Credit risk assessment method and device, electronic equipment and computer program product

Abstract

The invention discloses a credit risk assessment method and device, electronic equipment and a computer program product. The method comprises the steps that a customer information summary sheet of a to-be-evaluated object is acquired according to a preset period, and the customer information summary sheet at least comprises index data corresponding to multiple evaluation dimensions; a target coefficient corresponding to the to-be-evaluated object is inquired in a plurality of preset coefficients recorded in a preset smoking index parameter table, and the preset coefficients comprise a combined dimension coefficient and individual dimension coefficients corresponding to a plurality of evaluation dimensions; evaluating the index data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension; and combining the plurality of individual dimension scores with the combined dimension score to obtain a smoking index of the to-be-evaluated object, the smoking index being used for evaluating the credit risk of the to-be-evaluated object. The technical problem that credit risk assessment cannot be carried out on enterprise customers in a credit scene is solved.

Description

Technical Field

[0001] The present invention relates to the field of big data, and in particular, to a method, device, electronic device, and computer program product for evaluating credit risk. Background Art

[0002] In the credit business scenario, the business targets corporate customers. During the credit cycle, the enterprise will continue its corporate operations. However, during the process of corporate operation, its operation status may change.

[0003] In the case of evaluating the credit risk of an enterprise itself, the existing solutions cannot accurately evaluate aspects such as the enterprise's credit and litigation situation.

[0004] In view of the problem that the credit risk of corporate customers in the credit scenario cannot be evaluated, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, electronic device, and computer program product for evaluating credit risk, so as to at least solve the technical problem that the credit risk of corporate customers in the credit scenario cannot be evaluated.

[0006] According to one aspect of the embodiments of the present invention, a method for evaluating credit risk is provided, including: obtaining a summary table of customer information of an object to be evaluated according to a preset period, where the summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions; querying a target coefficient corresponding to the object to be evaluated among multiple preset coefficients recorded in a preset smoke index parameter table, where the preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to the multiple evaluation dimensions respectively; evaluating the index data corresponding to the same evaluation dimension based on the individual dimension coefficients to obtain an individual dimension score corresponding to each evaluation dimension; combining the multiple individual dimension scores with the combined dimension coefficient to obtain a smoke index of the object to be evaluated, where the smoke index is used to evaluate the credit risk of the object to be evaluated.

[0007] Optionally, querying the target coefficient corresponding to the object to be evaluated among multiple preset coefficients recorded in the preset smoke index parameter table includes: obtaining a target label of the object to be evaluated; querying the target coefficient corresponding to the target label among the multiple preset coefficients recorded in the preset smoke index parameter table, where the preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object with different preset labels.

[0008] Optionally, the preset smoke index parameter table includes: a preset individual dimension parameter table and a preset combination dimension parameter table. Among the multiple preset coefficients recorded in the preset smoke index parameter table, querying the target coefficient corresponding to the object to be evaluated includes: querying the multiple individual dimension coefficients corresponding to the object to be evaluated among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, wherein the preset individual dimension parameter table is used to record the correspondence between each of the preset objects and the multiple preset individual dimension coefficients; querying the combination dimension coefficient corresponding to the object to be evaluated among the multiple preset combination dimension coefficients pre-recorded in the preset combination dimension parameter table, wherein the preset combination dimension parameter table is used to record the correspondence between the preset combination dimension coefficient and the preset object; and determining the combination dimension coefficient corresponding to the object to be evaluated and the multiple individual dimension coefficients as the target coefficient corresponding to the object to be evaluated.

[0009] Optionally, querying the multiple individual dimension coefficients corresponding to the object to be evaluated among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table includes: querying the multiple preset individual dimension coefficients corresponding to the object to be evaluated among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, respectively as the individual dimension coefficients to be queried; based on the multiple evaluation dimensions in the customer information summary table, querying the individual dimension coefficient to be queried corresponding to each evaluation dimension among the multiple individual dimension coefficients to be queried, as the individual dimension coefficient, wherein each individual dimension coefficient to be queried is pre-configured with a corresponding preset evaluation dimension.

[0010] Optionally, among the multiple preset combination dimension coefficients pre-recorded in the preset combination dimension parameter table, querying the combination dimension coefficient corresponding to the object to be evaluated includes: querying the multiple target dimension items corresponding to the object to be evaluated among the multiple preset dimension items recorded in the preset combination dimension parameter table, wherein the preset combination dimension parameter table is used to record the correspondence between each of the preset objects and the multiple preset dimension items; and determining the product of the multiple target dimension items as the combination dimension coefficient.

[0011] Optionally, the indicator data corresponding to the same evaluation dimension is evaluated based on the individual dimension coefficient to obtain the individual dimension score corresponding to each evaluation dimension, including: using the preset evaluation rules pre-configured for each evaluation dimension to score each indicator data to obtain the single dimension score corresponding to each evaluation dimension; multiplying the single dimension score corresponding to the same evaluation dimension by the individual dimension coefficient to determine the single dimension revised score corresponding to the evaluation dimension; and accumulating the single dimension revised scores corresponding to multiple evaluation dimensions to obtain the individual dimension score.

[0012] Optionally, determining the smoking index of the object to be evaluated by combining the individual dimension score with the combined dimension coefficient in the target coefficient includes: determining the product of the individual dimension score of the object to be evaluated and the combined dimension coefficient as the smoking index.

[0013] According to another aspect of the embodiments of the present invention, there is also provided an apparatus for evaluating credit risk, including: an acquisition module configured to acquire a summary table of customer information of an object to be evaluated according to a preset period, where the summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions; a query module configured to query a target coefficient corresponding to the object to be evaluated from a plurality of preset coefficients recorded in a preset smoking index parameter table, where the preset smoking index parameter table is used to record the correspondence between each preset coefficient and a preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to the multiple evaluation dimensions respectively; an evaluation module configured to evaluate the index data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension; a determination module configured to combine the multiple individual dimension scores with the combined dimension coefficient to obtain the smoking index of the object to be evaluated, where the smoking index is used to evaluate the credit risk of the object to be evaluated.

[0014] According to another aspect of the embodiments of the present invention, there is also provided an electronic device including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to execute the above-mentioned method for evaluating credit risk through the computer program.

[0015] According to another aspect of the embodiments of the present invention, there is also provided a computer program product including computer instructions, and when the computer instructions are executed by a processor, the steps of the above-mentioned method for evaluating credit risk are implemented.

[0016] In an embodiment of the present invention, a summary table of customer information of an object to be evaluated is obtained according to a preset period. The summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions. Among the multiple preset coefficients recorded in a preset smoke index parameter table, a target coefficient corresponding to the object to be evaluated is queried. The preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object. The preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions respectively. The index data corresponding to the same evaluation dimension is evaluated based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension. The multiple individual dimension scores are combined with the combined dimension coefficient to obtain a smoke index of the object to be evaluated. The smoke index is used to evaluate the credit risk of the object to be evaluated. Thus, within a credit cycle, according to the preset period, the index data of the object to be evaluated in multiple evaluation dimensions can be evaluated according to the individual dimension coefficient and the combined dimension coefficient to obtain the smoke index for evaluating the object to be evaluated. Furthermore, this smoke index can represent the credit risk of the object to be evaluated, achieving the technical effect of evaluating the credit risk of the object to be evaluated, and further solving the technical problem of being unable to evaluate the credit risk of enterprise customers in a credit scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0018] Figure 1 is a flowchart of a method for evaluating credit risk according to an embodiment of the present invention;

[0019] Figure 2 is a schematic diagram of a calculation process of a smoke index according to an embodiment of the present invention;

[0020] Figure 3 is a schematic diagram of an apparatus for evaluating credit risk according to an embodiment of the present invention;

[0021] Figure 4 is a structural block diagram of a computer terminal according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present invention, an embodiment of a method for evaluating credit risk is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0025] Figure 1 is a flowchart of a method for evaluating credit risk according to an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0026] Step S102, obtain a summary table of customer information of the object to be evaluated according to a preset period, where the summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions;

[0027] Step S104, query the target coefficient corresponding to the object to be evaluated among the multiple preset coefficients recorded in the preset smoke index parameter table, where the preset smoke index parameter table is used to record the corresponding relationship between each preset coefficient and a preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions respectively;

[0028] Step S106, evaluate the index data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension;

[0029] Step S108, combine multiple individual dimension scores with the combined dimension coefficient to obtain the smoke index of the object to be evaluated, where the smoke index is used to evaluate the credit risk of the object to be evaluated.

[0030] In an embodiment of the present invention, a summary table of customer information of an object to be evaluated is obtained at a preset period. The summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions. Among multiple preset coefficients recorded in a preset smoke index parameter table, a target coefficient corresponding to the object to be evaluated is queried. The preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object. The preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions respectively. The index data corresponding to the same evaluation dimension is evaluated based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension. The multiple individual dimension scores are combined with the combined dimension coefficient to obtain a smoke index of the object to be evaluated. The smoke index is used to evaluate the credit risk of the object to be evaluated. Therefore, within a credit cycle, the index data of the object to be evaluated in multiple evaluation dimensions can be evaluated according to the individual dimension coefficient and the combined dimension coefficient at a preset period to obtain the smoke index of the object to be evaluated. Furthermore, the smoke index can represent the credit risk of the object to be evaluated, achieving the technical effect of evaluating the credit risk of the object to be evaluated, and further solving the technical problem of being unable to evaluate the credit risk of enterprise customers in the credit scenario.

[0031] In the above step S102, the object to be evaluated can be an enterprise customer of a credit business, and the enterprise customer is within the credit cycle of the credit business.

[0032] In the above step S102, the preset period is within the credit cycle, and the time granularity of the preset period is smaller than the time granularity of the credit cycle.

[0033] In the above step S102, the summary table of customer information can be updated at a preset period. The smaller the time granularity of the preset period, the higher the update frequency of the summary table of customer information, and even real-time update of the summary table of customer information can be achieved.

[0034] Optionally, the summary table of customer information can be obtained in the following manner:

[0035] Step S1021, obtain enterprise industrial and commercial data from an external data source according to the list of enterprise customers of the credit business. Perform in-house and external data association through the "organization code" in the basic customer information, and preferentially use the "organization code" to obtain relevant data of associated customers in the external data source. If the "organization code" cannot be successfully associated, then switch to using the "customer name" for association and acquisition. If data cannot be associated in both of the above two methods, it is regarded that the data cannot be obtained, and corresponding records and processing will be carried out.

[0036] Step S1022: Further process the industrial and commercial information issued by the open platform to form a summary table of enterprise industrial and commercial information (i.e., the summary table of customer information). This summary table comprehensively covers various industrial and commercial information of enterprise customers, providing a basis for subsequent extraction of indicator data.

[0037] In the above step S102, the indicator data in the summary table of customer information at least includes: enterprise information change and risk information identification; among them, the risk information identification is further refined into periodic statistical indicators, new addition indicators, and change indicators; the periodic statistical indicators are statistically calculated based on the historical data accumulated within the bank at regular intervals; the new addition indicators are used to identify newly added risk information; the change indicators are used to identify the situation where there was an existing identification before but the information has changed.

[0038] Optionally, based on the summary table of customer information, tags can be assigned to enterprise customers according to key information items, such as enterprise scale, industry, etc., for more refined classification and analysis.

[0039] Optionally, the indicator data extracted from the summary table of customer information is processed through a multi - rule strict - from, data - missing handling mechanism, and anti - duplicate triggering mechanism for early warning rule processing. This process aims to deeply analyze the indicator data according to preset rules and algorithms to determine the triggered risk level.

[0040] Optionally, according to the strict - from rules, different rules are compared under the same early warning signal, and the result of the highest risk level is taken as the result of this early warning signal. Classifying signals according to the risk level helps users more intuitively understand the risk situation and take corresponding countermeasures.

[0041] In the above step S104, the preset smoke - index parameter table is used to record the corresponding relationships between multiple groups of preset objects and preset coefficients. According to the preset smoke - index parameter table, the preset object matching the object to be evaluated can be determined, and the preset coefficient corresponding to this preset object is the target coefficient corresponding to the object to be evaluated. Furthermore, based on the determined target coefficient, the indicator data of the object to be evaluated can be evaluated for the smoke index.

[0042] In the above step S104, the target coefficient at least includes: individual - dimension coefficient and combined - dimension coefficient, where each object to be evaluated includes: multiple individual - dimension coefficients and a unique combined - dimension coefficient.

[0043] In the above step S104, the evaluation dimensions targeted by the individual - dimension coefficient at least include: enterprise financial data, enterprise governance data, enterprise judicial public opinion data, enterprise credit behavior data, enterprise account risk data, enterprise associated risk data.

[0044] In the above step S104, the evaluation dimensions targeted by the individual dimension coefficients may further include: enterprise market performance data and real estate enterprise-specific data.

[0045] In the above step S104, the combined dimension coefficients may include: a plurality of preset dimension items, where the plurality of preset dimension items at least include: industry adjustment coefficient, regional adjustment coefficient, enterprise nature adjustment coefficient, and enterprise scale adjustment coefficient.

[0046] In the above step S106, the individual dimension score is jointly determined according to the single dimension scores obtained by the object to be evaluated in multiple evaluation dimensions and the individual dimension coefficients corresponding to each evaluation dimension.

[0047] In the above step S106, the influence degree of each evaluation dimension on the evaluation result is different. Therefore, an individual dimension coefficient corresponding to each evaluation dimension can be set.

[0048] In the above step S106, the smoke index at least includes: the product of the individual dimension score of the object to be evaluated and the combined dimension coefficient.

[0049] The above steps S104 and S108 can be implemented by a smoke index model, and the smoke index model can be a neural network model.

[0050] It should be noted that the smoke index model proposed in this application comprehensively considers the risk changes in multiple fields such as enterprise industry and commerce, justice, credit, and finance, applies big data technology and advanced algorithms, combines traditional offline risk assessment experience with a quantitative model, identifies abnormal risk behaviors of customers, and completes risk assessment and early warning of customers.

[0051] Optionally, the above smoke index model can be loaded into the memory. For example, the original data of the smoke index model can be loaded from a non-volatile memory to a volatile memory so that the processor can run the smoke index model. The original data of the smoke index model refers to the data that has not been processed, usually including the parameters and structure data of the smoke index model. The structure data can be the calculation relationship based on the parameters, such as the forward propagation calculation relationship between intermediate layers and between neurons. Specifically, the structure data can include the code related to the structure of the smoke index model, such as the code used to execute the relevant calculations between intermediate layers and between neurons.

[0052] In one implementation, an area for loading the smoke index model can be divided in the memory, which can include a structure data storage area and a parameter storage area. The structure data storage area is used to store the code related to the structure, and the parameters it references can point to the addresses of specific parameters in the parameter storage area through pointers. During the training process of the smoke index model, the parameters may need to be updated frequently, so only the parameter values in the parameter storage area need to be updated.

[0053] As an alternative embodiment, among the multiple preset coefficients recorded in the preset smoke index parameter table, querying the target coefficient corresponding to the object to be evaluated includes: obtaining the target label of the object to be evaluated; among the multiple preset coefficients recorded in the preset smoke index parameter table, querying the target coefficient corresponding to the target label, where the preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object with different preset labels.

[0054] In the above embodiment of the present application, the preset smoke index parameter table can use labels to represent the preset objects corresponding to the preset coefficients. Thus, when querying the target coefficient corresponding to the object to be evaluated using the preset smoke index parameter table, the target label of the object to be evaluated can be determined first, and then based on this target label, the target coefficient corresponding to this target label can be queried from the preset smoke index parameter table to achieve the selection of the target coefficient.

[0055] Optionally, the target label can represent the type of the object to be evaluated, such as the enterprise scale of the object to be evaluated, the industry, etc.

[0056] As an alternative embodiment, the preset smoke index parameter table includes: a preset individual dimension parameter table and a preset combined dimension parameter table. Among the multiple preset coefficients recorded in the preset smoke index parameter table, querying the target coefficient corresponding to the object to be evaluated includes: among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, querying the multiple individual dimension coefficients corresponding to the object to be evaluated, where the preset individual dimension parameter table is used to record the correspondence between each preset object and the multiple preset individual dimension coefficients; among the multiple preset combined dimension coefficients pre-recorded in the preset combined dimension parameter table, querying the combined dimension coefficient corresponding to the object to be evaluated, where the preset combined dimension parameter table is used to record the correspondence between the preset combined dimension coefficient and the preset object; determining the combined dimension coefficient and the multiple individual dimension coefficients corresponding to the object to be evaluated as the target coefficient corresponding to the object to be evaluated.

[0057] In the above embodiment of the present application, the target coefficient includes: an individual dimension coefficient and a combined dimension coefficient. The preset smoke index parameter table includes: a preset individual dimension parameter table for determining the individual dimension coefficient and a preset combined dimension parameter table for determining the combined dimension coefficient. Thus, the individual dimension coefficient of the object to be evaluated can be obtained through the preset individual dimension parameter table, and the combined dimension coefficient of the object to be evaluated can be obtained through the preset combined dimension parameter table, to achieve the determination of the individual dimension coefficient and the combined dimension coefficient.

[0058] As an alternative embodiment, the preset individual dimension parameter table further includes: a preset correction coefficient table. Among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, querying the multiple individual dimension coefficients corresponding to the object to be evaluated includes: querying the target correction coefficient corresponding to the object to be evaluated among the multiple preset correction coefficients recorded in the preset correction coefficient table, where the preset correction coefficient table is used for the corresponding relationship between each preset correction coefficient and a preset object; performing a multiplication operation on the target correction coefficient and each of the preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table respectively to obtain the multiple individual dimension coefficients corresponding to the object to be evaluated.

[0059] In the above embodiment of the present application, the preset individual dimension coefficients recorded in the preset correction coefficient table can be used by multiple preset objects. The preset correction coefficient table can be a sub-table in the preset individual dimension parameter table, used to represent the differences between different preset objects. Thus, when determining the individual dimension coefficients of the object to be evaluated, the target correction coefficient representing the difference between the object to be evaluated and other preset objects can be determined in the preset correction coefficient table first, and then a multiplication operation is performed on the target correction coefficient and each of the preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table respectively, so as to obtain the multiple individual dimension coefficients specific to the object to be evaluated.

[0060] Optionally, there can be multiple target correction coefficients corresponding to each object to be evaluated. The number of target correction coefficients of the same object to be evaluated can be the same as the number of evaluation dimensions of the object to be evaluated. For example, the number of target correction coefficients of the same object to be evaluated can be the same as the number of preset individual dimension coefficients of the object to be evaluated. The target correction coefficients and the preset individual dimension coefficients are in one-to-one correspondence. The product of the target correction coefficient of the same evaluation dimension and the corresponding preset individual dimension coefficient is the individual dimension coefficient of this evaluation dimension.

[0061] As an alternative embodiment, among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, querying the multiple individual dimension coefficients corresponding to the object to be evaluated includes: querying the multiple preset individual dimension coefficients corresponding to the object to be evaluated among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, and respectively taking them as the individual dimension coefficients to be queried; according to the multiple evaluation dimensions in the customer information summary table, querying the individual dimension coefficient to be queried corresponding to each evaluation dimension among the multiple individual dimension coefficients to be queried as the individual dimension coefficient, where each individual dimension coefficient to be queried is pre-configured with a corresponding preset evaluation dimension.

[0062] In the above embodiments of the present application, the preset individual dimension parameter table can record multiple preset individual dimension coefficients, and the preset individual dimension coefficients recorded in the preset individual dimension parameter table may exceed the individual dimension coefficients required to evaluate the object to be evaluated. Therefore, when querying multiple individual dimension coefficients corresponding to the object to be evaluated from the preset individual dimension parameter table, the multiple individual dimension coefficients to be queried corresponding to the object to be evaluated can be determined first, and then based on the evaluation dimensions of the object to be evaluated recorded in the customer information summary table, the individual dimension coefficients to be queried corresponding to each evaluation dimension are queried one by one as the individual dimension coefficients, and then the query results of the multiple individual dimension coefficients to be queried are summarized to obtain the individual dimension coefficients of the object to be evaluated.

[0063] As an alternative embodiment, querying the combined dimension coefficient corresponding to the object to be evaluated among the multiple preset combined dimension coefficients pre-recorded in the preset combined dimension parameter table includes: querying multiple target dimension items corresponding to the object to be evaluated among the multiple preset dimension items recorded in the preset combined dimension parameter table, where the preset combined dimension parameter table is used to record the corresponding relationship between each preset object and the multiple preset dimension items; determining the product of the multiple target dimension items as the combined dimension coefficient.

[0064] In the above embodiments of the present application, the preset combined dimension parameter table can record multiple preset dimension items, and the combined dimension coefficient can be the product of the multiple preset dimension items. Therefore, when determining the combined dimension coefficient of the object to be evaluated, the multiple target dimension items of the object to be evaluated can be determined first, and then the product of the multiple target dimension items can be calculated to obtain the combined dimension coefficient of the object to be evaluated.

[0065] It should be noted that the preset combined dimension parameter table is used to record the corresponding relationship between the preset object and the multiple preset dimension items, and the multiple preset dimension items corresponding to different preset objects can be different. Therefore, when it is necessary to determine the combined dimension coefficient of the object to be evaluated, the multiple target dimension items corresponding to the object to be evaluated can be queried from the preset combined dimension parameter table first, and then the product operation is performed on the multiple queried target dimension items to obtain the unique combined dimension coefficient of the object to be evaluated.

[0066] As an alternative embodiment, evaluating the index data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain the individual dimension score corresponding to each evaluation dimension includes: using the preset evaluation rules pre-configured for each evaluation dimension to score each index data to obtain the single dimension score corresponding to each evaluation dimension; determining the product of the single dimension score corresponding to the same evaluation dimension and the individual dimension coefficient as the single dimension correction score corresponding to the evaluation dimension; accumulating the single dimension correction scores corresponding to the multiple evaluation dimensions to obtain the individual dimension score.

[0067] In the above embodiments of the present application, by using the preset evaluation rules pre-configured for each evaluation dimension, the index data of this evaluation dimension can be scored to obtain the single-dimension score corresponding to this evaluation dimension. Then, the product of the single-dimension score of the same evaluation dimension and the individual dimension coefficient is the single-dimension corrected score corresponding to this evaluation dimension. Then, by performing a summation operation on the single-dimension corrected scores corresponding to all evaluation dimensions, the individual dimension score of the object to be evaluated can be obtained.

[0068] Optionally, the individual dimension score can be obtained by performing a weighted summation on the single-dimension scores corresponding to all evaluation dimensions in the object to be evaluated with the individual dimension coefficient as the weight.

[0069] As an alternative embodiment, determining the smoke index of the object to be evaluated by combining the individual dimension score with the combined dimension coefficient in the target coefficient includes: determining the product of the individual dimension score of the object to be evaluated and the combined dimension coefficient as the smoke index.

[0070] In the above embodiments of the present application, the smoke index can be obtained by performing a multiplication operation on the individual dimension score and the combined dimension coefficient.

[0071] Optionally, the smoke index = f(individual dimension smoke sum B, combined dimension adjustment C); where, Dimension single signal b i can be the single-dimension score corresponding to each evaluation dimension, and dimension weight k i can be the individual dimension coefficient corresponding to each evaluation dimension, The adjustment coefficient can be a preset dimension item (or the target dimension item of the object to be evaluated).

[0072] Figure 2 is a schematic diagram of a calculation process of a smoke index according to an embodiment of the present invention, as Figure 2 shown, and includes the following steps:

[0073] Step S201, screening all customers that meet the calculation conditions according to the customer information data (that is, determining the object to be evaluated). For example, by identifying the customer type of the object to be evaluated and obtaining the customer information table of the object to be evaluated according to the identified customer type, this customer information table is used to record customer information data. By identifying the customer information data, a warning signal can be triggered, and then the customer who triggers the warning signal is used as the object to be evaluated, and the smoke index is determined based on the customer information summary table of this customer.

[0074] Step S202, for the index data of each evaluation dimension, obtain the single-dimension score of each evaluation dimension.

[0075] Step S203: For the individual dimension weight parameter of the smoke index, query the adjusted weight of the individual dimension (i.e., the individual dimension coefficient) according to the smoke index parameter table (i.e., the preset smoke index parameter table).

[0076] Table 1 is a schematic table of an individual dimension coefficient according to an embodiment of the present invention. As shown in Table 1, for different dimension weight parameters (such as individual dimension coefficients), set the individual dimensions (such as preset individual dimension coefficients) as b1, b2, b3... Let the adjusted weight (such as the target correction coefficient corresponding to the object to be evaluated) be M1, M2, M3... Then the individual dimension coefficient corresponding to the b1 evaluation dimension is b1*M1, and the individual dimension coefficient corresponding to the b2 evaluation dimension is b2*M2... The adjustment methods of the individual dimension coefficients for different evaluation dimensions are shown in the following table:

[0077] Table 1

[0078] Adjustment weight of individual dimension 1 b1*M1 ... ... Adjustment weight of individual dimension 2 b2*M2 ... ... Adjustment weight of individual dimension 3 b3*M3 ... ... Adjustment weight of individual dimension 4 b4*M4 ... ... Adjustment weight of individual dimension 5 b5*M5 ... ...

[0079] Optionally, after determining the single-dimension scores of each evaluation dimension and the adjusted weight of the individual dimension (i.e., the individual dimension coefficient), weighted calculation can be performed to obtain the individual dimension total (i.e., the individual dimension score).

[0080] Specifically, where i = individual dimension 1, individual dimension 2,..., individual dimension N.

[0081] It should be noted that for the customer scores of each evaluation dimension, those exceeding 100 points are calculated as 100 points.

[0082] In the above embodiments of the present application, early warning monitoring is carried out with the customer as the center. For the sub-dimension scores other than the individual dimension N, before counting the number of customer early warnings, the data is de-duplicated, and only one record is retained for the same customer and the same signal name.

[0083] Optionally, for the sub-dimension scores other than the individual dimension N, count the data with a triggering time within the past 1 year, a triggering method of system triggering, system triggering & manual triggering, and a signal status of to be determined, being determined, already determined, already determined (system determined).

[0084] Step S204: Determine the combined dimension coefficient. For example, query the combined dimension coefficient according to the smoke index parameter table (i.e., the preset smoke index parameter table).

[0085] Specifically,

[0086] Optionally, according to different customer tags, weight parameter tables (such as preset combined dimension parameter tables) are designed respectively, weights are assigned to the tags, and finally the combined dimension adjustment measurement result is calculated.

[0087] Step S205, determine the smoke index.

[0088] Specifically, the smoke index = f(total individual - dimension smoke B, adjusted combined - dimension C).

[0089] According to an embodiment of the present invention, there is also provided an embodiment of an apparatus for evaluating credit risk. It should be noted that this apparatus for evaluating credit risk can be used to execute the method for evaluating credit risk in the embodiment of the present invention, and the method for evaluating credit risk in the embodiment of the present invention can be executed in this apparatus for evaluating credit risk.

[0090] Figure 3 is a schematic diagram of an apparatus for evaluating credit risk according to an embodiment of the present invention, as Figure 3 shown. The apparatus may include: an acquisition module 32, configured to acquire a summary table of customer information of an object to be evaluated according to a preset period, where the summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions; a query module 34, configured to query a target coefficient corresponding to the object to be evaluated from a plurality of preset coefficients recorded in a preset smoke - index parameter table, where the preset smoke - index parameter table is used to record the corresponding relationship between each preset coefficient and a preset object, and the preset coefficients include: a combined - dimension coefficient and individual - dimension coefficients corresponding to multiple evaluation dimensions respectively; an evaluation module 36, configured to evaluate the index data corresponding to the same evaluation dimension based on the individual - dimension coefficient to obtain an individual - dimension score corresponding to each evaluation dimension; a determination module 38, configured to combine a plurality of individual - dimension scores and the combined - dimension coefficient to obtain a smoke index of the object to be evaluated, where the smoke index is used to evaluate the credit risk of the object to be evaluated.

[0091] It should be noted that the acquisition module 32 in this embodiment can be used to execute step S102 in the embodiment of the present application, the query module 34 in this embodiment can be used to execute step S104 in the embodiment of the present application, the evaluation module 36 in this embodiment can be used to execute step S106 in the embodiment of the present application, and the determination module 38 in this embodiment can be used to execute step S108 in the embodiment of the present application. The examples and application scenarios implemented by the above - mentioned modules and the corresponding steps are the same, but are not limited to the content disclosed in the above - mentioned embodiments.

[0092] In an embodiment of the present invention, a summary table of customer information of an object to be evaluated is obtained at a preset period, where the summary table of customer information at least includes: index data corresponding to multiple evaluation dimensions; among multiple preset coefficients recorded in a preset smoke index parameter table, a target coefficient corresponding to the object to be evaluated is queried, where the preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions respectively; the index data corresponding to the same evaluation dimension is evaluated based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension; multiple individual dimension scores are combined with the combined dimension coefficient to obtain a smoke index of the object to be evaluated, where the smoke index is used to evaluate the credit risk of the object to be evaluated. Thus, within a credit cycle, the index data of the object to be evaluated in multiple evaluation dimensions can be evaluated according to the individual dimension coefficient and the combined dimension coefficient at a preset period to obtain the smoke index for evaluating the object to be evaluated. Furthermore, the smoke index can represent the credit risk of the object to be evaluated, achieving the technical effect of evaluating the credit risk of the object to be evaluated, and further solving the technical problem of being unable to evaluate the credit risk of enterprise customers in a credit scenario.

[0093] As an alternative embodiment, the query module includes: an acquisition unit, configured to acquire a target label of the object to be evaluated; a query unit, configured to query, among multiple preset coefficients recorded in a preset smoke index parameter table, a target coefficient corresponding to the target label, where the preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object with different preset labels.

[0094] As an alternative embodiment, the preset smoke index parameter table includes: a preset individual dimension parameter table and a preset combined dimension parameter table. The query module includes: an individual dimension query unit, configured to query, among multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, multiple individual dimension coefficients corresponding to the object to be evaluated, where the preset individual dimension parameter table is used to record the correspondence between each preset object and multiple preset individual dimension coefficients; a combined dimension query unit, configured to query, among multiple preset combined dimension coefficients pre-recorded in the preset combined dimension parameter table, a combined dimension coefficient corresponding to the object to be evaluated, where the preset combined dimension parameter table is used to record the correspondence between the preset combined dimension coefficient and the preset object; a first determination unit, configured to determine the combined dimension coefficient and multiple individual dimension coefficients corresponding to the object to be evaluated as the target coefficient corresponding to the object to be evaluated.

[0095] As an alternative embodiment, the individual dimension query unit includes: a first query subunit, configured to query, from multiple preset individual dimension coefficients pre-recorded in a preset individual dimension parameter table, multiple preset individual dimension coefficients corresponding to an object to be evaluated, and respectively use them as the individual dimension coefficients to be queried; a second query subunit, configured to query, from the multiple individual dimension coefficients to be queried, an individual dimension coefficient corresponding to each evaluation dimension according to multiple evaluation dimensions in a customer information summary table, as the individual dimension coefficient, where each individual dimension coefficient to be queried is pre-configured with a corresponding preset evaluation dimension.

[0096] As an alternative embodiment, the combined dimension query unit includes: a third query subunit, configured to query, from multiple preset dimension items recorded in a preset combined dimension parameter table, multiple target dimension items corresponding to an object to be evaluated, where the preset combined dimension parameter table is used to record the corresponding relationship between each preset object and multiple preset dimension items; a determination subunit, configured to determine the product of the multiple target dimension items as the combined dimension coefficient.

[0097] As an alternative embodiment, the evaluation module includes: an evaluation unit, configured to score each indicator data by using a preset evaluation rule pre-configured for each evaluation dimension, to obtain a single-dimension score corresponding to each evaluation dimension; a second determination unit, configured to determine the product of the single-dimension score corresponding to the same evaluation dimension and the individual dimension coefficient as the single-dimension corrected score corresponding to the evaluation dimension; an accumulation unit, configured to accumulate the single-dimension corrected scores corresponding to multiple evaluation dimensions to obtain the individual dimension score.

[0098] As an alternative embodiment, the determination module includes: a third determination module, configured to determine the product of the individual dimension score of the object to be evaluated and the combined dimension coefficient as the smoke index.

[0099] An embodiment of the present invention may provide an electronic device, which may be a computer terminal, and the computer terminal may be any one of the computer terminal devices in a computer terminal group. Optionally, in this embodiment, the above computer terminal may also be replaced with a terminal device such as a mobile terminal.

[0100] Optionally, in this embodiment, the above computer terminal may be located in at least one of multiple network devices in a computer network.

[0101] In this embodiment, the above computer terminal may execute the program code of the following steps in the credit risk assessment method:

[0102] Obtain a summary table of customer information of the object to be evaluated according to a preset period. The summary table of customer information includes at least: index data corresponding to multiple evaluation dimensions. Among the multiple preset coefficients recorded in the preset smoke index parameter table, query the target coefficient corresponding to the object to be evaluated. The preset smoke index parameter table is used to record the corresponding relationship between each preset coefficient and the preset object. The preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions respectively. Evaluate the index data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain the individual dimension score corresponding to each evaluation dimension. Combine the multiple individual dimension scores with the combined dimension coefficient to obtain the smoke index of the object to be evaluated, where the smoke index is used to evaluate the credit risk of the object to be evaluated.

[0103] Figure 4 is a structural block diagram of a computer terminal according to an embodiment of the present invention, as Figure 4 shown. The computer terminal 42 may include: one or more (only one is shown in the figure) processors 42 and a memory 44.

[0104] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the credit risk assessment method and device in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above-mentioned credit risk assessment method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely set relative to the processor, and these remote memories can be connected to the terminal 40 through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0105] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: Obtain a summary table of customer information of the object to be evaluated according to a preset period. The summary table of customer information includes at least: index data corresponding to multiple evaluation dimensions. Among the multiple preset coefficients recorded in the preset smoke index parameter table, query the target coefficient corresponding to the object to be evaluated. The preset smoke index parameter table is used to record the corresponding relationship between each preset coefficient and the preset object. The preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions respectively. Evaluate the index data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain the individual dimension score corresponding to each evaluation dimension. Combine the multiple individual dimension scores with the combined dimension coefficient to obtain the smoke index of the object to be evaluated, where the smoke index is used to evaluate the credit risk of the object to be evaluated.

[0106] Optionally, the above-mentioned processor may also execute the program code of the following steps: Obtain the target label of the object to be evaluated; Among the multiple preset coefficients recorded in the preset smoke index parameter table, query the target coefficient corresponding to the target label, where the preset smoke index parameter table is used to record the correspondence between each preset coefficient and the preset object with different preset labels.

[0107] Optionally, the preset smoke index parameter table includes: a preset individual dimension parameter table and a preset combined dimension parameter table. The above-mentioned processor may also execute the program code of the following steps: Among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, query the multiple individual dimension coefficients corresponding to the object to be evaluated, where the preset individual dimension parameter table is used to record the correspondence between each preset object and the multiple preset individual dimension coefficients; Among the multiple preset combined dimension coefficients pre-recorded in the preset combined dimension parameter table, query the combined dimension coefficient corresponding to the object to be evaluated, where the preset combined dimension parameter table is used to record the correspondence between the preset combined dimension coefficient and the preset object; Determine the combined dimension coefficient corresponding to the object to be evaluated and the multiple individual dimension coefficients as the target coefficient corresponding to the object to be evaluated.

[0108] Optionally, the above-mentioned processor may also execute the program code of the following steps: Among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, query the multiple preset individual dimension coefficients corresponding to the object to be evaluated, and respectively use them as the individual dimension coefficients to be queried; According to the multiple evaluation dimensions in the customer information summary table, among the multiple individual dimension coefficients to be queried, query the individual dimension coefficient corresponding to each evaluation dimension as the individual dimension coefficient, where each individual dimension coefficient to be queried is pre-configured with a corresponding preset evaluation dimension.

[0109] Optionally, the above-mentioned processor may also execute the program code of the following steps: Among the multiple preset dimension items recorded in the preset combined dimension parameter table, query the multiple target dimension items corresponding to the object to be evaluated, where the preset combined dimension parameter table is used to record the correspondence between each preset object and the multiple preset dimension items; Determine the product of the multiple target dimension items as the combined dimension coefficient.

[0110] Optionally, the above-mentioned processor may also execute the program code of the following steps: Use the preset evaluation rules pre-configured for each evaluation dimension to score each index data to obtain the single dimension score corresponding to each evaluation dimension; Determine the product of the single dimension score corresponding to the same evaluation dimension and the individual dimension coefficient as the single dimension corrected score corresponding to the evaluation dimension; Accumulate the single dimension corrected scores corresponding to the multiple evaluation dimensions to obtain the individual dimension score.

[0111] Optionally, the above-mentioned processor may also execute the program code of the following steps: determining the product of the individual dimension score of the object to be evaluated and the combined dimension coefficient as the smoke index.

[0112] Those of ordinary skill in the art can understand that Figure 4 the structure shown is only schematic, and the computer terminal can also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, and terminal devices such as Mobile Internet Devices (MID), PAD, etc. Figure 4 It does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 40 may further include more or fewer components (such as a network interface, a display device, etc.) than those shown in Figure 4 or have a different configuration from that shown in Figure 4 shown.

[0113] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a computer program, and the computer program can be stored in a non-volatile medium. The non-volatile storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc, etc.

[0114] An embodiment of the present invention also provides a non-volatile storage medium. Optionally, in this embodiment, the above-mentioned non-volatile storage medium may be used to store the program code executed by the credit risk assessment method provided in the above embodiment.

[0115] Optionally, in this embodiment, the above-mentioned non-volatile storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0116] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: obtaining a customer information summary table of the object to be evaluated according to a preset period, wherein the customer information summary table includes at least: indicator data corresponding to multiple evaluation dimensions; querying a target coefficient corresponding to the object to be evaluated among multiple preset coefficients recorded in a preset smoke index parameter table, wherein the preset smoke index parameter table is used to record the correspondence between each preset coefficient and the preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to multiple evaluation dimensions; evaluating the indicator data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain an individual dimension score corresponding to each evaluation dimension; combining multiple individual dimension scores with the combined dimension coefficient to obtain a smoke index of the object to be evaluated, wherein the smoke index is used to evaluate the credit risk of the object to be evaluated.

[0117] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining a target label of the object to be evaluated; querying a target coefficient corresponding to the target label among multiple preset coefficients recorded in a preset smoke index parameter table, wherein the preset smoke index parameter table is used to record the correspondence between each preset coefficient and a preset object with a different preset label.

[0118] Optionally, in this embodiment, the preset smoke index parameter table includes: a preset individual dimension parameter table and a preset combination dimension parameter table, and the non-volatile storage medium is configured to store a program code for executing the following steps: querying multiple individual dimension coefficients corresponding to the object to be evaluated among multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, wherein the preset individual dimension parameter table is used to record the correspondence between each preset object and the multiple preset individual dimension coefficients; querying the combination dimension coefficient corresponding to the object to be evaluated among the multiple preset combination dimension coefficients pre-recorded in the preset combination dimension parameter table, wherein the preset combination dimension parameter table is used to record the correspondence between the preset combination dimension coefficient and the preset object; determining the combination dimension coefficient and the multiple individual dimension coefficients corresponding to the object to be evaluated as the target coefficient corresponding to the object to be evaluated.

[0119] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining a target label of the object to be evaluated; querying a plurality of preset individual dimension coefficients corresponding to the object to be evaluated from a plurality of preset individual dimension coefficients pre-recorded in a preset individual dimension parameter table, respectively serving as individual dimension coefficients to be queried; based on a plurality of evaluation dimensions in a customer information summary table, querying a plurality of individual dimension coefficients to be queried corresponding to each evaluation dimension as an individual dimension coefficient, wherein each individual dimension coefficient to be queried is pre-configured with a corresponding preset evaluation dimension.

[0120] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a target label of an object to be evaluated; querying, among a plurality of preset dimension items recorded in a preset combined dimension parameter table, a plurality of target dimension items corresponding to the object to be evaluated, where the preset combined dimension parameter table is used to record the correspondence between each preset object and the plurality of preset dimension items; and determining the product of the plurality of target dimension items as the combined dimension coefficient.

[0121] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a target label of an object to be evaluated; scoring each metric data by using a preset evaluation rule preconfigured for each evaluation dimension to obtain a single-dimension score corresponding to each evaluation dimension; determining the product of the single-dimension score corresponding to the same evaluation dimension and the individual dimension coefficient as the single-dimension corrected score corresponding to the evaluation dimension; and accumulating the single-dimension corrected scores corresponding to the plurality of evaluation dimensions to obtain an individual dimension score.

[0122] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining a target label of an object to be evaluated; and determining the product of the individual dimension score of the object to be evaluated and the combined dimension coefficient as the smoke index.

[0123] An embodiment of the present invention further provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it implements the steps of the credit risk assessment method provided in the above embodiment.

[0124] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0125] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0126] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division, and in actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0127] The unit described as a separating component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed over multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in various embodiments of the present invention may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0129] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned non-volatile storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks or optical discs and other various media that can store program codes.

[0130] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A credit risk assessment method, characterized in that: include: Obtaining a customer information summary table of the object to be evaluated according to a preset period, wherein the customer information summary table at least includes: indicator data corresponding to multiple evaluation dimensions respectively; Among the multiple preset coefficients recorded in the preset smoke index parameter table, query the target coefficient corresponding to the object to be evaluated, wherein the preset smoke index parameter table is used to record the corresponding relationship between each of the preset coefficients and the preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to the multiple evaluation dimensions respectively; Evaluate the indicator data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain the individual dimension score corresponding to each evaluation dimension; The multiple individual dimension scores are combined with the combined dimension coefficient to obtain the smoke index of the object to be evaluated, wherein the smoke index is used to evaluate the credit risk of the object to be evaluated.

2. The method according to claim 1, characterized in that Among the multiple preset coefficients recorded in the preset smoke index parameter table, querying the target coefficient corresponding to the object to be evaluated includes: Obtaining a target label of the object to be evaluated; Among the multiple preset coefficients recorded in the preset smoke index parameter table, the target coefficient corresponding to the target label is queried, wherein the preset smoke index parameter table is used to record the corresponding relationship between each of the preset coefficients and the preset objects with different preset labels.

3. The method according to claim 1, characterized in that The preset smoke index parameter table includes: a preset individual dimension parameter table and a preset combination dimension parameter table. Among the multiple preset coefficients recorded in the preset smoke index parameter table, querying the target coefficient corresponding to the object to be evaluated includes: Inquiring the multiple individual dimension coefficients corresponding to the object to be evaluated among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, wherein the preset individual dimension parameter table is used to record the corresponding relationship between each of the preset objects and the multiple preset individual dimension coefficients; Inquiring the combination dimension coefficient corresponding to the object to be evaluated among a plurality of preset combination dimension coefficients pre-recorded in a preset combination dimension parameter table, wherein the preset combination dimension parameter table is used to record the corresponding relationship between the preset combination dimension coefficient and the preset object; The combined dimension coefficient and the plurality of individual dimension coefficients corresponding to the object to be evaluated are determined as the target coefficient corresponding to the object to be evaluated.

4. The method according to claim 3, characterized in that Among the plurality of preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, querying the plurality of individual dimension coefficients corresponding to the object to be evaluated comprises: Among the multiple preset individual dimension coefficients pre-recorded in the preset individual dimension parameter table, query the multiple preset individual dimension coefficients corresponding to the object to be evaluated, and use them as the individual dimension coefficients to be queried respectively; Based on the multiple evaluation dimensions in the customer information summary table, among the multiple individual dimension coefficients to be queried, the individual dimension coefficient to be queried corresponding to each evaluation dimension is queried as the individual dimension coefficient, wherein each individual dimension coefficient to be queried is pre-configured with a corresponding preset evaluation dimension.

5. The method according to claim 3, characterized in that: Among the plurality of preset combination dimension coefficients pre-recorded in the preset combination dimension parameter table, querying the combination dimension coefficient corresponding to the object to be evaluated comprises: In the multiple preset dimension items recorded in the preset combination dimension parameter table, query the multiple target dimension items corresponding to the object to be evaluated, wherein the preset combination dimension parameter table is used to record the corresponding relationship between each of the preset objects and the multiple preset dimension items; The product of multiple target dimension items is determined as the combined dimension coefficient.

6. The method according to claim 1, characterized in that Evaluating the indicator data corresponding to the same evaluation dimension based on the individual dimension coefficient to obtain the individual dimension score corresponding to each evaluation dimension includes: Using the preset evaluation rules pre-configured for each evaluation dimension, score each indicator data to obtain a single dimension score corresponding to each evaluation dimension; Determine the product of the single dimension score corresponding to the same assessment dimension and the individual dimension coefficient as the single dimension modified score corresponding to the assessment dimension; The single dimension modified scores corresponding to the multiple evaluation dimensions are accumulated to obtain the individual dimension score.

7. The method according to claim 1, characterized in that Combining the individual dimension scores with the combined dimension coefficient to obtain the smoke index of the object to be evaluated includes: The product of the individual dimension score of the object to be evaluated and the combined dimension coefficient is determined as the smoke index.

8. A credit risk assessment device, characterized in that: include: An acquisition module is used to acquire a customer information summary table of the object to be evaluated according to a preset period, wherein the customer information summary table at least includes: indicator data corresponding to multiple evaluation dimensions respectively; A query module, used for querying the target coefficient corresponding to the object to be evaluated from a plurality of preset coefficients recorded in a preset smoke index parameter table, wherein the preset smoke index parameter table is used for recording the corresponding relationship between each of the preset coefficients and the preset object, and the preset coefficients include: a combined dimension coefficient and individual dimension coefficients corresponding to the plurality of evaluation dimensions respectively; An evaluation module, used to evaluate the indicator data corresponding to the same evaluation dimension based on the individual dimension coefficient, and obtain an individual dimension score corresponding to each evaluation dimension; The determination module is used to combine the multiple individual dimension scores with the combined dimension coefficient to obtain the smoke index of the object to be evaluated, wherein the smoke index is used to evaluate the credit risk of the object to be evaluated.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to execute the credit risk assessment method according to any one of claims 1 to 7 through the computer program.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the credit risk assessment method described in any one of claims 1 to 7 are implemented.

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