CN116797380A - Financial data processing method and related equipment thereof - Google Patents
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Abstract
The embodiment of the application belongs to the technical field of financial science and technology, and relates to a financial data processing method and related equipment thereof, wherein the method comprises the steps of identifying the evaluation quality and scale level of an auditor; taking the evaluation quality, the scale level and the expense budget corresponding to each auditor as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model; acquiring characterization information of a historical client, and constructing a client risk prediction model; selecting an auditor to carry out risk audit on the new client according to the risk prediction model and the resource allocation model; and acquiring and judging whether to carry out service subscription on the new client according to the auditing result. The application combines the customer risk prediction model and the resource allocation model to scientifically carry out the verification and the protection, not only can carry out the risk verification on the customer, but also can avoid wasting the verification resources of the company.
Description
Technical Field
The application relates to the technical field of financial science and technology, in particular to a financial data processing method and related equipment thereof.
Background
The check and protection is an important link in the insurance company underwriting process, and in the process of purchasing products, the insurance company can audit the client information, so that risks such as counter selection and fraud are avoided, and sustainable development of the insurance company is realized. If each client is subjected to strict auditing and investigation, on one hand, the auditing and investigation cost of an insurance company in an underwriting link is increased, on the other hand, the auditing link is heavy, the auditing time is long, the client experience is reduced, and the client loss is possibly caused.
The risk management is carried out on the clients, the risk rating is carried out on the clients, the risk classification is carried out on the clients with low risk, unnecessary links in auditing and investigation are reduced, and the client experience is improved; for high-risk clients, auditing and investigation contents are required to be enhanced, and losses caused by counter selection and underwriting of fraudulent clients are avoided. In the current industry, experience-based rules are formulated in the process mainly through experience knowledge of service post experience personnel, risk levels are judged through regions, ages, sexes and the like, and corresponding auditing strategies are adopted according to different risk levels. There are two disadvantages to doing this: the actual risk distribution cannot be well fitted, the problem of one cut exists, and the accuracy of judging the risk is low; the insurance companies have different areas, different companies and departments, and the corresponding auditing and investigation resources and expenses are different, so that the conditions of internal resource waste and insufficient resources of the companies exist simultaneously.
Disclosure of Invention
The embodiment of the application aims to provide a financial data processing method and related equipment thereof, so that a customer risk prediction model and a resource allocation model are combined to carry out verification scientifically, risk auditing can be carried out on the customer, and waste of company auditing resources can be avoided.
In order to solve the above technical problems, an embodiment of the present application provides a financial data processing method, which adopts the following technical schemes:
a financial data processing method comprising the steps of:
identifying the evaluation quality of the audit data provider according to the audit quality evaluation rule;
identifying the scale level of the audit data provider according to the audit scale evaluation rule;
taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model;
acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data;
predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model;
and acquiring and judging whether to carry out service subscription on the new client according to the auditing result.
Further, the step of identifying the evaluation quality of the audit data provider according to the audit quality evaluation rule specifically includes:
acquiring historical audit data of the audit data provider;
according to the historical auditing data, counting the proportional relation between the number of the audited risk clients in the audited passenger group and the number of all risk clients in all audited individuals, and taking the proportional relation as the risk client identification rate corresponding to the supplier;
and taking the risk client identification rate as the evaluation quality of the audit data provider.
Further, the step of identifying the scale level of the audit data provider according to the audit scale evaluation rule specifically includes:
acquiring historical audit data of the audit data provider;
and according to the historical auditing data, counting the maximum auditing quantity corresponding to the supplier, and representing the scale grade of the supplier according to the maximum auditing quantity.
Further, the step of constructing a resource allocation model by using the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm specifically includes:
Obtaining the auditing total budget cost provided by the sponsor for all responsible branch companies, wherein all responsible branch companies are sub-companies of the sponsor in each place;
obtaining budget cost provided by the sponsor for each responsible branch company respectively;
obtaining auditing cost budget provided by each responsible branch company for each supplier respectively;
based on preset constraint conditions:setting a resource allocation constraint for a pre-constructed resource allocation model, wherein X ij Indicating the scale level, X, of the jth provider of the ith responsible branch max_ij Representing a preset maximum audit amount corresponding to the scale level of the j-th supplier of the i-th responsible branch,represents an arbitrary X ij Meets X of 0 to less than or equal to ij ≤X max_ij Condition of->Representing the total audit cost of the ith responsible branch, C i Representing the budget costs offered by said sponsor for the ith responsible branch,/for->Representing the total audit cost of all responsible branch companies, C representing the audit total budget cost provided by the sponsor for all responsible branch companies;
based on a preset optimization algorithm: x is X 11 ×r 11 +…+X ij ×r ij +…+X IJ ×r IJ Optimizing a customer risk identification rate corresponding to the resource allocation model, wherein X is the same as the customer risk identification rate ij Indicating the scale level of the jth provider of the ith responsible branch The evaluation quality of the J-th supplier of the I-th responsible branch company is represented, I is not less than 1 and not more than I, J is not less than 1 and not more than J, I is the maximum value of I, and J is the maximum value of J;
and taking the historical audit data of the audit data provider as an input variable, inputting the resource allocation model with the constraint conditions and the optimization algorithm set, and training to obtain a trained resource allocation model.
Further, the step of obtaining the characterization information of the history clients, performing feature engineering on the characterization information, obtaining a feature value corresponding to each history client, and constructing a client risk prediction model according to the feature value corresponding to each history client specifically includes:
acquiring the underwriting and claim settlement data of historical clients from a preset service information database, wherein the underwriting and claim settlement data comprises client attribute information, client historical behavior information, client relationship information and attribute information of corresponding dangerous products;
preprocessing the underwriting and claim settling data, and taking the preprocessed underwriting and claim settling data as a characteristic value to be input of the client risk prediction model, wherein the preprocessing step comprises outlier processing, null processing and numerical processing;
Different risk prediction grades are set in advance according to the client risk prediction model of the risk class behaviors existing in the historical clients, and the different risk prediction grades are used as output variables;
and completing the construction and training of the client risk prediction model according to the characteristic value to be input, the output variable and a preset machine learning classification algorithm, wherein the preset machine learning classification algorithm is a decision tree algorithm.
Further, the step of predicting a risk level corresponding to a new client according to the risk prediction model, and selecting an audit data provider corresponding to the risk level to perform risk audit on the new client according to the resource allocation model specifically includes:
acquiring the corresponding pre-processed characterization information of the new client;
inputting the characterization information into the trained client risk prediction model, obtaining an output variable corresponding to the new client, and predicting a risk prediction grade corresponding to the new client according to the output variable;
and inputting the representation information and the risk prediction grade corresponding to the new client after preprocessing into the trained resource allocation model as input variables, and screening a corresponding auditing data provider to audit the risk grade of the new client.
Further, the step of obtaining and judging whether to sign up the service for the new client according to the auditing result specifically includes:
acquiring a risk level auditing result of the auditing data provider for the new client;
judging whether the new client meets service subscription conditions or not according to the risk level auditing result and a preset risk threshold;
if the new client meets the service signing condition, sending a service signing prompt to the new client;
and if the new client does not meet the service subscription condition, sending a service rejection prompt to the new client.
In order to solve the above technical problems, the embodiment of the present application further provides a financial data processing apparatus, which adopts the following technical scheme:
a financial data processing apparatus comprising:
the quality evaluation module is used for identifying the evaluation quality of the auditing data supplier according to the auditing quality evaluation rule;
the scale evaluation module is used for identifying the scale grade of the auditing data supplier according to the auditing scale evaluation rule;
the resource allocation model construction module is used for constructing a resource allocation model by taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm;
The risk prediction model construction module is used for acquiring characterization information of historical clients, carrying out feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data;
the risk auditing module is used for predicting the risk grade corresponding to the new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to carry out risk auditing on the new client according to the resource allocation model;
and the service subscription judging module is used for acquiring and judging whether to carry out service subscription on the new client according to the auditing result.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
a computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the financial data processing method described above.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor perform the steps of a financial data processing method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
according to the financial data processing method, the evaluation quality of the auditing data provider is identified according to the auditing quality evaluation rule; identifying the scale level of the audit data provider according to the audit scale evaluation rule; taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model; acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data; predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model; and acquiring and judging whether to carry out service subscription on the new client according to the auditing result. The application combines the customer risk prediction model and the resource allocation model to scientifically carry out the verification and the protection, not only can carry out the risk verification on the customer, but also can avoid wasting the verification resources of the company.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a financial data processing method according to the present application;
FIG. 3 is a schematic diagram illustrating the construction of one embodiment of a financial data processing apparatus in accordance with the present application;
FIG. 4 is a schematic diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio LayerIV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the financial data processing method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the financial data processing apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a financial data processing method according to the present application is shown. The financial data processing method comprises the following steps:
Step 201, according to the auditing quality evaluation rule, the evaluation quality of the auditing data provider is identified.
In this embodiment, the step of identifying the evaluation quality of the audit data provider according to the audit quality evaluation rule specifically includes: acquiring historical audit data of the audit data provider; according to the historical auditing data, counting the proportional relation between the number of the audited risk clients in the audited passenger group and the number of all risk clients in all audited individuals, and taking the proportional relation as the risk client identification rate corresponding to the supplier; and taking the risk client identification rate as the evaluation quality of the audit data provider.
In this embodiment, the audit data provider is an audit result provider.
By directly taking the risk client identification rate as the evaluation quality of the auditing data provider, obviously, if the risk client identification rate is high, the evaluation quality is high, otherwise, if the risk client identification rate is low, the evaluation quality is low, and by identifying the evaluation quality of the auditing data provider, auditing institutions with more accurate auditing results can be screened out.
And step 202, identifying the scale level of the audit data provider according to the audit scale evaluation rule.
In this embodiment, the step of identifying the scale level of the audit data provider according to the audit scale evaluation rule specifically includes: acquiring historical audit data of the audit data provider; and according to the historical auditing data, counting the maximum auditing quantity corresponding to the supplier, and representing the scale grade of the supplier according to the maximum auditing quantity.
And characterizing the scale grade of the supplier by counting the maximum auditing quantity corresponding to the supplier, wherein the supplier with more auditing quantity corresponds to the scale grade, otherwise, the supplier with less auditing quantity corresponds to the scale grade, and the auditing task is conveniently and scientifically and reasonably distributed to each auditing party by identifying the scale grade of each auditing data supplier.
And 203, constructing a resource allocation model by taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm.
In this embodiment, the step of constructing the resource allocation model by using the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm specifically includes: obtaining the auditing total budget cost provided by the sponsor for all responsible branch companies, wherein all responsible branch companies are sub-companies of the sponsor in each place; obtaining budget cost provided by the sponsor for each responsible branch company respectively; obtaining auditing cost budget provided by each responsible branch company for each supplier respectively; based on preset constraint conditions: Setting a resource allocation constraint for a pre-constructed resource allocation model, wherein X ij Indicating the scale level, X, of the jth provider of the ith responsible branch max_ij Representing a preset maximum audit amount corresponding to the scale level of the j-th supplier of the i-th responsible branch,represents an arbitrary X ij Meets X of 0 to less than or equal to ij ≤X max_ij Condition of->Representing the total audit cost of the ith responsible branch, C i Representing the budget costs offered by said sponsor for the ith responsible branch,/for->Representing the total audit cost of all responsible branch companies, C representing the audit total budget cost provided by the sponsor for all responsible branch companies; based on a preset optimization algorithm: x is X 11 ×r 11 +…+X ij ×r ij +…+X IJ ×r IJ Optimizing a customer risk identification rate corresponding to the resource allocation model, wherein X is the same as the customer risk identification rate ij Indicating the scale level of the jth supplier of the ith responsibility branch company, indicating the evaluation quality of the jth supplier of the ith responsibility branch company, wherein I is equal to or more than 1 and equal to or less than I, J is equal to or less than 1 and less than J, I is the maximum value of I, and J is the maximum value of J; and taking the historical audit data of the audit data provider as an input variable, inputting the resource allocation model with the constraint conditions and the optimization algorithm set, and training to obtain a trained resource allocation model.
The resource allocation model aims at providing a reasonable allocation scheme for each risk assessment data provider, and setting auditing limiting conditions for the reasonable allocation scheme, namely, the risk level limiting conditions for clients' auditing by the providers with different resource allocation.
By setting constraint conditions and using an optimization algorithm, the accuracy of the resource allocation model is further guaranteed, and the auditing task of the client is scientifically allocated to a corresponding auditing mechanism.
And 204, obtaining characterization information of the historical clients, performing feature engineering on the characterization information, obtaining feature values corresponding to each historical client, and constructing a client risk prediction model according to the feature values corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data.
In this embodiment, the step of obtaining the characterization information of the history clients, performing feature engineering on the characterization information, obtaining a feature value corresponding to each history client, and constructing a client risk prediction model according to the feature value corresponding to each history client specifically includes: acquiring the underwriting and claim settlement data of historical clients from a preset service information database, wherein the underwriting and claim settlement data comprises client attribute information, client historical behavior information, client relationship information and attribute information of corresponding dangerous products; preprocessing the underwriting and claim settling data, and taking the preprocessed underwriting and claim settling data as a characteristic value to be input of the client risk prediction model, wherein the preprocessing step comprises outlier processing, null processing and numerical processing; different risk prediction grades are set in advance according to the client risk prediction model of the risk class behaviors existing in the historical clients, and the different risk prediction grades are used as output variables; and completing the construction and training of the client risk prediction model according to the characteristic value to be input, the output variable and a preset machine learning classification algorithm.
In this embodiment, the preset machine learning classification algorithm is a decision tree algorithm, and by using the decision tree algorithm to construct and train the client risk prediction model, a decision node can be set for each risk level, so as to better conform to the prediction classification scene of the service.
By acquiring and according to the underwriting and claim settlement data of the historical clients, the construction and training of the client risk prediction model are carried out, and the accuracy and the scientificity of carrying out risk prediction on new clients are ensured.
And step 205, predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model.
In this embodiment, the step of predicting a risk level corresponding to a new client according to the risk prediction model, and selecting, according to the resource allocation model, an audit data provider corresponding to the risk level to perform risk audit on the new client specifically includes: acquiring the corresponding pre-processed characterization information of the new client; inputting the characterization information into the trained client risk prediction model, obtaining an output variable corresponding to the new client, and predicting a risk prediction grade corresponding to the new client according to the output variable; and inputting the representation information and the risk prediction grade corresponding to the new client after preprocessing into the trained resource allocation model as input variables, and screening a corresponding auditing data provider to audit the risk grade of the new client.
The risk grade of the new client is predicted firstly, then the characterization information of the new client and the risk prediction grade are input into the resource allocation model, the corresponding auditing data provider is screened to audit the risk grade of the new client, and auditing institutions are scientifically screened, so that the clients can be audited scientifically, the auditing resource waste can be avoided, and the method is more reasonable.
And step 206, acquiring and judging whether to carry out service subscription on the new client according to the auditing result.
In this embodiment, the step of obtaining and determining, according to the auditing result, whether to sign up for the service for the new client specifically includes: acquiring a risk level auditing result of the auditing data provider for the new client; judging whether the new client meets service subscription conditions or not according to the risk level auditing result and a preset risk threshold; if the new client meets the service signing condition, sending a service signing prompt to the new client; and if the new client does not meet the service subscription condition, sending a service rejection prompt to the new client.
According to the auditing quality evaluation rule, the application identifies the evaluation quality of an auditing data provider; identifying the scale level of the audit data provider according to the audit scale evaluation rule; taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model; acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data; predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model; and acquiring and judging whether to carry out service subscription on the new client according to the auditing result. The application combines the customer risk prediction model and the resource allocation model to scientifically carry out the verification and the protection, not only can carry out the risk verification on the customer, but also can avoid wasting the verification resources of the company.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
In the embodiment of the application, the customer risk prediction model and the resource allocation model can be combined for scientifically conducting the verification, the risk verification of the customer can be conducted, the waste of company verification resources can be avoided, and the risk prediction and verification are conducted by adopting the artificial intelligence model, so that the method is more intelligent and automatic.
With further reference to fig. 3, as an implementation of the method shown in fig. 2 described above, the present application provides an embodiment of a financial data processing apparatus, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 3, the financial data processing apparatus 300 according to the present embodiment includes: a quality assessment module 301, a scale assessment module 302, a resource allocation model construction module 303, a risk prediction model construction module 304, a risk auditing module 305 and a business subscription judgment module 306. Wherein:
the quality evaluation module 301 is configured to identify an evaluation quality of the audit data provider according to the audit quality evaluation rule;
the scale evaluation module 302 is configured to identify a scale level of the audit data provider according to the audit scale evaluation rule;
the resource allocation model construction module 303 is configured to construct a resource allocation model by using the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm;
the risk prediction model construction module 304 is configured to obtain characterization information of historical clients, perform feature engineering on the characterization information, obtain feature values corresponding to each historical client, and construct a client risk prediction model according to the feature values corresponding to each historical client, where the characterization information includes underwriting and claim settlement data;
the risk auditing module 305 is configured to predict a risk level corresponding to a new client according to the risk prediction model, and select an auditing data provider corresponding to the risk level according to the resource allocation model to perform risk auditing on the new client;
And the service subscription judgment module 306 is used for acquiring and judging whether to conduct service subscription on the new client according to the auditing result.
According to the auditing quality evaluation rule, the application identifies the evaluation quality of an auditing data provider; identifying the scale level of the audit data provider according to the audit scale evaluation rule; taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model; acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data; predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model; and acquiring and judging whether to carry out service subscription on the new client according to the auditing result. The application combines the customer risk prediction model and the resource allocation model to scientifically carry out the verification and the protection, not only can carry out the risk verification on the customer, but also can avoid wasting the verification resources of the company.
Those skilled in the art will appreciate that implementing all or part of the above described embodiment methods may be accomplished by computer readable instructions, stored on a computer readable storage medium, that the program when executed may comprise the steps of embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a financial data processing method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the financial data processing method.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
The embodiment provides computer equipment, belongs to finance science and technology nuclear insurance application technical field. The application identifies the evaluation quality of the auditing data supplier according to the auditing quality evaluation rule; identifying the scale level of the audit data provider according to the audit scale evaluation rule; taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model; acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data; predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model; and acquiring and judging whether to carry out service subscription on the new client according to the auditing result. The application combines the customer risk prediction model and the resource allocation model to scientifically carry out the verification and the protection, not only can carry out the risk verification on the customer, but also can avoid wasting the verification resources of the company.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by a processor to cause the processor to perform the steps of the financial data processing method as described above.
The embodiment provides a computer readable storage medium, which belongs to the technical field of financial science and technology nuclear insurance application. According to the auditing quality evaluation rule, the application identifies the evaluation quality of an auditing data provider; identifying the scale level of the audit data provider according to the audit scale evaluation rule; taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model; acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data; predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model; and acquiring and judging whether to carry out service subscription on the new client according to the auditing result. The application combines the customer risk prediction model and the resource allocation model to scientifically carry out the verification and the protection, not only can carry out the risk verification on the customer, but also can avoid wasting the verification resources of the company.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (10)
1. A method of financial data processing comprising the steps of:
identifying the evaluation quality of the audit data provider according to the audit quality evaluation rule;
identifying the scale level of the audit data provider according to the audit scale evaluation rule;
taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm, and constructing a resource allocation model;
acquiring characterization information of historical clients, performing feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data;
predicting a risk grade corresponding to a new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to conduct risk auditing on the new client according to the resource allocation model;
and acquiring and judging whether to carry out service subscription on the new client according to the auditing result.
2. The method according to claim 1, wherein the step of identifying the estimated quality of the audit data provider according to the audit quality estimation rule comprises:
Acquiring historical audit data of the audit data provider;
according to the historical auditing data, counting the proportional relation between the number of the audited risk clients in the audited passenger group and the number of all risk clients in all audited individuals, and taking the proportional relation as the risk client identification rate corresponding to the supplier;
and taking the risk client identification rate as the evaluation quality of the audit data provider.
3. The method according to claim 2, wherein the step of identifying the scale level of the audit data provider according to the audit scale evaluation rule, specifically comprises:
acquiring historical audit data of the audit data provider;
and according to the historical auditing data, counting the maximum auditing quantity corresponding to the supplier, and representing the scale grade of the supplier according to the maximum auditing quantity.
4. A financial data processing method according to claim 3, wherein the step of constructing a resource allocation model using the evaluation quality, the scale level and the spending budget corresponding to each supplier as constraints of a preset linear optimization algorithm specifically comprises:
Obtaining the auditing total budget cost provided by the sponsor for all responsible branch companies, wherein all responsible branch companies are sub-companies of the sponsor in each place;
obtaining budget cost provided by the sponsor for each responsible branch company respectively;
obtaining auditing cost budget provided by each responsible branch company for each supplier respectively;
based on preset constraint conditions:is pre-constructedSetting a resource allocation constraint by the built resource allocation model, wherein X ij Indicating the scale level, X, of the jth provider of the ith responsible branch max_ij Representing a preset maximum audit amount corresponding to the scale level of the jth supplier of the ith responsible branch office,/I> Represents an arbitrary X ij Meets X of 0 to less than or equal to ij ≤X max_ij Condition of->Representing the total audit cost of the ith responsible branch, C i Representing the budget costs offered by said sponsor for the ith responsible branch,/for->Representing the total audit cost of all responsible branch companies, C representing the audit total budget cost provided by the sponsor for all responsible branch companies;
based on a preset optimization algorithm: x is X 11 ×r 11 +…+X ij ×r ij +…+X IJ ×r IJ Optimizing a customer risk identification rate corresponding to the resource allocation model, wherein X is the same as the customer risk identification rate ij Indicating the scale level of the jth supplier of the ith responsibility branch company, indicating the evaluation quality of the jth supplier of the ith responsibility branch company, wherein I is equal to or more than 1 and equal to or less than I, J is equal to or less than 1 and less than J, I is the maximum value of I, and J is the maximum value of J;
And taking the historical audit data of the audit data provider as an input variable, inputting the resource allocation model with the constraint conditions and the optimization algorithm set, and training to obtain a trained resource allocation model.
5. The financial data processing method according to claim 1, wherein the step of obtaining characterization information of historical clients, performing feature engineering on the characterization information, obtaining feature values corresponding to each historical client, and constructing a client risk prediction model according to the feature values corresponding to each historical client specifically comprises:
acquiring the underwriting and claim settlement data of historical clients from a preset service information database, wherein the underwriting and claim settlement data comprises client attribute information, client historical behavior information, client relationship information and attribute information of corresponding dangerous products;
preprocessing the underwriting and claim settling data, and taking the preprocessed underwriting and claim settling data as a characteristic value to be input of the client risk prediction model, wherein the preprocessing step comprises outlier processing, null processing and numerical processing;
different risk prediction grades are set in advance according to the client risk prediction model of the risk class behaviors existing in the historical clients, and the different risk prediction grades are used as output variables;
And completing the construction and training of the client risk prediction model according to the characteristic value to be input, the output variable and a preset machine learning classification algorithm, wherein the preset machine learning classification algorithm is a decision tree algorithm.
6. The financial data processing method according to claim 1, wherein the step of predicting a risk level corresponding to a new customer according to the risk prediction model, and selecting an audit data provider corresponding to the risk level according to the resource allocation model to perform risk audit on the new customer specifically includes:
acquiring the corresponding pre-processed characterization information of the new client;
inputting the characterization information into the trained client risk prediction model, obtaining an output variable corresponding to the new client, and predicting a risk prediction grade corresponding to the new client according to the output variable;
and inputting the representation information and the risk prediction grade corresponding to the new client after preprocessing into the trained resource allocation model as input variables, and screening a corresponding auditing data provider to audit the risk grade of the new client.
7. The financial data processing method according to claim 1, wherein the step of acquiring and determining whether to sign up for the new customer according to the result of the audit comprises:
Acquiring a risk level auditing result of the auditing data provider for the new client;
judging whether the new client meets service subscription conditions or not according to the risk level auditing result and a preset risk threshold;
if the new client meets the service signing condition, sending a service signing prompt to the new client;
and if the new client does not meet the service subscription condition, sending a service rejection prompt to the new client.
8. A financial data processing apparatus, comprising:
the quality evaluation module is used for identifying the evaluation quality of the auditing data supplier according to the auditing quality evaluation rule;
the scale evaluation module is used for identifying the scale grade of the auditing data supplier according to the auditing scale evaluation rule;
the resource allocation model construction module is used for constructing a resource allocation model by taking the evaluation quality, the scale level and the expense budget corresponding to each supplier as constraint conditions of a preset linear optimization algorithm;
the risk prediction model construction module is used for acquiring characterization information of historical clients, carrying out feature engineering on the characterization information, acquiring a feature value corresponding to each historical client, and constructing a client risk prediction model according to the feature value corresponding to each historical client, wherein the characterization information comprises underwriting and claim settlement data;
The risk auditing module is used for predicting the risk grade corresponding to the new client according to the risk prediction model, and selecting an auditing data provider corresponding to the risk grade to carry out risk auditing on the new client according to the resource allocation model;
and the service subscription judging module is used for acquiring and judging whether to carry out service subscription on the new client according to the auditing result.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed implement the steps of the financial data processing method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor perform the steps of the financial data processing method of any one of claims 1 to 7.
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