A local exchange risk warning method, device and equipment

Abstract

The present invention relates to the field of computer information technology, and discloses a method, device and equipment for risk warning of local exchanges, the method comprising: receiving a risk analysis request sent by a user terminal; obtaining regulatory data of target customers based on the risk analysis request, wherein the regulatory data includes basic data of the target customers in the local exchange and external analysis data of the target customers outside the local exchange; preprocessing the regulatory data to obtain indicator values ​​corresponding to each monitoring indicator included in a preset risk prediction model; determining the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model; and executing corresponding risk warning actions based on the risk level. The present invention solves the technical problems in the prior art that the risk indicators in the risk warning of local exchanges are unrelated, the warning indicators are single, and the data used is a single summary data, and achieves the technical effect of improving the accuracy and correctness of risk warnings of local exchanges.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of computer information technology, and in particular, to a local exchange risk warning method, device and equipment. Background Art

[0002] The general process of existing risk warnings for various local exchanges is as follows: (1) Process and handle the collected data, perform basic formatting and standardization on the data, and perform data cleaning on abnormal data. The processed data is then stored in a structured or unstructured database. (2) For different types of data, the risk warning system uses different calculation rules to extract features from the data, calculate indicators, and store them in a structured database. (3) The calculation results of the indicators are aggregated to form an index, and the relationship between the index and the threshold is used to determine the risk identification of various local trading venues.

[0003] However, the current risk warning model identification method for local exchanges has certain limitations, including: (1) Calculation of a single indicator. Assuming that there is no correlation between the indicators, the calculation is relatively simple and cannot identify concurrent correlation risks; (2) Risks are generally warned by a single indicator, and the identification model is relatively fixed, which stipulates the direction that needs to be warned. However, the business forms of various local trading venues are different, and the risks are diverse. It is difficult to achieve timely warning effects through a single universal indicator. (3) The data used for risk warning is generally summary data rather than detailed data, which has a certain impact on the accuracy and pertinence of the warning. Summary of the invention

[0004] The embodiments of the present invention provide a local exchange risk warning method, device and equipment, which solve the technical problems existing in the prior art of risk warning of local exchanges, that is, risk indicators are unrelated, warning indicators are single and the data used is single summary data.

[0005] In a first aspect, an embodiment of the present application provides a local exchange risk warning method, the method comprising:

[0006] Receiving a risk analysis request sent by a user terminal;

[0007] Obtaining regulatory data of the target customer based on the risk analysis request, wherein the regulatory data includes basic data of the target customer in the local exchange and external analysis data of the target customer outside the local exchange;

[0008] Preprocessing the regulatory data to obtain indicator values ​​corresponding to each monitoring indicator included in the preset risk prediction model;

[0009] Determine the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model;

[0010] Based on the risk level, corresponding risk warning actions are performed.

[0011] In a second aspect, an embodiment of the present application provides a risk warning device for a local exchange, the device comprising:

[0012] A request receiving unit, used to receive a risk analysis request sent by a user terminal;

[0013] A data acquisition unit, configured to acquire regulatory data of a target customer based on the risk analysis request, wherein the regulatory data includes basic data of the target customer in a local exchange and external analysis data of the target customer outside the local exchange;

[0014] A preprocessing unit, used to preprocess the supervision data to obtain an indicator value corresponding to each monitoring indicator included in a preset risk prediction model;

[0015] A level determination unit, configured to determine the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model;

[0016] The risk warning unit is used to perform corresponding risk warning actions based on the risk level.

[0017] In a third aspect, an embodiment of the present application provides a local exchange risk warning device, wherein the local exchange risk warning device comprises:

[0018] one or more processors;

[0019] A storage device for storing one or more programs;

[0020] When the one or more programs are executed by the one or more processors, the one or more processors implement any local exchange risk warning method as described in the first aspect of the embodiment of the present application.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any local exchange risk warning method as described in the first aspect of the embodiment of the present application.

[0022] The embodiment of the present invention discloses a method, device and equipment for risk warning of local exchanges, the method comprising: receiving a risk analysis request sent by a user terminal; obtaining regulatory data of target customers based on the risk analysis request, wherein the regulatory data includes basic data of target customers in local exchanges and external analysis data of target customers outside local exchanges; preprocessing the regulatory data to obtain indicator values ​​corresponding to each monitoring indicator included in a preset risk prediction model; determining the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model; and executing corresponding risk warning actions based on the risk level. The present invention solves the technical problems in the prior art that risk indicators in risk warnings of local exchanges are unrelated, warning indicators are single, and the data used is single summary data, and achieves the technical effect of improving the accuracy and correctness of risk warnings of local exchanges. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a local exchange risk early warning method provided by an embodiment of the present invention;

[0024] Figure 2 is a flow chart of another local exchange risk warning method provided by an embodiment of the present invention;

[0025] Figure 3 It is a flow chart of another local exchange risk warning method provided by an embodiment of the present invention;

[0026] Figure 4 It is a flow chart of another local exchange risk warning method provided by an embodiment of the present invention;

[0027] Figure 5 It is a structural diagram of a risk early warning device for a local exchange provided by an embodiment of the present invention;

[0028] Figure 6 A schematic diagram of the structure of a risk warning device for a local exchange provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] Local exchanges refer to various types of trading venues approved by various regions to engage in property rights transactions, cultural and art transactions, and medium- and long-term commodity transactions in order to promote the development of rights and interests (such as equity, property rights, etc.) and commodity markets. With the full application of new-generation information technology in the financial industry, it has greatly promoted the innovation of various financial formats, and various traditional offline financial business behaviors have been continuously transferred and integrated online. Among them, various local exchanges have emerged and developed rapidly, and business innovations have emerged in an endless stream. Local financial supervision, as an institutional arrangement provided by the government to maintain market economic order, faces new challenges and higher standards.

[0030] In order to meet higher standard requirements and to carry out risk warning of local exchanges more accurately and correctly, the present invention proposes a risk warning method, device and equipment for local exchanges.

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0032] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present invention are used to distinguish different objects, rather than to limit a specific order. The following embodiments of the present invention can be implemented separately, or in combination with each other, and the embodiments of the present invention do not impose specific limitations on this.

[0033] Figure 1 This is a flow chart of a local exchange risk warning method provided by an embodiment of the present invention. The local exchange risk warning method can be applied to all situations where local exchanges need to provide risk warnings. The local exchange risk warning method can be executed by a local exchange risk warning device, which can be implemented in hardware and / or software and can generally be integrated into a server.

[0034] like Figure 1 As shown, the risk warning method of the local exchange specifically includes the following steps:

[0035] S101: receiving a risk analysis request sent by a user terminal.

[0036] Specifically, the user terminal is generally a host computer, a mobile terminal or a corresponding human-computer interaction unit installed on the host computer and the mobile terminal at the regulatory department personnel. The human-computer interaction unit can be in the form of an APP (Application) or other device that can send risk analysis requests; the target customers to be monitored are all local trading venues and their branches within the jurisdiction of the regulatory department. The regulatory department personnel send risk analysis requests for the target local exchanges that need to predict risks to the local exchange risk warning device through the user terminal.

[0037] S102, obtaining regulatory data of the target customer based on the risk analysis request, wherein the regulatory data includes basic data of the target customer in the local exchange and external analysis data of the target customer outside the local exchange.

[0038] Optionally, S102, obtaining the regulatory data of the target customer based on the risk analysis request includes: obtaining the regulatory data of the target customer from multiple preset dimensions based on the risk analysis request through centralized acquisition and continuous acquisition, wherein the preset dimensions include at least two of the following: transaction events, transaction institutions, capital flows, product information, transaction status, account changes, industry trends, event reporting status, event investigation information, event-related personnel information, advertising, collection, and reports.

[0039] Specifically, regulatory data is mainly divided into status data and detailed data, among which the basic data of target customers in local exchanges is status data, and the external analysis data of target customers outside local exchanges is detailed data.

[0040] The basic data mainly refers to all non-empty valid data of local exchanges, which are obtained regularly and automatically through centralized acquisition. They include basic information of local exchanges, business data, financial data, public opinion data, government data disclosed by government departments such as the public security, procuratorial and judicial departments, Internet public data, daily regulatory reports and other report data, and other supplementary data, such as transaction contracts, customer information, member management information, etc. It should be noted that the basic data can be the current data of local exchanges, and also include the historical transaction data of local exchanges. When storing and / or processing the above-mentioned regulatory data, it complies with the relevant provisions of national laws and regulations.

[0041] Optionally, the external analytical data of the target customers outside the local exchange includes at least one of the following: relevant data of the target customers pushed by the external data exchange center; relevant government data collected by the government data resource sharing and exchange center; relevant data generated based on various data dimensions, wherein the data dimensions include organizational dimension, matter dimension, natural person dimension and administrative agency dimension.

[0042] S103, pre-processing the supervision data to obtain the indicator value corresponding to each monitoring indicator included in the preset risk prediction model.

[0043] Specifically, after obtaining various regulatory data, the preprocessing of the regulatory data includes converting the regulatory data into a unified format, then extracting feature vectors from the regulatory data in the unified format, and obtaining corresponding cross-correlation features and feature vector weights based on the feature vectors, wherein the feature vectors, cross-correlation features and feature vector weights are the above-mentioned indicator values.

[0044] The process of establishing the preset risk prediction model is as follows: collect a large amount of regulatory data, construct supervised machine learning labels and training samples based on the existing regulatory data, and the training samples include training sets, test sets and verification samples. Among them, relevant data of historical risky enterprises are extracted as training samples, relevant data of existing enterprises are extracted as test samples, and relevant data of existing enterprises with regulatory ratings of A and D are extracted as verification samples.

[0045] After dividing the samples, the business, capital and industrial and commercial data corresponding to the local exchanges are extracted according to the object type and industry attributes, and the data is classified. Data is extracted according to different analysis dimensions for risk indicator processing, including business operations, financial risks, public opinion risks, etc. When initializing the model, the indicators processed from the regulatory data are used to extract model features using the definitions of relevant industry experts, and the cross-features, deviation value features of the association relationship and the implicit features existing in the actual business scenario are constructed. It should be noted that the current basis for the division of various features is mainly based on the historical risk-incurred enterprises and expert judgments to define the corresponding model features, cross-features, deviation features and implicit features. For example, if the external litigation indicator value of a debt dispute is large during the indicator processing, a feature of a difficult business operation is extracted, and the difficult business operation trait is one of the model features.

[0046] Specifically, feature crossover is a synthetic feature formed by combining two or more features. The feature dimension is increased by feature combination to achieve better training effect. Therefore, crossover feature is mainly used to solve nonlinear problems and separate data by establishing feature combination. Deviation feature is a data to observe the test environment and system stability. The smaller the eigenvalue of the deviation feature, the more stable the environment and the more effective the measured data. On the contrary, if the eigenvalue of the deviation feature is too large, it means that the environment is unstable. In this case, the measured data is of little value. Therefore, when testing, it is necessary to ensure that the eigenvalue of the deviation feature is small. Correspondingly, features with large eigenvalues ​​of the deviation feature will be gradually eliminated during training. Hidden feature refers to a feature that cannot be identified from the data alone and needs 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 shareholder. When other companies held by the shareholder face risks, the enterprise will also face a crisis quickly. In previous regulatory scenarios, at this time, the enterprise may be found to be a shell company and the financial report data are all forged, so it is necessary to judge some obscure features to identify it early.

[0047] According to the preset risk prediction model based on the machine learning algorithm trained in the training sample, the model is tuned in the test set. Optionally, the validation sample is used to verify the prediction accuracy of the preset risk prediction model. The model generalization performance of the basic preset risk prediction model is verified based on the validation sample, and the model is continuously trained to return results to improve the accuracy of the basic preset risk prediction model.

[0048] When the accuracy of the basic early warning model does not reach the preset accuracy threshold, that is, it does not reach the three dimensions of accuracy, precision, and recall rate, the basic preset risk prediction model needs to be trained again. There are two training methods: check whether the parameter configuration in the process of building the preset risk prediction model is unreasonable. If it is unreasonable, it is necessary to readjust the cross-feature correlation method and evidence weight conversion between indicators, and check whether the selected feature fields are not in line with economic significance; another method is to check whether the sample structure in the process of building the preset risk prediction model is reasonable, including label definition, sample quantity, and sample splitting. Among them, sample splitting includes the splitting of training samples and verification samples, and the splitting of training sets and test sets in training samples. If there are unreasonable parts, it is necessary to reorganize and match the regulatory data to generate new training samples, and retrain the basic preset risk prediction model based on the new training samples.

[0049] Finally, if there is new regulatory data reading and accumulation, use the new regulatory data to build an optimized preset risk prediction model, and use the new regulatory data as a sample to fine-tune the model to obtain an optimized preset risk prediction model. Compare and challenge the basic preset risk prediction model and the optimized preset risk prediction model to obtain the final preset risk prediction model. The preset risk prediction model of the local trading venue shall be based on the application of the final preset risk prediction model.

[0050] S104: Determine the risk level corresponding to the indicator value according to the indicator value and a preset risk prediction model.

[0051] Specifically, it is assumed that all indicator values ​​are risk indicators that may cause trading risks in local exchanges, but the probability of causing risks is different. The indicator values ​​are ranked in importance based on expert experience, and then the indicator values ​​are evaluated based on the importance ranking, the preferences of experts and the preset risk prediction model, and finally the risk level corresponding to the indicator value is obtained.

[0052] S105: Execute corresponding risk warning actions based on the risk level.

[0053] Specifically, after obtaining the risk level, risk warnings are issued to each regulatory object based on the risk level.

[0054] In summary, in the embodiments of the present invention, each time the regulatory data is acquired, the latest data is obtained, and optimization is performed based on the reading of the newly added data. After all the data are standardized according to the regulatory data standards, the collected regulatory data are uniformly processed into regulatory indicators, the indicator parameters are subjected to risk characteristic analysis, and then the risk characteristic quantity is extracted, and the measurement and analysis are performed based on the risk characteristic quantity. If a risk event occurs, it is necessary to determine the cause and location of the risk and other risk transmission trends that may be caused, so as to make corresponding risk warning actions.

[0055] The present invention solves the technical problems in the prior art that risk indicators in risk warnings of local exchanges are unrelated, warning indicators are single, and the data used is single summary data, thereby achieving the technical effect of improving the accuracy and correctness of risk warnings of local exchanges.

[0056] On the basis of the above technical solutions, Figure 2 is a flow chart of another local exchange risk warning method provided by an embodiment of the present invention. Figure 2 As shown, the above step S103 specifically includes:

[0057] S201, convert regulatory data into a unified format.

[0058] Specifically, after the corresponding regulatory data is obtained, the original regulatory data is stored, and then the regulatory data is converted according to the prescribed unified standards.

[0059] S202, extracting features of the unified-format regulatory data based on preset dimensions to obtain corresponding feature vectors.

[0060] Specifically, features of preset dimensions are extracted from the regulatory data, that is, transaction events, transaction institutions, capital flows, product information, transaction status, account changes, industry trends, event reporting status, event investigation information, information on event-related personnel, advertising, collection, reports, etc. in the preset dimensions are taken as corresponding targets, and features are extracted from the regulatory data collected under each target to obtain corresponding feature vectors.

[0061] S203, creating a cross-domain classification based on the feature vector, and determining a corresponding cross-correlation feature based on the cross-domain classification.

[0062] Specifically, using the influence of the previous analysis of the feature space, when creating a cross-domain classification, one or more conditions that must be met before returning a value from a source object can be specified, and conditions can be defined for the source object and the target object. If the conditions are met, a cross-domain classification is automatically created. After the cross-domain classification is determined, the corresponding cross-domain association features can be determined based on the cross-domain classification.

[0063] S204, determining the weight of the feature vector based on a preset risk prediction model, wherein the feature vector, the cross-correlation feature and the weight of the feature vector are indicator values.

[0064] Optionally, the preset risk prediction model includes training samples and verification samples, and the training samples include training sets and test sets. S204, determining the weight of the feature vector based on the preset risk prediction model includes: training the feature vector through the training set, and obtaining classifiers that lack the nth feature vector, where n is the number of feature vectors, and n≥1; testing the classification effect of each classifier through the test set, and counting the number of incorrect classifications of each classifier; normalizing the number of incorrect classifications to obtain the weight of the feature vector.

[0065] On the basis of the above technical solutions, Figure 3 is a flow chart of another local exchange risk warning method provided by an embodiment of the present invention, such as Figure 3 As shown, the above step S202 specifically includes:

[0066] S301, performing principal component analysis on the regulatory data based on the preset dimensions, and forming a feature space with the eigenvectors corresponding to the maximum eigenvalues ​​obtained by the analysis;

[0067] S302, using the influence of each regulatory data in the respective feature space as the classification basis of the feature vector;

[0068] S303, extracting features from the supervision data based on the classification basis to obtain corresponding feature vectors.

[0069] Specifically, the preset dimensions are taken as corresponding targets, and principal component analysis is performed on the regulatory data collected under each target. The eigenvectors corresponding to the maximum eigenvalues ​​obtained from the analysis are used to form a feature space. The impact of each target data (i.e., the regulatory data collected under each target) in the respective feature space is used as the classification basis of the feature vector. Based on the classification basis, features of the regulatory data are extracted to obtain the corresponding feature vector.

[0070] On the basis of the above technical solutions, Figure 4 is a flow chart of another local exchange risk warning method provided by an embodiment of the present invention, such as Figure 4 As shown, the above step S104 specifically includes:

[0071] S401, taking the indicator value as the risk indicator, and ranking the risk indicator by importance according to the expert experience, wherein the number of the expert experience is greater than 1, and the number of the importance ranking of the obtained risk indicator is greater than 1;

[0072] S402, performing risk assessment on multiple importance rankings according to a preset risk prediction model to obtain corresponding risk levels.

[0073] For example, among the various indicator values, it is assumed that all indicator values ​​are risk indicators that may cause local exchanges, and the indicator values ​​are used as risk indicators. Then, the risk indicators are ranked in order of importance based on expert experience. Assuming that there are n possible risk indicators, a complete risk indicator set A is defined, and n possible risk indicators are defined as u1, u2, ..., u n , then the complete risk indicator set is expressed as: A = {u1, u2, ..., u n}, let each expert rank the importance of all risk indicators in advance, and then form the preference of the expert group according to the preference of each expert, so as to obtain the key factor set U = {u1, u2, ..., u m}, where m<n. At the same time, the risk assessment set established is a set of possible assessment results, expressed as V={v1,v2,……,v m}, where each element represents various possible evaluation results.

[0074] In the risk assessment of local exchanges, the points system can be used in accordance with the convention to divide the fuzzy judgment set into levels, which are represented by v1 to v9, namely AAA, AA, A, BB, B, CC, C, DD, D. In order to facilitate quantitative calculation, with reference to other risk level data, these five levels are assigned V = {0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09}. Finally, the corresponding risk level is obtained based on the preset risk prediction model and the level assignment of risk assessment.

[0075] Figure 5 is a structural diagram of a risk warning device for a local exchange provided by an embodiment of the present invention, such as Figure 5 As shown, the risk warning device of the local exchange includes:

[0076] A request receiving unit 51, configured to receive a risk analysis request sent by a user terminal;

[0077] A data acquisition unit 52 is used to acquire the regulatory data of the target customer based on the risk analysis request, wherein the regulatory data includes the basic data of the target customer in the local exchange and the external analysis data of the target customer outside the local exchange;

[0078] A preprocessing unit 53 is used to preprocess the supervision data to obtain the indicator value corresponding to each monitoring indicator included in the preset risk prediction model;

[0079] A level determination unit 54, used to determine the risk level corresponding to the indicator value according to the indicator value and a preset risk prediction model;

[0080] The risk warning unit 55 is used to perform corresponding risk warning actions based on the risk level.

[0081] Optionally, the data acquisition unit 52 is specifically used to obtain regulatory data of target customers from multiple preset dimensions based on risk analysis requests through centralized and continuous acquisition, wherein the preset dimensions include at least two of the following: transaction events, transaction institutions, capital flows, product information, transaction status, account changes, industry trends, event reporting status, event investigation information, event-related personnel information, advertising, collection, and reports.

[0082] Optionally, the preprocessing unit 53 includes:

[0083] a conversion subunit, which is used to convert regulatory data into a uniform format;

[0084] A feature extraction subunit is used to extract features of the unified formatted regulatory data based on preset dimensions to obtain corresponding feature vectors;

[0085] A feature determination subunit, used for creating a cross-domain classification based on the feature vector, and determining a corresponding cross-correlation feature based on the cross-domain classification;

[0086] The weight determination subunit is used to determine the weight of the feature vector based on a preset risk prediction model, wherein the feature vector, the cross-correlation feature and the weight of the feature vector are indicator values.

[0087] Optionally, the feature extraction subunit is specifically used for:

[0088] Perform principal component analysis on the regulatory data based on the preset dimensions, and form a feature space with the eigenvectors corresponding to the maximum eigenvalues ​​obtained from the analysis;

[0089] The impact of each regulatory data in the respective feature space is used as the classification basis of the feature vector;

[0090] Based on the classification basis, the features of the supervision data are extracted to obtain the corresponding feature vector.

[0091] Optionally, the preset risk prediction model includes a training sample and a validation sample, the training sample includes a training set and a test set, and the weight determination subunit is specifically used for:

[0092] The feature vectors are trained through the training set to obtain classifiers that lack the nth feature vector, where n is the number of feature vectors, n≥1;

[0093] Test the classification effect of each classifier through the test set, and count the number of incorrect classifications of each classifier;

[0094] The number of misclassifications is normalized to obtain the weight of the feature vector.

[0095] Optionally, the level determination unit 54 is specifically configured to:

[0096] The indicator value is used as the risk indicator, and the risk indicator is ranked by importance according to the expert experience, wherein the number of expert experience is greater than 1, and the number of importance rankings of the obtained risk indicators is greater than 1;

[0097] Risk assessment is performed on multiple importance rankings based on a preset risk prediction model to obtain corresponding risk levels.

[0098] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0099] The local exchange risk warning device provided in the embodiment of the present invention has the same technical features as the local exchange risk warning method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0100] Figure 6 A schematic diagram of a risk warning device for a local exchange provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown, the local exchange risk warning device includes a processor 61, a memory 62, an input device 63 and an output device 64; the number of processors 61 in the local exchange risk warning device can be one or more. Figure 6 A processor 61 is taken as an example; the processor 61, memory 62, input device 63 and output device 64 in the risk warning device of the local exchange can be connected by a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0101] The memory 62, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the local exchange risk warning method in the embodiment of the present invention (for example, the request receiving unit 51, the data acquisition unit 52, the preprocessing unit 53, the level determination unit 54 and the risk warning unit 55 in the local exchange risk warning device). The processor 61 executes the various functional applications and data processing of the local exchange risk warning device by running the software programs, instructions and modules stored in the memory 62, that is, realizes the above-mentioned local exchange risk warning method.

[0102] The memory 62 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 62 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 62 may further include a memory remotely arranged relative to the processor 61, and these remote memories may be connected to the local exchange risk warning device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] The input device 63 can be used to receive input digital or character information, and generate key signal input related to user settings and function control of the local exchange risk warning device. The output device 64 can include display devices such as display screens.

[0104] An embodiment of the present invention also provides a storage medium containing computer executable instructions, which are used to execute a local exchange risk warning method when executed by a computer processor.

[0105] Specifically, the local exchange's risk warning methods include:

[0106] Receiving a risk analysis request sent by a user terminal;

[0107] Obtaining regulatory data of target customers based on risk analysis requests, where regulatory data includes basic data of target customers on local exchanges and external analytical data of target customers outside local exchanges;

[0108] Preprocess the regulatory data to obtain the indicator values ​​corresponding to each monitoring indicator included in the preset risk prediction model;

[0109] Determine the risk level corresponding to the indicator value based on the indicator value and the preset risk prediction model;

[0110] Execute corresponding risk warning actions based on risk level.

[0111] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the local exchange risk warning method provided in any embodiment of the present invention.

[0112] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FLASH), a hard disk or an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0113] It is worth noting that in the embodiment of the above-mentioned search device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0114] In the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0115] Finally, it should be noted that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A local exchange risk early warning method, characterized in that: The method comprises: Receiving a risk analysis request sent by a user terminal; Obtaining regulatory data of the target customer based on the risk analysis request, wherein the regulatory data includes basic data of the target customer in the local exchange and external analysis data of the target customer outside the local exchange; Preprocessing the regulatory data to obtain indicator values ​​corresponding to each monitoring indicator included in the preset risk prediction model; Determine the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model; Execute corresponding risk warning actions based on the risk level; The obtaining of the regulatory data of the target customer based on the risk analysis request comprises: obtaining the regulatory data of the target customer from multiple preset dimensions based on the risk analysis request by centralized acquisition and continuous acquisition, wherein the preset dimensions include at least two of the following: transaction events, transaction institutions, capital flows, product information, transaction status, account changes, industry trends, event reporting status, event investigation information, event-related personnel information, advertisements, collection, and reports; The preprocessing of the supervision data to obtain the indicator values ​​corresponding to the monitoring indicators included in the preset risk prediction model includes: converting said regulatory data into a uniform format; Extracting features of the unified-formatted supervision data based on the preset dimension to obtain corresponding feature vectors; Creating a cross-domain classification based on the feature vector, and determining a corresponding cross-correlation feature based on the cross-domain classification; The weight of the feature vector is determined based on the preset risk prediction model, wherein the feature vector, the cross-correlation feature and the weight of the feature vector are the indicator value.

2. The local exchange risk early warning method according to claim 1, characterized in that: The external analytical data of the target customer outside the local exchange includes at least one of the following: The relevant data of the target customers pushed by the external data exchange center; Relevant government data collected by the Government Data Resource Sharing and Exchange Center; Relevant data generated based on various data dimensions, wherein the data dimensions include organizational dimension, matter dimension, natural person dimension and administrative agency dimension.

3. The local exchange risk early warning method according to claim 1, characterized in that: The extracting of the unified formatted supervision data based on the preset dimension features to obtain the corresponding feature vector includes: Performing principal component analysis on the supervision data based on the preset dimension, and forming a feature space with the eigenvectors corresponding to the maximum eigenvalues ​​obtained by the analysis; Using the influence of each of the regulatory data in the respective feature space as a classification basis for the feature vector; Based on the classification basis, feature extraction is performed on the supervision data to obtain the corresponding feature vector.

4. The local exchange risk early warning method according to claim 1, characterized in that: The preset risk prediction model includes a training sample and a verification sample, the training sample includes a training set and a test set, and the weight of the feature vector determined based on the preset risk prediction model includes: Training the feature vectors through the training set to obtain classifiers that lack the nth feature vector, where n is the number of the feature vectors, and n≥1; Using the test set to test the classification effect of each classifier, and counting the number of incorrect classifications of each classifier; The misclassification number is normalized to obtain the weight of the feature vector.

5. The local exchange risk early warning method according to claim 1, characterized in that: The step of determining the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model includes: Taking the indicator value as the risk indicator, and ranking the risk indicator by importance according to expert experience, wherein the number of expert experience is greater than 1, and the number of importance rankings of the risk indicator obtained is greater than 1; A risk assessment is performed on the plurality of importance rankings according to the preset risk prediction model to obtain the corresponding risk levels.

6. The local exchange risk early warning method according to claim 4, characterized in that: The verification sample is used to verify the prediction accuracy of the preset risk prediction model.

7. A risk warning device for a local exchange, characterized in that: The device comprises: A request receiving unit, used to receive a risk analysis request sent by a user terminal; A data acquisition unit, configured to acquire regulatory data of a target customer based on the risk analysis request, wherein the regulatory data includes basic data of the target customer in a local exchange and external analysis data of the target customer outside the local exchange; A preprocessing unit, used to preprocess the supervision data to obtain an indicator value corresponding to each monitoring indicator included in a preset risk prediction model; A level determination unit, configured to determine the risk level corresponding to the indicator value according to the indicator value and the preset risk prediction model; A risk warning unit, configured to execute corresponding risk warning actions based on the risk level; The data acquisition unit is specifically used to: acquire the regulatory data of the target customer from multiple preset dimensions based on the risk analysis request by centralized acquisition and continuous acquisition, wherein the preset dimensions include at least two of the following: transaction events, transaction institutions, capital flows, product information, transaction status, account changes, industry trends, event reporting status, event investigation information, event-related personnel information, advertisements, collection, and reports; The pre-processing unit comprises: A conversion subunit, used for converting the regulatory data into a unified format; A feature extraction subunit, configured to extract features of the unified-formatted supervision data based on the preset dimension to obtain a corresponding feature vector; A feature determination subunit, configured to create a cross-domain classification based on the feature vector, and determine a corresponding cross-correlation feature based on the cross-domain classification; A weight determination subunit is used to determine the weight of the feature vector based on the preset risk prediction model, wherein the feature vector, the cross-correlation feature and the weight of the feature vector are the indicator value.

8. A local exchange risk warning device, characterized in that: The local exchange risk warning equipment includes: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the local exchange risk warning method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a local exchange risk warning method as described in any one of claims 1-6 is implemented.

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