greatly shortened, and the resource application period is greatly reduced.
Description
Technical Field
The present invention relates to the field of information technology, and in particular, to a method, an apparatus, a device, and a storage medium for allocating service resources.
Background
The existing business resource allocation method mainly depends on the man-sea tactics to investigate the utilization capacity of business resource application parties to resources, and analyzes through the experience and judgment of people, so that the problems of long decision period and strong subjectivity exist.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, a device and a storage medium for allocating service resources, so as to solve the problems of long decision period and strong subjectivity in micro-loan.
In view of the above object, a first aspect of the present invention provides a method for allocating service resources, where the method includes:
acquiring original data related to a resource application party, wherein the original data comprises one or more of bank data, credit investigation data, tax data, industrial and commercial data and judicial data;
processing the original data, and calculating index data according to the processed original data;
obtaining the score of the resource applicant according to the index data;
carrying out rating mapping based on the scores of the resource applicants to obtain rating results of the resource applicants;
and calculating a resource allocation result based on the rating result of the resource applicant, and outputting the resource allocation result to a resource allocation formula so that the resource allocator allocates resources based on the resource allocation result.
Optionally, the obtaining of the original data related to the resource applicant includes:
acquiring a data source and a data source interface of the data source, and calling the data source interface; sending an original data request message to the data source through the data source interface; receiving an original data return message fed back by the data source based on the original data request message; analyzing the original data return message to obtain the original data;
or the like, or, alternatively,
sending a calling request to the data source so that the data source generates a specified file and synchronizes to a specified shared area; and downloading the specified file from the specified sharing area, and analyzing the specified file to obtain the original data.
Optionally, the processing the original data includes:
and cleaning and converting original data with wrong data format or inconsistent data format and target format to obtain target data with consistent data format and target format, and presenting the target data in a standard table form.
Optionally, when the original data includes judicial data, the processing the original data further includes:
and analyzing the format of the judicial data after cleaning conversion, and matching and extracting key data items in the judicial data after cleaning conversion by adopting a regular expression.
Optionally, before the step of obtaining the raw data related to the resource applicant is performed, the method further includes:
receiving a resource application of a resource application party, and recording the resource application in a resource application table.
Optionally, the method further comprises:
and integrating the scores of the resource application parties, the rating results of the resource application parties and the resource allocation results to obtain a resource allocation proposal, and sending the resource allocation proposal to a resource allocation formula.
In accordance with the same purpose, a second aspect of the present invention provides an apparatus for allocating service resources, the apparatus comprising:
the data acquisition module is used for acquiring original data related to a resource application party, wherein the original data comprises one or more of bank data, credit investigation data, tax data, industrial and commercial data and judicial data;
the index data calculation module is used for processing the original data and calculating index data according to the processed original data;
the score obtaining module is used for obtaining the score of the resource applicant according to the index data;
the rating result obtaining module is used for carrying out rating mapping based on the scores of the resource applicants to obtain the rating result of the resource applicants;
and the resource allocation result calculation module is used for calculating a resource allocation result based on the rating result of the resource applicant and outputting the resource allocation result to the resource allocation formula so as to enable the resource allocator to allocate resources based on the resource allocation result.
Optionally, the data obtaining module includes:
the data source interface calling unit is used for acquiring a data source and a data source interface of the data source and calling the data source interface; a request message sending unit, configured to send an original data request message to the data source through the data source interface; a return message receiving unit, configured to receive an original data return message fed back by the data source based on the original data request message; the analysis unit is used for analyzing the original data return message to obtain the original data;
or the like, or, alternatively,
the calling request sending unit is used for sending a calling request to the data source so that the data source generates a specified file and synchronizes the specified file to a specified shared area; and the designated file analyzing unit is used for downloading the designated file from the designated sharing area and analyzing the designated file to obtain the original data.
Optionally, the index data calculation module includes a cleaning conversion unit, and the cleaning conversion unit is configured to perform cleaning conversion on original data with an error data format or inconsistent data format and a target format to obtain target data with a data format consistent with the target format, and the target data is presented in a standard table.
Optionally, when the original data includes judicial data, the index data calculation module further includes a judicial data analysis unit, and the judicial data analysis unit is configured to analyze a format of the judicial data after the cleaning conversion, and match and extract key data items in the judicial data after the cleaning conversion by using a regular expression.
Optionally, the apparatus further includes a resource application receiving module, configured to receive a resource application of the resource application party before the data obtaining module obtains the original data related to the resource application party, and record the resource application in a resource application table.
Optionally, the apparatus further includes a resource allocation suggestion sending module, configured to integrate the score of the resource applicant, the rating result of the resource applicant, and the resource allocation result into a resource allocation suggestion, and send the resource allocation suggestion to a resource allocation formula.
Based on the same objective, a third aspect of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the processor implements the method for allocating service resources according to the first aspect of the present invention.
With the same objective in mind, a fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method for allocating traffic resources according to the first aspect of the present invention.
It can be seen from the above that, according to the business resource allocation method, apparatus, device and storage medium provided by the present invention, one or more of bank data, credit investigation data, tax bureau, industry and commerce data and judicial data related to a resource applicant are obtained online, the obtained raw data is processed, index data is extracted from the processed raw data, then a score of the resource applicant is obtained according to the index data, a rating mapping is performed based on the score of the resource applicant to obtain a rating result of the resource applicant, and finally a resource allocation result is calculated from the rating result of the resource applicant and output the resource allocation result; the original data of the resource application party is obtained on line, the resource distribution result obtained by calculation according to the rating result is more comprehensive and objective, the accuracy is higher, the decision period is greatly shortened, and the resource application period is greatly reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 shows a schematic view of aA flow diagram of a method for allocating service resources provided by the embodiment of the present invention;
FIG. 2The model module architecture diagram provided by the embodiment of the invention;
FIG. 3The invention provides a decomposition level structure diagram of a loan amount calculation formula and a loan interest rate calculation formula;
FIG. 4The invention provides a composition diagram of a loan amount calculation formula and a loan interest rate neutron formula;
FIG. 5The structure diagram of the service resource allocation device provided by the embodiment of the invention is shown.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
It should be noted that all expressions using "first" and "second" in the embodiments of the present invention are used for distinguishing two entities with the same name but different names or different parameters, and it should be noted that "first" and "second" are merely for convenience of description and should not be construed as limitations of the embodiments of the present invention, and they are not described in any more detail in the following embodiments.
The existing business resource allocation method mainly depends on the human-sea tactics, surveys the actual utilization capacity and the resource returning capacity of the resource application party on the business resources, attaches importance to whether the logical relationship behind the information submitted by the resource application party can be mutually verified, and analyzes through the experience and judgment of people, but not an objective scoring mechanism.
However, since the relevant data of the resource application party needs to be collected, verified and evaluated offline, the resource allocation formula needs to be manually operated by multiple persons and multiple positions, and a lot of time is consumed, so that the decision period is long.
In order to solve the problems, the invention provides a method, a device, equipment and a storage medium for distributing business resources, which are used for processing the acquired original data by acquiring one or more of bank data, credit investigation data, tax bureau, industrial and commercial data and judicial data related to a resource applicant on line, extracting index data from the processed original data, then acquiring the score of the resource applicant according to the index data, carrying out rating mapping based on the score of the resource applicant to acquire the rating result of the resource applicant, finally calculating the rating result of the resource applicant to acquire the resource distribution result, and outputting the resource distribution result. The method and the device can be applied to various electronic devices such as mobile phones and tablet computers, and are not limited specifically. The service mentioned in the method and the apparatus may be a credit service, which is not specifically limited, and the method for allocating the service resource will be described in detail later by taking the credit service as an example.
For the convenience of understanding, the method for allocating the service resources is described in detail below with reference to the accompanying drawings.
FIG. 1 shows a schematic view of aThe method for allocating service resources provided by the embodiment of the invention comprises the following steps:
and S1, acquiring original data related to the resource application party, wherein the original data comprises one or more of bank data, credit investigation data, tax data, industry and commerce data and judicial data.
In the embodiment of the present invention, a credit service is taken as an example for detailed description. The credit is credit loan, the resource application party in the credit business is the credit application party, and the resource allocation formula is the credit operator.
The credit applicant refers to a party who submits a credit application from a credit operator, for example, the credit applicant may be a business or an individual, etc., and is not limited in particular. Accordingly, the credit operator refers to a party providing credit to the credit applicant, for example, the credit operator may be a bank or a regular credit operator outside the bank, and the like, without limitation. Raw data refers to raw credit applicant-related data obtained directly from a data source.
In order to carry out more comprehensive, more objective and more accurate evaluation on a credit applicant, original data related to the credit applicant needs to be acquired firstly; the raw data may originate from tax authorities, banks, and third party data service providers; the tax bureau can obtain tax data, the bank can obtain credit data and bank data, and the third-party data service provider can obtain industrial and commercial data and judicial data. When the credit application direction applies for credit to the bank, the bank to which the credit application direction applies can acquire inline data, and meanwhile, other bank data can also be acquired by banks other than the bank to which the credit application direction applies.
In practical application, the original data can be obtained in an interface mode or a file mode, when the real-time performance of the interaction of the original data is good, the original data is obtained in the interface mode, and when the original data is large-batch data, the original data is obtained in the file mode.
The specific manner of acquiring the original data will be described in detail later, and will not be described herein.
And S2, processing the original data, and calculating index data according to the processed original data.
The above steps are continued, and the credit service is taken as an example for detailed description.
In the embodiment of the invention, the electronic equipment (hereinafter referred to as the electronic equipment) executing the method is internally provided with the standard table, the standard table is a set of constructed data table structure, and the target format of the data meeting the business requirements is preset in the standard table, so that the original data can be converted into the target data meeting the business requirements.
After the original data related to the credit applicant is obtained, the original data needs to be further processed, and in practical application, the processing mode of the original data includes data cleaning conversion, data analysis and the like, and is not limited specifically. The specific process of processing the raw data will be described in detail later, and will not be described herein again.
The original data related to the credit applicant acquired in step S1 is presented in the form of an original table, which is a data table that retains the original format of the original data. After the original data are processed, target data with a target format can be obtained, and the target data are presented in a standard table form. An index configuration table is preset in the electronic equipment, the index configuration table provides index variable parameters adopted in index data calculation, and index variable parameters corresponding to the index data are provided for different index data respectively; and associating one or more tables in the standard tables carrying the target data and bringing the tables into index variable parameters for processing and calculation to obtain index data, wherein the index data can objectively reflect the operating condition and the credit condition of a credit applicant.
In practical application, the information of the index data includes an index name, a data table name and a calculation process. The index name is the name of the index data, and can reflect the main meaning of the index data; the data table names indicate that the index data is mainly calculated based on which data tables in the standard table, that is, indicate which data tables in the standard table are used; the index calculation process specifies the calculation logic based on the data table, such as the data fields used, and the judgment and calculation logic based on the data fields, and further generates the index data.
The calculation process of the index data will be described by taking a part of the calculation process of the credit investigation index data as an example.
Such as the index name: whether the personal credit report is invalid; the data table name is: STD _ ICR _ CREDIT _ CUE: credit prompting; the index calculation process is as follows:
1. report of no investigation
The credit report is "null" or "" or "-", returning null.
2. Can find a credit report
(1) A non-credit record. if STD _ ICR _ CREDIT _ CUE, first loan issuing month [ FIRSTTLOANOPENTENONTH ], first CREDIT card issuing month [ FIRSTTLOANCARDOPENTH ], and first quasi CREDIT card issuing month [ FIRSTTSTANNDARDLOANCARDOPENTH ] are all null, defining the personal CREDIT report as an invalid report, and returning to 1; else defines the personal credit report as a valid report and returns 0.
For example, the index name is the amount of outstanding interest-type loans of the applicant; the data table name is: STD _ ICR _ long _ INFO: loan information; the index calculation process is as follows:
count (STD _ ICR _ long _ info. five-level classification [ CLASS5STATE ] 02)
Note: if the [ CLASS5STATE ] is empty, the record is not counted; and if no record meeting the condition exists, returning to 0.
For example, the index name: the applicant does not clear the amount of the bad loan; the data table name is: STD _ ICR _ long _ INFO: loan information; the index calculation process is as follows:
count (STD _ ICR _ LOAN _ INFO. five-CLASS classification [ CLASS5STATE ] in [03, 04, 05])
Note: if the [ CLASS5STATE ] is empty, the record is not counted; and if no record meeting the condition exists, returning to 0.
And S3, obtaining the score of the resource applicant according to the index data.
The above steps are continued, and the credit service is taken as an example for detailed description.
In the embodiment of the invention, a model module system is preset in the electronic equipment, and the model score of a credit applicant can be obtained through the model module system based on index data. A model module rule configuration table is preset in a model module system, provides model variable parameters or module variable parameters adopted in model or module calculation, and provides model variable parameters or module variable parameters corresponding to different models or modules respectively.
FIG. 2A model module architecture diagram is shown. Such asFIG. 2As shown, the model module system is provided with five models, namely an admission model, an anti-fraud model, a scoring model, a pricing model and a post-credit early warning model. The admission model is used for determining whether the credit applicant allows admission; the anti-fraud model is used to determine whether the credit applicant is involved in fraud; the scoring model is used for calculating the score of the credit applicant according to the index data of the credit applicant; the pricing model is used for carrying out pricing configuration according to the rating result of the credit applicant and determining the loan line, interest rate and term; and the post-loan early warning model is used for carrying out risk early warning on a credit applicant after loan. The result of the admission model and the result of the anti-fraud model are preconditions for performing scoring calculations based on the scoring model, the result of the scoring model is a pairAnd (3) on the premise that the credit applicant rates, and after the rating result of the credit applicant is obtained, pricing configuration is carried out by using a pricing model.
Selecting multiple index rules can construct a module, and the multiple rules in the module calculate the comprehensive result according to predefined weight to obtain the module score. A model can be constructed by selecting a plurality of modules, and the plurality of modules in the model calculate the comprehensive result to obtain a model score according to the predefined weight.
When calculating the model score of the credit applicant based on the index data of the credit applicant, firstly calculating to obtain the index score based on the index data of the credit applicant according to the index rule, then calculating to obtain a module score based on a plurality of index scores, and calculating to obtain a model score based on a plurality of module scores, namely a model score of the credit applicant.
In practical application, an index rule is determined based on the index item, the index rule is a formula or a method for calculating index data, and the index rule uses one or more index data as an input parameter for calculation to obtain an index score.
The process of calculating the index score according to the index rule by adopting the index data is as follows:
firstly, judging the index data type:
a) if the index data is a continuous variable, applying a continuous variable scoring rule;
b) if the index data is a classification variable, applying a classification variable scoring rule;
the continuous variable score rule is that index scores are calculated according to linear difference of index data: appointing the reference points and the scores thereof, and calculating the scores in the middle of the reference points according to a linear difference rule.
The following examples are used to describe the continuous variable score rule and the categorical variable score rule, respectively.
In practical applications, when the index data is a continuous variable and the calculation is performed by using a continuous variable score rule, for example, the step numbers of the index data are respectively set to 0, 1, 2, … …, and 98, the value ranges of the corresponding index data are respectively 1, (1 to 3), (3 to 5), … …, and 5, and the corresponding index scores are respectively 10, (10 to 30), (30 to 70), … …, and 70.
When one index data is used as an input parameter and input into the index rule to calculate the index score, the corresponding grading number and the index score range can be determined according to the index data, and then the index score is calculated according to the linear difference. For example, when the index data is 4, the corresponding step number is 2, the index score range is 30-70, and the corresponding index score calculation formula when the index data is 4 is: 30+ ((70-30)/(5-3)). times.4-3), i.e., the index score of 50 was assigned to index data of 4.
When a plurality of index data are used as input parameters and input into the index rule to calculate the index score, the index score needs to be calculated by substituting the weight corresponding to each index data.
In practical applications, when the index data is classified variables and the calculation is performed by using a classified variable score rule, for example, the step numbers of the index data may be set to 1 and 2, the corresponding index data may be male and female, and the corresponding index scores may be 60 and 100; when the index data is determined to be male, the index score may be determined to be 60.
For a module, the calculation process of the score before adjustment (MS) and the score after adjustment (MSA) is as follows:
step 1: calculating a weighted score for the scoring index in the module assuming that there are k scoring indices (FS) in the module1,FS2,…,FSk) If the score index is MS ═ FS in weight ratio1×WF1+FS2×WF2…+FSk×WFk；；
Wherein (WF)1,WF2,…,WFk) The index weight, namely the module variable parameter provided by the rule configuration table of the model module; the scoring index refers to an index for participating in module scoring, namely an index selected from a public index pool and used for calculating module scoring;FSscoring the index.
Step 2: calculate the rule set in this Module (B)Class index) summary score (adjScore, AS for short), since the rule index has no weight, it is only necessary to directly sum up, and if there are j rule indexes in the module, the summary score of the rule set is AS or AS1+AS2…+ASj；
And step 3: the module adjusted score was calculated AS MSA Min (Max (MS + AS,0), 100).
The score before adjustment is a module score calculated according to a standard formula, and the score after adjustment is a module score obtained by adjustment according to actual conditions, for example, after actual measurement is performed according to index data, adjustment is performed based on a measurement result that the module score is expected to fall within an expected area.
For one model, the calculation of the pre-adjustment score (CS) and the post-adjustment score (CSA) is as follows:
step a: calculating the weighted scores of the modules in the model, assuming that there are s scoring Modules (MSA) in the model1,MSA2,…,MSAs) The scoring module then weights the CS ═ MSA1×WM1+MSA2×WM2…+MSAS×WMs(ii) a Wherein (WM)1,WM2…, WMs) are module weights, i.e., model variable parameters provided by a model module rule configuration table;
step b: calculating the model level rule set summary score, assuming there are t second rule indexes (AS ″) in the module1, AS`2,…, AS`t) If the rule set is summarized and scored AS AS ═ AS ″1+ AS`2…+ AS`t；
Step c: forming the model and then grading the model AS CSA ═ Min (Max (CS + AS', 0), 100); i.e. a model score of the credit applicant.
Similarly, the pre-adjustment score is a model score calculated according to a standard formula, and the post-adjustment score is a model score adjusted according to actual conditions, for example, after actual measurement is performed according to a module score, the model score is adjusted to be within an expected area based on the measurement result.
And S4, performing rating mapping based on the scores of the resource applicants to obtain the rating result of the resource applicants.
And continuing the steps, taking the credit business as an example to explain in detail, wherein the score of the resource applicant is the model score of the credit applicant.
In the embodiment of the invention, the electronic equipment is internally preset with the rating mapping table, and different ratings are set in the rating mapping table according to different model scoring ranges.
And the rating mapping is to perform rating judgment on the corresponding credit applicant according to the requirements of the credit operator such as risk preference and the like and according to the result of the admission model, the result of the anti-fraud model and the rating of the rating model, and the rating is used as a final comprehensive rating result of the credit applicant status. The evaluation process is a multi-dimensional matrix, and mapping is carried out through a plurality of model values. And carrying out rating mapping based on the scores of the scoring model on the premise that the result of the admission model is admission permission and the result of the anti-fraud model is no fraud.
The rating mapping process is illustrated by the following example.
For example, in practical applications, when the rating mapping table is set, the rating of the rating model is set to be R1 when the rating of the rating model is in the range of 80-100 points, the rating of the rating model is set to be R2 when the rating of the rating model is in the range of 70-80 points, the rating of the rating model is set to be R3 when the rating of the rating model is in the range of 60-70 points, the rating of the rating model is set to be R4 when the rating of the rating model is in the range of 50-60 points, the rating of the rating model is set to be R5 when the rating of the rating model is in the range of 40-50 points, and the rating of the rating model.
And S5, calculating a resource allocation result based on the rating result of the resource applicant, and outputting the resource allocation result to the resource allocation formula so that the resource allocation party performs resource allocation based on the resource allocation result.
The above steps are continued, and the credit service is taken as an example for detailed description. For credit services, the resource allocation result is the pricing configuration result.
In the embodiment of the invention, the pricing configuration result comprises the loan line and the loan interest rate, and the pricing configuration result can also comprise the loan term according to different requirements of different credit application parties.
In practical application, the pricing configuration result may be output in the form of an email or a short message, which is not limited specifically. And outputting the pricing configuration result to the credit operator so that the credit operator determines the loan line and the loan interest rate of the credit applicant according to the pricing configuration result.
And after the rating result of the credit applicant is obtained, pricing configuration is carried out by adopting a pricing model in a model module system, and the loan amount and the loan interest rate are calculated. And a pricing parameter configuration table and a pricing formula configuration table are preset in the pricing model, the pricing parameter configuration table provides parameter configuration for a pricing configuration process, and the pricing formula configuration table provides formula configuration for the pricing configuration process.
FIG. 3The decomposition hierarchy of the loan amount calculation formula and the loan interest rate calculation formula is shown.FIG. 4The composition schematic diagram of the sub-formula in the loan amount calculation formula and the loan interest rate calculation formula is shown.
Such asFIG. 3The loan amount calculation formula and the loan interest rate calculation formula can be jointly obtained based on a sub-formula 1, a sub-formula 2 and a sub-formula 3, and the sub-formula 1 can be jointly obtained based on a sub-formula 4 and a sub-formula 4; wherein each sub-formula is derived from a pricing formula configuration table.
For example, assume that the quota calculation formula is as follows:
initial credit limit for single user(annual sales income basic tax rate))*MIN(basic loan multiplier [ (+ 1+ revision order) Column 1 variables) ((1 + revision sequence 2 variables)The highest loan multiplier of industry)
And when the annual sales income cannot be directly acquired but has the tax amount, the annual sales income can be acquired by calculating the tax amount and the tax rate information, and the annual sales income calculation is a sub-formula, namely the calculation of the single-user credit initial amount formula needs to acquire a result based on the calculation of the annual sales income sub-formula.
Such asFIG. 4The sub-formula is shown as one or more of index data, index data coefficients, decision result values, general parameters, and sub-formula calculation resultsAnd calculating through logical combination.
The index data coefficient is a parameter for adjusting the weight of the index data, is derived from a pricing parameter configuration table, and the decision result value is a model score, a module score or a mapping rating result; the general parameters refer to common adjusting parameters, and are also derived from a pricing parameter configuration table to adjust a calculation result; the formula m represents that the result of the term is calculated by other formulas.
In the embodiment of the invention, one or more of bank data, credit investigation data, tax bureau, industrial and commercial data and judicial data related to a credit applicant are obtained on line, the obtained original data are processed, index data are extracted from the processed original data, then the index data are input into a credit scoring model to obtain model scoring, the rating mapping is carried out based on the model scoring to obtain a rating result of the credit applicant, finally, a loan amount and a loan interest rate are calculated according to the rating result of the credit applicant, and the loan amount and the loan interest rate are output; the original data of the credit applicant is obtained on line, the loan amount and the loan interest rate obtained by calculation according to the rating result are more comprehensive and objective, the accuracy is higher, and the decision period is greatly shortened, so that the financing cost and the financing period of an enterprise are greatly reduced.
As an embodiment, before the step of obtaining raw data related to the resource applicant is performed, the method further includes:
and receiving the resource application of the resource application party, and recording the resource application in a resource application table.
Taking the credit business as an example, after receiving a credit application, the tax bureau, the bank and the third-party data service provider can obtain the original data related to the credit application party based on the credit application.
In practical application, the original data related to the resource applicant can be obtained in various ways; then, in some possible embodiments, obtaining raw data related to the resource applicant includes:
acquiring a data source and a data source interface of the data source, and calling the data source interface;
sending the original data request message to a data source through a data source interface;
receiving an original data return message fed back by a data source based on an original data request message;
analyzing the original data return message to obtain original data;
or the like, or, alternatively,
sending a calling request to a data source so that the data source generates a specified file and synchronizes to a specified shared area;
and downloading the specified file from the specified sharing area, and analyzing the specified file to obtain original data.
The following is a detailed description of this embodiment by taking a credit service as an example.
Acquiring in different modes according to the characteristics of different original data; the method is suitable for acquiring raw data with better interactive real-time property by adopting a data source interface, and is suitable for acquiring large-batch raw data by adopting a mode of downloading a specified file from a specified sharing area.
In one case, in order to acquire original data related to a credit applicant in a data source interface mode, firstly, a data source and a data source interface of the data source are acquired; in practical applications, the original data related to the credit applicant may originate from the tax bureau, the bank and the third-party data facilitator, i.e. the data source may be the tax bureau, the bank and the third-party data facilitator. The interface protocol adopted by the data source interface may be http, socket, webservice, or the like, and is not limited specifically.
After the data source interface is obtained, the data source interface can be called, the original data request message is sent to the data source through the data source interface, so that the data source receives the original data request message, generates and feeds back an original data return message based on the original data message, and then receives and analyzes the original data return message to obtain original data. After the original data request message is sent to the data source through the data source interface, the data source interface may be immediately called, or the data source interface may be called after the data source feeds back the original data return message, which is not limited specifically.
The analysis of the original data return message can be performed by adopting an sqlload tool, when the original data return message is received, the sqlload tool is started in a script calling mode, and an instruction is sent to read the data file.
Taking the http protocol as an example of the data source interface, the process of calling the data source interface is as follows: firstly, defining a data source interface name and data source interface description information, and secondly defining a data source interface access address, a data source interface access method and data source interface access parameters; the access address is a URL address issued by the data source interface, the method is a get, post, delete, update and other methods in an http protocol, and the parameter content is content information which needs to be transmitted when the data source interface is requested, namely an original data request message.
In one case, in practical applications, in order to obtain some bulk of raw data of the bank side, negotiation is performed with the bank side in advance to determine the designated sharing area. When a large amount of original data of a bank party is needed, a calling request is sent to the bank party so that the bank party generates an appointed file and synchronizes the appointed file to an appointed sharing area, then the appointed file is downloaded from the appointed sharing area, and the appointed file is analyzed to obtain the original data.
Correspondingly, the analysis of the designated file can also be carried out by adopting an sqlload tool, when the original data return message is received, the sqlload tool is started in a script calling mode, and an instruction is sent to read the data file.
It can be understood that by simultaneously acquiring the original data related to the credit application party in the tax bureau, the bank and the third-party data service provider, the obtained original data can be ensured to be more complete and accurate, the data collection time can be saved, and the decision period can be shortened.
In practical application, after the original data of the resource application party is obtained, the original data may have a format problem and cannot be directly applied, and the original data needs to be processed; then, in some possible embodiments, the raw data is processed, including: and cleaning and converting original data with wrong data format or inconsistent data format and target format to obtain target data with consistent data format and target format, and presenting the target data in a standard table form.
The following is a detailed description of this embodiment by taking a credit service as an example.
In order to utilize the original data related to the credit applicant, the original data with data format errors or inconsistent data format with the target format in the tax data, judicial data, credit investigation data and industrial and commercial data needs to be cleaned and converted to obtain the target data with the data format consistent with the target format, and the target data is loaded in the standard table. The standard table carries a destination format, and destination data is data which is expected to be obtained and can be used for index calculation.
The data cleansing rules may include general rules and special cleansing rules, and the general rules may uniformly process all fields in the original data, such as removing head and tail spaces, full angle and half angle conversion, and the like, which is not limited specifically. The special cleaning rule may include, but is not limited to, a go-space, a sum to comma, a unified mapping rule that is not null or field value, and the like.
The data can be cleaned in SQL statement mode, the cleaning rule is completed by calling Oracle function, if a certain field value in the table is judged to be empty, and a default value is returned when the value is empty, then the writing method is as follows:
select nvl (a.name, 'default value') as name from std _ a where id '123';
when multiple cleaning methods are configured on one original data item, then multiple methods can be called simultaneously.
It is understood that the accuracy of the index data calculated from the raw data can be further ensured by performing the cleaning process on the raw data. In practical application, judicial data is a normative document such as a decision document, and both the document and the expression have certain normative, so that the judicial data needs to be analyzed; then, in some possible embodiments, when the judicial data is included in the raw data, processing the raw data further includes:
and analyzing the format of the judicial data after cleaning conversion, and matching and extracting key data items in the judicial data after cleaning conversion by adopting a regular expression.
In order to apply the judicial data, after the judicial data is cleaned and converted, the format of the judicial data after cleaning and conversion is analyzed, and then a regular expression is adopted to match and extract key data items in the judicial data after cleaning and conversion. In practical applications, the key data items may include case classification, case amount unit, trial result, trial program, court level, case influence, checking of case result influence on the client or accuracy of client litigation role, and the like, without limitation.
For example, the "referred amount" is out of the judicial data documents: 1000.00 yuan, then when parsing the judicial data, full text search matches "involved amount: "+ value +" element ", and extract the value among them as the analytic result; in order to avoid errors, the character information which appears for multiple times can be cross-verified, namely the' amount involved: if the format of the + numerical value plus the 'element' or other characters capable of representing the involved amount may appear for many times, the comparison and verification are carried out on a plurality of extraction results, and the accuracy is improved.
It can be understood that the use ratio of judicial data can be improved and the accuracy of index data calculated according to the original data can be further improved by further analyzing, matching and extracting the key data items of the judicial data after cleaning and processing.
In practical application, after the resource allocation result is obtained, the resource allocation result can be further sent to a credit operator; then, in some possible embodiments, further comprising:
and integrating the scores of the resource application parties, the rating results of the resource application parties and the resource allocation results to obtain a resource allocation proposal, and sending the resource allocation proposal to a resource allocation formula.
The following is a description of this embodiment by taking a credit service as an example.
In order to facilitate the credit operator to view the results, the model scoring, the rating results of the credit applicant and the pricing configuration results can be integrated to obtain a credit application strategy, and then the credit application strategy is sent to the credit operator.
In practical applications, the credit application policy may be sent to the credit operator in the form of an email or a short message, which is not limited specifically.
It can be understood that the model scoring, the rating result and the pricing configuration result are integrated to form a credit application strategy and then sent to the credit operator, so that the credit operator can conveniently check the decision result and make a credit strategy.
FIG. 5The device for allocating service resources provided by the embodiment of the invention comprises:
the data acquiring module 501 is configured to acquire original data related to a resource applicant, where the original data includes one or more of bank data, credit investigation data, tax data, industry and commerce data, and judicial data;
an index data extraction module 502, configured to process the original data and calculate index data according to the processed original data;
a score obtaining module 503, configured to obtain a score of the resource applicant according to the index data;
a rating result obtaining module 504, configured to perform rating mapping based on the score of the resource applicant, and obtain a rating result of the resource applicant;
and the resource allocation result calculation module 505 is configured to calculate a resource allocation result based on the rating result of the resource applicant, and output the resource allocation result to the resource allocation formula, so that the resource allocation party performs resource allocation based on the resource allocation result.
As an embodiment, the data obtaining module 501 includes:
the data source interface calling unit is used for acquiring a data source and a data source interface of the data source and calling the data source interface;
a request message sending unit, configured to send an original data request message to a data source via a data source interface;
a return message receiving unit, configured to receive an original data return message fed back by a data source based on an original data request message;
the analysis unit is used for analyzing the original data return message to obtain original data;
or the like, or, alternatively,
the calling request sending unit is used for sending a calling request to the data source so that the data source generates a specified file and synchronizes the specified file to the specified shared area;
and the designated file analyzing unit is used for downloading the designated file from the designated sharing area and analyzing the designated file to obtain original data.
In a possible implementation manner, the index data calculation module includes a cleaning conversion unit, the cleaning conversion unit is configured to perform cleaning conversion on original data with an error data format or with a data format inconsistent with a destination format to obtain destination data with a data format consistent with the destination format, and the destination data is presented in a standard table.
As an implementation manner, when the original data includes judicial data, the index data calculation module further includes a judicial data analysis unit, and the judicial data analysis unit is configured to analyze a format of the judicial data after the cleaning conversion, and match and extract the key data items in the judicial data after the cleaning conversion by using a regular expression.
In a possible implementation manner, the apparatus further includes a resource application receiving module (not shown in the figure), configured to receive a resource application of the resource applicant before the data obtaining module obtains the raw data related to the resource applicant, and record the resource application in the resource application table.
As an embodiment, the apparatus further includes a resource allocation suggestion sending module (not shown in the figure), configured to integrate the score of the resource applicant, the rating result of the resource applicant, and the resource allocation result into a resource allocation suggestion, and send the resource allocation suggestion to the resource allocation formula.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
In an embodiment of the present invention, there is also provided an electronic device, including 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 any of the above-mentioned service resource allocation methods.
In an embodiment of the present invention, there is also provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform any of the above-described method for allocating a service resource.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (14)
1. A method for allocating service resources, the method comprising:
acquiring original data related to a resource application party, wherein the original data comprises one or more of bank data, credit investigation data, tax data, industrial and commercial data and judicial data;
processing the original data, and calculating index data according to the processed original data;
obtaining the score of the resource applicant according to the index data;
carrying out rating mapping based on the scores of the resource applicants to obtain rating results of the resource applicants;
calculating a resource allocation result based on the rating result of the resource applicant, and outputting the resource allocation result to a resource allocation formula so that the resource allocator allocates resources based on the resource allocation result;
the obtaining of the score of the resource applicant according to the index data includes:
firstly, calculating to obtain index scores based on index data of a resource applicant according to index rules, then calculating to obtain a module score based on the index scores, and obtaining model scores based on a plurality of module scores, namely the scores of the resource applicant;
the calculation mode of the module score comprises the following steps: step 1: weighted score, false, of score indicators in a computing moduleLet the module have k scoring indices (FS)1,FS2,…,FSk) If the score index is MS ═ FS in weight ratio1×WF1+FS2×WF2…+FSk×WFk；
Wherein (WF)1,WF2,…,WFk) The index weight, namely the module variable parameter provided by the rule configuration table of the model module; the scoring index refers to an index for participating in module scoring, namely an index selected from a public index pool and used for calculating module scoring; FS is an index score;
step 2: the aggregate score of the rule set in the module is calculated, assuming that there are j first rule indexes (AS) in the module1,AS2,…ASj) If the rule set summary score is AS ═ AS1+AS2…+ASj；
And step 3: calculating the adjusted score of the module AS MSA ═ Min (Max (MS + AS,0), 100);
the model score is calculated in a manner that includes: step a: calculating the weighted scores of the modules in the model, assuming that there are s scoring Modules (MSA) in the model1,MSA2,…,MSAs) The scoring module then weights the CS ═ MSA1×WM1+MSA2×WM2…+MSAS×WMs(ii) a Wherein (WM)1,WM2…, WMs) are module weights, i.e., model variable parameters provided by a model module rule configuration table;
step b: calculating the model level rule set summary score, assuming there are t second rule indexes (AS ″) in the module1, AS`2,…, AS`t) If the rule set is summarized and scored AS AS ═ AS ″1+ AS`2…+ AS`t；
Step c: forming the model and then grading the model AS CSA ═ Min (Max (CS + AS', 0), 100);
the model module system is provided with five models which are respectively an admission model, an anti-fraud model, a grading model, a pricing model and a post-credit early warning model.
2. The method according to claim 1, wherein the obtaining of the original data related to the resource applying party comprises:
acquiring a data source and a data source interface of the data source, and calling the data source interface;
sending an original data request message to the data source through the data source interface;
receiving an original data return message fed back by the data source based on the original data request message;
analyzing the original data return message to obtain the original data;
or the like, or, alternatively,
sending a calling request to the data source so that the data source generates a specified file and synchronizes to a specified shared area;
and downloading the specified file from the specified sharing area, and analyzing the specified file to obtain the original data.
3. The method according to claim 1, wherein the processing the original data comprises:
and cleaning and converting original data with wrong data format or inconsistent data format and target format to obtain target data with consistent data format and target format, and presenting the target data in a standard table form.
4. The method according to claim 3, wherein when the original data includes judicial data, the processing the original data further comprises:
and analyzing the format of the judicial data after cleaning conversion, and matching and extracting key data items in the judicial data after cleaning conversion by adopting a regular expression.
5. The method of claim 1, wherein before the step of obtaining the original data related to the resource application party, the method further comprises:
receiving a resource application of a resource application party, and recording the resource application in a resource application table.
6. The method for allocating service resources according to claim 1, wherein the method further comprises:
and integrating the scores of the resource application parties, the rating results of the resource application parties and the resource allocation results to obtain a resource allocation proposal, and sending the resource allocation proposal to a resource allocation formula.
7. An apparatus for allocating service resources, the apparatus comprising:
the data acquisition module is used for acquiring original data related to a resource application party, wherein the original data comprises one or more of bank data, credit investigation data, tax data, industrial and commercial data and judicial data;
the index data calculation module is used for processing the original data and calculating index data according to the processed original data; the score obtaining module is used for obtaining the score of the resource applicant according to the index data;
the rating result obtaining module is used for carrying out rating mapping based on the scores of the resource applicants to obtain the rating result of the resource applicants;
the resource allocation result calculation module is used for calculating a resource allocation result based on the rating result of the resource applicant and outputting the resource allocation result to a resource allocation formula so as to enable the resource allocator to allocate resources based on the resource allocation result; the obtaining of the score of the resource applicant according to the index data includes:
firstly, calculating to obtain index scores based on index data of a resource applicant according to index rules, then calculating to obtain a module score based on the index scores, and obtaining model scores based on a plurality of module scores, namely the scores of the resource applicant;
the calculation mode of the module score comprises the following steps: step 1: meterWeighted scoring of scoring metrics in a computation module, assuming there are k scoring metrics (FS) in the module1,FS2,…,FSk) If the score index is MS ═ FS in weight ratio1×WF1+FS2×WF2…+FSk×WFk；
Wherein (WF)1,WF2,…,WFk) The index weight, namely the module variable parameter provided by the rule configuration table of the model module; the scoring index refers to an index for participating in module scoring, namely an index selected from a public index pool and used for calculating module scoring; FS is an index score;
step 2: the aggregate score of the rule set in the module is calculated, assuming that there are j first rule indexes (AS) in the module1,AS2,…ASj) If the rule set summary score is AS ═ AS1+AS2…+ASj；
And step 3: calculating the adjusted score of the module AS MSA ═ Min (Max (MS + AS,0), 100);
the model score is calculated in a manner that includes: step a: calculating the weighted scores of the modules in the model, assuming that there are s scoring Modules (MSA) in the model1,MSA2,…,MSAs) The scoring module then weights the CS ═ MSA1×WM1+MSA2×WM2…+MSAS×WMs(ii) a Wherein (WM)1,WM2…, WMs) are module weights, i.e., model variable parameters provided by a model module rule configuration table;
step b: calculating the model level rule set summary score, assuming there are t second rule indexes (AS ″) in the module1, AS`2,…, AS`t) If the rule set is summarized and scored AS AS ═ AS ″1+ AS`2…+ AS`t；
Step c: forming the model and then grading the model AS CSA ═ Min (Max (CS + AS', 0), 100);
the model module system is provided with five models which are respectively an admission model, an anti-fraud model, a grading model, a pricing model and a post-credit early warning model.
8. The apparatus for allocating service resources according to claim 7, wherein the data obtaining module comprises:
the data source interface calling unit is used for acquiring a data source and a data source interface of the data source and calling the data source interface; a request message sending unit, configured to send an original data request message to the data source through the data source interface;
a return message receiving unit, configured to receive an original data return message fed back by the data source based on the original data request message;
the analysis unit is used for analyzing the original data return message to obtain the original data;
or the like, or, alternatively,
the calling request sending unit is used for sending a calling request to the data source so that the data source generates a specified file and synchronizes the specified file to a specified shared area;
and the designated file analyzing unit is used for downloading the designated file from the designated sharing area and analyzing the designated file to obtain the original data.
9. The apparatus according to claim 7, wherein the index data calculation module includes a cleansing conversion unit, the cleansing conversion unit is configured to perform cleansing conversion on original data with an error data format or inconsistent data format with a destination format to obtain destination data with a consistent data format and destination format, and the destination data is presented in a standard table.
10. The apparatus according to claim 9, wherein when the raw data includes judicial data, the index data calculation module further includes a judicial data analysis unit, and the judicial data analysis unit is configured to analyze a format of the judicial data after the cleaning conversion, and match and extract key data items in the judicial data after the cleaning conversion by using a regular expression.
11. The apparatus for allocating service resources according to claim 7, further comprising a resource application receiving module, configured to receive a resource application from a resource application party before the data obtaining module obtains the raw data related to the resource application party, and record the resource application in a resource application table.
12. The apparatus according to claim 7, further comprising a resource allocation suggestion sending module, configured to integrate the score of the resource applicant, the rating result of the resource applicant, and the resource allocation result into a resource allocation suggestion, and send the resource allocation suggestion to a resource allocation formula.
13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 6 when executing the program.
14. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 6.
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| CN111598488A (en) * | 2020-06-30 | 2020-08-28 | 新奥（中国）燃气投资有限公司 | Tax risk monitoring method and device and computer storage medium |
| CN112017063B (en) * | 2020-07-15 | 2024-04-26 | 北京淇瑀信息科技有限公司 | Resource allocation method and device based on comprehensive risk score and electronic equipment |
| CN112016791B (en) * | 2020-07-15 | 2024-04-26 | 北京淇瑀信息科技有限公司 | Resource allocation method and device and electronic equipment |
| CN112051992A (en) * | 2020-09-02 | 2020-12-08 | 天元大数据信用管理有限公司 | Data processing method and system based on credit investigation report generation |
| CN113962503A (en) * | 2020-09-25 | 2022-01-21 | 京东科技控股股份有限公司 | Method, device and equipment for resource allocation and computer readable storage medium |
| CN112613988B (en) * | 2020-12-31 | 2024-07-12 | 中国农业银行股份有限公司 | Method and device for generating credit investigation index information |
| CN113034289A (en) * | 2021-03-23 | 2021-06-25 | 深圳微众信用科技股份有限公司 | Equipment financing service processing device, method and equipment |
| CN113590310A (en) * | 2021-07-02 | 2021-11-02 | 上海淇玥信息技术有限公司 | Resource allocation method and device based on rule touch rate scoring and electronic equipment |
| CN114022158A (en) * | 2021-09-28 | 2022-02-08 | 东方微银科技股份有限公司 | Resource allocation method and equipment for individual targets |
| CN114022159A (en) * | 2021-09-28 | 2022-02-08 | 东方微银科技股份有限公司 | Target qualification confirmation method and device |
| CN114139883B (en) * | 2021-11-10 | 2024-03-29 | 云南电网有限责任公司信息中心 | Calculation method of material domain evaluation index of power enterprise |
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| CN107240014A (en) * | 2017-04-28 | 2017-10-10 | 天合泽泰（厦门）征信服务有限公司 | A kind of credit rating method based on enterprise's reference business |
| CN109727116A (en) * | 2018-08-17 | 2019-05-07 | 平安普惠企业管理有限公司 | Credit analysis method, device, equipment and computer readable storage medium |
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Address after: 10-7, 10-8, 10 Qingyun Road, Jiangbei District, Chongqing 400025 Applicant after: Dongfang Weiyin Technology Co.,Ltd. Address before: 10-7, 10-8, 10 Qingyun Road, Jiangbei District, Chongqing 400025 Applicant before: Oriental micro bank technology (Chongqing) Co.,Ltd. Address after: 10-7, 10-8, 10 Qingyun Road, Jiangbei District, Chongqing 400025 Applicant after: Oriental micro bank technology (Chongqing) Co.,Ltd. Address before: 100044 4th floor, Ronghui international building, building 1, courtyard 42, gaoliangxie street, Haidian District, Beijing Applicant before: Oriental micro silver technology (Beijing) Co.,Ltd. |
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Address after: 400025 unit 08, floor 23, No. 1, Qingyun Road, Jiangbei District, Chongqing Patentee after: Dongfang Weiyin Technology Co.,Ltd. Address before: 10-7, 10-8, 10 Qingyun Road, Jiangbei District, Chongqing 400025 Patentee before: Dongfang Weiyin Technology Co.,Ltd. |
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