62 results about "Risk type" patented technology

The invention discloses a financial risk control method and system, and relates to the technical field of enterprise financial management. The method comprises the following steps: acquiring related data; the related data comprises financial data, market data, operation data and external data; wherein the external data comprises an industry report, competitive product data and policy and regulation change data; constructing a knowledge graph according to the related data; the knowledge graph comprises financial risk types and corresponding risk levels and risk influence factors; based on the knowledge graph, constructing a financial risk prediction model by using a credit risk model and a quantification algorithm; the credit risk model adopts a KMV model; the quantization algorithm comprises a historical simulation method and a Monte Carlo simulation method; and performing real-time monitoring on a target enterprise by using the financial risk prediction model, and performing visual display on a prediction result in a set period. According to the invention, the risk monitoring accuracy and timeliness can be improved.

The invention provides an intelligent contract risk identification method and device based on a big language model, and the method comprises the steps: constructing a legal knowledge graph and a risk case library, carrying out the structural processing of a contract text, carrying out the preliminary risk assessment through combining with a big language model, and carrying out the deep reasoning and case verification through combining with the knowledge graph. And generating a final report containing the risk type, the confidence coefficient, the reasoning chain and the modification suggestion, and continuously optimizing the model through user feedback data, so that efficient, accurate, explainable and adaptive optimization of contract risk identification is realized.

The invention relates to the technical field of risk management and control and data processing, and discloses a data management and control system for post-loan risk monitoring, and the system comprises a multi-source data fusion module which is used for obtaining post-loan financial information of a borrower and external association information, and generating a multi-dimensional feature vector; the post-loan risk assessment module is used for constructing and training an integrated learning model comprising a feature decoupling layer, a base model layer and a meta-model layer, obtaining a post-loan risk assessment model, inputting a multi-dimensional feature vector, and generating a post-loan risk score, a risk contribution degree weight and a post-loan risk type; and the post-loan risk management and control module is used for matching disposal strategies according to post-loan risk types when the post-loan risk score is greater than a dynamic threshold value, performing priority ranking on the disposal strategies based on the risk contribution degree weights, generating a risk disposal scheme and performing real-time adjustment according to the disposal feedback of the risk disposal scheme. According to the invention, accurate evaluation and efficient management and control of post-loan risks can be realized, and the risk management capability of financial institutions is effectively improved.

The invention belongs to the technical field of user management, and provides a risk type identification method and device for a social session, and the method comprises the steps: obtaining to-be-detected information which comprises session information and related information; according to the to-be-detected information, obtaining a first risk type label and an initial confidence coefficient thereof; if the initial confidence coefficient is not smaller than a first threshold value, obtaining a cue word character string according to the first risk type label; inputting the cue word character string into a large language model to obtain a first confidence coefficient of the first risk type tag; and if the first confidence coefficient is not less than a second threshold value, determining that the to-be-detected information is a risk type corresponding to the first risk type label.

The invention discloses an intelligent acquisition risk identification system based on deep learning, and the system comprises a time sequence sample construction module which is used for building a time sequence sample sequence; the hierarchical attention structural feature coding module is used for carrying out structural feature coding on the sequential sample sequence by utilizing a hierarchical attention network; the time sequence feature extraction module is used for inputting the structural feature vector into an ETSform model to perform time sequence feature extraction; the hierarchical time sequence collaborative attention adaptive fusion module is used for performing bidirectional attention interaction and dynamically generating a hierarchical weight and a time sequence weight through a meta-learning controller; the improved CatBoost risk identification module is used for outputting a risk score and a risk type label; and the system fusion module is used for summarizing and merging the risk scores and the risk type labels. The method and the device are suitable for merchant transaction risk identification in an acquiring business scene.

The embodiment of the invention provides a refining heating furnace risk supervision method and system, and belongs to the technical field of equipment risk supervision. The risk supervision method for the refining and chemical heating furnace is characterized by comprising the following steps: collecting real-time operation data of the refining and chemical heating furnace, and preprocessing the real-time operation data to obtain preprocessed data; classifying the preprocessed data based on risk types to obtain a data set under each risk type; determining risk levels of the corresponding risk types one by one based on the data sets under the risk types, and calling corresponding supervision schemes in sequence for the determined risk levels based on a preset risk knowledge graph; and executing the called supervision scheme, and outputting corresponding feedback information based on an execution result.

The invention discloses a finance and taxation data processing method and system, relates to the technical field of data storage processing, and classifies finance and taxation information resources into structured and unstructured data, so that an enterprise can more clearly know the composition of own data. The classification is beneficial to subsequent storage, query and analysis, data confusion is avoided, and the internal relation between structured and unstructured finance and tax data can be accurately found out through relevance evaluation. Therefore, the enterprise does not ignore data which seems to be independent but actually correlated, the data with remarkable correlation is integrated, the finance and taxation conditions of the enterprise can be reflected more comprehensively, the correlated data are stored in a unified mode, financial evaluation values and insight evaluation values are analyzed, multi-dimensional data factors are considered in the mode, and the enterprise finance and taxation evaluation efficiency is improved. Potential risks are judged according to the comprehensive risk assessment value, early warning display is carried out according to the risk types, and an enterprise can take corresponding measures according to specific risk conditions.

The invention relates to the technical field of risk management and control, and particularly discloses a to-be-early-warned insurance policy query method and device based on geometric cutting, equipment and a medium. According to the method, screening is carried out according to the disaster risk type, the to-be-screened insurance policy list is obtained, the number of insurance policies is reduced, geometric cutting is carried out on the disaster risk area according to the preset cutting parameters, at least one convex polygon area is obtained, the disaster risk area with a complex boundary is converted into a simple convex polygon area, and the risk of disaster risk is reduced. The complexity of polygons and the space judgment calculation amount are reduced, so that the insurance policy query efficiency is improved; moreover, the cut convex polygon geometry can completely coincide with the original risk area, so that the edge precision of the risk area is prevented from being reduced, and the policy query accuracy is ensured. The scheme is applied to financial insurance services such as property insurance and agricultural insurance, the insurance policy to be pre-warned with the underwriting address located in the disaster risk area can be quickly inquired, and pre-warning is sent to the personnel to be pre-warned, so that the loss caused by disasters is reduced.

The present invention relates to the field of data processing technology, and the present invention relates to a supply chain financial transaction security early warning method and system, comprising: obtaining financial data of the supply chain and its corresponding risk score, dividing the financial data into continuous data and discrete data, and calculating its importance coefficient. The importance coefficient is positively correlated with the Spearman rank correlation coefficient of the risk score, and is negatively correlated with the Pearson correlation coefficient in continuous data, the standardized mutual information in discrete data, and the point biserial correlation coefficient between continuous data and discrete data. An improved XGBoost model is used to predict the probability of risk type of financial data, and the risk type corresponding to the maximum value of the probability is the final risk type of the financial data. The present invention solves the problem of inaccurate risk assessment of supply chain financial transactions by improving the XGBoost model.

The present application proposes an enterprise risk judgment method, device, electronic device and storage medium based on artificial intelligence. The enterprise risk judgment method based on artificial intelligence includes: querying the historical registration information of the enterprise from the business database, and obtaining the historical public information of the enterprise based on the open source information; comparing the historical registration information and historical public information of the enterprise to construct the risk feature vector of the enterprise; labeling the enterprise to obtain the label of each risk feature vector; calculating the preferred dimension factor based on the risk feature vector and the label, reducing the dimension of the risk feature vector to the preferred dimension factor to obtain the preferred risk feature vector; training the enterprise risk judgment network based on the preferred risk feature vector and the enterprise label; collecting the preferred risk feature vector of the enterprise to be judged and inputting it into the enterprise risk judgment network to obtain the risk judgment result. The present application can enhance the difference in feature vectors between enterprises of different risk types, thereby improving the accuracy of enterprise risk judgment.

This application provides a credit risk analysis method applicable to the fields of big data and artificial intelligence. The method includes: responding to a credit risk analysis request for a target enterprise, acquiring multi-dimensional credit data of the target enterprise, and extracting a set of credit risk features for each dimension based on the credit data. These multiple dimensions include financial, cash flow, and financing dimensions. The method also involves inputting the set of credit risk features for each dimension into a corresponding sub-model to obtain analysis results for that dimension. These results include the initial credit risk level of the target enterprise under different performance periods and risk types in each dimension. Finally, the analysis results are input into a fusion model to obtain the final credit risk level of the target enterprise under different performance periods and risk types across multiple dimensions. This application also provides a credit risk analysis device, equipment, storage medium, and program product.

The present invention discloses a procurement risk management method and system based on AI technology, which relates to the field of bidding technology. The method includes: obtaining enterprise procurement-related information collected by a bidding smart supervision system; analyzing the enterprise procurement-related information to obtain key information related to procurement risks, and determining the corresponding risk name and risk level based on the key information and the risk information set in the preset risk type; when the risk level of one target risk is higher than the preset level threshold, enabling supervision authority and sending a supervision pop-up window after receiving the supervision instruction. The supervision pop-up window is used to receive supervision suggestions for the risk and send the supervision suggestions to the corresponding target unit. The present invention solves the problem of poor accuracy in procurement risk management in the existing technology.

The invention discloses a real-time all-weather risk control system based on high-frequency data and news public opinions, and the system comprises a quantitative intelligent transaction module, a risk income calculation module, a multi-asset risk control module, and an intelligent evaluation module. The quantitative intelligent transaction module comprises a strategy combination module, a strategy matching fund module, an automatic transaction module and a strategy compliance real-time review sub-module, the sub-module obtains financial supervision compliance requirements in real time, carries out compliance verification on financial product combination and fund putting schemes, blocks execution of uncompliance rules, and feeds back the uncompliance rules; the risk income calculation module comprises a core data collection module and a data traceability evidence storage sub-module, the sub-module marks a source identifier, collection time and personnel information for core data to form non-tampering traceability chain associated storage, and the multi-asset risk control module comprises a risk control module and a risk slow release scheme automatic generation sub-module. The sub-module receives the risk signal and analyzes the risk type and range.

The invention relates to the technical field of data prediction, and particularly provides a contract risk prediction method and device, a medium, equipment and a product, and the method can comprise the steps: determining a risk conduction path corresponding to a risk type of a contract based on a risk knowledge graph; wherein the risk knowledge graph comprises a space-time weight of influence of each event on the contract; the risk conduction path represents a series of chain reactions caused by the risk type; obtaining a contract income risk value based on a key space-time parameter corresponding to the risk conduction path, the risk conduction path, a contract product category and a risk time parameter; wherein the key space-time parameter is determined based on the space-time weight. According to the embodiment of the invention, accurate prediction of the contract risk can be realized.

This specification provides a risk processing method, device, and server for data objects, which can be used in the financial field. Specifically, the first node server first obtains the serial number information of the target object as the first type of feature data; and by matching the first type of feature data with the preset local database, if the match is successful, further determines the target risk type that matches the target object; then distinguishes whether the above-mentioned target risk type is a first type of risk type involving the attribute characteristics of the target object or a second type of risk type involving the behavioral characteristics of the target object; if it is determined that the target risk type belongs to the first type of risk type, according to the first processing rule that matches the first type of risk type, the second type of feature data involving the attributes of the target object is extracted in a targeted manner; and according to the first processing rule and the second type of feature data, the risk processing for the target object is performed. In this way, the risk processing of the target object is achieved intelligently and efficiently.

The invention relates to the technical field of risk assessment, and discloses a power grid provincial management industry stock right investment risk assessment method, which comprises the following steps: a system identifies stock right investment risk types, determines main influence factors of different risks, constructs a GDP growth prediction model based on ARIMA, forms market risk quantitative assessment standard formulation, constructs a load prediction model based on SAO optimization LSTM, and provides a power grid provincial management industry stock right investment risk assessment system. The method comprises the following steps: establishing an operation risk level evaluation model based on Markov and LS-SVM, forming operation risk quantitative evaluation standard formulation, constructing an operation risk level evaluation model based on Markov and LS-SVM, realizing operation risk quantitative evaluation standard formulation, applying a neural network model, realizing scientific setting of market risk, operation risk and operation risk score weight, and obtaining a final risk evaluation result. According to the method, the actual current situation of the power grid equity investment is combined, the main risk type of the power grid equity investment is systematically evaluated through a series of advanced intelligent prediction methods, and therefore scientific and quantitative support is provided for investment decision making.

The present invention provides a training method and related device for an enterprise violation risk prediction model, the method comprising: obtaining enterprise contract data, and inputting the enterprise contract data into a pre-trained enterprise contract data expansion model to obtain expanded contract data; combining the enterprise contract data and the expanded contract data to obtain a first contract sample set; using a pre-trained feature extraction model to extract features from the first contract sample set to obtain contract features of each contract sample in the first contract sample set; labeling the contract features according to the risk type of the contract sample, and combining all the labeled contract features to obtain a training feature set; using the training feature set to train a classification model to obtain an enterprise violation risk prediction model; the present invention can train an enterprise violation risk prediction model that can make high-accuracy judgments on enterprise contract risks.

The invention provides a monitoring and early warning method and device for a financial risk control model, electronic equipment and a medium, and the method comprises the steps: obtaining financial monitoring data, and carrying out the preprocessing of the financial monitoring data; wherein the preprocessing comprises encryption processing; inputting the preprocessed financial monitoring data into a pre-trained financial risk control model to obtain early warning information; wherein the early warning information at least comprises an event triggering risk early warning, risk occurrence time, a risk type and a risk level; and sending the early warning information to a worker, so that the worker processes an event triggering risk early warning. According to the invention, the timeliness and accuracy of risk early warning are improved.

The embodiment of the invention provides an enterprise data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring multi-modal data of a target enterprise; performing feature extraction on the multi-modal data to obtain different multi-modal features; performing feature fusion on different multi-modal features to obtain fused features; identifying feature attributes of the multi-modal features, wherein the feature attributes comprise data integrity, timestamps and risk types; determining a risk assessment type according to the risk assessment task demand of the target enterprise; determining a target processing strategy according to the feature attribute and the risk assessment type; and performing risk assessment processing on the fusion feature according to the target processing strategy through a risk assessment model, and generating a risk assessment result of the corresponding target enterprise. According to the embodiment of the invention, the comprehensiveness and accuracy of enterprise risk assessment can be improved, the resource allocation weight of the risk assessment model is dynamically adjusted according to the target processing strategy, and efficient and accurate enterprise risk assessment can be realized in a financial scene.

The invention provides a bidding file risk early warning method based on machine learning, and relates to the technical field of risk management, and the method comprises the steps: obtaining real-time bidding files, carrying out the file classification, obtaining a first classification file set, processing each bidding file in the first classification file set, and obtaining a second classification file set; determining a risk keyword and a corresponding risk rule based on the bidding type of the bidding file to obtain a first risk type; screening a matched machine learning algorithm based on the first risk type to obtain a first learning algorithm, and performing risk assessment on the bidding document; and analyzing the first risk type in combination with the risk assessment result to obtain a risk early warning scheme of the corresponding bidding file, thereby performing risk early warning. By classifying the bidding files, the risk type of each bidding type is determined, and the corresponding machine learning algorithm is matched to carry out risk assessment, so that the risk assessment efficiency can be improved, and risk early warning can be carried out more quickly and accurately.

The invention discloses an SQL statement intelligent auditing and risk early warning method for financial services, and relates to the technical field of financial service intelligent auditing, and the method comprises the following steps: 1, defining a financial SQL core risk type and a quantitative auditing dimension; 2, a full-dimensional sensing module is constructed, multi-source data related to the SQL to be audited are collected, and the multi-source data at least comprise SQL statement data, database operation environment data, financial service rule data, supervision compliance data and historical SQL execution log data. According to the method, financial SQL auditing scene elements are mapped into four types of element individuals of SQL, risk, compliance and business, association strength among the individuals is quantified in combination with a connection weight algorithm, a virtual social network is constructed, four types of core risks of multi-risk-dimension collaborative judgment, sensitive data coverage, compliance authorization, business logic and performance overload are realized, and the risk level of the financial SQL auditing scene is improved. And the one-sidedness of a traditional static rule is avoided.

The invention provides a property insurance premium calculation method and device, computer equipment and a storage medium. The method comprises the steps of determining attribute information of related articles of an insurance object and position information of the insurance object; determining target risk levels, corresponding to different risk types, of the insurance object from a multi-dimensional risk database according to the position information; determining a risk score corresponding to the related article according to the attribute information and the target risk levels corresponding to the different risk types; and according to the attribute information of the related article and the risk score, calculating and determining the insurance premium rate of the insurance object. Therefore, the target risk levels, corresponding to the different risk types, of the insurance object can be rapidly determined based on the multi-dimensional risk database, then the risk scores corresponding to the related articles are determined, and the insurance premium rate of the insurance object is finally obtained, so that differential pricing of the personalized insurance object is realized while the claim settlement efficiency is improved to a great extent, and the cost is reduced. And the customer experience is effectively improved.

The application provides a smart contract risk identification method and device based on a large language model, which constructs a legal knowledge graph and a risk case library, performs structured processing on a contract text, performs preliminary risk evaluation by using a joint large language model, performs deep reasoning and case verification in combination with the knowledge graph, generates a final report containing a risk type, a confidence, a reasoning chain and a modification suggestion, and continuously optimizes the model through user feedback data, so that efficient, accurate, interpretable and self-adaptive optimization of contract risk identification are realized.

The invention discloses a risk control decision-making method based on a large model, and the method comprises the steps: receiving a risk control decision-making request from a financial business platform, and extracting a scene identifier, business information and behavior characteristics; matching a preset model cluster based on the scene identifier, the model cluster comprising at least one risk assessment model, and each risk assessment model corresponding to a risk type; forming risk control feature vectors by the business information and the behavior features, inputting the risk control feature vectors into each risk assessment model in the matched model cluster, respectively outputting a risk quantized value of each risk type, forming a basic risk value sequence as a basic risk vector, and binding with a risk type identification sequence; and outputting a risk control decision result by using a preset risk control decision mechanism based on the basic risk vector bound with the risk type identification sequence and a predefined risk incidence matrix. Therefore, the quality and efficiency of financial business risk control decision making are improved.

The invention discloses a procurement risk management method and system based on an AI technology, and relates to the technical field of bidding, and the method comprises the steps: obtaining enterprise procurement related information collected by a bidding intelligent supervision system; analyzing the enterprise purchase related information to obtain key information related to the purchase risk, and comparing the key information with risk information set in preset risk types to determine a corresponding risk name and a risk level; when the risk level of one of the target risks is higher than a preset level threshold value, the supervision authority is started, a supervision pop-up window is sent after a supervision instruction is received, and the supervision pop-up window is used for receiving supervision suggestions for the risks and sending the supervision suggestions to the corresponding target unit. According to the invention, the problem of poor accuracy during purchase risk management in the prior art is solved.

The invention relates to an anti-fraud risk identification method and system based on multi-modal interaction, and the method comprises the steps: configuring an interaction template which comprises a prompt type interaction template and a verification type interaction template; configuring an expert rule, wherein the expert rule comprises a transaction channel, a rule name, an interaction strategy, a prompt strategy, a disposal strategy, a post strategy and a notification strategy; and a transaction is initiated, a risk control center is called to carry out risk identification, differentiated early warning processing modes are configured according to the expert rules based on crowd differences, and an interaction mode is selected to carry out secondary risk identification. Multi-dimensional information is captured through multi-modal interaction, multi-dimensional risk features are extracted, risks are finely identified, risk education and persuasion of different modals are realized according to risk levels and risk types, and the payment stopping rate of risk transactions and the anti-fraud awareness of users are improved.

An electronic apparatus is configured to track risk factors of a real-world event. The apparatus has utility to business-to-business sale transactions such that it reduces the likelihood of lost sales opportunities. The apparatus display's a risk visualization area subdivided into distinct segments, each representing a unique type of risk associated with the real-world event. Each segment indicates a risk level based on inputted or received data relating to the event. The electronic apparatus utilizes an Expert System to improve the accuracy of the inputted data. The apparatus is communicable with a server such that: multiple users may access the same data and receive real-time updates on the event and any associated changes in risk; and the apparatus can communicate with third-party nodes, either to retrieve information or for data analysis, which may be carried out via artificial intelligence (AI). The risk-management is effected using the P6™ / methodology.

The invention discloses a judicial risk identification method and device and a medium. The method comprises the following steps: determining a value object related to a target risk rule and a risk judgment rule; defining a business index corresponding to the value object and a risk index corresponding to the risk judgment rule; determining risk items corresponding to the plurality of target risk rules based on the risk indexes corresponding to the target risk rules at intervals of preset time; and generating a risk list of the plurality of target risk rules in a preset time based on the risk items. According to the method, the user can flexibly define the risk index rule by himself / herself, the risk index rule can be defined from different perspectives of industries, services, risk types and the like, the risk existing in each service field is identified based on the rule, and the service risk list is automatically formed.

The invention discloses an enterprise-level application operation and maintenance risk management and control method and device, a program and a storage medium. The method comprises the following steps: acquiring a plurality of risk characteristics of a target risk event; determining the risk type of the target risk event; determining a risk coping strategy for the target risk event according to the risk field and a preset risk coping strategy set; under the condition that the risk field is the known risk, obtaining operation environment data of the operation and maintenance carrier; determining a target diagnosis script according to the operation environment data, the risk coping strategy and a preset diagnosis script library; determining a target solution for the target risk event based on the target diagnosis script and a preset solution set; sending the target solution to the front end, and in response to a received confirmation instruction sent by the front end, generating a risk disposal script according to the target solution; and controlling the operation and maintenance carrier to operate the risk disposal script, and outputting a risk disposal result. According to the method, the risk blind area in enterprise-level application operation and maintenance can be reduced.

The present invention provides a method and device for processing credit business data based on artificial intelligence, which relates to the field of intelligent decision-making technology and the financial field. The method includes: extracting risk factors corresponding to the credit business to be approved; determining the risk level corresponding to the credit business to be approved based on the risk factors and a preset risk mapping relationship; and approving the credit business to be approved according to the approval method corresponding to the risk level to obtain an approval processing result. The artificial intelligence-based credit business data processing method and device of the present application improves computing efficiency by distinguishing business risk types, adopting a more accurate calculation method for high-risk businesses, and adopting a computing model with a simple algorithm and small amount of computing for low-risk businesses, thereby reducing the amount of system computing and improving the operating efficiency of the automatic approval system.