CN112766716A - Credit scoring method considering compatibility of various service applications - Google Patents
Credit scoring method considering compatibility of various service applications Download PDFInfo
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- CN112766716A CN112766716A CN202110057059.4A CN202110057059A CN112766716A CN 112766716 A CN112766716 A CN 112766716A CN 202110057059 A CN202110057059 A CN 202110057059A CN 112766716 A CN112766716 A CN 112766716A
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Abstract
The invention relates to the technical field of logistics industry credit evaluation, in particular to a credit scoring method considering compatibility with various service applications, which comprises the steps of configuring scoring indexes, calculating weight values and calculating score values of main bodies, carrying out corresponding scoring according to a configured scoring index algorithm, selecting indexes of the main bodies for credit from the configured scoring indexes according to real service characteristics of application scenes, appointing the sequence and the importance degree ratio of the selected indexes, and processing corresponding index values one by one according to the appointment of the indexes by using data in all credit main body files. The credit scoring method considering the compatibility of various business applications replaces manual assessment through system calculation, improves the data processing efficiency, can be reused by different business scenes, enhances the expandability of indexes, breaks through the problems that assessment standards of road transportation practitioners are simplified, assessment is easy to be subjectively intervened by assessment committees, and meets the market demands that the credit conditions of related practitioners are known in different business situations in a road transportation ecological chain.
Description
Technical Field
The invention relates to the technical field of credit assessment in the logistics industry, in particular to a credit scoring method considering compatibility with various service applications.
Background
In the road transportation industry, the existing credit information system usually scores according to various assessment indexes by manpower to assess the quality credit condition of a road passenger enterprise, score control is adopted for integrity assessment of road transportation practitioners, and a road transportation management mechanism deducts corresponding scores according to score standards on violation behaviors of road transportation drivers; on the other hand, the credit evaluation process has low efficiency, the number of manually-evaluated objects in unit time is limited, nearly ten million logistics practitioners exist in the logistics transportation industry, and if the credit evaluation completely depends on the manual judgment of the evaluation personnel, not only all the logistics practitioners cannot be completely covered, but also a large amount of manpower and material resources are wasted; in addition, the credit evaluation and assessment standard is single, the number is limited, the expandability is poor, the credit evaluation and assessment method is only limited in the aspect of road transportation safety and is not suitable for the attention requirements of other social fields on the credit of road transportation practitioners, therefore, the credit evaluation method which is compatible with various service applications is designed, the credit evaluation method is urgently needed in the technical field of credit evaluation of logistics industries, and the credit evaluation method is also significant for the construction, innovation and innovation of credit systems in various industries.
Disclosure of Invention
The invention provides a credit scoring method considering compatibility with various service applications, which aims to solve the problems in the prior art.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
according to the embodiment of the invention, a credit scoring method considering compatibility with various service applications comprises configuring scoring indexes, calculating weight values and calculating the scoring values of a main body, wherein the configured scoring indexes comprise the age, the driving age, the marital condition, whether to refund soldiers, legal disputes, credit conditions, guarantee conditions, abnormal transportation administration information, traffic liability accidents, traffic violations, overrun overload records, late stage rate, tracking equipment positioning abnormity, abnormal stop times, shipping accuracy, goods damage and goods difference rate, average insurance line, platform settlement rate, accident compensation rate, complained rate, platform total mileage, platform monthly mileage, platform total amount of orders, platform monthly amount of orders, platform consumption amount, historical use platform financial service conditions, transportation process satisfaction, service attitude satisfaction, customer comprehensive degree and customer evaluation total number, carrying out corresponding scoring according to a configured scoring index algorithm, selecting indexes of a credit main body from the configured scoring indexes according to real service characteristics of an application scene, appointing the sequence and importance degree ratio of the selected indexes, and processing corresponding index values one by one according to the appointment of the indexes by using data in all credit main body files;
the process of calculating the weight value is as follows:
according to the reverse order, processing the last index to the first index one by one, accumulating the numerical value corresponding to the ratio of the importance degrees of each index by using a direct product solving algorithm, and calculating the direct product solving result corresponding to the index:
wherein i is the ranking number of the index, r (i) is the value corresponding to the ratio of the importance degrees of the corresponding indexes, and after the direct product calculation result of each index is calculated, the direct product calculation results of the indexes from head to tail are accumulated by using a summation algorithm:
after the accumulated result is obtained, 1 is added to the result to obtain the reciprocal, and the reciprocal is rounded and reserved to 3 bits after the decimal point, and the value is the weight value of the last index:
w (m) represents the weight value of the index with the rank number m, and the weight value of the index one bit before the last index is obtained one by using the value corresponding to the ratio of the weight value of the last index multiplied by the importance degree, namely the weight value of the index one bit after the last index until the weight value of the first index is obtained:
w(j-1)＝r(j)*w(i)
wherein, the weight value of the last index is equal to the value corresponding to the ratio of the importance degree of the last index multiplied by the weight of the index;
the process of calculating the score value of the subject is as follows:
calculating the scoring value of the main body according to the weighted value obtained in the process and the scoring index corresponding to the real scene, and calculating the final result:
x＝(x1f1+x2f2+x3f3+...+xkfk)p
wherein x represents the index value of the index, f represents the weight value of the index, k represents the total number of the index in the application scene, p represents the result calibration coefficient, when the p value is 1000, the result is rounded to an integer value which is more than 0 and less than 1000, the steps are repeated to calculate the score value of the next main body until the data of all the main bodies are processed, the main bodies are graded according to the score value of the calculated main bodies to distinguish the credit conditions of different main bodies, the credit grades can be divided into five grades of AAAA, AAA, AA and A from high to low, the grade difference between the credit grades is determined according to the data, the evaluation grade divisions of different credit grades are determined to the left and the right by taking the dividing point between AAA and AA as the center, the evaluation credit grade is determined, wherein the determination of the grade difference and the determination between the evaluation grade divisions are included, and Basis is represented as the grade difference, max (x)i)、min(xi) Respectively representing the maximum value and the minimum value of the evaluation score, and then the calculation formula of the grade difference Basis is as follows:
the distribution of the evaluation samples in the grade interval is subject to bell-shaped distribution, and is generally evaluated as the most AAAA grade and AAA grade, so that the evaluation score mean value is selected as the critical point of the AAAA grade and the AAA grade, about 50% of the evaluation samples can be guaranteed to be gathered near the AAAA grade and the AAA grade according to the bell-shaped image characteristics of high middle and low two ends of the bell-shaped distribution, the phenomenon that most samples are gathered near the AAAAA grade and the A grade and unreasonable phenomenon can be avoided, and the grade of pulling the credit grades of different credit subjects is guaranteed.
Further, the level difference is the difference between the evaluation scores of each adjacent levels and is determined according to two extreme values of the maximum value and the minimum value of the sample.
The invention has the following advantages:
the credit scoring method considering the compatibility of various service applications replaces manual assessment through system calculation, improves the data processing efficiency, in addition, indexes can be reused by different scenes, enhances the expandability of the indexes, breaks through the simplification of assessment standards of road transportation practitioners, and solves the problem that assessment is easy to be subjectively intervened by appraisers, meets the market demand on credit conditions of related practitioners in a road transportation ecological chain, simultaneously aims at different service characteristics, the indexes, scenes and applicable bodies required in the assessment process can be adjusted according to credit scoring actual application services, selects corresponding indexes for credit bodies, sets the sequence between the indexes and adjusts the ratio of importance degrees according to the difference between different service fields, the process of calculating the weighted values avoids the defect of manually defining the weighted values, provides the scientificity of assessment results, and the process of calculating the weighted values is processed by the system, the moral risk of manual assessment is avoided, and the processing efficiency is improved.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. 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.
In the present specification, the terms "upper", "lower", "left", "right", "middle", and the like are used for clarity of description, and are not intended to limit the scope of the present invention, and changes or modifications in the relative relationship may be made without substantial changes in the technical content.
The invention provides a technical scheme that:
a credit scoring method considering compatibility with multiple service applications comprises configuring scoring indexes, calculating weight values and calculating scoring values of a main body, wherein the configured scoring indexes comprise the age, the driving age, the marital condition, whether or not to return military personnel, legal disputes, credit conditions, guarantee conditions, abnormal operation information, traffic liability accidents, traffic violations, overrun overload records, late stage rate, tracking equipment positioning abnormity, abnormal stop times, freight accuracy rate, freight loss rate, average insurance line, platform processing odds, accident odds rates, complaint rates, platform total mileage, platform total amount of orders, platform month amount of orders, platform consumption amount, historical platform financial service conditions, transportation process satisfaction, service attitude satisfaction, customer comprehensive satisfaction and customer evaluation total number of a driver, corresponding scoring is carried out according to a configured scoring index algorithm, selecting the indexes of the credit main body from the configured scoring indexes according to the real service characteristics of the application scene, appointing the sequence and the importance degree ratio between the selected indexes, and processing corresponding index values one by one according to the appointment of the indexes by using data in all credit main body files;
the process of calculating the weight value is as follows:
according to the reverse order, processing the last index to the first index one by one, accumulating the numerical value corresponding to the ratio of the importance degrees of each index by using a direct product solving algorithm, and calculating the direct product solving result corresponding to the index:
wherein i is the ranking number of the index, r (i) is the value corresponding to the ratio of the importance degrees of the corresponding indexes, and after the direct product calculation result of each index is calculated, the direct product calculation results of the indexes from head to tail are accumulated by using a summation algorithm:
after the accumulated result is obtained, 1 is added to the result to obtain the reciprocal, and the reciprocal is rounded and reserved to 3 bits after the decimal point, and the value is the weight value of the last index:
w (m) represents the weight value of the index with the rank number m, and the weight value of the index one bit before the last index is obtained one by using the value corresponding to the ratio of the weight value of the last index multiplied by the importance degree, namely the weight value of the index one bit after the last index until the weight value of the first index is obtained:
w(j-1)＝r(j)*w(i)
wherein, the weight value of the last index is equal to the value corresponding to the ratio of the importance degree of the last index multiplied by the weight of the index;
the process of calculating the score value of the subject is as follows:
calculating the scoring value of the main body according to the weighted value obtained in the process and the scoring index corresponding to the real scene, and calculating the final result:
x＝(x1f1+x2f2+x3f3+...+xkfk)p
wherein x represents the index value of the index, f represents the weight value of the index, k represents the total number of the indexes in the application scene, p represents the result calibration coefficient, when the value of p is 1000, the result is rounded to obtain an integer value which is more than 0 and less than 1000, the steps are repeated to calculate the score value of the next main body until the data of all the main bodies are processed, and the data of all the main bodies are calculated according to the score valueCalculating the scoring value of the main body to grade the credit of the main body, so as to distinguish the credit conditions of different main bodies, dividing the credit rating into AAAA, AAA, AA and A five grades from high to low, determining the grade difference between the credit ratings according to the data, determining the evaluation scoring intervals of different credit ratings towards the left and the right by taking the dividing point between the AAA and the AA as the center, determining the credit rating of the evaluation, wherein the determination of the grade difference and the determination of the evaluation scoring intervals are included, and setting Basis to represent the grade difference, max (x)i)、min(xi) Respectively representing the maximum value and the minimum value of the evaluation score, and then the calculation formula of the grade difference Basis is as follows:
the distribution of the evaluation samples in the grade interval is subject to bell-shaped distribution, and is generally evaluated as the most AAAA grade and AAA grade, so that the evaluation score mean value is selected as the critical point of the AAAA grade and the AAA grade, about 50% of the evaluation samples can be guaranteed to be gathered near the AAAA grade and the AAA grade according to the bell-shaped image characteristics of high middle and low two ends of the bell-shaped distribution, the phenomenon that most samples are gathered near the AAAAA grade and the A grade and unreasonable phenomenon can be avoided, and the grade of pulling the credit grades of different credit subjects is guaranteed.
In the invention: the level difference is the difference of the evaluation scores between each two adjacent levels and is determined according to two extreme values of the maximum value and the minimum value of the sample.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (2)
1. A credit scoring method considering compatibility with various service applications comprises the steps of configuring scoring indexes, calculating weight values and calculating score values of a main body, and is characterized in that: the configured scoring indexes comprise the age, the driving age, the marital condition, whether to refund soldiers, legal disputes, credit conditions, guarantee conditions, abnormal transportation administration information, traffic liability accidents, traffic violations, overrun overload records, late stage rate, abnormal tracking equipment positioning, abnormal stop times, freight accuracy, goods damage and goods difference rate, average insurance application limit, platform processing claim rate, accident claim rate, complaint rate, total platform mileage, total platform amount, platform consumption amount, platform financial service conditions, transportation process satisfaction, service attitude satisfaction, comprehensive customer satisfaction and customer evaluation total number of a driver, corresponding scoring is carried out according to a configured scoring index algorithm, and the indexes of a credit subject are selected from the configured scoring indexes according to the real business characteristics of an application scene, the sequence and the importance degree ratio of the selected indexes are agreed, and corresponding index values are processed one by one according to the agreement of the indexes by using data in all credit subject files;
the process of calculating the weight value is as follows:
according to the reverse order, processing the last index to the first index one by one, accumulating the numerical value corresponding to the ratio of the importance degrees of each index by using a direct product solving algorithm, and calculating the direct product solving result corresponding to the index:
wherein i is the ranking number of the index, r (i) is the value corresponding to the ratio of the importance degrees of the corresponding indexes, and after the direct product calculation result of each index is calculated, the direct product calculation results of the indexes from head to tail are accumulated by using a summation algorithm:
after the accumulated result is obtained, 1 is added to the result to obtain the reciprocal, and the reciprocal is rounded and reserved to 3 bits after the decimal point, and the value is the weight value of the last index:
w (m) represents the weight value of the index with the rank number m, and the weight value of the index one bit before the last index is obtained one by using the value corresponding to the ratio of the weight value of the last index multiplied by the importance degree, namely the weight value of the index one bit after the last index until the weight value of the first index is obtained:
w(j-1)＝r(j)*w(j)
wherein, the weight value of the last index is equal to the value corresponding to the ratio of the importance degree of the last index multiplied by the weight of the index;
the process of calculating the score value of the subject is as follows:
calculating the scoring value of the main body according to the weighted value obtained in the process and the scoring index corresponding to the real scene, and calculating the final result:
x＝(x1f1+x2f2+x3f3+...+xkfk)p
wherein x represents the index value of the index, f represents the weight value of the index, k represents the total number of the index in the application scene, p represents the result calibration coefficient, when the p value is 1000, the result is rounded to an integer value which is more than 0 and less than 1000, the steps are repeated to calculate the score value of the next main body until the data of all the main bodies are processed, the main bodies are graded according to the score value of the calculated main bodies to distinguish the credit conditions of different main bodies, the credit grades can be divided into five grades of AAAA, AAA, AA and A from high to low, the grade difference between the credit grades is determined according to the data, the evaluation grade divisions of different credit grades are determined to the left and the right by taking the dividing point between AAA and AA as the center, the evaluation credit grade is determined, wherein the determination of the grade difference and the determination between the evaluation grade divisions are included, and Basis is represented as the grade difference, max (x)i)、min(xi) Respectively representing the maximum value and the minimum value of the evaluation score, and then the calculation formula of the grade difference Basis is as follows:
the distribution of the evaluation samples in the grade interval is subject to bell-shaped distribution, and is generally evaluated as the most AAAA grade and AAA grade, so that the evaluation score mean value is selected as the critical point of the AAAA grade and the AAA grade, about 50% of the evaluation samples can be guaranteed to be gathered near the AAAA grade and the AAA grade according to the bell-shaped image characteristics of high middle and low two ends of the bell-shaped distribution, the phenomenon that most samples are gathered near the AAAAA grade and the A grade and unreasonable phenomenon can be avoided, and the grade of pulling the credit grades of different credit subjects is guaranteed.
2. The credit scoring method in consideration of compatibility with multiple service applications according to claim 1, wherein: the level difference is the difference of the evaluation scores between each two adjacent levels and is determined according to two extreme values of the maximum value and the minimum value of the sample.
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| CN114021998A (en) * | 2021-11-09 | 2022-02-08 | 中交智运有限公司 | Method and system for evaluating integrity status of network freight operation enterprise |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114021998A (en) * | 2021-11-09 | 2022-02-08 | 中交智运有限公司 | Method and system for evaluating integrity status of network freight operation enterprise |
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