89 results about "Risk profiling" patented technology

The invention discloses a risk analysis method and device for a financial system, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: obtaining multi-source financial data which at least comprises structured data and unstructured text data; performing semantic analysis on the multi-source financial data to obtain an analysis result containing semantic information; extracting risk entities, association relationships among the risk entities, risk events and attribute information of the risk events from the analysis result; constructing a financial risk knowledge graph based on the risk entities, the association relationships, the risk events and the attribute information; and performing association analysis based on the financial risk knowledge graph to obtain a risk analysis result, and executing risk early warning based on the risk analysis result. Through the method and the device, the technical problem of low risk analysis accuracy of the financial transaction system in related technologies is solved.

The invention relates to the technical field of intelligent risk analysis, in particular to an intelligent risk analysis system of a financial system. The system comprises a data flow analysis module, a relational graph construction module, an abnormal mode detection module, a security boundary monitoring module, a staged strategy planning module, a scene simulation evaluation module, a decision support module and a comprehensive risk management module. A graph database and a graph convolutional network are utilized to deeply analyze a financial entity relationship, reveal a risk association network, improve abnormal behavior recognition through a K-mean value and an isolated forest algorithm, enhance security boundary monitoring through a random forest algorithm, enable risk threshold adjustment to be more dynamic, combine a decision tree with a genetic algorithm, optimize a risk management strategy, and improve risk management efficiency. The system dynamics and proxy model technology in scene simulation evaluation strengthens risk prediction and increases foresight, principal component analysis and risk matrix evaluation are applied in a comprehensive risk management module, a quantitative analysis tool is provided, and more systematic and comprehensive risk management is realized.

The invention relates to the technical field of cost assessment, in particular to a civil construction project cost assessment optimization system, which comprises a feature analysis module, a cost estimation module, a real-time monitoring module, a model training and tuning module, a risk analysis module, a decision support module, a financial simulation module and a reinforcement learning cost adjustment module. According to the invention, a convolutional neural network algorithm and a principal component analysis method in the feature analysis module excavate building information model data and screen key features, and the cost estimation module combines a multi-layer perceptron neural network and a linear regression model to enhance the cost mode analysis capability and calculate the project cost. The digital twinborn technology and the dynamic programming algorithm realize tracking of project progress and real-time monitoring of cost change in the real-time monitoring module, and the genetic algorithm and the machine learning algorithm realize continuous optimization and self-learning of model parameters in the model training and tuning module. And a Monte Carlo simulation method and a sensitivity analysis method are used for identifying and evaluating project risks in the risk analysis module.

The invention discloses a debt risk real-time analysis system and method based on big data and artificial intelligence. The system comprises a data acquisition and preprocessing module, a credit risk analysis module, a market risk analysis module, a mobility risk analysis module, a comprehensive risk scoring and early warning module and a risk optimization and decision support module. Each module of the system can perform real-time data analysis and prediction by using machine learning and deep learning algorithms, can generate a credit risk analysis result, a market risk analysis result and a mobility risk analysis result respectively, finally performs weighted summation on each result, and can realize dynamic monitoring and early warning based on the summation result. And enterprises, financial institutions and investors are helped to effectively avoid risks in commercial transactions. Therefore, by adopting the embodiment of the invention, the default risk in the commercial transaction can be obviously reduced, and the decision-making efficiency and the robustness of the financial market can be improved.

The invention discloses an enterprise financial risk determination method and a related device, and relates to the technical field of artificial intelligence. Six dimensions of mobility, credit, market, operation, industry macroscopic and public opinion reputation are respectively processed through six special and precise agents including a mobility risk analysis agent, a credit risk analysis agent, a market risk analysis agent, an operation risk agent, an industry macroscopic risk analysis agent and a public opinion and reputation risk analysis agent; and full-quadrant scanning of enterprise financial risks is realized. The cross validation coordinator and the dynamic cooperation engine form a double-wheel drive, and the cross validation coordinator can obtain a cooperation agent identifier set, a cooperation task type and cooperation configuration information; the dynamic collaboration engine dispatches multi-agent parallel collaboration in real time based on the collaboration agent identifier set, the collaboration task type and the collaboration configuration information, information islands are eliminated, and misjudgment and missing report are remarkably reduced. Therefore, accurate enterprise financial risks can be obtained.

The invention provides an anti-fraud method and system based on multi-modal perception and cross-time feature modeling. The method comprises the following steps: receiving a multi-modal financial transaction graph sent by an edge computing node; inputting the multi-modal financial transaction graph into a risk control model for risk analysis to obtain a node risk score of each node, an edge risk score of each edge and a gang risk score of each sub-graph; the risk control model is a multi-scale ST-Transform-GCN framework, and the risk control model is a multi-scale ST-Transform-GCN framework; processing the node risk score, the edge risk score and the gang risk score through a gating fusion mechanism to obtain a comprehensive risk score; based on the node risk score, the edge risk score and the gang risk score, extracting a suspicious transaction sub-graph from the multi-modal financial transaction graph; and inputting the comprehensive risk score and the suspicious transaction sub-graph into a preset reinforcement learning decision maker to obtain an anti-fraud strategy, and executing the anti-fraud strategy to achieve the purpose of accurately identifying the complex collaborative fraud behavior.

The invention discloses a risk assessment method and device for a financial service, electronic equipment and a storage medium, and relates to the technical field of big data and artificial intelligence or other related fields, and the method comprises the steps: carrying out the information collection of a target service main body from N heterogeneous data sources after receiving a financial service application submitted by the target service main body, n is a positive integer; performing multi-modal feature extraction processing on the business main body information to obtain business main body features; the business main body features are input to a risk assessment model, a risk assessment result is output, the risk assessment model comprises a feature fusion layer and a risk quantification layer, the risk assessment model is used for cooperatively calculating assessment scores of M risk dimensions through the feature fusion layer and the risk quantification layer, and M is a positive integer; and triggering a risk early warning signal based on the risk assessment result. Through the method and the device, the technical problem of low efficiency of risk analysis of business data of different data sources and different structures in related technologies is solved.

The invention discloses a financial risk early warning method, and relates to the technical field of financial management, and a risk early warning system adopted by the method comprises a financial data acquisition module, an analysis sensitivity adjustment module and an auxiliary risk identification module. The financial data acquisition module is used for performing data acquisition on financial data change of a fund circulation path and distributed fund flow time points, and the analysis sensitivity adjustment module is used for adjusting the financial analysis frequency of the risk analysis unit according to the acquired data. The auxiliary risk identification module is used for detecting the head end of a financial risk signal by adopting a manual processing method when the risk analysis unit fails to accurately identify the front point of the risk signal; and the financial data acquisition module comprises a fund access recording module, a financial data change acquisition module, a distributed detection unit, a financial structure mapping module and a financial risk analysis unit. The system has the characteristic of automatic risk response.

The present application relates to a financial risk analysis method and system based on a big data analysis model. The method includes obtaining enterprise fiscal and tax data and external industry data within a certain time period and performing data preprocessing to obtain preprocessed internal enterprise data and external enterprise data, randomly screening the internal enterprise data and the external enterprise data for correlation analysis, constructing a big data analysis model for enterprise finance and taxation, obtaining fiscal and tax risk data in the internal enterprise data that exceeds the preset fiscal and tax item indicators, inputting the fiscal and tax risk data into the big data analysis model, performing risk decision-making processing on the fiscal and tax risk data, performing classification and iterative processing on the fiscal and tax risk data according to the risk decision result, calculating a fiscal and tax risk decision deviation coefficient according to the iterative result and correcting the risk decision result to obtain financial risk analysis data. The present application has the effect of improving the accuracy of financial risk analysis.

The invention relates to the technical field of financing risk analysis, in particular to a financing risk report dynamic graph generation method based on multi-source invoice data. Obtaining input invoice data and output invoice data, and extracting a first commercial feature and a second commercial feature; using the financing event as a time anchor point, aiming at each financing event, applying an operation risk analysis model to process the first commercial feature and the second commercial feature, and generating a pre-financing risk set and a post-financing risk set including risk types; performing first-order differentiation analysis on the pre-financing risk set and the post-financing risk set, and identifying risk state parameters; for multiple financing events, executing second-order differentiation analysis, and identifying a cross-cycle change trend of the risk mode of the target enterprise; and based on the first-order and second-order differentiation analysis, generating a multi-layer time sequence knowledge graph, and forming a financing risk dynamic graph. According to the invention, dynamic analysis of financing risks is realized through multi-source invoice data identification.

The invention relates to the technical field of data processing, in particular to a financial credit risk analysis early warning method and system based on artificial intelligence, and the method comprises the steps: dividing the historical data of each financial index of a target enterprise into a hyper-parameter adjustment optimal set and a training set; training an XGBoost model by using the training set to obtain the importance degree of each financial index, and obtaining the dynamic relative importance degree according to the importance degree of each financial index in the feature subset corresponding to any decision tree; obtaining a data distribution confusion degree according to the distribution condition of the historical data in the hyper-parameter tuning set, and obtaining a complexity degree according to the correlation between the financial indexes and the data distribution confusion degree; the self-adaptive penalty factor is obtained by combining the dynamic relative importance degree and the complexity degree, and the XGBoost model is optimized according to the self-adaptive penalty factor of each decision tree, so that the credit risk of the target enterprise is pre-warned, and the accuracy of pre-warning the credit risk of the enterprise is improved.

The invention discloses a financial document review method, a related device and a system, and relates to the technical field of data processing, after a to-be-reviewed multi-modal financial document is processed into structured financial data, related domain knowledge and risk analysis specification information of the structured financial data are retrieved from a dynamic knowledge graph, and the structured financial data are reviewed according to the related domain knowledge and risk analysis specification information. And generating a risk analysis instruction command based on the structured financial data, the related domain knowledge and the risk analysis specification information, and inputting the risk analysis instruction command into the generative pre-training model to obtain a risk analysis result. The process does not need manual participation in auditing, the auditing efficiency is greatly improved, the indication information of risk analysis is generated according to the latest related domain knowledge retrieved from the dynamic knowledge graph and the risk analysis specification information, and a more accurate risk analysis result of the structured financial data can be obtained in real time.

The invention belongs to the field of big data, and relates to an agent-based report generation method and device, equipment and a medium, and the method comprises the steps: receiving an analysis demand of a user, and generating a structured demand description through semantic analysis; matching a target analysis process template from the analysis process template set according to the demand description; generating a risk analysis step document based on the demand description and the target analysis process template; sending the risk analysis step document to the user to obtain confirmation information of the user; based on the confirmation information, distributing a data processing task corresponding to the risk analysis step document to a corresponding tool agent through a routing agent so as to execute the data processing task; and receiving a processing result of the data processing task returned by each tool agent, and generating a structured analysis report based on a plurality of processing results. The method can be applied to the business field of financial insurance and the like, and can realize full-process intelligent cooperation of insurance risk section analysis and improve the analysis efficiency and quality.

The invention relates to the field of contract risk analysis and the technical field of large model fine tuning, in particular to a contract risk analysis method and system based on a large model in the sales industry, and the method comprises the following steps: preparing an electronic sales contract file; clauses in the contract are extracted; risk information existing in the terms is manually marked; finely adjusting the base model by using the marked clause information, and outputting the finely-adjusted model; carrying out risk analysis on terms in a sales contract which needs to be signed subsequently by carrying out dynamic fine tuning on the output model; the method has the beneficial effects that manual examination is replaced by automatic analysis, and the contract risk analysis period is greatly shortened; a large model technology is utilized to comprehensively capture potential risks in a contract, and omission and misjudgment are reduced; the dependence on professional laws and financial staff is reduced, and the operation cost of enterprises is reduced; and a scientific and accurate risk assessment basis is provided for decision-making processes such as contract examination, negotiation and signing.

The invention relates to a financial system drill scene key fault risk analysis method based on a tree model. The method comprises the steps of collecting fault data of a tested module of a financial system in chaos engineering fault drilling, obtaining a fault data set, classifying the fault data set by using a Gaussian mixture model and a K-means clustering analysis method, determining a fault type to which each piece of fault data in the fault data set belongs, and determining the fault type of each piece of fault data in the fault data set; analyzing each piece of fault data in the fault data set by using a decision tree constructed based on a random forest, determining the feature importance of each piece of fault data, inputting the feature importance of each piece of fault data into an XGBoost model for prediction analysis, and obtaining a risk prediction value of each piece of fault data; and performing comprehensive analysis according to the fault type to which each fault data belongs and the risk prediction value, and determining a key fault and a key fault path. And the accuracy and efficiency of fault risk analysis of the financial system are improved.

The invention relates to the technical field of information technology and big data analysis, and discloses a financial risk identification method and system based on big data, a data source module, a risk analysis module, an early warning module and a management response module are established, the data source module is divided into a financial data unit and a non-financial data unit, the risk analysis module is divided into a risk modeling unit, a real-time monitoring adjustment unit and a risk assessment unit and calculates a financial risk identification index Za, a real-time monitoring adjustment coefficient Zl and a comprehensive data security assessment score Zo, the early warning module is divided into an early warning prompt unit and an early warning emergency processing unit, and the early warning module is connected with the management response module through a network. And the management response module adjusts the transmission frequency of the real-time monitoring equipment according to the size of the real-time monitoring adjustment coefficient Zl, and adds safety protection walls for the financial data unit, the non-financial data unit and the network transmission process according to the comprehensive data safety evaluation score Zo.

The invention discloses a supply chain artificial intelligence financial risk decision-making system, and belongs to the technical field of data processing. A learning data customization module and a learning data label module are arranged, so that a user can customize a risk analysis task, personalized data learning can be realized, and the user experience is improved. And meanwhile, the intelligent library module, the data learning module and the model operation module are arranged, so that the data relationship specified by the user can be accurately learned, the user is assisted to make a financial risk decision, and the processing efficiency of the user can be effectively improved.

This invention relates to the field of privacy computing and discloses a collaborative analysis method for potential financial risks based on fuzzy privacy set intersection. The method first protects the privacy of deposit and loan data from various financial institutions through Paillier homomorphic encryption. Then, it utilizes a fuzzy privacy set intersection algorithm based on Unintentional Key-Value Storage (OKVS) and the discrete logarithm problem to achieve fuzzy matching of user identities across multiple institutions. While ensuring the privacy of user feature data, it identifies records of the same user that meet a predetermined distance threshold. Finally, it aggregates the encrypted deposit and loan data of the matched records and compares them with the threshold. A trusted third party decrypts the data and completes the judgment of potential financial risks, thus realizing financial privacy and security risk analysis under cross-institutional data collaboration.

A method for analyzing investment opportunities includes receiving an identifier of a security of interest. The method further includes identifying an address associated with the security of interest based on public filings by an issuer of the security of interest. The method also includes retrieving public ratings of, and additional public information regarding, the security of interest by using the identifier and the identified address of the security of interest, the method also includes calculating an expected return for the security of interest based on the public ratings and the additional public information. The method also includes generating a proprietary score of the security of interest based on the public ratings, the additional public information, and the expected return.

The embodiment of the invention discloses a data processing method and a related device, and is applied to the field of artificial intelligence, in the method, first data of a first customer is acquired, and the first data comprises credit scores corresponding to a plurality of time points of the first customer; according to the first data, a target prompt prompt is generated based on prompt engineering processing, the target prompt is used for describing the credit condition indicated by the first data, and the prompt engineering processing is used for carrying out annotation output of the credit condition for the credit score; and inputting the target prompt into the large language model to obtain a risk analysis result of the first customer. According to the method, the financial institution can conveniently and comprehensively know the credit risk condition of the customer in the credit decision-making process.

The invention relates to the technical field of data analysis systems, and particularly discloses a financial risk data analysis system supporting dynamic modeling. Comprising a multi-source heterogeneous financial data acquisition and fusion module, a risk factor dynamic modeling and weight optimization module, a risk conduction relation modeling and influence prediction module, a risk prediction and dynamic evaluation module, a risk coping strategy generation and optimization module and a closed-loop learning and model updating module. According to the method, on the basis of efficient fusion tamping risk analysis of multi-source heterogeneous financial data, risk factor evolution characteristics under market changes are accurately captured by means of dynamic modeling, and the risk prediction capability is depicted and strengthened through a refined conduction path; the timeliness of coping with suggestions and the long-term adaptability of the system are guaranteed by relying on strategy dynamic optimization and a closed-loop learning mechanism, the usability and the risk management and control initiative are improved by matching with a perfect visual interaction and abnormity early warning function, and accurate identification, real-time monitoring and efficient coping of financial risks are comprehensively realized.

The invention discloses an insurance agent data processing method, device and equipment and a medium, which are applied to an insurance service platform, comprise a management agent, an analysis agent and an adjustment agent, and send customer information, historical insurance data and inherent risk data to the analysis agent based on the management agent, the historical claim settlement data is sent to an adjustment agent, an analysis agent is used to carry out risk analysis on a client to obtain a target analysis result, an initial insurance policy is generated according to the target analysis result and is sent to the adjustment agent, the adjustment agent is used to adjust the initial insurance policy to obtain an adjusted insurance policy, and a management agent is used to carry out risk analysis on the client to obtain the adjusted insurance policy. And checking the adjusted insurance policy to obtain a checking result, and if a preset condition is not met, executing the step of using the analysis agent until the preset condition is met to obtain a target insurance policy. The method can be applied to the field of financial science and technology, and the insurance policy generation reliability is improved while the insurance policy generation efficiency is improved.

The invention discloses an economics analysis and risk evaluation method and system based on an enhanced retrieval agent, and belongs to the technical field of crossing of artificial intelligence and sociology. According to the method, a knowledge graph is constructed by integrating sociology and financial data, a thinking chain template is trained, and cross-layer calculation and evaluation are realized by using a layer-by-layer conduction model. The core of the method lies in establishing evaluation indexes and methods from a sociology calculation model to social financial data fluctuation, and the problem that an effective evaluation system is lacked in the prior art is solved. The method can more accurately reveal the influence of social factors on financial data, improves the reliability and practicability of the model, and is mainly applied to financial risk analysis and decision support.

Disclosed in the embodiments of the present application are a data processing method and a related apparatus, which are applied to the field of artificial intelligence. The method comprises: acquiring first data of a first client, the first data comprising credit scores of the first client respectively corresponding to a plurality of time points; according to the first data, generating a target prompt on the basis of prompt engineering processing, the target prompt being used for describing a credit status indicated by the first data, and the prompt engineering processing being used for performing labeling output of the credit status for the credit scores; and inputting the target prompt into a large language model to obtain a risk analysis result of the first client. The method can facilitate financial institutions to obtain comprehensive understanding of the credit risk status of clients during a credit approval decision process.

The invention discloses a business risk analysis method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: obtaining target multi-source data related to a target financial business from N financial data sources, N being a positive integer; the target multi-source data is input into a preset target model, a data analysis result in a natural language form is output, and the preset target model is a large model adjusted in advance according to the financial field direction and is used for analyzing risk data relevance of the target multi-source data and expressing the risk data relevance in the natural language form; and filling a preset report template with the data analysis result in the natural language form to obtain a business risk analysis report of the target financial business. Through the method and the device, the technical problem of low accuracy of a business risk report caused by insufficient data integration analysis in related technologies is solved.

The invention relates to a bank cash business-oriented public opinion risk insight system and method, and the system comprises an edge end which comprises a data collection module and an edge processing module, and a cloud end which comprises a cloud end analysis module; the data acquisition module acquires cash business data and local public opinion information in real time; the edge processing module performs feature extraction on the cash business data and the local public opinion information to obtain risk features; inputting the risk features into a preset multi-modal risk marking model for risk assessment to obtain a preliminary risk result; and the cloud analysis module analyzes according to the preliminary risk result to obtain a global risk report, and queries based on a preset dynamic knowledge graph to obtain a risk response strategy corresponding to the global risk report. According to the method, feature extraction and preliminary risk assessment are firstly carried out by utilizing the computing power of the edge end, and then global risk analysis is carried out and a risk response strategy is formulated through the cloud end, so that the computing pressure and processing time of the cloud end are reduced, the response speed of risk identification is improved, and public opinion risk prevention is facilitated.

This invention discloses a method and apparatus for generating transaction feature maps, relating to the field of financial risk analysis and identification technology. One specific implementation of the method includes: determining multiple early warning indicators corresponding to a financial transaction; merging the parsed text corresponding to each early warning indicator to generate a summary text; extracting multiple feature words of the financial transaction from the summary text; determining the correlation between each feature word and the financial transaction; and generating a feature map corresponding to the financial transaction based on each feature word and its corresponding correlation. This implementation displays transaction features in a visual feature map format, making the feature map more intuitive and specific, which is beneficial for relevant personnel to make correct analysis and decisions regarding financial transactions.

The invention relates to the technical field of financial risk analysis, in particular to a risk analysis method and system for financial data. Comprising the following steps: S1, collecting internal financial data of internal enterprises, and performing related enterprise list identification according to the internal financial data; s2, performing external financial data collection and external enterprise information collection according to the related enterprise list, and screening the internal financial data in combination with the related enterprise list to obtain related financial data corresponding to each external enterprise; by dynamically calculating a risk compression resistance residual value and monitoring the overall risk openness of the enterprise in real time, if the amount of a single risk transaction exceeds the residual compression resistance value, the risk level is automatically improved to the highest, the transaction is frozen, and the management layer needs to perform approval personally, so that the facture of an enterprise fund chain and the improvement of the overall risk tolerance caused by a single high-risk transaction are avoided.

The invention discloses a risk control decision generation method and device, electronic equipment and a storage medium, and belongs to the technical field of financial risk control. The method comprises the following steps: acquiring multi-source data of an insurance applicant; the multi-source data comprises insurance applicant information and insurance applicant claim settlement related information; based on the multi-source data, performing semantic analysis and recognition through a pre-trained first large model to obtain a structured event fragment, and based on the structured event fragment and the multi-source data, performing entity recognition and relation attribute extraction through the pre-trained first large model to obtain a triple; based on the multi-source data, performing emotion analysis through a pre-trained first large model to obtain emotion risk label information; generating a risk control association knowledge graph based on the structured event fragment, the triple and the emotional risk tag information; and performing risk analysis based on the risk control association knowledge graph to obtain target risk information, and generating a risk control decision based on the target risk information.