CN117094816A - Enterprise financial assessment method and system based on big data - Google Patents
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Abstract
The embodiment of the application relates to the technical field of computers, and discloses an enterprise financial evaluation method and system based on big data, wherein the method comprises the following steps: acquiring financial data of an enterprise; performing primary evaluation based on the financial data to obtain a first evaluation value; performing multidimensional compensation on the first evaluation value to obtain a second evaluation value; when the second evaluation value does not accord with the first lending expectation of the credit institution, performing multidirectional correction evaluation based on the financial data to obtain a third evaluation value; and outputting a quasi-credit evaluation result when the third evaluation value accords with the second lending expectation of the credit institution. Based on the evaluation of the financial data value, multidimensional compensation and multidirectional correction are carried out to pull the financial evaluation value of enterprises apart by a certain gap, so that the effective evaluation of the finance of the enterprises is realized, the loan qualification of the enterprises is effectively distinguished, and different loan services are adopted for the enterprises with different financial evaluation values, so that the adaptive loan services can be accurately provided for the social enterprises.
Description
Technical Field
The application relates to the technical field of computers, in particular to an enterprise financial assessment method and system based on big data.
Background
The loan organization generally pays attention to the financial data of the enterprise in recent years, such as sales and equity, when providing loan services to the enterprise, but the high-quality enterprises on the market are more and have close qualification, and when providing loan services, the loan service organization does not have an effective assessment method to distinguish loan qualification of the enterprise, so that the enterprise cannot be accurately provided with the adapted loan services, especially the loan services valuable for social development.
Disclosure of Invention
The application mainly aims to provide an enterprise financial assessment method and system based on big data, and aims to solve the technical problem that enterprise qualification can not be effectively judged and distinguished in the prior art.
To achieve the above object, in a first aspect, an embodiment of the present application provides a method for evaluating corporate finance based on big data, including:
acquiring financial data of an enterprise, wherein the financial data comprises tax payment data, social security payment data and profit data of a preset year;
performing primary evaluation based on the financial data to obtain a first evaluation value, wherein the first evaluation value is used for representing the current value created by the enterprise;
multidimensional compensation is carried out on the first evaluation value to obtain a second evaluation value, and the second evaluation value is used for representing expected value which can be created by the enterprise;
determining whether the second evaluation value meets a first lending expectation of a credit institution;
if the second evaluation value does not accord with the first lending expectation of the credit institution, performing multidirectional correction evaluation based on the financial data to obtain a third evaluation value, wherein the third evaluation value is used for representing the relative value of the enterprise;
determining whether the third evaluation value meets a second lending expectation of a credit institution;
and if the third evaluation value accords with the second lending expectation of the credit institution, outputting a quasi-lending evaluation result.
Preferably, the big data based enterprise financial assessment method of claim 1, wherein the tax data, social security payment data and profit data of the preset year include corresponding absolute value data and base growth rate data; the first evaluation value is obtained by performing initial evaluation based on the financial data, and the first evaluation value comprises the following steps:
based on the tax data, social security payment data and absolute value data of profit data, corresponding first absolute pair evaluation values M are obtained respectively 10 、M 11 、M 12 ；
Based on the tax payment data, social security payment data and the base increment rate data of profit data, corresponding first relative sub-evaluation values M are obtained respectively 100 、M 110 、M 120 ；
Calculating a first sub-evaluation value based on the first opposite sub-evaluation value and the first opposite sub-evaluation value;
acquiring evaluation weights P of tax payment data, social security payment data and profit data 1 、P 2 、P 3 ；
Carrying out weighted summation on the first sub-evaluation values to obtain a first evaluation value M1; wherein the first evaluation value M1 satisfies the following expression:
preferably, the multidimensional compensation includes two or more of supply expected compensation of industry core talents, demand expected compensation of enterprise products and supply expected compensation of supply chains, and the multidimensional compensation of the first evaluation value obtains a second evaluation value, including:
randomly selecting more than two compensation modes;
based on the compensation mode, acquiring the corresponding expected saturation W (W is more than or equal to 0 and less than or equal to 1) of big data;
determining corresponding compensation amounts to be M respectively based on the saturation 0X Wherein M is 0X =(expected to be positive), or M 0X =0 (expected to be negative);
and calculating a second evaluation value according to the compensation quantity, wherein the second evaluation value M2 meets the following expression:
preferably, said determining whether said second evaluation value meets a first lending expectation of a credit institution further comprises:
and if the second evaluation value accords with the first loan repayment expectation of the credit institution, outputting a first quasi-loan evaluation result, wherein the first quasi-loan evaluation result is used for indicating that the enterprise is a high-quality enterprise, and the first-grade credit service can be recommended.
Preferably, said determining whether said second evaluation value meets a first lending expectation of a credit institution comprises:
inputting the second evaluation value into a pre-trained first lending expected model, wherein the first lending expected model is a two-dimensional point cloud image, and the two-dimensional point cloud image comprises a core area;
if the second evaluation value is located in the core area, determining that the second evaluation value accords with a first lending expectation of a credit agency;
if it is determined that the second evaluation value is not located in the core area, it is determined that the second evaluation value does not conform to the first lending expectation of the credit institution.
Preferably, the financial data further includes social activity financial cost data, and the performing the multi-directional correction evaluation based on the financial data obtains a third evaluation value, including:
performing horizontal correction and vertical correction evaluation based on the financial data to obtain a third evaluation value; the transverse correction is to correct the second evaluation value according to the social activity condition of enterprises in the same industry, and the longitudinal correction is to correct the second evaluation value according to the social activity condition of enterprises in the same region.
Preferably, the correcting the second evaluation value according to the social activity condition of the enterprises in the same industry includes:
when the average number of social activity financial fees of the enterprises in the same industry is larger than that of the enterprises, the second evaluation value of the enterprises is regulated down to obtain a third evaluation value, and otherwise, the second evaluation value is regulated up; and/or, the correcting the second evaluation value according to the social activity condition of the enterprises in the same area includes:
and when the average number of the social activity financial fees of the enterprises in the same area is larger than that of the enterprises, the second evaluation value of the enterprises is regulated down to obtain a third evaluation value, and otherwise, the second evaluation value is regulated up.
Preferably, said determining whether said third evaluation value meets a second lending expectation of a credit institution comprises:
inputting the third evaluation value into a pre-trained second lending expected model, wherein the second lending expected model is a two-dimensional point cloud image, and the two-dimensional point cloud image comprises a core area;
if the third evaluation value is located in the core area, determining that the third evaluation value meets a second lending expectation of a credit agency;
if it is determined that the third evaluation value is not located in the core area, it is determined that the third evaluation value does not conform to a second lending expectation of a credit institution.
In a second aspect, an embodiment of the present application further provides a loan service system, including: a processor and a memory; wherein the memory is for storing program code and the processor is for invoking the program code to perform the method according to the first aspect.
In a third aspect, there is also provided in an embodiment of the present application a computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the method according to the first aspect.
Different from the prior art, the enterprise financial assessment method based on big data provided by the embodiment of the application firstly acquires financial data of an enterprise; then, performing primary evaluation according to the financial data to obtain a first evaluation value; then carrying out multidimensional compensation on the first evaluation value to obtain a second evaluation value; when the second evaluation value accords with the first lending expectation of the credit institution, the first grade credit service is directly provided, when the second evaluation value does not accord with the first lending expectation of the credit institution, the multi-way correction evaluation is carried out based on the financial data to obtain a third evaluation value, so that whether the second lending expectation of the credit institution accords with the second lending expectation of the credit institution is judged through the third evaluation value, and when the third evaluation value accords with the second lending expectation of the credit institution, the second grade credit service is provided, so that the financial evaluation value of the enterprise is pulled by a certain gap by multi-dimensional compensation and multi-way correction on the basis of the financial data value evaluation, the effective evaluation of the financial of the enterprise is realized, the effective differentiation of loan qualification is carried out on the enterprise, and different loan services are adopted for the enterprises of different financial evaluation values, so that the adapted loan service can be accurately provided for the social enterprises.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an enterprise financial assessment method in accordance with some embodiments of the present application;
fig. 2 is a schematic diagram of a hardware architecture of a loan servicing system, according to some embodiments of the application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, "and/or" throughout this document includes three schemes, taking a and/or B as an example, including a technical scheme, a technical scheme B, and a technical scheme that both a and B satisfy; in addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
In recent years, global economy has tended to be lower, residents' consumption is soft, social stability is poor, social investment and loan are cautious, and enterprise loan to banks or other financial institutions is one of the conventional means for maintaining enterprise development. The loan organization generally pays attention to the financial data of recent years of enterprises, such as sales and equity data, etc., when providing loan services for enterprises, but at present, high-quality enterprises on the market are more and have close qualification, and when providing loan services, the loan service organization does not have an effective assessment method to distinguish loan qualification for enterprises, so that the enterprises cannot accurately provide adaptive loan services, especially valuable loan services for social development.
Aiming at the problems, the embodiment of the application provides an enterprise financial evaluation method based on big data, which is applied to a loan service system.
The specific steps of the big data based enterprise financial assessment method will be described primarily below, with the understanding that although a logical order is shown in the flow chart, in some cases the steps shown or described may be performed in an order different than what is shown or described herein. Referring to fig. 1, the enterprise financial assessment method based on big data includes the following steps:
s100, acquiring financial data of an enterprise, wherein the financial data comprises tax data of a preset year, social security payment data and profit data;
specifically, there are various ways to obtain the financial data of the enterprise, such as the loan applicant inputting the corresponding financial data into the loan service system one by one; or the loan applicant uploads the financial statement to a loan service system, and the system obtains the enterprise financial data through image and text recognition.
It should be noted that, different financial data can represent different business states and different social values of the enterprise, for example, the enterprise tax can provide financial support for social development, so that the enterprise tax data can represent social development value; social security payment can provide employment for society, so that enterprise social security payment data can characterize social stability value; enterprise profits can provide financial resources for enterprise and social sustainable development, and thus, profit data can characterize social sustainable value.
Therefore, the embodiment of the application reflects the value of the enterprise by acquiring financial data such as tax payment data, social security payment data, profit data and the like, and can intuitively reflect the value created by the enterprise in the society.
S200, performing primary evaluation based on the financial data to obtain a first evaluation value, wherein the first evaluation value is used for representing the current value created by the enterprise;
in the embodiment of the application, the first evaluation value is obtained by evaluating the value of the financial data and is inquired through a mapping table, and in one embodiment, the evaluation standard of the financial data-evaluation value can be formulated in advance, for example, the annual tax payment is 0-100 ten thousand 30 minutes, the annual tax payment is 100-200 ten thousand 50 minutes, the annual tax payment is 200-500 ten thousand 70 minutes, and the annual tax payment is more than 500 ten thousand 100 minutes; the social security payment data and profit data can also be subjected to scoring evaluation in stages, and the description is omitted here; it will be appreciated that since the financial data is in recent years and is currently real data, the evaluation value obtained in the value evaluation using the currently existing financial data is a current real value evaluation, which represents the current value or the current created value of the business.
S300, performing multidimensional compensation on the first evaluation value to obtain a second evaluation value, wherein the second evaluation value is used for representing expected value which can be created by the enterprise;
since there are many enterprises in the market, and when the present value (first evaluation value) is obtained by performing value evaluation based on the present financial data, the inventors found that the first evaluation value differs little, and it is difficult to distinguish the enterprises effectively. Therefore, the embodiment of the application carries out multidimensional compensation on the basis of the financial data to obtain the second evaluation value, so that the concept of expected value is introduced, and when the expected value (the second evaluation value) of an enterprise is larger, the enterprise can create larger value for the enterprise in the future, and the loan qualification is better.
It can be understood that the multidimensional compensation compensates the first evaluation value in multiple dimensions, for example, further evaluation is performed in terms of talents, markets, costs, technical advancement, etc., so that after the multidimensional compensation is performed on the high-quality enterprise, the high-quality enterprise can be better (the evaluation value is higher), and the evaluation and differentiation of the enterprise are facilitated.
S400, judging whether the second evaluation value accords with a first lending expectation of a credit agency;
various ways of determining whether the second evaluation value meets the first lending expectation of the credit agency, such as by comparing the values, and when the second evaluation value is greater than the preset lending evaluation value, indicating that the second evaluation value meets the lending expectation of the credit agency; the determination can also be made by a two-dimensional point cloud image, and when the second evaluation value falls in a preset point cloud image area, the statement accords with the lending expectation of the credit agency.
S500, if the second evaluation value does not accord with the first lending expectation of the credit agency, performing multidirectional correction evaluation based on the financial data to obtain a third evaluation value, wherein the third evaluation value is used for representing the relative value of the enterprise;
since the number of relatively high-quality enterprises (i.e., enterprises in a rapid growth stage) on the market is the largest, it is still difficult to distinguish their effective evaluations by only the second evaluation value; therefore, in the embodiment of the application, when the second evaluation value does not accord with the first lending expectation of the credit agency, the second evaluation value is further corrected to obtain the third evaluation value, so that the gap between the enterprise value evaluation values in the rapid growth stage is enlarged.
It will be appreciated that the multi-directional correction corrects the second evaluation value from multiple directions and dimensions, and the correction may be positive or negative, i.e. the second evaluation value may be increased by a third evaluation value, or the second evaluation value may be decreased by a third evaluation value.
S600, judging whether the third evaluation value accords with a second lending expectation of a credit agency;
the principle and method for determining whether the third evaluation value meets the second lending expectation of the credit institution are the same as those in step S400, and will not be described again here.
S700, if the third evaluation value accords with the second lending expectation of the credit institution, outputting a second quasi-lending evaluation result.
After the third evaluation value is obtained by multidimensional compensation and multidirectional correction on the basis of the financial data, if the third evaluation value accords with the second lending expectation of the credit institution, a second quasi-lending evaluation result is output, and the evaluation result is used for representing the credit service which accords with the second grade.
According to the enterprise financial assessment method based on big data, financial data of an enterprise is firstly obtained; then, performing primary evaluation according to the financial data to obtain a first evaluation value; then carrying out multidimensional compensation on the first evaluation value to obtain a second evaluation value; when the second evaluation value accords with the first lending expectation of the credit institution, the first grade credit service is directly provided, when the second evaluation value does not accord with the first lending expectation of the credit institution, the multi-way correction evaluation is carried out based on the financial data to obtain a third evaluation value, so that whether the second lending expectation of the credit institution accords with the second lending expectation of the credit institution is judged through the third evaluation value, and when the third evaluation value accords with the second lending expectation of the credit institution, the second grade credit service is provided, so that the financial evaluation value of the enterprise is pulled by a certain gap by multi-dimensional compensation and multi-way correction on the basis of the financial data value evaluation, the effective evaluation of the financial of the enterprise is realized, the effective differentiation of loan qualification is carried out on the enterprise, and different loan services are adopted for the enterprises of different financial evaluation values, so that the adapted loan service can be accurately provided for the social enterprises.
It will be appreciated that financial data has a variety of expressions such as absolute value, cyclic ratio growth rate, base growth rate, homonymous growth rate, etc., and that different expressions can express different meanings, and therefore different financial data have different effects on different index evaluations. In the embodiment of the application, the tax payment data, the social security payment data and the profit data of the preset year comprise corresponding absolute value data and fixed base growth rate data; step 200, performing primary evaluation based on the financial data to obtain a first evaluation value, including:
s210, respectively obtaining corresponding first absolute pair evaluation values M based on the tax payment data, social security payment data and absolute value data of profit data 10 、M 11 、M 12 ；
In the embodiment of the application, the mapping table is used for inquiring to obtain the first absolute pair of estimated values M 10 、M 11 、M 12 The mapping relation can be subjected to standard formulation through historical data.
S220, respectively obtaining corresponding first relative sub-evaluation values M based on the tax payment data, social security payment data and the base increment rate data of profit data 100 、M 110 、M 120 ；
Also, the first relative sub-evaluation value M can be obtained by mapping table query 100 、M 110 、M 120 The mapping relationship can also be standardized by historical data.
S230, calculating a first sub-evaluation value based on the first relative sub-evaluation value and the first relative sub-evaluation value;
it can be understood that the value of the enterprise is related to the absolute value and the growth rate of the financial data, and both are in positive correlation, so that the first sub-evaluation value can be obtained by calculating the first absolute sub-evaluation value and the first relative sub-evaluation value in a manner of taking an average value or taking a large value.
S240, acquiring evaluation weights P of tax payment data, social security payment data and profit data 1 、P 2 、P 3 ；
Because the tax data, the social security payment data and the profit data are inconsistent in influence degree on the enterprise value, the evaluation weights are inconsistent, and the evaluation weight P is also inconsistent 1 、P 2 、P 3 Can be stored in a memory, and related data can be recalled from the memory at any time when needed.
S250, carrying out weighted summation on the first sub-evaluation values to obtain a first evaluation value M1; wherein the first evaluation value M1 satisfies the following expression:
in the embodiment of the present application, the value range of the first evaluation value M1 is between 0 and 100, so that attention is paid to the rationality of the absolute sub-evaluation value and the relative sub-evaluation value when making the mapping standard of each financial data-evaluation value.
In the embodiment of the application, the multidimensional compensation comprises two or more of the expected compensation of the supply of industry core talents, the expected compensation of the demand of enterprise products and the expected compensation of the supply chain, wherein the expected supply of industry core talents refers to how much talents can be provided for the industry of the enterprise in the future, the expected supply can be determined by acquiring big data, for example, how many related professional Gramines, major or doctor graduates can be analyzed by the big data, and the number of graduates in recent years is in an ascending trend or a decreasing trend; when the number of graduates of related professions is stable or trend in recent years, the relative saturation of industry core talents is indicated, and the saturation can be determined through a preset mapping relation, for example, when the graduate growth rate is lower than 20%, the saturation is 80%, when the graduate growth rate is lower than 10%, the saturation is 90%, and when the graduate growth is negative, the saturation is 100%; the greater the saturation of talent supply,
the lower the chance that the enterprise can absorb high-quality talents from society, the more unfavorable the enterprise development, and therefore, the lower the future creative value, the smaller the compensation amount. The demand of the enterprise product is expected to be the demand of society for the product produced by the enterprise, and if the demand is more saturated, the smaller the future value of the enterprise can be created, the smaller the compensation amount. The supply expectation of the supply chain refers to the supply expectation of suppliers that the society can supply parts or manufacture for enterprises, and the more saturated the supply chain supply, the slower the number of suppliers that can provide the service increases, the more difficult it is to further reduce the supply cost, the more unfavorable it is to reduce the cost of the enterprises, the lower the interest rate of the enterprises is, the lower the value of the enterprises that can be created in the future is, and the smaller the compensation amount is.
The three expected saturation degrees are all obtained through the growth trend of related supply or demand of big data, and corresponding saturation degree values are obtained through the query of a mapping relation table of the growth trend.
In the embodiment of the present application, step 300, performing multidimensional compensation on the first evaluation value to obtain a second evaluation value includes:
s310, randomly selecting more than two compensation modes;
s320, acquiring corresponding expected saturation W (W is more than or equal to 0 and less than or equal to 1) of big data based on the compensation mode;
s330, determining corresponding compensation amounts to be M based on the saturation 0X Wherein M is 0X =(expected to be positive), or M 0X =0 (expected to be negative);
s340, calculating to obtain a second evaluation value according to the compensation quantity, wherein the second evaluation value M2 meets the following expression:
it will be appreciated that in the short term, there is a growing trend in the development of enterprises when the supply of industry core talents, the demand for enterprise products, and the supply of supply chains are in a certain trend (i.e., expected to be positive)The method is convenient; therefore, in the embodiment of the application, the second evaluation value is obtained by multidimensional compensation on the basis of the first evaluation value, and the high quality can be effectively evaluated and distinguished through multidimensional compensation; when the supply of industry core talents, the demand of enterprise products and the supply chain show a decreasing trend (i.e. are expected to be negative), no compensation is performed. Specifically, when the first evaluation value is subjected to multidimensional compensation, the system randomly selects two or three compensation modes to compensate, and obtains corresponding saturation through big data to calculate and obtain a compensation quantity M 0X (the above three modes correspond to M 01 、M 02 、M 03 ）。
As can be seen from the above, when the trend of supply of industry core talents, demand of enterprise products and supply of supply chains tends to be relaxed, i.e. saturated, the acceleration of the development of the enterprise is not favorable, so that, in one embodiment, the compensation amount M is controlled when desired 0X In negative correlation with the corresponding saturation W, i.e. when the saturation is larger, the first evaluation value is less compensated to more accurately feed back the expected value of the enterprise, in this embodiment, the compensation amount M 0X =So that the compensation amount M 0X Is between 0 and 10, thereby matching the range of values of the first evaluation value M1.
With continued reference to fig. 1, in one embodiment, the step S400: determining whether the second evaluation value meets a first lending expectation of a credit institution further comprises:
and S900, outputting a first quasi-credit evaluation result if the second evaluation value accords with the first lending expectation of the credit institution, wherein the first quasi-credit evaluation result is used for indicating that the enterprise is a high-quality enterprise and can recommend a first-grade credit service.
Specifically, when the second evaluation value meets the first lending expectation of the credit agency, the enterprise is indicated to be a high-quality enterprise, and the first-level credit service is recommended.
In one embodiment, the step S400 of determining whether the second evaluation value meets a first lending expectation of a credit institution includes:
s410, inputting the second evaluation value into a pre-trained first lending expected model, wherein the first lending expected model is a two-dimensional point cloud image, and the two-dimensional point cloud image comprises a core area;
s420, if the second evaluation value is located in the core area, determining that the second evaluation value accords with a first lending expectation of a credit agency;
and S430, if the second evaluation value is not located in the core area, determining that the second evaluation value does not accord with the first lending expectation of the credit agency.
Specifically, the loan model training is performed through multiple times of data in advance, the abscissa represents the enterprises with successful loan, the ordinate corresponds to the loan evaluation value, so that a two-dimensional point cloud chart is formed, a core area is defined on the two-dimensional point cloud chart, the core area can be divided according to the requirements of the loan institution, when the second evaluation value is positioned in the core area, the second evaluation value is determined to be in accordance with the first loan expectation of the loan institution, otherwise, the second evaluation value is not in accordance with the first loan expectation of the loan institution, and whether the loan quality of the enterprises is in accordance with the loan requirements can be accurately judged through the judging mode.
In another embodiment, the financial data further includes social activity financial cost data, and the higher the social activity financial cost of the enterprise, the more active the social activity, the more beneficial to social development, and therefore the higher the social value. Therefore, when the enterprises still cannot be effectively distinguished after the second evaluation value is obtained, correction of the financial data can be performed by the social activity financial cost data to obtain the third evaluation value. And S500, performing multi-directional correction evaluation on the financial data to obtain a third evaluation value, wherein the method comprises the following steps of:
s510, performing horizontal correction and vertical correction evaluation based on the financial data to obtain a third evaluation value; the transverse correction is to correct the second evaluation value according to the social activity condition of enterprises in the same industry, and the longitudinal correction is to correct the second evaluation value according to the social activity condition of enterprises in the same area;
s520, when the average number of social activity financial fees of enterprises in the same industry is larger than that of the enterprises, the second evaluation value of the enterprises is adjusted down to obtain a third evaluation value, and otherwise, the second evaluation value is adjusted up;
and S530, when the average number of the social activity financial fees of the enterprises in the same area is larger than that of the enterprises, the second evaluation value of the enterprises is regulated down to obtain a third evaluation value, and otherwise, the second evaluation value is regulated up.
Specifically, in the embodiment of the application, the corrected amplitude is approximately between 0 and 10, and the second evaluation value is corrected through the activity financial cost data, so that the value evaluation values of enterprises are further distinguished, and the effective evaluation of the finance of the enterprises is realized.
It should be noted that, the manner of determining whether the third evaluation value meets the second lending expectation of the credit agency is the same as the manner of determining whether the second evaluation value meets the first lending expectation of the credit agency, and will not be described herein. And outputting a second evaluation result when the third evaluation value accords with the second lending expectation of the credit agency, wherein the second evaluation result is used for indicating that the enterprise is a relatively high-quality enterprise, and a second-grade credit service can be recommended.
In other embodiments, determining whether the third evaluation value meets a second lending expectation of a credit agency at step S600, further comprises:
s800, if the third evaluation value does not accord with the second lending expectation of the credit institution, outputting an inaccurate lending evaluation result.
In this way, the embodiment of the application carries out primary evaluation through the financial data, carries out multidimensional compensation on the basis of the primary evaluation value to obtain a second evaluation value, carries out primary distinction on enterprises through the second evaluation value, carries out multidirectional rectification on the basis of the second evaluation value to obtain a third evaluation value, and carries out secondary distinction on the enterprises through the third evaluation value so as to realize effective evaluation on finance of the enterprises, thereby carrying out effective distinction on loan qualification of the enterprises.
Referring to fig. 2, fig. 2 is a schematic hardware structure of the loan service system according to the embodiment of the application.
Wherein the processor 101 is configured to provide computing and control capabilities for performing corresponding tasks with the loan servicing system, e.g., for controlling the loan servicing system to perform the enterprise financial assessment method of any of the method embodiments described above, the method comprising: acquiring financial data of an enterprise, wherein the financial data comprises tax payment data, social security payment data and profit data of a preset year; performing primary evaluation based on the financial data to obtain a first evaluation value, wherein the first evaluation value is used for representing the current value created by the enterprise; multidimensional compensation is carried out on the first evaluation value to obtain a second evaluation value, and the second evaluation value is used for representing expected value which can be created by the enterprise; determining whether the second evaluation value meets a first lending expectation of a credit institution; if the second evaluation value does not accord with the first lending expectation of the credit institution, performing multidirectional correction evaluation based on the financial data to obtain a third evaluation value, wherein the third evaluation value is used for representing the relative value of the enterprise; determining whether the third evaluation value meets a second lending expectation of a credit institution; and if the third evaluation value accords with the second lending expectation of the credit institution, outputting a quasi-lending evaluation result.
The processor 101 may be a general purpose processor including a central processing unit (CentralProcessingUnit, CPU), a network processor (NetworkProcessor, NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DigitalSignalProcessing, DSP), an application specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), a programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (field-programmable gate array, FPGA), general-purpose array logic (generic array logic, GAL), or any combination thereof.
The memory 102 serves as a non-transitory computer readable storage medium, and may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the methods of determining operating parameters in embodiments of the present application. Processor 101 may implement the enterprise financial assessment method of any of the method embodiments described above by running non-transitory software programs, instructions, and modules stored in memory 102.
In particular, the memory 102 may include Volatile Memory (VM), such as random access memory (random access memory, RAM); the memory 102 may also include a non-volatile memory (NVM), such as read-only memory (ROM), flash memory (flash memory), hard disk (HDD) or Solid State Drive (SSD), or other non-transitory solid state storage devices; the memory 102 may also include a combination of the types of memory described above.
In summary, the loan service system adopts the technical scheme of any one of the embodiments of the enterprise financial evaluation method, so that the loan service system at least has the beneficial effects brought by the technical scheme of the embodiments, and the description is omitted herein.
Embodiments of the present application also provide a computer readable storage medium, such as a memory, including program code executable by a processor to perform the method of corporate financial assessment of the embodiments described above. For example, the computer readable storage medium may be Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CDROM), magnetic tape, floppy disk, optical data storage device, etc.
Embodiments of the present application also provide a computer program product comprising one or more program codes stored in a computer-readable storage medium. The program code is read from the computer readable storage medium by a processor of the electronic device, which executes the program code to perform the method steps of corporate financial assessment provided in the above embodiments.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by program code related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and where the program may include processes implementing the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the application, and all equivalent structural changes made by the description of the present application and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the application.
Claims (10)
1. The enterprise financial assessment method based on big data is applied to a loan service system and is characterized by comprising the following steps:
acquiring financial data of an enterprise, wherein the financial data comprises tax payment data, social security payment data and profit data of a preset year;
performing primary evaluation based on the financial data to obtain a first evaluation value, wherein the first evaluation value is used for representing the current value created by the enterprise;
multidimensional compensation is carried out on the first evaluation value to obtain a second evaluation value, and the second evaluation value is used for representing expected value which can be created by the enterprise;
determining whether the second evaluation value meets a first lending expectation of a credit institution;
if the second evaluation value does not accord with the first lending expectation of the credit institution, performing multidirectional correction evaluation based on the financial data to obtain a third evaluation value, wherein the third evaluation value is used for representing the relative value of the enterprise;
determining whether the third evaluation value meets a second lending expectation of a credit institution;
and if the third evaluation value accords with the second loan repayment expectation of the credit institution, outputting a second quasi-loan evaluation result.
2. The big data based financial assessment method of claim 1, wherein the tax data, social security payment data and profit data of the preset year include corresponding absolute value data and base growth rate data; the first evaluation value is obtained by performing initial evaluation based on the financial data, and the first evaluation value comprises the following steps:
based on the tax data, social security payment data and absolute value data of profit data, corresponding first absolute pair evaluation values M are obtained respectively 10 、M 11 、M 12 ；
Based on the tax payment data, social security payment data and the base increment rate data of profit data, corresponding first relative sub-evaluation values M are obtained respectively 100 、M 110 、M 120 ；
Calculating a first sub-evaluation value based on the first opposite sub-evaluation value and the first opposite sub-evaluation value;
acquiring evaluation weights P of tax payment data, social security payment data and profit data 1 、P 2 、P 3 ；
Carrying out weighted summation on the first sub-evaluation values to obtain a first evaluation value M1; wherein the first evaluation value M1 satisfies the following expression:
3. the big data based financial assessment method of claim 2, wherein,
the multidimensional compensation comprises two or more of supply expected compensation of industry core talents, demand expected compensation of enterprise products and supply expected compensation of supply chains, and the multidimensional compensation of the first evaluation value is carried out to obtain a second evaluation value, which comprises the following steps:
randomly selecting more than two compensation modes;
based on the compensation mode, acquiring the corresponding expected saturation W (W is more than or equal to 0 and less than or equal to 1) of big data;
determining corresponding compensation amounts to be M respectively based on the saturation 0X Wherein M is 0X =Is expected to be positive, or M 0X =0, expected to be negative;
and calculating a second evaluation value according to the compensation quantity, wherein the second evaluation value M2 meets the following expression:
4. the big data based business financial assessment method of claim 1, wherein said determining whether said second assessment value meets a first lending expectation of a credit institution further comprises:
and if the second evaluation value accords with the first loan repayment expectation of the credit institution, outputting a first quasi-loan evaluation result, wherein the first quasi-loan evaluation result is used for indicating that the enterprise is a high-quality enterprise, and the first-grade credit service can be recommended.
5. The big data based business financial assessment method of claim 1, wherein said determining whether said second assessment value meets a first lending expectation of a credit institution comprises:
inputting the second evaluation value into a pre-trained first lending expected model, wherein the first lending expected model is a two-dimensional point cloud image, and the two-dimensional point cloud image comprises a core area;
if the second evaluation value is located in the core area, determining that the second evaluation value accords with a first lending expectation of a credit agency;
if it is determined that the second evaluation value is not located in the core area, it is determined that the second evaluation value does not conform to the first lending expectation of the credit institution.
6. A method of corporate financial assessment based on big data as claimed in claim 1, wherein said financial data further comprises social activity financial cost data, and wherein said performing a multi-way corrective assessment based on said financial data results in a third assessment value, comprising:
performing horizontal correction and vertical correction evaluation based on the financial data to obtain a third evaluation value; the transverse correction is to correct the second evaluation value according to the social activity condition of enterprises in the same industry, and the longitudinal correction is to correct the second evaluation value according to the social activity condition of enterprises in the same region.
7. The big data based business financial assessment method of claim 6, wherein said correcting the second assessment value based on social activity of the same business enterprise comprises:
when the average number of social activity financial fees of the enterprises in the same industry is larger than that of the enterprises, the second evaluation value of the enterprises is regulated down to obtain a third evaluation value, and otherwise, the second evaluation value is regulated up; and/or, the correcting the second evaluation value according to the social activity condition of the enterprises in the same area includes:
and when the average number of the social activity financial fees of the enterprises in the same area is larger than that of the enterprises, the second evaluation value of the enterprises is regulated down to obtain a third evaluation value, and otherwise, the second evaluation value is regulated up.
8. The big data based business financial assessment method of claim 1, wherein said determining whether said third assessment value meets a second lending expectation of a credit institution comprises:
inputting the third evaluation value into a pre-trained second lending expected model, wherein the second lending expected model is a two-dimensional point cloud image, and the two-dimensional point cloud image comprises a core area;
if the third evaluation value is located in the core area, determining that the third evaluation value meets a second lending expectation of a credit agency;
if it is determined that the third evaluation value is not located in the core area, it is determined that the third evaluation value does not conform to a second lending expectation of a credit institution.
9. A loan servicing system, comprising: a processor and a memory; wherein the memory is for storing program code, the processor is for invoking the program code to perform the method of any of claims 1 to 8.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method according to any one of claims 1 to 8.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109543516A (en) * | 2018-10-16 | 2019-03-29 | 深圳壹账通智能科技有限公司 | Signing intention judgment method, device, computer equipment and storage medium |
| CN112348654A (en) * | 2020-09-23 | 2021-02-09 | 民生科技有限责任公司 | Automatic assessment method, system and readable storage medium for enterprise credit line |
| CN112561681A (en) * | 2020-12-08 | 2021-03-26 | 爱信诺征信有限公司 | Method, device, electronic equipment and storage medium for determining potential loan enterprise |
| CN112767120A (en) * | 2020-12-31 | 2021-05-07 | 山东数字能源交易中心有限公司 | Enterprise evaluation data processing method and device |
| KR20220074176A (en) * | 2020-11-27 | 2022-06-03 | 상명대학교산학협력단 | Enterprise analysis using finance big data analysis and investment portfolio optimization system and method |
| KR20220097005A (en) * | 2020-12-31 | 2022-07-07 | 주식회사 세윤씨앤에스 | Valuation system using company information data |
| CN114971883A (en) * | 2022-06-27 | 2022-08-30 | 贵州省农村信用社联合社 | Small and micro enterprise credit risk assessment analysis system based on big data |
| CN115631029A (en) * | 2022-09-19 | 2023-01-20 | 湖南省财信科技小额贷款有限公司 | Method and device for evaluating risk of scientific loan |
| CN115759750A (en) * | 2022-11-29 | 2023-03-07 | 江西汉辰信息技术股份有限公司 | Financial risk assessment method, system, computer and readable storage medium |
| CN115860889A (en) * | 2021-09-21 | 2023-03-28 | 武汉谦屹达管理咨询有限公司 | Financial loan big data management method and system based on artificial intelligence |
| CN116128627A (en) * | 2022-12-21 | 2023-05-16 | 中信银行股份有限公司 | Risk prediction method, risk prediction device, electronic equipment and storage medium |
-
2023
- 2023-10-18 CN CN202311345106.0A patent/CN117094816B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109543516A (en) * | 2018-10-16 | 2019-03-29 | 深圳壹账通智能科技有限公司 | Signing intention judgment method, device, computer equipment and storage medium |
| CN112348654A (en) * | 2020-09-23 | 2021-02-09 | 民生科技有限责任公司 | Automatic assessment method, system and readable storage medium for enterprise credit line |
| KR20220074176A (en) * | 2020-11-27 | 2022-06-03 | 상명대학교산학협력단 | Enterprise analysis using finance big data analysis and investment portfolio optimization system and method |
| CN112561681A (en) * | 2020-12-08 | 2021-03-26 | 爱信诺征信有限公司 | Method, device, electronic equipment and storage medium for determining potential loan enterprise |
| CN112767120A (en) * | 2020-12-31 | 2021-05-07 | 山东数字能源交易中心有限公司 | Enterprise evaluation data processing method and device |
| KR20220097005A (en) * | 2020-12-31 | 2022-07-07 | 주식회사 세윤씨앤에스 | Valuation system using company information data |
| CN115860889A (en) * | 2021-09-21 | 2023-03-28 | 武汉谦屹达管理咨询有限公司 | Financial loan big data management method and system based on artificial intelligence |
| CN114971883A (en) * | 2022-06-27 | 2022-08-30 | 贵州省农村信用社联合社 | Small and micro enterprise credit risk assessment analysis system based on big data |
| CN115631029A (en) * | 2022-09-19 | 2023-01-20 | 湖南省财信科技小额贷款有限公司 | Method and device for evaluating risk of scientific loan |
| CN115759750A (en) * | 2022-11-29 | 2023-03-07 | 江西汉辰信息技术股份有限公司 | Financial risk assessment method, system, computer and readable storage medium |
| CN116128627A (en) * | 2022-12-21 | 2023-05-16 | 中信银行股份有限公司 | Risk prediction method, risk prediction device, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 徐秋慧;王迪;邓之音;: "供应链金融模式下中小企业融资问题分析――以44家农业中小企业为例", 农村金融研究, no. 09 * |
| 王冠男;: "基于企业财务指标的贷款评价模型研究", 时代金融, no. 05 * |
| 符建斌;: "关于商业银行小微企业信贷风险评估模型的实证研究", 黑龙江科技信息, no. 30 * |
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|---|---|
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