CN112561681A - Method, device, electronic equipment and storage medium for determining potential loan enterprise - Google Patents
Method, device, electronic equipment and storage medium for determining potential loan enterprise Download PDFInfo
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Abstract
The embodiment of the application provides a method, a device, electronic equipment and a storage medium for determining a potential loan enterprise, wherein the method for determining the potential loan enterprise comprises the following steps: inputting information of an enterprise into a loan intention evaluation model, predicting loan intention through the loan intention evaluation model, and outputting a loan intention score of the enterprise; inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise; and determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise. The scheme provided by the embodiment can be used for rapidly and accurately predicting the potential target enterprises with loan requirements, so that high-quality potential target enterprises can be searched for financial institutions, loan-aid institutions and the like.
Description
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a method, a device, electronic equipment and a storage medium for determining a potential loan enterprise.
Background
With the development of times, the small and medium-sized enterprise population is increasingly strong. In daily operation, medium and small enterprises often adopt the mode that commodities and invoices are provided firstly, and then the customers pay quarterly or according to the fiscal year. Compared with large-scale enterprises, the fund chain stability of small and medium-sized enterprises is lower, and the fund support is often required to be obtained in modes of financing, loan and the like, and the repayment is carried out after the settlement of the client.
When services such as financing and loan are provided, the current customer-obtaining modes of financial institutions are generally offline visiting, telemarketing and the like, and the mode cannot avoid that most visiting and telemarketing are enterprises without loan requirements, so that a large amount of resources are wasted.
In view of the above, a technical problem to be solved in the prior art is how to provide another objective solution.
Disclosure of Invention
In view of the above, one of the technical problems to be solved by the embodiments of the present application is to provide a method, an apparatus, an electronic device and a storage medium for determining a potential loan enterprise, which overcome some of the drawbacks of the prior art.
In one aspect, the present application provides a method for determining a potential loan enterprise, including: inputting information of an enterprise into a loan intention evaluation model, predicting loan intention through the loan intention evaluation model, and outputting a loan intention score of the enterprise; inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise; and determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise.
Optionally, in this embodiment of the application, the information of the enterprise includes invoice data, the information of the enterprise is input to the loan intention evaluation model, loan intention prediction is performed through the loan intention evaluation model, and a loan intention score of the enterprise is output, including: determining a plurality of operation index data of the enterprise according to the invoice data of the enterprise through the loan intention evaluation model; and forecasting the loan intention according to the plurality of operation index data through the loan intention evaluation model, and outputting the loan intention score of the enterprise.
Optionally, in this embodiment of the application, the operation index includes at least one of the following: sales, purchase amount, number of distributors in a preset time range, number of suppliers, fluctuation rate, customer churn degree, activity degree, share ratio of sales in a preset time range, share ratio of purchase amount, share ratio of number of distributors in a preset time range, share ratio of number of suppliers, share ratio of fluctuation rate, share ratio of customer churn degree, share ratio of activity degree, and at least one of the following operation indexes: the ring ratio of sales, the ring ratio of purchase amount, the ring ratio of number of dealers, the ring ratio of number of suppliers, the ring ratio of fluctuation rate, the ring ratio of customer churn degree and the ring ratio of activity degree in a preset time range.
Optionally, in this embodiment of the application, the information of the enterprise includes information of a plurality of industrial and commercial companies, the inputting of the information of the enterprise into a qualification evaluation model, performing loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise includes: determining a score corresponding to each industrial and commercial information of the enterprise through the qualification evaluation model; and performing qualification evaluation on the enterprises according to the scores corresponding to the business information, and outputting loan qualification scores of the enterprises.
Optionally, in this embodiment of the application, the qualification evaluation is performed on the enterprise according to the score corresponding to each piece of the business information, and a loan qualification score of the enterprise is output, including; according to the value corresponding to each piece of business information, performing qualification evaluation on the enterprise; and adjusting the qualification evaluation result of the enterprise according to the transaction data between the enterprise and the predetermined core enterprise, and outputting the loan qualification score of the enterprise.
Optionally, in this embodiment of the application, the business information includes at least one of the following: registered capital, expiration date, type of business.
Optionally, in this embodiment of the application, determining, according to the loan intention score and the loan qualification score of the enterprise, that the enterprise is a potential target enterprise with a loan requirement includes: summarizing the loan willingness scores and the loan qualification scores of the enterprises to obtain the value scores of the enterprises; and determining whether the enterprise is a potential target enterprise with loan requirements according to the value score of the enterprise.
In another aspect, the present application provides an apparatus for determining a potential loan enterprise, including:
the system comprises a loan intention prediction module, a loan intention assessment module and a loan intention assessment module, wherein the loan intention prediction module is used for inputting information of an enterprise into the loan intention assessment module, predicting the loan intention through the loan intention assessment module and outputting a loan intention score of the enterprise;
the qualification evaluation module is used for inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model and outputting a loan qualification score of the enterprise;
and the potential target enterprise determining module is used for determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise.
In another aspect, the present application provides an electronic device, including a memory and a processor, where the memory stores an executable program, and the processor executes the executable program to perform the steps corresponding to the method described above.
In another aspect, the present application provides a storage medium having a computer program stored thereon, which when executed by a processor implements the method as described above.
According to the scheme provided by the embodiment, information of an enterprise is input into a loan intention evaluation model, loan intention prediction is carried out through the loan intention evaluation model, and a loan intention score of the enterprise is output; inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise; according to the loan intention score and the loan qualification score of the enterprise, the enterprise is determined to be a potential target enterprise with loan requirements, so that the loan intention of the enterprise can be predicted through the loan intention evaluation model, the loan qualification of the enterprise can be judged through the qualification evaluation model, and the potential target enterprise with the loan requirements can be predicted quickly and accurately by combining the loan intention evaluation model and the qualification evaluation model, so that high-quality potential target enterprises can be searched for financial institutions, loan-assisting institutions and the like, and high-precision marketing is realized.
Drawings
Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
fig. 1 is a schematic flow chart illustrating a method for determining potential loan enterprises in one embodiment of the present application;
fig. 2 is a flowchart illustrating a method for determining potential loan enterprises in the second embodiment of the present application;
fig. 3 is a schematic structural diagram of an apparatus for determining a potential loan enterprise in a third embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device in the fourth embodiment of the present application.
Detailed Description
It is not necessary for any particular embodiment of the invention to achieve all of the above advantages at the same time.
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application shall fall within the scope of the protection of the embodiments in the present application.
The following further describes specific implementations of embodiments of the present application with reference to the drawings of the embodiments of the present application.
Fig. 1 is a schematic flow chart illustrating a method for determining potential loan enterprises in one embodiment of the present application; as shown in fig. 1, it comprises the following steps:
s101, inputting information of an enterprise into a loan intention evaluation model, predicting loan intention through the loan intention evaluation model, and outputting a loan intention score of the enterprise.
In this embodiment, under the condition of obtaining permission of the enterprise, the information of the enterprise may include information such as transaction data, asset data, and purchase record of the enterprise; but may also include corporate information, registered funds, etc. for the enterprise. In this embodiment, the information of the enterprise is only required to be used for determining the loan intention score and the loan qualification score, and this embodiment is not limited to this.
In order to ensure that the determined score is accurate, the information of the enterprise should be as comprehensive as possible; to improve the failure, the information of the enterprise may include information of the last year.
In this embodiment, the loan intention evaluation model may be a machine learning model. Before the step is executed, the enterprise information of the sample enterprise and the loan condition of the sample enterprise can be collected, and accordingly, a loan intention evaluation model is trained by utilizing a big data technology. In the step, loan intention prediction can be performed by using a trained loan intention evaluation model by using a big data technology.
In this embodiment, the loan intention score of the enterprise is used to represent the degree of the enterprise's will to apply for loans. Generally, when the fund chain of an enterprise is tense, a stronger loan will be presented. For example, the loan intention score may be proportional to how strong the enterprise's will apply for the loan, i.e., the stronger the willingness to make the loan, the higher the loan intention score. Of course, the above description is merely illustrative and not restrictive of the present application.
And S102, inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise.
For the specific content of the enterprise information, refer to the above steps, and are not described herein again.
In this embodiment, the qualification evaluation model may also be a machine learning model. Before the step is executed, the enterprise information of the sample enterprise and the repayment condition of the sample enterprise can be collected, and the qualification evaluation model is trained by utilizing the big data technology. In the step, loan qualification evaluation can be performed by using a trained qualification evaluation model by using a big data technology.
In this embodiment, generally, the better the business condition of the enterprise, or the closer the enterprise transacts with a predetermined core enterprise (for example, a core enterprise identified by another financial institution), or the better the historical repayment condition of the enterprise, or the higher the registered fund of the enterprise, the higher the probability that the enterprise can repay on time. Of course, the above description is merely illustrative and not restrictive of the present application.
S103, determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise.
In the embodiment, when the enterprise is determined to have higher loan willingness according to the loan willingness score and the possibility of timely repayment of the enterprise is determined to be higher according to the loan qualification score of the enterprise, the enterprise can be determined to be a potential target enterprise with loan requirements; on the contrary, if the willingness of the enterprise to loan is determined to be low according to the loan willingness score, the enterprise can be determined to be an enterprise without loan requirements, or if the possibility of timely repayment of the enterprise is determined to be low according to the loan qualification score of the enterprise, the enterprise is determined to be not a potential target enterprise.
After the potential target enterprise is determined, the staff of the lending institution may communicate with the staff of the target enterprise to ask the target enterprise whether a loan requirement exists.
According to the scheme provided by the embodiment, information of an enterprise is input into a loan intention evaluation model, loan intention prediction is carried out through the loan intention evaluation model, and a loan intention score of the enterprise is output; inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise; according to the loan intention score and the loan qualification score of the enterprise, the enterprise is determined to be a potential target enterprise with loan requirements, so that the loan intention of the enterprise can be predicted through the loan intention evaluation model, the loan qualification of the enterprise can be judged through the qualification evaluation model, and the potential target enterprise with the loan requirements can be predicted quickly and accurately by combining the loan intention evaluation model and the qualification evaluation model, so that high-quality potential target enterprises can be searched for financial institutions, loan-assisting institutions and the like, and high-precision marketing is realized.
Fig. 2 is a flowchart illustrating a method for determining potential loan enterprises in the second embodiment of the present application; as shown in fig. 2, it comprises the following steps:
s201, acquiring information of enterprises.
The information for the business may include invoice data and business information.
In this embodiment, the invoice data is used to characterize the business behavior of the enterprise. In the case of obtaining the business license, contract information, logistics information, and the like associated with the invoice data may be included in the information of the business.
The business information is information registered to relevant departments when enterprises are registered, and the business information comprises at least one of the following information: registered capital, expiration date, type of business.
In this embodiment, in the case of obtaining the enterprise license, the information of the enterprise may further include: loan records, repayment records, credit information, tax information, etc. of the enterprise. This embodiment does not limit this.
S202, preprocessing the information of the enterprise.
In this embodiment, the information of the enterprise may be imported into a related data processing platform, such as Hive, Mysql, or the like, and the related processing may be directly performed through the data processing platform. Pretreatment processes include, but are not limited to: missing variable filling, abnormal value processing, box separation and the like.
And S203, inputting the preprocessed information of the enterprise into a loan intention evaluation model, predicting the loan intention through the loan intention evaluation model, and outputting a loan intention score of the enterprise.
Specifically, in this embodiment, if the information of the enterprise includes invoice data, step S203 includes: determining a plurality of operation index data of the enterprise according to the invoice data of the enterprise through the loan intention evaluation model; and forecasting the loan intention according to the plurality of operation index data through the loan intention evaluation model, and outputting the loan intention score of the enterprise. Through the quantified operation indexes, the information of the enterprise is better mined from a plurality of angles corresponding to a plurality of operation indexes, so that the loan intention scoring accuracy of the enterprise is improved.
Specifically, in this embodiment, the operation index includes at least one of the following: sales, purchase amount, number of distributors in a preset time range, number of suppliers, fluctuation rate, customer churn degree, activity degree, share ratio of sales in a preset time range, share ratio of purchase amount, share ratio of number of distributors in a preset time range, share ratio of number of suppliers, share ratio of fluctuation rate, share ratio of customer churn degree, share ratio of activity degree, and at least one of the following operation indexes: the ring ratio of sales, the ring ratio of purchase amount, the ring ratio of number of dealers, the ring ratio of number of suppliers, the ring ratio of fluctuation rate, the ring ratio of customer churn degree and the ring ratio of activity degree in a preset time range.
The "preset time range" may be determined by a person skilled in the art according to a requirement, and is not limited in this embodiment.
In this embodiment, the customer churn degree is used to represent the churn degree of the customers of the current enterprise; the client churn degree in this embodiment may be a client churn degree within a preset time range.
The customer churn degree in the preset time range is the coincidence quantity of the customers in the preset time range and the customers before the preset time range/the number of the customers before the preset time range.
Illustratively, a customer churn of approximately 6 months is the number of business counterparties that are coincident with approximately 7-12 months/approximately 7-12 months. The number of business transaction opponents is the number of opponent businesses that also currently have a transaction to and from.
In this embodiment, the liveness is used to represent the liveness of the enterprise transaction; the activity level in this embodiment may be an activity level within a preset time range, or an activity level established since an enterprise.
The activity degree in the preset time range is equal to the sum of the transaction month number in the preset time range/the number of the customers in the preset time range.
Illustratively, the activity of approximately 12 months is the sum of the number of months traded per enterprise customer of approximately 12 months/(the number of enterprise customers of approximately 12 months 12).
In this embodiment, the fluctuation rate is used to represent the amount fluctuation condition of the enterprise transaction, and the fluctuation rate may be the activity within a preset time range, or may be the fluctuation rate from the establishment of the enterprise. The volatility may be a standard deviation of the aggregate transaction amount. Illustratively, the fluctuation rate of approximately 12 months is the standard deviation of the monthly aggregated transaction amount of approximately 12 months.
The above is only illustrated by a part of the operation indexes, and those skilled in the art can determine other operation indexes according to the above description, which is also within the protection scope of the present application.
After a plurality of operation index data of an enterprise are determined through the loan intention evaluation model, loan intention prediction can be carried out according to the operation index data, and loan intention scores of the enterprise are output.
In this embodiment, the loan intention evaluation model may adopt an artificial neural network, a decision tree, an XGboost, and the like, which is not limited in this embodiment.
An Artificial Neural Network (ANN) is an operational model formed by a large number of nodes (or neurons) connected with each other, each node representing a specific output function called an excitation function. Each connection between two nodes represents a weighted value, called weight, for the signal passing through the connection. The process of training the artificial neural network can be a process of adjusting the weight, and information of an enterprise can be calculated and loan intention scores can be output by combining the nodes in the artificial neural network and the weights among the nodes.
Decision Tree (Decision Tree) is a Decision analysis method for evaluating the risk of a project and judging the feasibility of the project by constructing a Decision Tree to obtain the probability that the expected value of the net present value is greater than or equal to zero on the basis of the known occurrence probability of various conditions, and is a graphical method for intuitively applying probability analysis. Decision trees may rely on data testing on the partitioning of source data. The data testing process is completed when no more splits can be made or a separate class can be applied to a branch. In this embodiment, the information of the enterprise may be used as the source data of the decision tree, and the decision tree makes a decision and outputs a loan intention score.
The XGboost is an optimized distributed gradient enhancement library, is an improvement on a gradient enhancement algorithm, uses first-order and second-order partial derivatives, the second-order derivative is beneficial to quicker and more accurate gradient reduction, a Taylor expansion acquisition function is used as a second-order derivative form of an independent variable, leaf splitting optimization calculation can be carried out only depending on the value of input data under the condition that the specific form of a loss function is not selected, the selection of the loss function and the decoupling of model algorithm optimization/parameter selection are realized, and the applicability is stronger. In this embodiment, the information of the enterprise may be used as the source data of the XGBoost, and the XGBoost performs calculation according to the information of the enterprise and outputs the loan intention score.
And S204, inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise.
Optionally, if the information of the enterprise includes a plurality of business information, step S204 may include: determining a score corresponding to each industrial and commercial information of the enterprise through the qualification evaluation model; and performing qualification evaluation on the enterprises according to the scores corresponding to the business information, and outputting loan qualification scores of the enterprises. Because the industrial and commercial information is information provided when the enterprise registers, the industrial and commercial information can be determined to be real and accurate, the industrial and commercial information can reflect information such as registered fund, operating age and the like of the enterprise, whether the enterprise meets loan qualification or not can be accurately determined according to the industrial and commercial information through the qualification evaluation model, and a loan qualification grade is output.
Optionally, in this embodiment, the qualification evaluation is performed on the enterprise according to the score corresponding to each piece of the business information, and the loan qualification score of the enterprise is output, including; according to the value corresponding to each piece of business information, performing qualification evaluation on the enterprise; and adjusting the qualification evaluation result of the enterprise according to the transaction data between the enterprise and the predetermined core enterprise, and outputting the loan qualification score of the enterprise. Therefore, if the stable transaction between the enterprise and the predetermined core enterprise is determined according to the transaction data between the enterprise and the predetermined core enterprise, the good fund state of the enterprise can be determined, or the good development prospect of the enterprise is determined, and the loan qualification grade of the enterprise can be adjusted according to the stable transaction between the enterprise and the predetermined core enterprise, so that the loan qualification of the enterprise is improved.
And S205, summarizing the loan intention score and the loan qualification score of the enterprise to obtain a value score of the enterprise.
The loan intention scores and the loan qualification scores of the enterprises can be aggregated in a weighted summation mode. Or the loan intention scores and the loan qualification scores of the enterprises can be graded according to the grade, each grade can correspond to a one-value score, and the value scores of all the grades are collected to obtain the value score of the enterprise.
And S206, determining whether the enterprise is a potential target enterprise with loan requirements according to the value score of the enterprise.
Specifically, the price values may be classified, and whether the enterprise is a potential target enterprise with loan requirements may be determined according to the corresponding grade of the enterprise.
When the enterprise includes a plurality of enterprises, the potential target enterprise with the loan requirement can be determined from the plurality of enterprises according to the corresponding level of each enterprise, and the priority of the potential target enterprise with the loan requirement can be determined according to the value score of each enterprise.
After determining that the enterprise is a potential target enterprise with loan requirements, the loan officer of the lender can contact the staff of the enterprise according to the priority order to guide the enterprise to carry out loan.
According to the scheme provided by the embodiment, information of an enterprise is input into a loan intention evaluation model, loan intention prediction is carried out through the loan intention evaluation model, and a loan intention score of the enterprise is output; inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise; according to the loan intention score and the loan qualification score of the enterprise, the enterprise is determined to be a potential target enterprise with loan requirements, so that the loan intention of the enterprise can be predicted through the loan intention evaluation model, the loan qualification of the enterprise can be judged through the qualification evaluation model, and the potential target enterprise with the loan requirements can be predicted quickly and accurately by combining the loan intention evaluation model and the qualification evaluation model, so that high-quality potential target enterprises can be searched for financial institutions, loan-assisting institutions and the like, and high-precision marketing is realized.
Fig. 3 is a schematic structural diagram of an apparatus for determining a potential loan enterprise in a third embodiment of the present application; as shown in fig. 3, the device for determining the potential loan enterprise comprises:
the intention prediction module 301 is configured to input information of an enterprise into a loan intention evaluation model, predict loan intention through the loan intention evaluation model, and output a loan intention score of the enterprise;
the qualification evaluation module 302 is used for inputting the information of the enterprise into a qualification evaluation model, performing loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise;
and the potential target enterprise determining module 303 is configured to determine that the enterprise is a potential target enterprise with a loan requirement according to the loan intention score and the loan qualification score of the enterprise.
Optionally, in any embodiment of the present application, the information of the enterprise includes invoice data, and the willingness prediction module includes: the index data determining module is used for determining a plurality of operating index data of the enterprise according to the invoice data of the enterprise through the loan intention evaluation model; and the index prediction module predicts the loan intention according to the plurality of operation index data through the loan intention evaluation model and outputs the loan intention score of the enterprise.
Optionally, in any embodiment of the present application, the operation index includes at least one of: sales, purchase amount, number of distributors in a preset time range, number of suppliers, fluctuation rate, customer churn degree, activity degree, share ratio of sales in a preset time range, share ratio of purchase amount, share ratio of number of distributors in a preset time range, share ratio of number of suppliers, share ratio of fluctuation rate, share ratio of customer churn degree, share ratio of activity degree, and at least one of the following operation indexes: the ring ratio of sales, the ring ratio of purchase amount, the ring ratio of number of dealers, the ring ratio of number of suppliers, the ring ratio of fluctuation rate, the ring ratio of customer churn degree and the ring ratio of activity degree in a preset time range.
Optionally, in any embodiment of the present application, the information of the enterprise includes information of a plurality of industrial and commercial companies, and the qualification evaluation module includes: the business and industry determining module is used for determining the value corresponding to each business and industry information of the enterprise through the qualification evaluation model; and the qualification evaluation sub-module is used for evaluating the qualification of the enterprise according to the scores corresponding to the business information and outputting the loan qualification score of the enterprise.
Optionally, in any embodiment of the present application, the qualification evaluation sub-module includes;
the business and business evaluation module is used for evaluating the qualification of the enterprise according to the value corresponding to each business and business information;
and the adjusting module is used for adjusting the qualification evaluation result of the enterprise according to the transaction data between the enterprise and the predetermined core enterprise and outputting the loan qualification score of the enterprise.
Optionally, in any embodiment of the present application, the business information includes at least one of: registered capital, expiration date, type of business.
Optionally, in any embodiment of the present application, the potential target enterprise determining module includes: the collection module is used for collecting the loan intention scores and the loan qualification scores of the enterprises to obtain the value scores of the enterprises; and the potential target enterprise determining submodule is used for determining whether the enterprise is a potential target enterprise with loan requirements according to the value score of the enterprise.
According to the scheme provided by the embodiment, information of an enterprise is input into a loan intention evaluation model, loan intention prediction is carried out through the loan intention evaluation model, and a loan intention score of the enterprise is output; inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise; according to the loan intention score and the loan qualification score of the enterprise, the enterprise is determined to be a potential target enterprise with loan requirements, so that the loan intention of the enterprise can be predicted through the loan intention evaluation model, the loan qualification of the enterprise can be judged through the qualification evaluation model, and the potential target enterprise with the loan requirements can be predicted quickly and accurately by combining the loan intention evaluation model and the qualification evaluation model, so that high-quality potential target enterprises can be searched for financial institutions, loan-assisting institutions and the like, and high-precision marketing is realized.
Fig. 4 is a hardware structure diagram of some electronic devices provided in the embodiments of the present application. According to fig. 4, the apparatus comprises:
one or more processors 410 and a memory 420, with one processor 410 being an example in fig. 4.
The electronic device may further include: an input device 430 and an output device 440.
The processor 410, the memory 420, the input device 430, and the output device 440 may be connected by a bus or other means, such as the bus connection in fig. 4.
The memory 420 may be used as a non-volatile computer readable storage medium for storing non-volatile software programs, non-volatile computer executable programs, and modules, such as program instructions/modules corresponding to the method for determining potential loan enterprises in the embodiments of the present application. The processor 410 executes various functional applications of the server and data processing by executing the nonvolatile software programs, instructions and modules stored in the memory 420, namely, the method for determining the potential loan enterprise in the above method embodiments.
The memory 420 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from the use of the device that determines the potential loan enterprise, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the memory 420 may optionally include memory 420 located remotely from the processor 410, and these remote memories 420 may be connected over a network to the means for determining the potential loan enterprise. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may receive input numeric or character information and generate key signal inputs related to determining business settings and functional control of the device for the potential lending business. The input device 430 may include a keyboard, a touch screen, and the like.
The one or more modules are stored in the memory 420 and, when executed by the one or more processors 410, perform a method of determining a potential lending enterprise in any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in the embodiments of the present application.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
mobile communication devices, which are characterized by mobile communication capabilities and are primarily targeted at providing voice and data communications. Such terminals include smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
The ultra-mobile personal computer equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include PDA, MID, and UMPC devices, such as ipads.
Portable entertainment devices such devices may display and play multimedia content. Such devices include audio and video players (e.g., ipods), handheld game consoles, electronic books, as well as smart toys and portable car navigation devices.
The server is similar to a general computer architecture, but has higher requirements on processing capability, stability, reliability, safety, expandability, manageability and the like because of the need of providing highly reliable services.
And other electronic devices with data interaction functions.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by enterprise-programmed devices. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The application may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for determining potential loan enterprises, comprising:
inputting information of an enterprise into a loan intention evaluation model, predicting loan intention through the loan intention evaluation model, and outputting a loan intention score of the enterprise;
inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise;
and determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise.
2. The method of claim 1, wherein the information of the enterprise comprises invoice data, the information of the enterprise is input into a loan intention evaluation model, loan intention is predicted through the loan intention evaluation model, and loan intention scores of the enterprise are output, and the method comprises the following steps:
determining a plurality of operation index data of the enterprise according to the invoice data of the enterprise through the loan intention evaluation model;
and forecasting the loan intention according to the plurality of operation index data through the loan intention evaluation model, and outputting the loan intention score of the enterprise.
3. The method of claim 2, wherein the business indicators comprise at least one of: sales, purchase amount, number of distributors in a preset time range, number of suppliers, fluctuation rate, customer churn degree, activity degree, share ratio of sales in a preset time range, share ratio of purchase amount, share ratio of number of distributors in a preset time range, share ratio of number of suppliers, share ratio of fluctuation rate, share ratio of customer churn degree, share ratio of activity degree, and at least one of the following operation indexes: the ring ratio of sales, the ring ratio of purchase amount, the ring ratio of number of dealers, the ring ratio of number of suppliers, the ring ratio of fluctuation rate, the ring ratio of customer churn degree and the ring ratio of activity degree in a preset time range.
4. The method of claim 1, wherein the information of the enterprise comprises a plurality of business information, the inputting the information of the enterprise into a qualification evaluation model, performing loan qualification evaluation through the qualification evaluation model, and outputting a loan qualification score of the enterprise comprises:
determining a score corresponding to each industrial and commercial information of the enterprise through the qualification evaluation model;
and performing qualification evaluation on the enterprises according to the scores corresponding to the business information, and outputting loan qualification scores of the enterprises.
5. The method according to claim 4, wherein the qualification evaluation is performed on the enterprises according to the scores corresponding to the business information, and the loan qualification scores of the enterprises are output, including;
according to the value corresponding to each piece of business information, performing qualification evaluation on the enterprise;
and adjusting the qualification evaluation result of the enterprise according to the transaction data between the enterprise and the predetermined core enterprise, and outputting the loan qualification score of the enterprise.
6. The method of claim 4, wherein the business information comprises at least one of: registered capital, expiration date, type of business.
7. The method of claim 1, wherein the determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise comprises:
summarizing the loan willingness scores and the loan qualification scores of the enterprises to obtain the value scores of the enterprises;
and determining whether the enterprise is a potential target enterprise with loan requirements according to the value score of the enterprise.
8. An apparatus for determining potential lending enterprises, comprising:
the system comprises a loan intention prediction module, a loan intention assessment module and a loan intention assessment module, wherein the loan intention prediction module is used for inputting information of an enterprise into the loan intention assessment module, predicting the loan intention through the loan intention assessment module and outputting a loan intention score of the enterprise;
the qualification evaluation module is used for inputting the information of the enterprise into a qualification evaluation model, carrying out loan qualification evaluation through the qualification evaluation model and outputting a loan qualification score of the enterprise;
and the potential target enterprise determining module is used for determining the enterprise as a potential target enterprise with loan requirements according to the loan intention score and the loan qualification score of the enterprise.
9. An electronic device comprising a memory having an executable program stored thereon and a processor that executes the executable program to perform steps corresponding to the method of any one of claims 1-7.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, carries out the method according to any one of claims 1-7.
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