CN114240609A - Credit decision method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a trust decision method, a trust decision device, a computer device, a storage medium and a computer program product. The method comprises the following steps: the method comprises the steps of obtaining customer information of a customer to be trusted, and determining a target customer group to which the customer to be trusted belongs based on the customer information; determining a target credit granting scheme corresponding to a target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model; acquiring credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on a credit granting index calculation model; and determining the credit line of the target credit scheme according to the credit index. By adopting the method of the embodiment of the application, the centralized and unified management of the clients to be credited can be realized, the determination efficiency of the crediting scheme is effectively improved, the accuracy of the credit line can be improved, and the credit decision efficiency is improved.
Description
Technical Field
The present application relates to the field of big data analysis technology, and in particular, to a trust decision method, apparatus, computer device, storage medium, and computer program product.
Background
With the rapid development of small and micro enterprises, the status and the function of the small and micro enterprises in the national economy and social development are increasingly enhanced, and the small and micro enterprises have the important functions of expanding employment, increasing income, improving the livelihood, promoting stability, national tax, market economy and the like. The financial institution reasonably provides short-term loan service for production, operation and turnover for high-quality small and micro enterprises, can promote the healthy development of the small and micro enterprises, is favorable for the development of self service, and realizes win-win.
At present, financial institutions can provide customized credit granting services aiming at different small and micro enterprises and different types of loan services, but because the small and micro enterprises have the characteristics of being multiple, disordered and miscellaneous, and along with the increasing of loan services, the financial institutions face the problems of complex loan service operation, difficult loan management and the like, so that the credit granting decision efficiency of the financial institutions is low.
Disclosure of Invention
In view of the above, it is desirable to provide a credit decision method, device, computer readable storage medium and computer program product capable of providing credit decision efficiency.
A trust decision method, the method comprising:
the method comprises the steps of obtaining customer information of a customer to be trusted, and determining a target customer group to which the customer to be trusted belongs based on the customer information;
determining a target credit granting scheme corresponding to the target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model;
obtaining credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on the credit granting index calculation model;
and determining the credit line of the target credit scheme according to the credit index.
In one embodiment, the obtaining the customer information of the customer to be trusted includes: obtaining sub-dimension information under each comprehensive dimension to which a client to be trusted belongs, wherein each comprehensive dimension comprises a Puhui dimension, a clustering dimension, and an attribution industry and an industry field;
the determining the target customer group to which the customer to be trusted belongs based on the customer information comprises: and synthesizing the sub-dimension information under each comprehensive dimension to which the customer to be trusted belongs, and determining a target customer group to which the customer to be trusted belongs.
In one embodiment, the determining a target trust scheme corresponding to the target guest group includes:
and searching and obtaining a target credit granting scheme corresponding to the target passenger group according to the stored corresponding relation between the passenger group and a preset credit granting scheme corresponding to the passenger group.
In one embodiment, the credit data types include composite data and risk data, the composite data includes at least two of asset data, business data, liability data and credit data, and the risk data includes expert scoring data, homed industry information and growth trend data.
In one embodiment, the calculating to obtain the credit granting indicator based on the credit granting indicator calculation model includes: performing data processing on the comprehensive data, and calculating comprehensive data indexes of the comprehensive data, wherein the data processing comprises at least one of data cleaning, data conversion and data integration, and the credit granting indexes comprise the comprehensive data indexes;
the method for calculating and obtaining the credit granting index based on the credit granting index calculation model comprises the following steps: and determining a risk index of the risk data corresponding to the risk type by adopting a judgment mode corresponding to the risk type according to the risk type, wherein the credit index comprises the risk index.
In one embodiment, the determining, according to a risk type and in a determination manner corresponding to the risk type, a risk indicator of the risk data corresponding to the risk type includes:
determining an attribution industry index of the attribution industry information according to the attribution industry information and a preset attribution industry index value corresponding to a preset attribution industry, wherein the risk index comprises the attribution industry index;
determining an expert scoring interval to which the expert scoring data belongs, and determining an expert scoring index corresponding to the expert scoring data according to a corresponding relation between a preset expert scoring interval and a preset expert scoring index value, wherein the risk index comprises the expert scoring index;
and determining a growth trend interval to which the growth trend data belongs, and determining a growth trend index corresponding to the growth trend data according to a corresponding relation between a preset growth trend interval and a preset growth trend index value, wherein the risk index comprises the growth trend index.
In one embodiment, after the determining, according to the credit granting index, a credit granting amount of the target credit granting scheme, the method further includes:
determining the maximum credit line corresponding to the target passenger group to which the customer to be credited belongs;
and if the credit line of the credit scheme is larger than the maximum credit line, determining the maximum credit line as the credit line of the client to be trusted.
A trust decision apparatus, the apparatus comprising:
the system comprises a client group determining module, a client group determining module and a client group determining module, wherein the client group determining module is used for acquiring client information of a client to be trusted and determining a target client group to which the client to be trusted belongs based on the client information;
the credit granting scheme determining module is used for determining a target credit granting scheme corresponding to the target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model;
the credit granting index calculation module is used for acquiring credit granting data required by the type of the credit granting data of the target credit granting scheme, and calculating to obtain a credit granting index based on the credit granting index calculation model;
and the credit line determining module is used for determining the credit line of the target credit scheme according to the credit index.
In one embodiment, the guest group determining module includes:
the dimension information acquisition unit is used for acquiring sub-dimension information under each comprehensive dimension to which a client to be trusted belongs, and each comprehensive dimension comprises a Hewlett packard dimension, a clustering dimension, and an attribution industry and an industry field;
and the target customer group determining unit is used for integrating the sub-dimension information under each integrated dimension to which the customer to be trusted belongs and determining the target customer group to which the customer to be trusted belongs.
In one embodiment, the trust scheme determining module is further configured to search for and obtain a target trust scheme corresponding to the target guest group according to a stored correspondence between the guest group and a preset trust scheme corresponding to the guest group.
In one embodiment, the credit granting scheme determining module is further configured to determine that the credit granting data type includes comprehensive data and risk data, the comprehensive data includes at least two of asset data, business data, liability data and credit data, and the risk data includes expert rating data, attribution industry information and growth trend data.
In one embodiment, the trust indicator calculating module includes:
the comprehensive data index calculation unit is used for performing data processing on the comprehensive data and calculating comprehensive data indexes of the comprehensive data, the data processing comprises at least one of data cleaning, data conversion and data integration, and the credit granting indexes comprise the comprehensive data indexes;
and the risk index calculation unit is used for determining a risk index of the risk data corresponding to the risk type by adopting a judgment mode corresponding to the risk type according to the risk type, and the credit granting index comprises the risk index.
In one embodiment, the risk indicator calculation unit is further configured to determine a home industry indicator of the home industry information according to the home industry information and a preset home industry indicator value corresponding to a preset home industry, where the risk indicator includes the home industry indicator;
the risk index calculation unit is further configured to determine an expert scoring interval to which the expert scoring data belongs, and determine an expert scoring index corresponding to the expert scoring data according to a corresponding relationship between a preset expert scoring interval and a preset expert scoring index value, where the risk index includes the expert scoring index;
the risk index calculation unit is further configured to determine a growth trend interval to which the growth trend data belongs, and determine a growth trend index corresponding to the growth trend data according to a correspondence between a preset growth trend interval and a preset growth trend index value, where the risk index includes the growth trend index.
In one embodiment, the apparatus further comprises:
the credit line adjusting unit is used for determining the maximum credit line corresponding to the target passenger group to which the client to be trusted belongs; and if the credit line of the credit scheme is larger than the maximum credit line, determining the maximum credit line as the credit line of the client to be trusted.
A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the trust decision method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned trust decision method.
A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the above-mentioned trust decision method.
According to the credit granting decision method, the device, the computer equipment, the storage medium and the computer program product, the client information of the client to be granted is obtained, and the target client group to which the client to be granted belongs is determined based on the client information; determining a target credit granting scheme corresponding to a target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model; acquiring credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on a credit granting index calculation model; and determining the credit line of the target credit scheme according to the credit index. By adopting the method of the embodiment, the clients to be trusted are divided into the predetermined client groups according to the client information of the clients to be trusted, the corresponding trust schemes can be determined directly according to the client groups to which the clients to be trusted belong, the centralized and unified management of the clients to be trusted can be realized, the determination efficiency of the trust schemes is effectively improved, the trust indexes corresponding to the target trust schemes of the clients to be trusted are obtained through calculation, the corresponding credit limits are determined according to the trust indexes, the accuracy of the credit limits can be improved, and the trust decision efficiency is improved.
Drawings
FIG. 1 is a diagram of an application environment of a trust decision method in one embodiment;
FIG. 2 is a flow diagram illustrating a trust decision method according to an embodiment;
FIG. 3 is a flow diagram illustrating a trust decision method in an exemplary embodiment;
FIG. 4 is a block diagram of a credit decision device according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, in the trust decision method provided by the embodiment of the present application, an application environment may relate to both the terminal 102 and the server 104, as shown in fig. 1. Wherein the terminal 102 may communicate with the server 104 over a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. Specifically, a client to be trusted inputs client information in the terminal 102, the server 104 acquires the client information of the client to be trusted through the terminal 102, and determines a target guest group to which the client to be trusted belongs based on the client information; determining a target credit granting scheme corresponding to a target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model; acquiring credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on a credit granting index calculation model; and determining the credit line of the target credit scheme according to the credit index.
In one embodiment, the trust decision method provided in the embodiments of the present application may be applied only to the terminal 102 or the server 104. Specifically, the terminal 102 or the server 104 may directly obtain the customer information of the customer to be trusted, and determine the target customer group to which the customer to be trusted belongs based on the customer information; determining a target credit granting scheme corresponding to a target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model; acquiring credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on a credit granting index calculation model; and determining the credit line of the target credit scheme according to the credit index.
The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, a trust decision method is provided, which is described by taking the method as an example applied to the terminal 102 and/or the server 104 in fig. 1, and includes:
step S202, customer information of the customer to be trusted is obtained, and a target customer group to which the customer to be trusted belongs is determined based on the customer information.
In one embodiment, the credit is the fund directly provided by the financial institution to the non-financial institution customer or the guarantee of the compensation and payment responsibility possibly generated by the customer in the related economic activity, including the in-table business of loan, trade financing, bill financing, financing lease, overdraft, various payment charges and the like, and the out-table business of bill acceptance, credit issuing guarantee, insurance letter, reserve credit, credit guarantee, bond issuing guarantee, loan guarantee, asset sale with recourse right, unused irrevocable promise and the like. The credit authorization according to the embodiment of the present application mainly refers to a loan transaction, but is not limited to a loan transaction, the financial institution mainly refers to a commercial bank, and the non-financial institution mainly refers to a small enterprise, but is not limited to a small enterprise.
In one embodiment, the small micro-enterprise is a general term of small enterprises, micro-enterprises and the like, and the small micro-enterprise has the characteristics of being multiple, messy and miscellaneous. In order to improve the credit granting decision efficiency of the financial institution for granting credit to the small micro-enterprise, the small micro-enterprises with common enterprise characteristics are divided into the same customer group in advance according to the enterprise characteristics of the small micro-enterprise, the customer group is called as a customer group for short, that is, a plurality of small micro-enterprises are divided into a plurality of customer groups in advance. The small micro-enterprise requiring the credit of the financial institution is called a client to be credited, and the client group to which the client to be credited belongs is called a target client group. The embodiment of the application can customize different credit granting schemes for different customer groups, realize differentiated management and enhance credit granting flexibility and expandability. In addition, a plurality of small micro-enterprises in the same passenger group can share the same credit granting scheme, so that the credit granting decision efficiency can be effectively improved, and the cost is reduced.
In one embodiment, after determining the customer to be trusted, it is necessary to determine a target customer group to which the customer to be trusted belongs. The target customer group to which the customer to be trusted belongs can be directly selected and determined by the customer to be trusted. For example, a client to be trusted transacts a related credit service through a terminal or a server, the terminal or the server includes a display area, the display area includes a plurality of preset guest groups, and the client to be trusted can select a target guest group through various touch control modes by touch control. Specifically, a target guest group to which the customer to be trusted belongs is determined according to a target guest group selection instruction in response to the target guest group selection instruction of the customer to be trusted.
In one embodiment, the target customer group to which the customer to be trusted belongs may also be determined according to customer information of the customer to be trusted. Specifically, customer information of a customer to be trusted is obtained, and a target customer group to which the customer to be trusted belongs is determined based on the customer information. In order to improve the accuracy of determining a target customer group, a more accurate credit granting scheme is obtained, the sub-dimension information of each comprehensive dimension to which a customer to be credited belongs is obtained, the sub-dimension information of each comprehensive dimension to which the customer to be credited belongs is integrated, and the target customer group to which the customer to be credited belongs is determined.
Specifically, in order to improve the target customer group recognition degree of the small and micro enterprise, each comprehensive dimension is preset. Each composite dimension includes, but is not limited to, a Hewlett packard dimension, a clustering dimension, a home industry and an industry domain. The sub-dimensions under the Hewlett packard dimension include poverty relief, scientific innovation, ministry and agriculture involvement. Sub-dimensions under a cluster dimension include business, government, data driven, internal and other clusters. Sub-dimensions under the home industry include the scientific and technological service industry, the strategic emerging industry, the agriculture and related industries, the health industry, the aging industry, the tourism and related industries, the productive service industry and the living service industry. Sub-dimensions in the industry field include the lodging catering industry, the manufacturing industry, the construction industry, the real estate industry, the education industry, the financial industry, the internet industry and the mining industry. For example, the obtained sub-dimension information of each comprehensive dimension to which the client to be trusted belongs is: and if the sub-dimension information in the popular dimension comprises scientific information, the sub-dimension information in the clustering dimension comprises government clustering information, the sub-dimension information in the attribution industry comprises scientific and technological service industry information, and the sub-dimension information in the industry field is education industry information, determining that the target customer group to which the customer to be trusted belongs is a target customer group corresponding to 'scientific and creative + government clustering + scientific and technological service industry + education industry'.
And step S204, determining a target credit granting scheme corresponding to the target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model.
In one embodiment, in order to improve credit granting decision efficiency, a credit granting scheme corresponding to a guest group is determined in advance according to common characteristics of small micro-enterprises in the guest group, and the predetermined credit granting scheme is called a preset credit granting scheme. After the preset credit granting scheme corresponding to the passenger group is determined, the passenger group and the preset credit granting scheme corresponding to the passenger group are correspondingly stored, so that the preset credit granting scheme corresponding to the passenger group can be directly determined in the following process. After a target guest group to which a guest to be trusted belongs is determined, a trust scheme of the guest to be trusted is determined, and the trust scheme corresponding to the target guest group is called a target trust scheme. Specifically, a target credit granting scheme corresponding to the target customer group is searched and obtained according to the stored correspondence between the customer group and a preset credit granting scheme corresponding to the customer group. The credit granting scheme can be comprehensively formulated according to the aspects of a risk slow-release mode, single household quota, security range, loan interest rate bottom line, limit longest term, loan longest term, examination and approval mode, limit circulation, repayment mode, repayment frequency, contract signing on line, contract selection and the like.
In one embodiment, different trust indexes included in the trust schemes of different clients to be trusted are different, the different trust indexes include different types of the trust indexes, the different types of the trust indexes are different, that is, the corresponding computing modes of the trust indexes are different, and the types of the trust data required for computing the trust indexes are different. The calculation mode of the corresponding credit granting index determined in advance according to the type of the credit granting index is called a credit granting index calculation model. Specifically, the target credit scheme includes a credit data type and a credit index calculation model, so as to further calculate and determine a credit limit in the target credit scheme of the client to be credited.
And step S206, acquiring the credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on a credit granting index calculation model.
In one embodiment, the credit line in the target credit scheme of the client to be credited needs to be determined comprehensively according to the related credit data of the client to be credited, so as to ensure that the client to be credited can make a payment smoothly in the later period, so as to avoid affecting the credit of the client to be credited. Specifically, the credit granting data required by the credit granting data type of the target credit granting scheme is obtained. The credit data types comprise comprehensive data and risk data, the comprehensive data comprises at least two of asset data, business data, liability data and credit data, and the risk data comprises expert scoring data, attribution industry information and growth trend data. Specifically, the expert scoring data refers to the scores of expert scoring cards, and the expert scoring cards are qualitative description quantification methods. The growth trend data is growth trend data within a preset time length, wherein the preset time length can be set to be approximately 3 to 12 months, and is specifically determined in advance according to a target customer group to which a customer to be trusted belongs, for example, there are some growth trend data corresponding to the target customer group which is growth trend data within approximately 6 months, and there are some growth trend data corresponding to the target customer group which is growth trend data within approximately 12 months, and the determination can be selected according to actual conditions.
In one embodiment, different credit schemes all include comprehensive data and risk data, but the types of the credit data included in the different credit schemes are not identical. Specifically, the types of risk data in different credit schemes are expert rating data, attribution industry information and growth trend data, while the types of comprehensive data in different credit schemes may be different, for example, the types of comprehensive data in a target credit scheme of some clients to be credited are asset data and business data, the types of comprehensive data in a target credit scheme of some clients to be credited are asset data and liability data, and the types of comprehensive data need to be predetermined according to a target customer group to which the clients to be credited belong.
In one embodiment, the credit granting data of different credit granting data types have different calculation modes of corresponding credit granting indexes. Specifically, the credit granting index is calculated and obtained based on the credit granting index calculation model. The credit granting indexes comprise comprehensive data indexes and risk indexes, and the comprehensive data indexes and the risk indexes respectively comprise more than two. The comprehensive data index is obtained by calculation according to the comprehensive data, and the risk index is obtained by calculation according to the risk data. Specifically, the comprehensive data is subjected to data processing, comprehensive data indexes of the comprehensive data are calculated, and the data processing comprises at least one of data cleaning, data conversion and data integration. The data cleaning comprises checking data consistency, processing invalid values, missing values and the like, and the data conversion comprises data smoothing processing, data aggregation processing, data generalization processing, data normalization processing, data attribute construction processing and the like. Data integration refers to a way of centralizing data from different sources and formats. Specifically, the calculated composite data index may be represented as x.
In one embodiment, according to the risk type, a judgment mode corresponding to the risk type is adopted to determine a risk index of the risk data corresponding to the risk type, and the risk index is represented as R. The risk data comprises expert scoring data, attribution industry information and growth trend data, namely the risk indexes comprise expert scoring indexes, attribution industry indexes and growth trend indexes, the expert scoring indexes are represented as R1, the attribution industry indexes are represented as R2, and the growth trend indexes are represented as R3. The risk indicator may also be referred to as a risk factor, and the specific value of the risk indicator is generally between 0 and 1.
In one embodiment, when the risk data is home industry information, a home industry index of the home industry information is determined according to the home industry information and a preset home industry index value corresponding to a preset home industry. The home industry index value corresponding to the home industry is preset, for example, the home industry index value corresponding to the internet industry is preset to 1.0. After the home industry is determined, the corresponding home industry index value can be determined.
In one embodiment, when the risk data is expert score data, an expert score interval to which the expert score data belongs is determined, and an expert score index corresponding to the expert score data is determined according to a corresponding relation between a preset expert score interval and a preset expert score index value. The expert score index value corresponding to the expert score interval is preset, for example, the expert score interval is 500 to 600, and the corresponding expert score index value is preset to 0.9. After the expert scoring interval is determined, the corresponding expert scoring index can be determined.
In one embodiment, when the risk data is growth trend data, a growth trend interval to which the growth trend data belongs is determined, and a growth trend index corresponding to the growth trend data is determined according to a corresponding relationship between a preset growth trend interval and a preset growth trend index value. Wherein, the corresponding growth trend index value of the growth trend interval is preset, for example, the growth trend interval in the last 6 months is 30% to 60%, and the corresponding growth trend index value is preset to 0.9.
And step S208, determining the credit line of the target credit scheme according to the credit index.
In one embodiment, a calculation model between the credit line and the credit index of the credit scheme is predetermined and is called as a credit line calculation model. The credit line calculation model comprises a risk index of the client to be credited and a functional relation between comprehensive data indexes of the client to be credited, and the functional relation is expressed as f. The credit line of the target credit scheme is the product of the values corresponding to the functional relationship between each risk index of the client to be credited and the comprehensive data index of the client to be credited. After the credit-granting index in the target credit-granting scheme is determined, the credit-granting index is substituted into the credit-granting amount calculation model, and the credit-granting amount of the target credit-granting scheme can be calculated. Specifically, the credit line calculation model is as follows:
Y＝R1*R2*R3*f(x1,x2,...,xn)
wherein Y represents the credit line of the target credit scheme, R1 represents the expert scoring index of the client to be trusted, R2 represents the attribution industry index of the client to be trusted, R3 represents the growth trend index of the client to be trusted, x1, x2,.. xn represents n comprehensive data indexes of the client to be trusted, and f (x1, x2,... xn) represents the functional relationship among the n comprehensive data indexes of the client to be trusted.
In one embodiment, the specific calculation manner of the functional relationship between the comprehensive data indexes of the clients to be trusted may be the sum of products of the comprehensive data indexes and corresponding comprehensive data index weights. The comprehensive data indexes and the weights corresponding to the comprehensive data indexes are preset according to a large number of actual service scenes, and the weights corresponding to the comprehensive data indexes can be determined after the comprehensive data indexes are determined.
In one embodiment, after the small-sized micro-enterprises are divided into a plurality of guest groups in advance, the maximum credit line corresponding to the guest groups is preset, and the calculated credit line of the customer to be credited needs to be less than or equal to the corresponding maximum credit line. Specifically, after determining the credit line of the target credit scheme according to the credit index, the method further includes: and determining the maximum credit line corresponding to the target customer group to which the customer to be credited belongs, comparing the calculated credit line of the credit scheme with the maximum credit line, and if the credit line of the credit scheme is greater than the maximum credit line, determining the maximum credit line as the credit line of the customer to be credited. Or, the credit line of the client to be trusted can be determined artificially according to the actual situation of the client to be trusted.
In one embodiment, after the customer to be trusted is trusted, the rate of bad loans of the target customer group to which the customer to be trusted belongs can be tracked and determined. The bad loan rate refers to the proportion of the bad loan of a financial institution in the total loan balance, the bad loan refers to the classification of the loan into five categories of normal, concerned, subordinate, suspicious and lost according to the risk basis when the loan quality is evaluated, wherein the latter three categories are called bad loans. Specifically, according to the bad loan rate of the target customer group to which the customer to be trusted belongs, the maximum credit line corresponding to the target customer group to which the customer to be trusted belongs is adjusted, so that the credit line of the customer group is subsequently managed.
In the credit granting decision method, the client information of the client to be granted is acquired, and the target client group to which the client to be granted belongs is determined based on the client information; determining a target credit granting scheme corresponding to a target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model; acquiring credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on a credit granting index calculation model; and determining the credit line of the target credit scheme according to the credit index. By adopting the method of the embodiment, the clients to be trusted are divided into the predetermined client groups according to the client information of the clients to be trusted, the corresponding trust schemes can be determined directly according to the client groups to which the clients to be trusted belong, the centralized and unified management of the clients to be trusted can be realized, the determination efficiency of the trust schemes is effectively improved, the trust indexes corresponding to the target trust schemes of the clients to be trusted are obtained through calculation, the corresponding credit limits are determined according to the trust indexes, the accuracy of the credit limits can be improved, and the trust decision efficiency is improved.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings and a specific embodiment. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, the financial institution gives credit to the small micro-enterprise, that is, the financial institution makes a credit-giving decision, wherein the financial institution divides the small micro-enterprises having the common enterprise characteristics into the same guest group in advance according to the enterprise characteristics of the small micro-enterprises, divides a plurality of the small micro-enterprises into a plurality of guest groups in advance, and indicates the guest groups as guest group 1, guest group 2 … and guest group N, and determines a preset credit-giving scheme corresponding to the guest groups in advance. Fig. 3 is a schematic flow chart of a credit granting decision method of a financial institution, which includes the following steps:
obtaining sub-dimension information under each comprehensive dimension to which a small micro enterprise to be trusted belongs, wherein each comprehensive dimension comprises a popular dimension, a clustering dimension, an attribution industry and an industry field, the sub-dimension information under the popular dimension comprises scientific information, the sub-dimension information under the clustering dimension comprises government clustering information, the sub-dimension information under the attribution industry comprises scientific and technological service industry information, and the sub-dimension information under the industry field is education industry information;
the method comprises the steps of integrating sub-dimension information under all integrated dimensions to which small micro-enterprises to be trusted belong, and determining a target customer group to which the small micro-enterprises belong as a target customer group corresponding to 'scientific creation + government clustering + scientific service industry + education industry';
searching and obtaining a target credit granting scheme corresponding to the target customer group according to the stored corresponding relation between the customer group and a preset credit granting scheme corresponding to the customer group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model; the credit granting data type comprises comprehensive data and risk data, the comprehensive data comprises asset data, business data, liability data and credit data in about 6 months, and the risk data comprises expert scoring data, attribution industry information and growth trend data in about 6 months;
acquiring credit granting data required by the credit granting data type of the target credit granting scheme, performing data cleaning, data conversion and data integration on the comprehensive data, calculating a comprehensive data index of the comprehensive data, representing the comprehensive data index of the first month as x1, the comprehensive data index of the second month as x2, the comprehensive data index of the third month as x3, the comprehensive data index of the fourth month as x4, the comprehensive data index of the fifth month as x5, and the comprehensive data index of the sixth month as x 6;
determining an expert scoring interval to which the expert scoring data belongs, and determining an expert scoring index R1 corresponding to the expert scoring data according to the corresponding relation between the preset expert scoring interval and the preset expert scoring index value; determining an attribution industry index R2 of the attribution industry information according to the attribution industry information and a preset attribution industry index value corresponding to the preset attribution industry; determining a growth trend interval to which the growth trend data belongs, and determining a growth trend index R3 corresponding to the growth trend data according to the corresponding relation between the preset growth trend interval and the preset growth trend index value;
substituting the credit index into a credit limit calculation model to determine the credit limit of the target credit scheme, wherein the credit limit calculation model is as follows:
Y＝R1*R2*R3*f(x1,x2,...,x6)
wherein Y represents the credit line of the target credit scheme, R1 represents an expert score index, R2 represents an attribution industry index, R3 represents an increase trend index, x1, x2,.. multidot.x 6 represents 6 comprehensive data indexes in about 6 months, and f (x1, x2,.. multidot.x 6) represents the functional relationship among the 6 comprehensive data indexes.
It should be understood that, although the various steps in the flowcharts related to the embodiments described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be alternated or performed with other steps or at least some of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a credit granting decision device for realizing the credit granting decision method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the trust decision apparatus provided below may refer to the limitations of the trust decision method in the foregoing, and details are not described here.
In one embodiment, as shown in fig. 4, there is provided a credit decision device, including: the system comprises an account group determining module 410, a trust scheme determining module 420, a trust index calculating module 430 and a credit limit determining module 440, wherein:
the guest group determining module 410 is configured to obtain guest information of a guest to be trusted, and determine a target guest group to which the guest to be trusted belongs based on the guest information.
And the trust scheme determining module 420 is configured to determine a target trust scheme corresponding to the target guest group, where the target trust scheme includes a trust data type and a trust index calculation model.
And the credit granting index calculation module 430 is configured to obtain the credit granting data required by the type of the credit granting data of the target credit granting scheme, and calculate to obtain a credit granting index based on the credit granting index calculation model.
And the credit line determining module 440 is configured to determine the credit line of the target credit scheme according to the credit indicator.
In one embodiment, the guest group determining module 410 includes:
and the dimension information acquisition unit is used for acquiring the sub-dimension information of each comprehensive dimension to which the client to be trusted belongs, wherein each comprehensive dimension comprises a Hewlett packard dimension, a clustering dimension, and an attribution industry and an industry field.
And the target customer group determining unit is used for integrating the sub-dimension information under each integrated dimension to which the customer to be trusted belongs and determining the target customer group to which the customer to be trusted belongs.
In one embodiment, the trust scheme determining module 420 is further configured to search for and obtain a target trust scheme corresponding to the target guest group according to a stored correspondence between the guest group and a preset trust scheme corresponding to the guest group.
In one embodiment, the trust scenario determination module 420 is further configured to determine that the trust data type includes composite data and risk data, the composite data includes at least two of asset data, business data, liability data and credit data, and the risk data includes expert rating data, homeland industry information and growth trend data.
In one embodiment, the trust indicator calculating module 430 includes:
and the comprehensive data index calculation unit is used for performing data processing on the comprehensive data and calculating a comprehensive data index of the comprehensive data, the data processing comprises at least one of data cleaning, data conversion and data integration, and the credit granting index comprises the comprehensive data index.
And the risk index calculation unit is used for determining a risk index of the risk data corresponding to the risk type by adopting a judgment mode corresponding to the risk type according to the risk type, and the credit granting index comprises the risk index.
In one embodiment, the risk indicator calculation unit is further configured to determine a home industry indicator of the home industry information according to the home industry information and a preset home industry indicator value corresponding to a preset home industry, where the risk indicator includes the home industry indicator.
In one embodiment, the risk indicator calculation unit is further configured to determine an expert scoring interval to which the expert scoring data belongs, and determine an expert scoring indicator corresponding to the expert scoring data according to a correspondence between a preset expert scoring interval and a preset expert scoring index value, where the risk indicator includes the expert scoring indicator.
In one embodiment, the risk indicator calculating unit is further configured to determine a growth trend interval to which the growth trend data belongs, and determine a growth trend indicator corresponding to the growth trend data according to a correspondence between a preset growth trend interval and a preset growth trend indicator value, where the risk indicator includes the growth trend indicator.
In one embodiment, the credit granting decision apparatus further includes:
the credit line adjusting unit is used for determining the maximum credit line corresponding to the target passenger group to which the client to be trusted belongs; and if the credit line of the credit scheme is larger than the maximum credit line, determining the maximum credit line as the credit line of the client to be trusted.
All or part of each module in the credit decision device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program when executed by a processor implements a credit decision device method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the trust decision method when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned trust decision method.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of the trust decision method described above.
It should be noted that, the user or client information (including but not limited to user or client device information, user or client personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data that are authorized by the user or client or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (17)
1. A trust decision method, the method comprising:
the method comprises the steps of obtaining customer information of a customer to be trusted, and determining a target customer group to which the customer to be trusted belongs based on the customer information;
determining a target credit granting scheme corresponding to the target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model;
obtaining credit granting data required by the credit granting data type of the target credit granting scheme, and calculating to obtain a credit granting index based on the credit granting index calculation model;
and determining the credit line of the target credit scheme according to the credit index.
2. The trust decision method of claim 1,
the obtaining of the customer information of the customer to be trusted includes: obtaining sub-dimension information under each comprehensive dimension to which a client to be trusted belongs, wherein each comprehensive dimension comprises a Puhui dimension, a clustering dimension, and an attribution industry and an industry field;
the determining the target customer group to which the customer to be trusted belongs based on the customer information comprises: and synthesizing the sub-dimension information under each comprehensive dimension to which the customer to be trusted belongs, and determining a target customer group to which the customer to be trusted belongs.
3. The credit granting decision method of claim 1, wherein the determining the target credit granting scheme corresponding to the target guest group comprises:
and searching and obtaining a target credit granting scheme corresponding to the target passenger group according to the stored corresponding relation between the passenger group and a preset credit granting scheme corresponding to the passenger group.
4. The credit decision method of claim 3, wherein the credit data types include composite data including at least two of asset data, business data, liability data and credit data, and risk data including expert scoring data, homeland industry information and growth trend data.
5. The trust decision method of claim 4,
the method for calculating and obtaining the credit granting index based on the credit granting index calculation model comprises the following steps: performing data processing on the comprehensive data, and calculating comprehensive data indexes of the comprehensive data, wherein the data processing comprises at least one of data cleaning, data conversion and data integration, and the credit granting indexes comprise the comprehensive data indexes;
the method for calculating and obtaining the credit granting index based on the credit granting index calculation model comprises the following steps: and determining a risk index of the risk data corresponding to the risk type by adopting a judgment mode corresponding to the risk type according to the risk type, wherein the credit index comprises the risk index.
6. The credit granting decision method according to claim 5, wherein the determining a risk indicator of the risk data corresponding to the risk type by using a determination method corresponding to the risk type according to the risk type comprises:
determining an attribution industry index of the attribution industry information according to the attribution industry information and a preset attribution industry index value corresponding to a preset attribution industry, wherein the risk index comprises the attribution industry index;
determining an expert scoring interval to which the expert scoring data belongs, and determining an expert scoring index corresponding to the expert scoring data according to a corresponding relation between a preset expert scoring interval and a preset expert scoring index value, wherein the risk index comprises the expert scoring index;
and determining a growth trend interval to which the growth trend data belongs, and determining a growth trend index corresponding to the growth trend data according to a corresponding relation between a preset growth trend interval and a preset growth trend index value, wherein the risk index comprises the growth trend index.
7. The credit decision method as claimed in claim 1, further comprising, after determining the credit line of the target credit scheme according to the credit indicator:
determining the maximum credit line corresponding to the target passenger group to which the customer to be credited belongs;
and if the credit line of the credit scheme is larger than the maximum credit line, determining the maximum credit line as the credit line of the client to be trusted.
8. A trust decision apparatus, the apparatus comprising:
the system comprises a client group determining module, a client group determining module and a client group determining module, wherein the client group determining module is used for acquiring client information of a client to be trusted and determining a target client group to which the client to be trusted belongs based on the client information;
the credit granting scheme determining module is used for determining a target credit granting scheme corresponding to the target passenger group, wherein the target credit granting scheme comprises a credit granting data type and a credit granting index calculation model;
the credit granting index calculation module is used for acquiring credit granting data required by the type of the credit granting data of the target credit granting scheme, and calculating to obtain a credit granting index based on the credit granting index calculation model;
and the credit line determining module is used for determining the credit line of the target credit scheme according to the credit index.
9. The trust decision apparatus of claim 8, wherein the guest group determination module comprises:
the dimension information acquisition unit is used for acquiring sub-dimension information under each comprehensive dimension to which a client to be trusted belongs, and each comprehensive dimension comprises a Hewlett packard dimension, a clustering dimension, and an attribution industry and an industry field;
and the target customer group determining unit is used for integrating the sub-dimension information under each integrated dimension to which the customer to be trusted belongs and determining the target customer group to which the customer to be trusted belongs.
10. The trust decision apparatus of claim 8,
the credit granting scheme determining module is further configured to search and obtain a target credit granting scheme corresponding to the target customer group according to a stored correspondence between the customer group and a preset credit granting scheme corresponding to the customer group.
11. The trust decision apparatus of claim 10,
the credit granting scheme determining module is further used for determining that the credit granting data type comprises comprehensive data and risk data, the comprehensive data comprises at least two of asset data, operation data, liability data and credit data, and the risk data comprises expert scoring data, attribution industry information and growth trend data.
12. The credit decision device as claimed in claim 11, wherein the credit indicator calculation module comprises:
the comprehensive data index calculation unit is used for performing data processing on the comprehensive data and calculating comprehensive data indexes of the comprehensive data, the data processing comprises at least one of data cleaning, data conversion and data integration, and the credit granting indexes comprise the comprehensive data indexes;
and the risk index calculation unit is used for determining a risk index of the risk data corresponding to the risk type by adopting a judgment mode corresponding to the risk type according to the risk type, and the credit granting index comprises the risk index.
13. The trust decision apparatus of claim 12,
the risk index calculation unit is further configured to determine an attribution industry index of the attribution industry information according to the attribution industry information and a preset attribution industry index value corresponding to a preset attribution industry, where the risk index includes the attribution industry index;
the risk index calculation unit is further configured to determine an expert scoring interval to which the expert scoring data belongs, and determine an expert scoring index corresponding to the expert scoring data according to a corresponding relationship between a preset expert scoring interval and a preset expert scoring index value, where the risk index includes the expert scoring index;
the risk index calculation unit is further configured to determine a growth trend interval to which the growth trend data belongs, and determine a growth trend index corresponding to the growth trend data according to a correspondence between a preset growth trend interval and a preset growth trend index value, where the risk index includes the growth trend index.
14. The trust decision apparatus of claim 8, further comprising:
the credit line adjusting unit is used for determining the maximum credit line corresponding to the target passenger group to which the client to be trusted belongs; and if the credit line of the credit scheme is larger than the maximum credit line, determining the maximum credit line as the credit line of the client to be trusted.
15. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the trust decision method of any one of claims 1 to 7.
16. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the trust decision method of any one of claims 1 to 7.
17. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the trust decision method of any one of claims 1 to 7.
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