CN114118793A - Local exchange risk early warning method, device and equipment - Google Patents
Local exchange risk early warning method, device and equipment Download PDFInfo
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Abstract
The invention relates to the technical field of computer information, and discloses a method, a device and equipment for warning local exchange risk, wherein the method comprises the following steps: receiving a risk analysis request sent by a user terminal; acquiring supervision data of the target client based on the risk analysis request, wherein the supervision data comprises basic data of the target client on the local exchange and external analysis data of the target client outside the local exchange; preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model; determining a risk level corresponding to the index value according to the index value and a preset risk prediction model; and executing corresponding risk early warning actions based on the risk level. The method and the device solve the technical problems that in the prior art, a risk indicator part is irrelevant, an early warning index is single, and the use data is single summarized data in the risk early warning of the local exchange, and achieve the technical effects of improving the accuracy and the accuracy of the risk early warning of the local exchange.
Description
Technical Field
The embodiment of the invention relates to the technical field of computer information, in particular to a method, a device and equipment for warning the risk of a local exchange.
Background
The existing general process of risk early warning of various local exchanges is as follows: (1) the collected data is processed and processed, basic formatting and standardization are carried out on the data, and data cleaning is needed for abnormal data. And storing the processed data into a structured or unstructured database. (2) Aiming at different types of data, the risk early warning system adopts different calculation rules to extract features from the data, calculate indexes and store the indexes in a structured database. (3) And aggregating the calculation results of the indexes to form an index, and judging the risk identification of various local trading places by using the relation between the index and a threshold value.
However, the current risk early warning model identification method for local exchanges has certain limitations, including: (1) and (4) calculating a single index. Assuming that each index has no correlation, the calculation is simple, and the concurrent correlation risk cannot be identified; (2) risks generally mainly adopt single index early warning, recognition modes are relatively fixed, the direction needing early warning is specified, but business forms of various local transaction places are different, risk forms are various, and timely early warning effect is difficult to achieve through a certain single index of universality. (3) The data used by risk early warning is generally summarized data rather than detailed data, and has certain influence on early warning accuracy and pertinence.
Disclosure of Invention
The embodiment of the invention provides a local exchange risk early warning method, a local exchange risk early warning device and local exchange risk early warning equipment, and solves the technical problems that in the prior art, a risk indicator part is irrelevant, an early warning index is single, and use data is single summarized data.
In a first aspect, an embodiment of the present application provides a local exchange risk early warning method, where the method includes:
receiving a risk analysis request sent by a user terminal;
acquiring supervision data of a target customer based on the risk analysis request, wherein the supervision data comprises basic data of the target customer at a local exchange and external analysis data of the target customer outside the local exchange;
preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
determining a risk level corresponding to the index value according to the index value and the preset risk prediction model;
and executing corresponding risk early warning actions based on the risk level.
In a second aspect, an embodiment of the present application provides a local exchange risk early warning apparatus, where the apparatus includes:
a request receiving unit, configured to receive a risk analysis request sent by a user terminal;
a data acquisition unit, configured to acquire supervision data of a target customer based on the risk analysis request, where the supervision data includes basic data of the target customer at a local exchange and external analysis data of the target customer outside the local exchange;
the preprocessing unit is used for preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
the grade determining unit is used for determining a risk grade corresponding to the index value according to the index value and the preset risk prediction model;
and the risk early warning unit is used for executing corresponding risk early warning actions based on the risk level.
In a third aspect, an embodiment of the present application provides a local exchange risk early warning device, where the local exchange risk early warning device includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a local exchange risk pre-warning method as any of the first aspect of embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a local exchange risk warning method as any of the first aspect of the embodiments of the present application.
The embodiment of the invention discloses a method, a device and equipment for warning the risk of a local exchange, wherein the method comprises the following steps: receiving a risk analysis request sent by a user terminal; acquiring supervision data of the target client based on the risk analysis request, wherein the supervision data comprises basic data of the target client on the local exchange and external analysis data of the target client outside the local exchange; preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model; determining a risk level corresponding to the index value according to the index value and a preset risk prediction model; and executing corresponding risk early warning actions based on the risk level. The method and the device solve the technical problems that in the prior art, a risk indicator part is irrelevant, an early warning index is single, and the use data is single summarized data in the risk early warning of the local exchange, and achieve the technical effects of improving the accuracy and the accuracy of the risk early warning of the local exchange.
Drawings
Fig. 1 is a flowchart of a local exchange risk early warning method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another local exchange risk warning method according to an embodiment of the present invention;
FIG. 3 is a flow chart of yet another local exchange risk warning method provided by an embodiment of the present invention;
FIG. 4 is a flow chart of yet another local exchange risk warning method provided by an embodiment of the present invention;
FIG. 5 is a block diagram of a local exchange risk warning device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a local exchange risk early warning device according to an embodiment of the present invention.
Detailed Description
The local exchange means various types of trading places approved to be set in various regions for promoting rights and interests (such as equity rights, property rights and the like) and developing commodity markets, performing property right trading, cultural artwork trading, long-term trading in bulk commodities and the like, various traditional offline financial business behaviors are continuously transferred and fused to the online along with the full application of a new generation of information technology in the financial industry, wherein various local exchanges are abnormally prominent and develop violently, the business innovation comes up endlessly, and local financial supervision is taken as the regulation arrangement for maintaining the economic order of the market provided by the government and faces new challenges and higher standard requirements.
In order to meet higher standard requirements and achieve more accurate and correct risk early warning of local exchanges, the invention provides a method, a device and equipment for risk early warning of local exchanges.
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
It should be noted that the terms "first", "second", and the like in the description and claims of the present invention and the accompanying drawings are used for distinguishing different objects, and are not used for limiting a specific order. The following embodiments of the present invention may be implemented individually, or in combination with each other, and the embodiments of the present invention are not limited in this respect.
Fig. 1 is a flowchart of a local exchange risk early warning method according to an embodiment of the present invention. The local exchange risk early warning method can be suitable for the condition that risk early warning is required to be carried out in all local transactions. The local exchange risk early warning method can be executed by a local exchange risk early warning device, which can be realized in a hardware and/or software mode and can be generally integrated in a server.
As shown in fig. 1, the local exchange risk early warning method specifically includes the following steps:
s101, receiving a risk analysis request sent by a user terminal.
Specifically, the user terminal is generally a host computer and a mobile terminal at the supervision department, or a corresponding human-computer interaction unit installed on the host computer and the mobile terminal, and the human-computer interaction unit may be in the form of an APP (Application), or may be other devices capable of sending a risk analysis request; target customers to be monitored are all local trading places and branch institutions thereof in the administrative district of the supervision department, and supervision department personnel send risk analysis requests of target local trading places needing risk prediction to the local trading place risk early warning device through the user terminal.
And S102, acquiring supervision data of the target client based on the risk analysis request, wherein the supervision data comprises basic data of the target client on the local exchange and external analysis data of the target client outside the local exchange.
Optionally, S102, the obtaining the supervision data of the target customer based on the risk analysis request includes: acquiring supervision data of a target customer from a plurality of preset dimensions based on a risk analysis request in a centralized acquisition and continuous acquisition mode, wherein the preset dimensions at least comprise the following two types: transaction event, transaction mechanism, fund flow direction, product information, transaction state, account transaction, industry trend, event reporting state, event investigation information, event related personnel information, advertisement, collection promotion and report form.
Specifically, the supervision data is mainly divided into status data and detail data, wherein the basic data of the target customer at the local exchange is the status data, and the external analysis data of the target customer outside the local exchange is the detail data.
The basic data mainly comprises all non-null effective data of the local exchange, and is automatically acquired regularly in a centralized acquisition mode, including basic information, business data, financial data, public opinion data, government data published by government departments such as public inspection, internet public data, daily supervision reports and other report data of the local exchange, and other supplementary data, such as: the basic data may be current data of the local exchange and also include historical transaction data of the local exchange, and when the above supervision data is stored and/or processed, the relevant regulations of the national laws and regulations are met.
Optionally, the external analytical data of the target customer outside the local exchange comprises at least one of: the external data exchange center pushes the relevant data of the target client; relevant government affair data collected by a government data resource sharing exchange center; and generating related data based on various data dimensions, wherein the data dimensions comprise an organization dimension, an item dimension, a natural person dimension and an administrative organ dimension.
S103, preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in the preset risk prediction model.
Specifically, after various pieces of supervision data are acquired, preprocessing the supervision data includes converting the supervision data into a uniform format, then extracting feature vectors from the supervision data in the uniform format, and obtaining corresponding cross-correlation features and weights of the feature vectors based on the feature vectors, where the weights of the feature vectors, the cross-correlation features, and the feature vectors are the index values.
The preset risk prediction model is established as follows: the method comprises the steps of collecting a large amount of supervision data, constructing a supervision machine learning label and a training sample according to the existing supervision data, wherein the training sample comprises a training set, a testing set and a verification sample, extracting relevant data of historical enterprises with risks as the training sample, extracting relevant data of the existing enterprises as the testing sample, and extracting relevant data of the enterprises with supervision grades A and supervision grades D as the verification sample.
After dividing the samples, extracting business, fund and business data corresponding to local exchanges according to the object types and the industry attributes, classifying the data, extracting the data according to different analysis dimensions, and processing risk indexes, wherein the dimensions are business operation, financial risk, public opinion risk and the like. When initializing the model, according to the indexes processed by the supervision data, the definition of related industry experts is utilized to extract the model characteristics, and the cross characteristics and deviation value characteristics of the incidence relation and the hidden characteristics existing in the actual business scene are constructed. For example, if the external litigation index value of the debt dispute is large in index processing, an enterprise operation difficult feature is extracted, and the operation difficult feature is one of the model features.
Specifically, feature crossing is a composite feature formed by combining two or more features, and the dimension of the features is increased by means of feature combination to obtain a better training effect, so that the feature crossing is mainly to solve the nonlinear problem and separate data by establishing feature combinations; the deviation characteristic is data for observing the stability of a test environment and a system, the smaller the characteristic value of the deviation characteristic is, the more stable the environment is, the more effective the measured data is, otherwise, if the characteristic value of the deviation characteristic is too large, the instability of the environment is indicated, and the measured data has little value under the condition, so that in the test, the characteristic value of the deviation characteristic must be ensured to be small, and correspondingly, the characteristic with the larger characteristic value of the deviation characteristic can be gradually eliminated in the training; the obscure characteristics are characteristics which cannot be recognized from data conditions and need to be judged by actual business experience, for example, all financial data of an enterprise are stable, but the enterprise is held by a single stockholder, when risks occur to other companies holding the stockholder, the enterprise can correspondingly and rapidly appear crisis, in the past supervision scene, the enterprise can be found to belong to an empty-shell enterprise, financial and newspaper data are forged, and therefore some obscure characteristics need to be judged to be recognized in advance.
And constructing a basic preset risk prediction model based on a machine learning algorithm according to training in the training samples, and performing model tuning in the test set. Optionally, the verification sample is used for verifying the prediction accuracy of the preset risk prediction model. And performing model generalization performance verification on the basic preset risk prediction model according to the verification sample, and continuously training the model return result to improve the accuracy of the basic preset risk prediction model.
When the accuracy of the basic early warning model does not reach the preset accuracy threshold value, namely three dimensions of accuracy, precision and recall rate are not reached, the basic preset risk prediction model needs to be trained again. The method comprises two training modes: checking whether the parameter configuration in the construction process of the preset risk prediction model is unreasonable, if not, readjusting the cross feature association mode and evidence weight conversion among the indexes, and simultaneously checking whether the selected feature field has the condition of not conforming to the economic significance; the other method is to check whether the sample structure in the process of constructing the preset risk prediction model is reasonable or not, and comprises label definition, sample quantity and sample splitting, wherein the sample splitting comprises splitting of a training sample and a verification sample and splitting of a training set and a test set in the training sample. And if an unreasonable part exists, the supervision data needs to be rearranged and matched to generate a new training sample, and the basic preset risk prediction model is trained again according to the new training sample.
And finally, if the newly added supervision data are read and accumulated, constructing an optimized preset risk prediction model by using the newly added supervision data, and performing model fine tuning by using the newly added supervision data as a sample to obtain the optimized preset risk prediction model. And comparing the basic preset risk prediction model with the optimized preset risk prediction model for challenging to obtain a final preset risk prediction model, wherein the preset risk prediction model of the local trading place is subject to the application of the final preset risk prediction model.
And S104, determining the risk level corresponding to the index value according to the index value and the preset risk prediction model.
Specifically, assuming that all index values are risk indexes which can cause transaction risks in local exchanges but have different risks, importance ranking is performed on the index values according to expert experience, then grade evaluation is performed on the index values according to the importance ranking, the preference of each expert and a preset risk prediction model, and finally the risk grade corresponding to the index values is obtained.
And S105, executing corresponding risk early warning actions based on the risk level.
Specifically, after the risk level is obtained, risk early warning is performed on each monitored object according to the risk level.
In short, in the embodiment of the present invention, the latest data is acquired every time the supervisory data is acquired, and optimization is performed according to reading of the newly added data. And after all data are subjected to standardization processing according to the supervision data standard, collected supervision data are processed into supervision indexes in a unified mode, the index parameters are subjected to risk characteristic analysis, then risk characteristic quantities are extracted, and measurement and analysis are carried out according to the risk characteristic quantities. If a risk event occurs, the reason, the position and other possible risk conduction situations of the risk occurrence are judged so as to make corresponding risk early warning actions.
The method and the device solve the technical problems that in the prior art, a risk indicator part is irrelevant, an early warning index is single, and the use data is single summarized data in the risk early warning of the local exchange, and achieve the technical effects of improving the accuracy and the accuracy of the risk early warning of the local exchange.
On the basis of the above technical solutions, fig. 2 is a flowchart of another local exchange risk early warning method provided by the embodiment of the present invention, and as shown in fig. 2, the step S103 specifically includes:
s201, the supervision data is converted into a uniform format.
Specifically, after the corresponding supervision data is acquired, the original supervision data is stored, and then the supervision data is converted according to a specified unified standard.
S202, extracting the supervision data with the uniform format based on preset dimension characteristics to obtain corresponding characteristic vectors.
Specifically, preset dimension feature extraction is performed on the supervision data, namely, transaction events, transaction institutions, fund flow directions, product information, transaction states, account transaction changes, industry trends, event reporting states, event troubleshooting information, event related personnel information, advertisements, collection urging, reports and the like in preset dimensions are taken as corresponding targets, and feature extraction is performed on the supervision data acquired under each target to obtain corresponding feature vectors.
S203, creating a cross domain classification based on the feature vector, and determining corresponding cross-correlation features based on the cross domain classification.
In particular, with the analysis impact on the feature space in the early stages, when creating the cross-domain classification, one or more conditions that must be satisfied before returning values from the source object may be specified, conditions may be defined for the source object and the target object, and if the conditions are satisfied, the cross-domain classification is automatically created. After the cross-domain classification is determined, corresponding cross-domain association features may be determined according to the cross-domain classification.
S204, determining the weight of the feature vector based on a preset risk prediction model, wherein the weight of the feature vector, the cross-correlation feature and the feature vector is an index value.
Optionally, the preset risk prediction model includes a training sample and a verification sample, the training sample includes a training set and a testing set, and S204, determining the weight of the feature vector based on the preset risk prediction model includes: training the feature vectors through a training set to respectively obtain classifiers lacking the nth feature vector, wherein n is the number of the feature vectors and is more than or equal to 1; respectively testing the classification effect of each classifier through the test set, and counting the number of wrong classifications of each classifier; and carrying out normalization processing on the error classification number to obtain the weight of the feature vector.
On the basis of the above technical solutions, fig. 3 is a flowchart of another local exchange risk early warning method provided in an embodiment of the present invention, and as shown in fig. 3, the step S202 specifically includes:
s301, performing principal component analysis on the supervision data based on preset dimensionality, and forming a feature space by feature vectors corresponding to maximum feature values obtained through analysis;
s302, taking the influence of each supervision data in the respective feature space as a classification basis of the feature vectors;
s303, carrying out feature extraction on the supervision data based on the classification basis to obtain corresponding feature vectors.
Specifically, the preset dimensionality and the like are used as corresponding targets, principal component analysis is carried out on the supervision data acquired under each target, feature vectors corresponding to the maximum feature values obtained through analysis form feature spaces, the influence of each target data (namely the supervision data acquired under each target) in each feature space is used as a classification basis of the feature vectors, feature extraction is carried out on the supervision data based on the classification basis, and the corresponding feature vectors are obtained.
On the basis of the above technical solutions, fig. 4 is a flowchart of another local exchange risk early warning method provided in an embodiment of the present invention, and as shown in fig. 4, the step S104 specifically includes:
s401, taking the index value as a risk index, and sorting the importance of the risk index according to expert experience, wherein the number of the expert experience is more than 1, and the number of the obtained importance sorting of the risk index is more than 1;
s402, carrying out risk assessment on the importance sequences according to a preset risk prediction model to obtain corresponding risk grades.
For example, in each index value, assuming that all index values are risk indexes which can cause local exchanges, the index values are used as risk indexes, and then the risk indexes are ranked according to the importance of indexes by expert experience. Assume that there are n possible risk indicatorsDefining a complete risk indicator set A, n possible risk indicators being defined as u1，u2，……，unThen, the complete risk indicator set is expressed as: a ═ u1，u2，……，unAnd ordering the importance of all risk indexes by each expert in advance, and forming the preference of an expert group according to the preference of each expert so as to obtain a key factor set U-U1，u2，……，umWhere m < n. Meanwhile, the established risk evaluation set is a set of possible evaluation results and is represented as V ═ V1，v2，……，vmAnd wherein each element represents a variety of possible evaluation results.
In the local exchange risk assessment, the fuzzy evaluation set can be divided into grades according to the convention of adopting a division, and the grades are respectively represented by v 1-v 9, namely AAA, AA, A, BB, B, CC, C, DD and D. In order to facilitate quantitative calculation, the five grades are assigned with V ═ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09 with reference to other risk level data, and finally, the corresponding risk grade is obtained based on the preset risk prediction model and the grade assignment of the risk assessment.
Fig. 5 is a structural diagram of a local exchange risk early warning apparatus according to an embodiment of the present invention, and as shown in fig. 5, the local exchange risk early warning apparatus includes:
a request receiving unit 51, configured to receive a risk analysis request sent by a user terminal;
a data obtaining unit 52, configured to obtain supervision data of the target customer based on the risk analysis request, where the supervision data includes basic data of the target customer at the local exchange and external analysis data of the target customer outside the local exchange;
the preprocessing unit 53 is configured to preprocess the supervision data to obtain index values corresponding to each monitoring index included in the preset risk prediction model;
the grade determining unit 54 is configured to determine a risk grade corresponding to the index value according to the index value and the preset risk prediction model;
and the risk early warning unit 55 is used for executing corresponding risk early warning actions based on the risk level.
Optionally, the data obtaining unit 52 is specifically configured to: acquiring supervision data of a target customer from a plurality of preset dimensions based on a risk analysis request in a centralized acquisition and continuous acquisition mode, wherein the preset dimensions at least comprise the following two types: transaction event, transaction mechanism, fund flow direction, product information, transaction state, account transaction, industry trend, event reporting state, event investigation information, event related personnel information, advertisement, collection promotion and report form.
Optionally, the preprocessing unit 53 includes:
the conversion subunit is used for converting the supervision data into a uniform format;
the characteristic extraction subunit is used for extracting the supervision data with the unified format based on preset dimensional characteristics to obtain corresponding characteristic vectors;
the characteristic determining subunit is used for creating cross domain classification based on the characteristic vector and determining corresponding cross correlation characteristics based on the cross domain classification;
and the weight determining subunit is used for determining the weight of the feature vector based on the preset risk prediction model, wherein the weight of the feature vector, the cross-correlation feature and the feature vector is an index value.
Optionally, the feature extraction subunit is specifically configured to:
performing principal component analysis on the supervision data based on preset dimensionality, and forming a feature space by feature vectors corresponding to maximum feature values obtained by analysis;
the influence of each supervision data on the respective feature space is used as a classification basis of the feature vectors;
and carrying out feature extraction on the supervision data based on the classification basis to obtain corresponding feature vectors.
Optionally, the preset risk prediction model includes a training sample and a verification sample, the training sample includes a training set and a testing set, and the weight determination subunit is specifically configured to:
training the feature vectors through a training set to respectively obtain classifiers lacking the nth feature vector, wherein n is the number of the feature vectors and is more than or equal to 1;
respectively testing the classification effect of each classifier through the test set, and counting the number of wrong classifications of each classifier;
and carrying out normalization processing on the error classification number to obtain the weight of the feature vector.
Optionally, the level determining unit 54 is specifically configured to:
taking the index value as a risk index, and sorting the importance of the risk index according to expert experience, wherein the number of the expert experience is more than 1, and the number of the obtained importance sorting of the risk index is more than 1;
and carrying out risk evaluation on the importance sequences according to a preset risk prediction model to obtain corresponding risk grades.
The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments.
The local exchange risk early warning device provided by the embodiment of the invention has the same technical characteristics as the local exchange risk early warning method provided by the embodiment, so that the same technical problems can be solved, and the same technical effect is achieved.
Fig. 6 is a schematic structural diagram of a local exchange risk early warning apparatus according to an embodiment of the present invention, as shown in fig. 6, the local exchange risk early warning apparatus includes a processor 61, a memory 62, an input device 63, and an output device 64; the number of the processors 61 in the local exchange risk early warning device can be one or more, and one processor 61 is taken as an example in fig. 6; the processor 61, the memory 62, the input device 63 and the output device 64 in the local exchange risk early warning device may be connected by a bus or other means, and the bus connection is taken as an example in fig. 6.
The memory 62 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the local exchange risk early warning method in the embodiment of the present invention (for example, the request receiving unit 51, the data obtaining unit 52, the preprocessing unit 53, the grade determining unit 54, and the risk early warning unit 55 in the local exchange risk early warning device). The processor 61 executes various functional applications and data processing of the local exchange risk early warning device by executing software programs, instructions and modules stored in the memory 62, that is, the local exchange risk early warning method is implemented.
The memory 62 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 62 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 62 may further include memory located remotely from the processor 61, which may be connected to a local exchange risk early warning device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 63 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function controls of the local exchange risk early warning apparatus. The output device 64 may include a display device such as a display screen.
Embodiments of the present invention also provide a storage medium containing computer-executable instructions that, when executed by a computer processor, perform a method for ground exchange risk early warning.
Specifically, the local exchange risk early warning method comprises the following steps:
receiving a risk analysis request sent by a user terminal;
acquiring supervision data of the target client based on the risk analysis request, wherein the supervision data comprises basic data of the target client on the local exchange and external analysis data of the target client outside the local exchange;
preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
determining a risk level corresponding to the index value according to the index value and a preset risk prediction model;
and executing corresponding risk early warning actions based on the risk level.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the local exchange risk warning method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the above search apparatus, each included unit and module are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
In the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention and the technical principles applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (11)
1. A local exchange risk early warning method, the method comprising:
receiving a risk analysis request sent by a user terminal;
acquiring supervision data of a target customer based on the risk analysis request, wherein the supervision data comprises basic data of the target customer at a local exchange and external analysis data of the target customer outside the local exchange;
preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
determining a risk level corresponding to the index value according to the index value and the preset risk prediction model;
and executing corresponding risk early warning actions based on the risk level.
2. The local exchange risk early warning method according to claim 1, wherein the acquiring of the target customer's supervision data based on the risk analysis request comprises: acquiring supervision data of the target client from a plurality of preset dimensions based on the risk analysis request in a centralized acquisition and continuous acquisition mode, wherein the preset dimensions at least comprise the following two types: transaction event, transaction mechanism, fund flow direction, product information, transaction state, account transaction, industry trend, event reporting state, event investigation information, event related personnel information, advertisement, collection promotion and report form.
3. The local exchange risk warning method according to claim 1, wherein the external analysis data of the target customer outside the local exchange comprises at least one of:
the external data exchange center pushes the relevant data of the target customer;
relevant government affair data collected by a government data resource sharing exchange center;
and generating related data based on various data dimensions, wherein the data dimensions comprise an organization dimension, an item dimension, a natural person dimension and an administrative organ dimension.
4. The local exchange risk early warning method according to claim 2, wherein the preprocessing the supervision data to obtain an index value corresponding to each monitoring index included in a preset risk prediction model comprises:
converting the supervisory data into a unified format;
extracting the supervision data with uniform format based on the preset dimension characteristics to obtain corresponding characteristic vectors;
creating a cross-domain classification based on the feature vectors, and determining corresponding cross-correlation features based on the cross-domain classification;
determining the weight of the feature vector based on the preset risk prediction model, wherein the weight of the feature vector, the cross-correlation feature and the feature vector is the index value.
5. The local exchange risk early warning method according to claim 4, wherein the extracting the supervision data in a unified format based on the preset dimension features to obtain corresponding feature vectors comprises:
performing principal component analysis on the supervision data based on the preset dimensionality, and forming a feature space by using feature vectors corresponding to maximum feature values obtained by analysis;
taking the influence of each supervision data in the respective feature space as a classification basis of the feature vectors;
and performing feature extraction on the supervision data based on the classification basis to obtain corresponding feature vectors.
6. The local exchange risk pre-warning method according to claim 4, wherein the preset risk prediction model comprises training samples and verification samples, the training samples comprise a training set and a testing set, and the determining the weight of the feature vector based on the preset risk prediction model comprises:
training the feature vectors through the training set to respectively obtain classifiers lacking the nth feature vector, wherein n is the number of the feature vectors and is more than or equal to 1;
respectively testing the classification effect of each classifier through the test set, and counting the number of wrong classifications of each classifier;
and carrying out normalization processing on the error classification number to obtain the weight of the feature vector.
7. The local exchange risk early warning method according to claim 1, wherein the determining the risk level corresponding to the index value according to the index value and the preset risk prediction model comprises:
taking the index value as a risk index, and ranking the risk index according to the importance of expert experience, wherein the number of the expert experience is more than 1, and the ranking number of the importance of the obtained risk index is more than 1;
and carrying out risk assessment on the importance sequences according to the preset risk prediction model to obtain corresponding risk grades.
8. The local exchange risk early warning method according to claim 6, wherein the verification sample is used for verifying the prediction accuracy of the preset risk prediction model.
9. A local exchange risk early warning device, the device comprising:
a request receiving unit, configured to receive a risk analysis request sent by a user terminal;
a data acquisition unit, configured to acquire supervision data of a target customer based on the risk analysis request, where the supervision data includes basic data of the target customer at a local exchange and external analysis data of the target customer outside the local exchange;
the preprocessing unit is used for preprocessing the supervision data to obtain index values corresponding to all monitoring indexes contained in a preset risk prediction model;
the grade determining unit is used for determining a risk grade corresponding to the index value according to the index value and the preset risk prediction model;
and the risk early warning unit is used for executing corresponding risk early warning actions based on the risk level.
10. A local exchange risk early warning device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the local exchange risk pre-warning method of any one of claims 1-8.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a local exchange risk warning method according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117437040A (en) * | 2023-12-21 | 2024-01-23 | 广州平云小匠科技股份有限公司 | Method, equipment and storage medium for updating trust risk level |
| CN117557086A (en) * | 2023-07-05 | 2024-02-13 | 北京忠业兴达科技有限公司 | Secret-related carrier supervision method, device, equipment and readable storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117557086A (en) * | 2023-07-05 | 2024-02-13 | 北京忠业兴达科技有限公司 | Secret-related carrier supervision method, device, equipment and readable storage medium |
| CN117557086B (en) * | 2023-07-05 | 2024-03-26 | 北京忠业兴达科技有限公司 | Secret-related carrier supervision method, device, equipment and readable storage medium |
| CN117437040A (en) * | 2023-12-21 | 2024-01-23 | 广州平云小匠科技股份有限公司 | Method, equipment and storage medium for updating trust risk level |
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