CN112767121A - Method and device for processing risk level data - Google Patents
Method and device for processing risk level data Download PDFInfo
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- CN112767121A CN112767121A CN202011637199.0A CN202011637199A CN112767121A CN 112767121 A CN112767121 A CN 112767121A CN 202011637199 A CN202011637199 A CN 202011637199A CN 112767121 A CN112767121 A CN 112767121A
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Abstract
The embodiment of the invention relates to a method and a device for processing risk grade data, wherein the method comprises the following steps: acquiring a first personal context data set and a first personal credit data set; according to the first personal background data set, carrying out personal background grade evaluation processing to generate first grade data; performing personal credit rating evaluation processing according to the first personal credit data set to generate second rating data; and analyzing and processing the personal risk grade according to the first grade data and the second grade data to generate first risk grade data. The embodiment of the invention assists the supply chain financial service mechanism to process the intermediate and downstream enterprise owner data to obtain the corresponding risk level data, so that the working efficiency of the supply chain financial service mechanism is improved, and the business experience of enterprise customers is improved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing risk level data.
Background
The core purpose of supply chain finance is to perform financing service on a core enterprise and other downstream enterprises by taking the core enterprise as a center, and the service purpose is to solve the problem of cash flow shortage in enterprise operation. Before the financial service organization of the supply chain provides the financing service, the financial service organization not only evaluates the credit level of the enterprise, but also evaluates the operator of the enterprise, and provides the corresponding financing service according to the evaluation level on the premise of ensuring the controllable risk. However, in the current service scenario, the supply chain financial service institution performs data sorting, calculation and evaluation on the evaluation processing flow of the enterprise, and also performs data processing, calculation and evaluation in a paper document office manner, so that not only is the working efficiency low, but also complaints of enterprise customers are easily caused due to problems such as calculation errors caused by human factors.
Disclosure of Invention
The present invention aims to overcome the defects of the prior art, and provides a method, an apparatus, an electronic device, a computer program product, and a computer-readable storage medium for processing risk level data, which assist a supply chain financial service organization to process intermediate and downstream enterprise owner data to obtain corresponding risk level data, so as to improve the work efficiency of the supply chain financial service organization and improve the business experience of enterprise customers.
In order to achieve the above object, a first aspect of the embodiments of the present invention provides a method for processing risk level data, where the method includes:
acquiring a first personal context data set and a first personal credit data set;
according to the first personal background data set, carrying out personal background grade evaluation processing to generate first grade data;
performing personal credit rating evaluation processing according to the first personal credit data set to generate second rating data;
and analyzing and processing the personal risk grade according to the first grade data and the second grade data to generate first risk grade data.
Preferably, the first and second liquid crystal materials are,
the first class data comprises a background class A1Background level A2Background three-level A3And background level four A4(ii) a From the background to a1The background level A2The background is tertiary A3To said background level four A4The risk levels rise sequentially;
the second level data includes credit level B1Credit level B2Credit level B3And credit level B4(ii) a From the credit level B1The credit level B2The credit level B3To the credit level B4The risk levels rise sequentially;
the first risk level data includes low risk, general risk, next highest risk, and high risk.
Preferably, the performing, according to the first personal context data set, personal context level evaluation processing to generate first level data specifically includes:
according to the first personal background data set, grading processing related to conditions such as age, marital state, cultural degree, unit property, job title, monthly income, physical condition and the like is respectively carried out to obtain a plurality of corresponding grading data;
and counting the plurality of grading data, and carrying out grading evaluation on the counting result according to a plurality of grading intervals from low to high to generate the first grade data.
Preferably, the performing personal credit rating evaluation processing according to the first personal credit data set to generate second rating data specifically includes:
according to the first personal credit data set, rating processing related to the number of credit cards and the number of loan institutions, the current overdue amount of the credit cards and the historical overdue times of the credit cards, the current overdue amount of the loans and the historical overdue times of the loans and other bad credit records is respectively carried out to obtain a plurality of corresponding rating data;
and combining the plurality of rating data, and performing grading evaluation according to a combined result to generate the second rating data.
Preferably, the analyzing and processing the personal risk level according to the first level data and the second level data to generate first risk level data specifically includes:
and combining the first grade data and the second grade data, and performing grading evaluation according to a combination result to generate the first risk grade data.
A second aspect of the embodiments of the present invention provides a device for processing risk level data, including:
the acquisition module is used for acquiring a first personal context data set and a first personal credit data set;
the first evaluation processing module is used for carrying out personal background grade evaluation processing according to the first personal background data set to generate first grade data;
the second evaluation processing module is used for carrying out personal credit grade evaluation processing according to the first personal credit data set to generate second grade data;
and the comprehensive evaluation processing module is used for analyzing and processing the personal risk level according to the first level data and the second level data to generate first risk level data.
A third aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;
the processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps of the first aspect;
the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.
A fourth aspect of embodiments of the present invention provides a computer program product comprising computer program code which, when executed by a computer, causes the computer to perform the method of the first aspect.
A fifth aspect of embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect.
Embodiments of the present invention provide a method and an apparatus for processing risk level data, an electronic device, a computer program product, and a computer-readable storage medium, which assist a supply chain financial service organization in processing intermediate and downstream enterprise owner data to obtain corresponding risk level data, so as to improve the work efficiency of the supply chain financial service organization and improve the business experience of enterprise customers.
Drawings
Fig. 1 is a schematic diagram of a method for processing risk level data according to an embodiment of the present invention;
fig. 2 is a block diagram of a risk level data processing apparatus according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present invention provides a method for processing risk level data, as shown in fig. 1, which is a schematic diagram of a method for processing risk level data according to an embodiment of the present invention, the method mainly includes the following steps:
step 1, acquiring a first personal background data set and a first personal credit data set;
wherein the first set of personal context data comprises first age data, first marital status data, first cultural degree data, first unit property data, first title data, first monthly income data, and first body condition data;
the first personal credit data set comprises first credit card quantity data, first loan institution quantity data, current overdue amount data of the first credit card, historical overdue number data of the first credit card, current overdue amount data of the first loan, historical overdue number data of the first loan, and first other bad credit record data.
Here, the first personal context data set includes basic personal information of the subject to be evaluated, including information such as age, marital status, cultural degree, unit property, job title, monthly income, and physical examination status, that is, physical status; the first personal credit data set comprises the number of credit cards of an object to be evaluated, the loan number, namely the number of loan institutions, the overdue condition of the credit cards, namely the current overdue amount data of the first credit card, the historical overdue data of the first credit card, the overdue condition of the loan, namely the historical overdue data of the first loan and first other bad credit record data.
Step 2, according to the first personal background data set, carrying out personal background grade evaluation processing to generate first grade data;
wherein the first class data comprises a background class A1Background level A2Background three-level A3And background level four A4(ii) a From background to level A1Background level A2Background three-level A3To background level four A4The risk levels rise sequentially;
the personal background grade evaluation processing is to extract first age data, first marital status data, first cultural degree data, first unit property data, first title data, first monthly income data and first body condition data from a first personal background data set, respectively score each data according to a corresponding scoring rule, sum all the scores at last, and grade according to a score interval where the sum data is located to obtain first grade data; the first class data is divided into a background class A1Background level A2Background three-level A3And background level four A4Four levels;
the method specifically comprises the following steps: step 21, according to the first personal background data set, respectively carrying out grading processing related to conditions such as age, marital state, cultural degree, unit property, job title, monthly income, physical condition and the like to obtain a plurality of corresponding grading data;
the method specifically comprises the following steps: step 211, according to the first personal background data set, performing rating processing related to the age condition to obtain corresponding first age rating data;
specifically, according to the first age data, a first corresponding relation table reflecting the corresponding relation between the age interval and the age score is inquired, and first scoring processing is carried out according to a first age scoring rule to generate first age scoring data;
here, the data format of the first age data is a natural number; the first correspondence table includes a plurality of first correspondence records, each first correspondence record containing two fields: the first age group records comprise a first age group interval field and a first age scoring field, wherein the first age group interval fields of all the first corresponding relationship records are not overlapped and the first age scoring fields are not repeated; the first age data is 0 point if the legal adult age is not reached; the higher the first age score data is, the better the stability of the object to be evaluated is; for example, the first correspondence table is shown in table one, where 45 of the first age data indicates that the age of the subject to be evaluated is 45 years, and the first age score data is 5 points;
watch 1
Step 212, according to the first personal background data set, performing scoring processing related to marital state conditions to obtain corresponding first marital scoring data;
specifically, according to the first marital state data, a second corresponding relation table reflecting the marital state and marital scoring corresponding relation is inquired, second scoring processing is carried out according to a first marital state scoring rule, and first marital scoring data are generated;
here, the data format of the first marital status data is an integer; the second correspondence table includes a plurality of second correspondence records, each of which contains two fields: the first marriage status fields and the first marriage scoring fields of all the second corresponding relationship records are not overlapped, and the first marriage status fields are not repeated; if the first age data does not reach the legal marital age, the first marital scoring data is 0 point; the higher the first marital score data is, the better the stability of the object to be evaluated is;
for example, as shown in table two, if the first marital status data is 2, which indicates that the marital status of the object to be evaluated is married or not, the first marital score data is 4;
watch two
Step 213, according to the first personal background data set, performing scoring processing related to the cultural degree condition to obtain corresponding first cultural degree scoring data;
specifically, according to the first cultural degree data, a third corresponding relation table reflecting the corresponding relation between the cultural degree and the cultural degree score is inquired, and third scoring processing is carried out according to the first cultural degree scoring rule to generate first cultural degree scoring data;
here, the data format of the first cultural degree data is an integer; the third correspondence table includes a plurality of third correspondence records, each third correspondence record including two fields: the first culture degree field and the first culture degree scoring field are not overlapped, and the first culture degree scoring field of all the third corresponding relation records is not overlapped; if the first cultural degree data is 0, the first marital scoring data is 0, and the cleartext degree of 0 is lower than the elementary education level; the higher the first culture degree score data is, the better the stability of the object to be evaluated is;
for example, the third correspondence table is shown in table three, if the first cultural degree data is 3, which indicates that the cultural degree of the object to be evaluated is education such as too high, the first cultural degree score data is 5 points;
watch III
Step 214, according to the first personal background data set, performing scoring processing related to the unit property condition to obtain corresponding first unit property scoring data;
specifically, according to the first unit property data, a fourth corresponding relation table reflecting the corresponding relation between the unit property and the unit property score is inquired, and fourth scoring processing is performed according to a first unit property scoring rule to generate first unit property scoring data;
here, the data format of the first unit property data is an integer; the fourth mapping table includes a plurality of fourth mapping records, each of which includes two fields: the first unit property field and the first unit property scoring field are not overlapped, and the first unit property scoring field of all the fourth corresponding relation records are not overlapped; if the first unit property data is 0, the first unit property scoring data is 0 points, and the 0 points indicate that the criminal service or the mandatory supervision stage is appointed by a judicial authority; the higher the first unit property score data is, the better the stability of the object to be evaluated is;
for example, the fourth correspondence table is as shown in table four, where the first unit property data is 4, which indicates that the type of the organization where the object to be evaluated is located is an enterprise unit, and the first unit property score data is 4 scores;
watch four
Step 215, according to the first personal background data set, performing scoring processing related to the job title condition to obtain corresponding first job title scoring data;
specifically, according to the first job title data, a fifth corresponding relation table reflecting the corresponding relation between the job titles and the job title scores is inquired, fifth scoring processing is carried out according to a first job title scoring rule, and first job title scoring data is generated;
here, the data format of the first title data is an integer; the fifth correspondence table includes a plurality of fifth correspondence records, each of which includes two fields: the first job title field and the first job title scoring field are not overlapped, and the first job title scoring field of all the fifth corresponding relationship records are not overlapped; if the first job title data is 0, the first job title scoring data is 0 points, and the 0 points indicate that no working experience and no working year limit evidence exist; the higher the first job title score data is, the better the stability of the object to be evaluated is;
for example, the fifth correspondence table is shown in table five, where the first job title data is 3, which indicates that the job title of the object to be evaluated is the senior job title, and the first job title score data is 5 scores;
watch five
Step 216, according to the first personal background data set, carrying out grading processing related to the monthly income condition to obtain corresponding first monthly income grading data;
specifically, according to the first monthly income data, a sixth corresponding relation table reflecting the corresponding relation between the monthly income and the monthly income score is inquired, sixth scoring processing is carried out according to the first monthly income scoring rule, and first monthly income scoring data are generated;
here, the data format of the first-month income data is a nonnegative real number, which corresponds to income data fixed per month by the person to be evaluated, such as the sum of monthly payroll income and other stable income obtained per month, and the first-month income data should be 0 in other cases such as no income source; the sixth correspondence table includes a plurality of sixth correspondence records, each sixth correspondence record including two fields: a first-month income interval field and a first-month income scoring field, wherein the first-month income fields recorded by all the sixth corresponding relations are not overlapped and the first-month income scoring fields are not repeated; if the first-month income data is 0, the first-month income scoring data is 0 point; the higher the first-month income score data is, the better the stability of the object to be evaluated is;
for example, the sixth correspondence table is shown in table six, where first monthly income data of 30,000.00 indicates that the monthly income of the subject to be evaluated is 30,000.00 yuan, and the first monthly income score data is 5 points;
watch six
Step 217, according to the first personal background data set, carrying out grading processing related to physical conditions to obtain corresponding first physical condition grading data;
specifically, according to the first body condition data, a seventh corresponding relation table reflecting the corresponding relation between the body condition and the body condition score is inquired, and seventh scoring processing is performed according to a first body condition scoring rule to generate first body condition scoring data;
here, the data format of the first body situation data is an integer; the seventh correspondence table includes a plurality of seventh correspondence records, each of which includes two fields: a first body condition field, a first body condition score field, the first body condition fields of all seventh correspondence records being non-overlapping, the first body condition score fields being non-overlapping; if the first body condition data is 0, the first body condition evaluation data should be 0, and 0 indicates that a major disease or life crisis has been diagnosed; the higher the first body condition score data is, the better the stability of the object to be evaluated is;
for example, the seventh correspondence table is shown in table seven, and if the first body condition data is 3, which indicates that the body condition of the subject to be evaluated is good, the first body condition score data is 5;
watch seven
the method specifically comprises the following steps: step 221, performing sum calculation on the first age scoring data, the first marital scoring data, the first culture degree scoring data, the first unit property scoring data, the first title scoring data, the first monthly income scoring data and the first body condition scoring data to generate first sum data;
wherein the first age scoring data, the first marital scoring data, the first cultural degree scoring data, the first unit property scoring data, the first title scoring data, the first monthly income scoring data, and the first body condition scoring data are all not null;
here, if all the rating data are empty, for example, 0, which indicates that there is a case where the corresponding rating rule is not satisfied in the corresponding rating process, the currently performed personal background level evaluation process should be immediately stopped, and information is presented to the evaluation operator by generating presentation information that the similar evaluation data are not satisfied;
if all the scoring data are not null, the situation that the scoring rules are not met in the corresponding scoring processing is described, the total scoring data of the object to be evaluated is calculated continuously, namely the first total data, and the first total data is first age scoring data, first marital scoring data, first cultural degree scoring data, first unit property scoring data, first job title scoring data, first monthly income scoring data and first body condition scoring data;
step 222, when the first sum data meets a preset first background scoring interval, setting the first grade data as a background grade A1(ii) a When the first sum data meets a preset second background scoring interval, setting the first grade data as a background grade A2(ii) a When the first sum data meets a preset third background scoring interval, setting the first grade data as background grade A3(ii) a When the first sum data meets a preset fourth background scoring interval, setting the first grade data as a background grade A4；
The score section of the first background score interval is higher than the second background score interval, the score section of the second background score interval is higher than the third background score interval, and the score section of the third background score interval is higher than the fourth background score interval.
Here, the four background score intervals are four score data intervals corresponding to four levels of data: the first background scoring interval corresponds to a background level A1The second background scoring interval corresponds to background level A2The third background scoring interval corresponds to the third background level A3The fourth background score interval corresponds to the background level A2(ii) a Background level A1Background level A2Background three-level A3To background level four A4May be incremental integer type data such as 1, 2, 3, 4, or character type data such as "first level", "second level", "third level", and "fourth level"; because of the first level A from the background1Background level A2Background three-level A3To background level four A4The risk level is sequentially higher, i.e. background level A1Highest stability, lowest risk, background level four A4The stability is the worst and the risk is the highest; therefore, the score section of the first background score interval is higher than the second background score interval, the score section of the second background score interval is higher than the third background score interval, and the score section of the third background score interval is higher than the fourth background score interval.
Step 3, performing personal credit grade evaluation processing according to the first personal credit data set to generate second grade data;
wherein the second level data comprises a credit level B1Credit level B2Credit level B3And credit level B4(ii) a From credit level B1Credit level B2Credit level B3To credit level B4The risk levels rise sequentially;
here, the personal credit rating evaluation process first ranks the data in the first personal credit data set separately: 1) rating data, i.e., first data, for rating relating to the number of credit cards and the number of lending institutions using the amount of the first credit card amount data and the first lending institution amount data, 2) using the current overdue amount data of the first credit card and the past overdue amount data of the first credit cardThe term data is used for grading related to the current overdue amount of the credit card and the historical overdue times of the credit card to obtain grading data, namely second data, 3) the current overdue amount data of the first loan and the historical overdue times data of the first loan are used for grading related to the current overdue amount of the loan and the historical overdue times of the loan to obtain grading data, namely third data, and 4) the first other bad credit record data is used for grading related to other bad credit records to obtain grading data, namely fourth data; then, the first, second, third and fourth data are combined and graded and evaluated to obtain a final grading result, namely second grade data; the second level data includes credit level B1Credit level B2Credit level B3And credit level B4；
The method specifically comprises the following steps: step 31, according to the first personal credit data set, respectively carrying out rating processing related to the number of the credit cards and the number of loan institutions, the current overdue amount of the credit cards and the historical overdue times of the credit cards, the current overdue amount of the loans and the historical overdue times of the loans and other bad credit records to obtain a plurality of corresponding rating data;
the method specifically comprises the following steps: step 311, performing rating processing related to the number of credit cards and the number of lending institutions according to the first personal credit data set;
specifically, when the first credit card quantity data is less than or equal to a preset credit card quantity threshold value and the first loan institution quantity data is less than or equal to a preset loan institution quantity threshold value, the first data is set as a first level S1(ii) a When the first credit card quantity data is larger than the credit card quantity threshold value or the first loan institution quantity data is larger than the loan institution quantity threshold value, setting the first data as a first second grade S2；
Wherein, from the first primary level S1To the first or second level S2The risk level is gradually improved;
here, the data format of the first credit card quantity data is an integer representing the quantity of all credit cards currently held by the subject to be evaluated, and the data format of the first loan institution quantity data is an integer representing the loan institution quantity of all loans currently handled by the subject to be evaluated, such as the loan bank quantity;
the higher the first credit card quantity data and the first loan institution quantity data are, the more the debt repayment objects of the object to be evaluated are, the higher the credit risk level is;
in the step, the threshold value of the number of the credit cards and the threshold value of the number of the loan institutions are all preset integer values, and the threshold value of the number of the credit cards is set as 4 by default and the threshold value of the number of the loan institutions is set as 8 under the conventional condition; the credit card quantity threshold and the loan institution quantity threshold are used as grade evaluation thresholds, if the credit card quantity and the loan institution quantity of the object to be evaluated can be controlled within the thresholds defined by the credit card quantity threshold and the loan institution quantity threshold, the risk is considered to be lower, and under the condition, the rating data obtained by combining and rating the sum of the first credit card quantity data and the first loan institution quantity data, namely the first data, is set as a first grade S1(ii) a If the number of the credit cards and the number of the loan institutions of the object to be evaluated can exceed the limit threshold, the risk is considered to be higher, and in this case, the first data is set as a first second grade S2；
From a first level S1To the first or second level S2The risk level is gradually improved; first grade S1And a first or second grade S2May be incremental integer type data such as 1, 2, or character type data such as "primary", "secondary";
step 312, according to the first personal credit data set, carrying out rating processing related to the current overdue amount of the credit card and the historical overdue times of the credit card;
specifically, when the current overdue amount data and the historical overdue times data of the first credit card are both empty, the second data is set to be at a second first level T1(ii) a When the current overdue amount data of the first credit card meets the preset current overdue amount interval of the first credit card, or the historical overdue times data of the first credit card is equal to the preset historical overdue times of the first credit cardSetting the second data to a second level T when the period number is a threshold value2(ii) a When the current overdue amount data of the first credit card meets a preset current overdue amount interval of the second credit card, or the historical overdue time data of the first credit card is equal to a preset historical overdue time threshold of the second credit card, setting the second data to be a second third grade T3(ii) a When the current overdue amount data of the first credit card exceeds the current overdue amount interval of the second credit card or the historical overdue time data of the first credit card is larger than the historical overdue time threshold of the second credit card, setting the second data as a second four-grade T4；
The amount of the current overdue amount interval of the first credit card is smaller than the current overdue amount interval of the second credit card, and the historical overdue time threshold of the first credit card is smaller than the historical overdue time threshold of the second credit card; from the second to the first level T1A second grade T2A second third grade T3To a second fourth level T4The risk level is gradually improved;
here, the data format of the data of the current overdue amount of the first credit card is a non-negative real number type, which represents the sum of overdue amounts of all credit cards currently held by the object to be evaluated, the overdue of the credit card refers to the behavior that payment is not made for the current amount after the latest payment date indicated by each credit card bill, and the current overdue amount of the credit card is the sum of the overdue amounts of all credit cards; the data format of the historical overdue times data of the first credit card is an integer, represents the times of overdue of the object to be evaluated in the credit card repayment history, the times can bring the current overdue into the times calculation, and the times of the overdue and the completed payment in the history can be calculated;
the higher the current overdue amount data and the historical overdue times data of the first credit card are, the worse the debt repayment stability and the higher the credit risk level of the object to be evaluated are;
the current overdue amount interval of the first credit card and the current overdue amount interval of the second credit card are two preset amount value intervals, the amount of money in the current overdue amount interval of the first credit card is smaller than the amount of money in the current overdue amount interval of the second credit card, and the two intervals are reference intervals for evaluating the current overdue amount data of the first credit card; the first credit card historical overdue threshold and the second credit card historical overdue threshold are two preset overdue threshold, the first credit card historical overdue threshold is smaller than the second credit card historical overdue threshold, and the two thresholds are reference thresholds for evaluating the first credit card historical overdue data;
in this step, if the data of the current overdue amount of the first credit card is empty, for example, 0, it indicates that there is no overdue amount currently, and the current bill is paid in time; if the historical overdue time data of the first credit card is null, for example, 0, the first credit card indicates that no overdue history exists, and the bills are paid on time; then, when the current overdue amount data and the historical overdue number data of the first credit card are both empty, for example, all 0 s, it indicates that the debt repayment stability of the object to be evaluated is high, the credit is good, and the credit risk level is low, in this case, the rating data obtained by performing combined rating using the current overdue amount data and the historical overdue number data of the first credit card, that is, the second data, is set to a second first level T1；
If the current overdue amount data of the first credit card meets the current overdue amount interval of the first credit card within the current overdue amount interval of the first credit card, for example, the current overdue amount interval of the first credit card is set to be greater than 0 and less than or equal to 200 yuan, and the current overdue amount data of the first credit card is 100 yuan, the current bill has the overdue amount but smaller related amount; if the historical overdue data of the first credit card is equal to the historical overdue threshold of the first credit card, for example, the historical overdue threshold of the first credit card is set to 1, and the historical overdue data of the first credit card is 1, it indicates that although overdue occurs in the history, the overall situation is stable; the two situations occur either, which shows that the debt repayment stability of the object to be evaluated is more stable but has flaws, and the credit risk level is risky but has windThe risk is low; in this case, the second data is set to the second level T2；
If the current overdue amount data of the first credit card meets the current overdue amount interval of the second credit card within the current overdue amount interval of the second credit card, for example, the current overdue amount interval of the second credit card is set to be more than 200 yuan and less than or equal to 1000 yuan, and the current overdue amount data of the first credit card is 800 yuan, the current bill has overdue amount and the related amount is not small; if the first credit card historical overdue data is equal to the second credit card historical overdue threshold, for example, the second credit card historical overdue threshold is set to be 2, and the first credit card historical overdue data is 2, the fact that more than 1 overdue occurs in the history record is indicated; either of the two situations occurs, which indicates that the liability repayment stability of the object to be evaluated is to be inquired, the credit risk level is risky, and the risk is possibly expanded; in this case, the second data is set to the second third level T3；
If the current overdue amount data of the first credit card exceeds the upper limit of the current overdue amount interval of the second credit card, namely exceeds the current overdue amount interval of the second credit card, for example, the current overdue amount interval of the second credit card is set to be more than 200 yuan and less than or equal to 1000 yuan, and the current overdue amount data of the first credit card is 3000 yuan, the current bill not only has overdue amount, but also has large related amount; if the historical overdue number data of the first credit card is larger than the historical overdue number threshold of the second credit card, for example, the historical overdue number threshold of the second credit card is set to be 2, and the historical overdue number data of the first credit card is 5, the fact that overdue occurs for multiple times in the history record and overdue habits are formed is described; either of the two situations is generated, which shows that the debt repayment stability, the credit and the credit risk level risk of the object to be evaluated are poor; in this case, the second data is set to the second fourth level T4；
From the second to the first level T1A second grade T2A second third grade T3To a second fourth level T4The risk level is gradually improved; second grade T1A second grade T2A second third grade T3And a second four-level T4May be incremental integer type data such as 1, 2, 3, 4, or may be character type data such as "primary", "secondary", "tertiary", "quaternary";
313, according to the first personal credit data set, carrying out rating processing related to the current overdue amount of the loan and the historical overdue times of the loan;
specifically, when the current overdue amount data and the historical overdue times data of the first loan are both empty, the third data is set to be a third level U1(ii) a When the current overdue amount data of the first loan is empty and the historical overdue number data of the first loan is equal to the preset historical overdue number threshold of the first loan, setting the third data as a third second level U2(ii) a When the current overdue amount data of the first loan is empty and the historical overdue number data of the first loan is equal to the preset historical overdue number threshold of the second loan, setting the third data as a third grade U3(ii) a When the current overdue amount data of the first loan is not empty or the historical overdue time data of the first loan is larger than the historical overdue time threshold of the second loan, setting the third data as a third fourth grade U4；
The first loan history overdue time threshold is smaller than the second loan history overdue time threshold; from the third first level U1A third grade U2And a third grade U3To the third fourth level U4The risk level is gradually improved;
the data format of the current overdue amount data of the first loan is a non-negative real number type and represents the sum of overdue and unreleased amounts of all loans currently transacted by a subject to be evaluated, the overdue of the loan refers to the behavior that the current amount of the first loan is not yet paid after the latest repayment date of each term for the term loan, and the current amount of the first loan is the sum of the overdue amounts of all the loans for the term loan; the data format of the first loan history overdue number data is an integer, represents the number of overdue times of the object to be evaluated in the loan repayment history, the number can be used for calculating the current overdue times and the number of times, and the number of times that the overdue times and the final repayment is completed in the history can be calculated;
the higher the current overdue amount data and the historical overdue times data of the first loan are, the worse the performance stability of the object to be evaluated is, and the higher the credit risk level is;
the first loan history overdue frequency threshold and the second loan history overdue frequency threshold are two preset overdue frequency thresholds, the first loan history overdue frequency threshold is smaller than the second loan history overdue frequency threshold, and the two thresholds are reference thresholds for evaluating the first loan history overdue frequency data;
in this step, if the current overdue amount data of the first loan is empty, for example, 0, it indicates that no overdue amount exists currently, and the current loan repayment is timely; if the first loan history overdue number data is null, for example 0, no overdue history is indicated; then, when the current overdue amount data of the first loan and the historical overdue number data of the first loan are both empty, for example, all 0 s, it indicates that the performance stability of the object to be evaluated is high, the reputation is good, and the credit risk level is low, in this case, the rating data obtained by performing combined rating using the current overdue amount data of the first loan and the historical overdue number data of the first loan is the third data, and is set to the third level U1；
On the premise that the current overdue amount data of the first loan is empty, if the historical overdue number data of the first loan is equal to the historical overdue number threshold of the first loan, for example, the historical overdue number threshold of the first loan is set to be 1, and the historical overdue number data of the first loan is 1, it is described that although overdue occurs in the history record, the overall situation is stable, the performance stability of the object to be evaluated is stable but has flaws, and the credit risk level is risky but has less risk; in this case, the third data is set to the third second level U2；
At the first creditOn the premise that the current overdue amount data of the loan is empty, if the historical overdue number data of the first credit card is equal to the historical overdue number threshold of the second loan, for example, the historical overdue number threshold of the second loan is set to be 2, and the current overdue amount data of the first loan is 2, the fact that overdue occurs for more than 1 time in the history record is shown, the performance stability of the object to be evaluated is to be inquired, the credit risk level is risky, and the risk is possibly expanded; in this case, the third data is set to the third level U3；
If the current overdue amount data of the first loan is not empty, for the loan, the place different from the credit card is that the repayment amount is high and the lowest repayment amount does not exist, and the amount is not small as long as overdue occurs, so that the first loan is regarded as a large risk event as long as the current overdue amount data of the first loan is not empty; if the first loan history overdue time data is larger than the second loan history overdue time threshold, for example, the second loan history overdue time threshold is set to be 2, and the first loan history overdue time data is 5, the fact that overdue occurs for multiple times in the history record and the overdue habit is formed is described; any one of the two situations shows that the object to be evaluated has poor performance stability, poor credit and high risk of credit risk level; in this case, the third data is set to the third fourth level U4；
From the third first level U1A third grade U2And a third grade U3To the third fourth level U4The risk level is gradually improved; third grade U1A third grade U2And a third grade U3To the third fourth level U4May be incremental integer type data such as 1, 2, 3, 4, or may be character type data such as "primary", "secondary", "tertiary", "quaternary";
step 314, performing rating processing related to other bad credit records according to the first personal credit data set;
specifically, when the first other bad credit record data is empty, the fourth data is set to the fourth first level V1(ii) a When the first other bad credit is recordedSetting the fourth data as a fourth second grade V when the recorded data is not empty2；
Wherein, from the fourth first level V1To a fourth second level V2The risk level is gradually improved;
here, the data format of the first other bad credit record data is a character type for recording other bad credit record information except overdue information of the object to be evaluated, such as a life facility expense arrearage record of water, electricity, gas, heating, etc., a communication expense arrearage record, other non-financial institution credit, an arrearage bad record, etc.;
if the first other bad credit record data are not null, the fact that the performance stability of the object to be evaluated is poor and the credit risk level is high is indicated;
in this step, if the first other bad credit record data is null, for example, 0, it indicates that the current social credit is good and the credit risk level is low; in this case, the fourth data, which is the rating data rated using the first other bad credit record data, is set to the fourth first rank V1；
If the first other bad credit record data are not null, the current social credit of the object to be evaluated is poor and the credit risk level is high; in this case, the fourth data is set to the fourth second level V2；
From the fourth first level V1To a fourth second level V2The risk level is gradually improved; fourth grade V1And a fourth second level V2May be incremental integer type data such as 1, 2, or character type data such as "primary", "secondary";
specifically, when the first data is the first level S1And the second data is the second first grade T1And the third data is the third first grade U1And the fourth data is a fourth first level V1Then, the second level data is set to credit oneStage B1(ii) a When the first data is the first second grade S2Or the second data is a second grade T2Or the third data is the third second grade U2And the fourth data is a fourth first level V1Then, the second level data is set as credit level B2(ii) a When the second data is the second third grade T3Or the third data is a third grade U3And the fourth data is a fourth first level V1Then, the second class data is set as credit class B3(ii) a When the second data is the second four-level T4Or the third data is a third four-level U4Or the fourth data is a fourth second level V2Then, the second level data is set as credit level B4。
Here, after completing the four sets of ratings of steps 311 to 314, the first, second, third and fourth data are combined and graded to obtain a final grading result, that is, second grade data;
the specific evaluation method is as follows:
when the first data is the first primary grade S1And the second data is the second first grade T1And the third data is the third first grade U1And the fourth data is a fourth first level V1When the evaluation result is, the final evaluation result is that the second-level data is set as the credit first-level B1；
When the first data is the first second grade S2Or the second data is a second grade T2Or the third data is the third second grade U2And the fourth data is a fourth first level V1When the evaluation result shows that the card number or the loan number of the person to be evaluated exceeds the preset threshold value, the low-risk credit card overdue or the low-risk loan overdue exists under the condition that no other bad credit records exist, at the moment, the pair to be evaluated isThe credit risk is controlled like social credit being good, but either the funding capacity rating is not good enough, or the debt repayment stability is flawed, or the performance stability is flawed, in which case the final assessment result, i.e. the second level data, will be set as credit level B2；
When the second data is the second third grade T3Or the third data is a third grade U3And the fourth data is a fourth first level V1When the evaluation result is the final evaluation result, namely the second-level data is set as the third-level B credit data, if no other bad credit records exist, either the credit card overdue or the loan overdue with the risk expansion possibility occurs, and at this time, the social credit of the evaluated object is good, but either the debt repayment stability or the performance stability is in the risk expansion trend, and the credit risk is large3；
When the second data is the second four-level T4Or the third data is a third four-level U4Or the fourth data is a fourth second level V2When the evaluation result is the final evaluation result, namely the second-class data is set as a credit fourth-class B, the evaluation result is the final credit fourth-class data4；
From credit level B1Credit level B2Credit level B3To credit level B4The risk levels rise sequentially; from credit level B1Credit level B2Credit level B3To credit level B4It may be an increasing integer type of data, such as 1, 2, 3, 4, or a character type of data, such as "first level", "second level", "third level", or fourth level ".
Step 4, analyzing and processing the personal risk grade according to the first grade data and the second grade data to generate first risk grade data;
wherein the first risk level data comprises low risk, general risk, next highest risk, and high risk;
the method specifically comprises the following steps: combining the first grade data and the second grade data, and performing grading evaluation according to a combination result to generate first risk grade data;
specifically, according to the first grade data and the second grade data, personal risk grade analysis is carried out; when the combination of the first grade data and the second grade data is A1B1Or A2B1Or A3B1When the first risk level data is set as a low risk; when the combination of the first grade data and the second grade data is A4B1Or A1B2Or A2B2Or A3B2Setting the first risk level data as a general risk; when the combination of the first grade data and the second grade data is A4B2Or A1B3Or A2B3Or A3B3Setting the first risk level data as a second highest risk; when the combination of the first grade data and the second grade data is A4B3Or A1B4Or A2B4Or A3B4Or A4B4The first risk level data is set to high risk.
In the step, the comprehensive analysis of the personal risk level is carried out by combining the first level data obtained by the personal background level evaluation processing and the second level data obtained by the personal credit level evaluation processing, and the final first risk level data is obtained; the adopted evaluation method is that the content of the corresponding first risk grade data is set according to the content of the combination of the first grade data and the second grade data, the corresponding relation is shown in table eight, the first risk grade data has four grades, and the four grades are sequentially from low to high: low risk, general risk, next highest risk, and high risk.
| Credit first level B1 | Credit level B2 | Credit level B3 | Credit level B4 | |
| Background level A1 | Low risk | General risks | Second highest risk | High risk |
| Background level two A2 | Low risk | General risks | Second highest risk | High risk |
| Background three levels A3 | Low risk | General risks | Second highest risk | High risk |
| Background four levels A4 | General risks | Second highest risk | High risk | High risk |
Table eight
Fig. 2 is a block diagram of a risk level data processing apparatus according to a second embodiment of the present invention, where the apparatus may be a terminal device or a server for implementing the method according to the second embodiment of the present invention, or an apparatus connected to the terminal device or the server for implementing the method according to the second embodiment of the present invention, and for example, the apparatus may be an apparatus or a chip system of the terminal device or the server. As shown in fig. 2, the apparatus includes:
the obtaining module 201 is used for obtaining a first personal context data set and a first personal credit data set.
The first evaluation processing module 202 is configured to perform personal context level evaluation processing according to the first personal context data set to generate first level data.
The second evaluation processing module 203 is used for carrying out personal credit rating evaluation processing according to the first personal credit data set to generate second rating data.
The comprehensive evaluation processing module 204 is configured to perform personal risk level analysis processing according to the first level data and the second level data, and generate first risk level data.
The risk level data processing apparatus provided in the embodiment of the present invention may execute the method steps in the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the determining module. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), etc. For another example, when some of the above modules are implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can invoke the program code. As another example, these modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, bluetooth, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), etc.
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. The electronic device may be the terminal device or the server, or may be a terminal device or a server connected to the terminal device or the server and implementing the method according to the embodiment of the present invention. As shown in fig. 3, the electronic device may include: a processor 31 (e.g., CPU), a memory 32, a transceiver 33; the transceiver 33 is coupled to the processor 31, and the processor 31 controls the transceiving operation of the transceiver 33. Various instructions may be stored in memory 32 for performing various processing functions and implementing the methods and processes provided in the above-described embodiments of the present invention. Preferably, the electronic device according to an embodiment of the present invention further includes: a power supply 34, a system bus 35, and a communication port 36. The system bus 35 is used to implement communication connections between the elements. The communication port 36 is used for connection communication between the electronic device and other peripherals.
The system bus mentioned in fig. 3 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM) and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
It should be noted that the embodiment of the present invention also provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method and the processing procedure provided in the above-mentioned embodiment.
The embodiment of the invention also provides a chip for running the instructions, and the chip is used for executing the method and the processing process provided by the embodiment.
Embodiments of the present invention also provide a program product, which includes a computer program stored in a storage medium, from which the computer program can be read by at least one processor, and the at least one processor executes the methods and processes provided in the embodiments.
Embodiments of the present invention provide a method and an apparatus for processing risk level data, an electronic device, a computer program product, and a computer-readable storage medium, which assist a supply chain financial service organization in processing intermediate and downstream enterprise owner data to obtain corresponding risk level data, so as to improve the work efficiency of the supply chain financial service organization and improve the business experience of enterprise customers.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for processing risk classification data, the method comprising:
acquiring a first personal context data set and a first personal credit data set;
according to the first personal background data set, carrying out personal background grade evaluation processing to generate first grade data;
performing personal credit rating evaluation processing according to the first personal credit data set to generate second rating data;
and analyzing and processing the personal risk grade according to the first grade data and the second grade data to generate first risk grade data.
2. The method of processing risk classification data according to claim 1,
the first class data comprises a background class A1Background level A2Background three-level A3And background level four A4(ii) a From the background to a1The background level A2The background is tertiary A3To said background level four A4The risk levels rise sequentially;
the second level data includes credit level B1Credit level B2Credit level B3And credit level B4(ii) a From the credit level B1The credit level B2The credit level B3To the credit level B4The risk levels rise sequentially;
the first risk level data includes low risk, general risk, next highest risk, and high risk.
3. The method for processing risk level data according to claim 2, wherein the performing of the personal context level evaluation processing according to the first personal context data set to generate the first level data specifically comprises:
according to the first personal background data set, grading processing related to conditions such as age, marital state, cultural degree, unit property, job title, monthly income, physical condition and the like is respectively carried out to obtain a plurality of corresponding grading data;
and counting the plurality of grading data, and carrying out grading evaluation on the counting result according to a plurality of grading intervals from low to high to generate the first grade data.
4. The method for processing risk level data according to claim 2, wherein the performing personal credit level assessment processing according to the first set of personal credit data to generate second level data specifically comprises:
according to the first personal credit data set, rating processing related to the number of credit cards and the number of loan institutions, the current overdue amount of the credit cards and the historical overdue times of the credit cards, the current overdue amount of the loans and the historical overdue times of the loans and other bad credit records is respectively carried out to obtain a plurality of corresponding rating data;
and combining the plurality of rating data, and performing grading evaluation according to a combined result to generate the second rating data.
5. The method for processing risk classification data according to claim 2, wherein the performing of the individual risk classification analysis processing according to the first classification data and the second classification data to generate first risk classification data specifically includes:
and combining the first grade data and the second grade data, and performing grading evaluation according to a combination result to generate the first risk grade data.
6. An apparatus for processing risk classification data, comprising:
the acquisition module is used for acquiring a first personal context data set and a first personal credit data set;
the first evaluation processing module is used for carrying out personal background grade evaluation processing according to the first personal background data set to generate first grade data;
the second evaluation processing module is used for carrying out personal credit grade evaluation processing according to the first personal credit data set to generate second grade data;
and the comprehensive evaluation processing module is used for analyzing and processing the personal risk level according to the first level data and the second level data to generate first risk level data.
7. An electronic device, comprising: a memory, a processor, and a transceiver;
the processor is used for being coupled with the memory, reading and executing the instructions in the memory to realize the method steps of any one of the claims 1-5;
the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.
8. A computer program product, characterized in that the computer program product comprises computer program code which, when executed by a computer, causes the computer to perform the method of any of claims 1-5.
9. A computer-readable storage medium having stored thereon computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-5.
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