113 results about "Risk profiling" patented technology

The invention relates to the field of business intelligence and artificial intelligence, and discloses an AI analysis technology-based store site selection accurate data comprehensive measurement and calculation system, which comprises a data acquisition module, a data preprocessing module, an AI analysis and optimization module, a financial prediction and risk assessment module and a report generation module. The data acquisition module acquires geographic information, human traffic, rent cost, competitor information and the like of candidate positions from a multi-dimensional data source; the data preprocessing module preprocesses the data; the AI analysis and optimization module generates a recommendation scheme of a candidate position by using multi-objective optimization, linear programming and a machine learning algorithm; the financial prediction and risk assessment module carries out financial income prediction and risk analysis on each position; and the report generation module displays the analysis result through a visual tool and generates a decision support report. According to the method, the optimal store site selection scheme can be dynamically and accurately recommended, and merchants are helped to make scientific decisions and adapt to market changes.

The invention relates to the field of financial risk management, and discloses a financial panoramic risk management and control system, the system integrates a data layer, a machine learning layer, an application layer and an interaction layer, and aims to provide comprehensive and efficient risk management for enterprises, the data layer is responsible for collection, preprocessing and storage of multi-dimensional financial data, and the machine learning layer is responsible for interaction with the interaction layer. The machine learning layer trains a fund chain and supply chain risk prediction model by using the data to realize scientific risk assessment, the application layer performs real-time risk analysis and early warning to ensure that an enterprise timely deals with potential risks, and the interaction layer provides a user-friendly interface and strict authority management to ensure data security and convenient operation, so that the system has a wide application prospect. The system not only improves the efficiency and accuracy of risk management and control, but also strengthens the ability of an enterprise to control panoramic financial risks, effectively prevents omission and misjudgment, and assists the enterprise to stably operate in a complex and changeable market environment.

This application involves the field of financial technology, and discloses a financial business approval and risk management system based on direct data connection, including: data integration module, risk analysis module, decision support module and feedback optimization module; the data integration module is used to collect and clean multi-source data in real time through direct data connection to generate key risk feature data; the risk analysis module uses multi-dimensional modeling and random fluctuation modeling based on the above data to dynamically monitor the risk factors of customers; the decision support module uses Markov decision process and game theory analysis to generate optimal approval decisions based on risk scores, balancing customer needs and risk control. The system realizes multi-source data integration, dynamic risk assessment and intelligent decision support, improves the approval efficiency and risk management level of financial institutions, and can be widely used in various financial service scenarios such as vehicle finance, corporate credit, and consumer finance.

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.

A system (100) for adaptive real-time risk assessment in insurance auditing, comprising the following: a data acquisition module configured to collect real-time data from multiple sources, including IoT-based health monitoring devices, telematics systems, financial transaction logs, and external risk databases; a risk analysis module configured to process and analyze the collected data using machine learning algorithms to create a dynamic risk assessment for a person or company; a policy recommendation module configured to generate personalized underwriting decisions and premium rates based on dynamic risk assessment; a fraud detection module configured to detect anomalies and potentially fraudulent claims using AI-based anomaly detection methods; an explainability and compliance module configured to ensure transparency in underwriting decisions by providing an interpretable AI framework that complies with regulatory standards; and a real-time feedback module configured to continuously update risk profiles and adjust underwriting parameters based on new data inputs.

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 relates to the technical field of financial risk analysis, and particularly discloses a financial risk early warning system and method and a storage medium, and the method comprises the steps: firstly obtaining a newest financial supervision file and newest financial punishment information, and extracting a risk index set and a punishment risk point set from the newest financial supervision file and the newest financial punishment information; and then semantic embedding coding is performed on the risk indicator set and the punishment risk point set by using a natural language processing technology based on deep learning, a financial risk assessment point semantic embedding representation set is constructed based on an association relationship between the risk indicators and punishment risk points, and then a financial risk assessment point semantic embedding representation set is constructed. Through rapid semantic query interaction between the financial service and each financial risk assessment point, potential risk information of the financial service is mined, so that intelligent identification and early warning of financial service risks are realized. According to the invention, the intelligent level of financial risk early warning can be effectively improved, the capability of identifying financial business risks is enhanced, and the compliance and security of financial businesses are ensured.

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 relates to the field of financial risk assessment, and discloses an industrial chain financial risk assessment method and system based on industrial chain data analysis, and the method comprises the steps: constructing an industrial chain graph, and obtaining the production efficiency information, inventory turnover rate information, sales data information and cash flow information of each supply node; according to the production efficiency information and the inventory turnover rate information of the plurality of supply nodes in the industry chain graph, determining production state identification indexes of the plurality of supply nodes in the industry chain graph; according to the inventory turnover rate information, the sales data information and the cash flow information of the plurality of supply nodes in the industry chain graph, determining fund state identification indexes of the plurality of supply nodes in the industry chain graph; and according to the production state identification indexes and the fund state identification indexes of the plurality of supply nodes in the industry chain graph, determining industry chain financial risk assessment results of the plurality of supply nodes in the industry chain graph. The financial risk analysis effect of the industrial chain can be improved by combining the characteristics of the industrial chain.

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 invention provides an enterprise financial risk intelligent analysis method and system based on big data, and relates to the technical field of enterprise financial risk analysis. The enterprise financial risk intelligent analysis system based on big data comprises a data collection layer, a data processing layer, a feature engineering layer, an intelligent analysis layer, a knowledge graph layer and an application display layer, the data collection layer is connected with the data processing layer, the data processing layer is connected with the feature engineering layer, the feature engineering layer is connected with the intelligent analysis layer, and the knowledge graph layer is connected with the application display layer. The intelligent analysis layer is connected with a knowledge graph layer, and the knowledge graph layer is connected with an application display layer. According to the technology, the financial risk of the enterprise can be identified and evaluated more accurately, a more intuitive and detailed analysis report can be provided, the enterprise is helped to make a more intelligent decision in a complex and changeable market environment, and the accuracy is higher.

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 relates to the technical field of financial risk prediction, in particular to a financial risk prediction method and system based on artificial intelligence, and the method comprises the following steps: S1, data collection and preprocessing: collecting multi-dimensional data from a plurality of financial markets in real time; s2, constructing a cross-market financial product association network: constructing the cross-market financial product association network, and identifying the association among the financial markets; s3, risk conduction path identification: identifying a risk conduction path between financial markets; s4, risk analysis: predicting risk fluctuations in different financial markets through the financial risk prediction model; and S5, risk prediction result output: outputting prediction results of risks of different financial markets, wherein the prediction results comprise risk scores and risk fluctuation trends of the markets. According to the invention, the potential risk conduction path in the market can be effectively identified, so that a more comprehensive risk early warning and management tool is provided for market participants.

The invention relates to the technical field of enterprise financial management and risk control, in particular to a financial data risk prediction method and system based on big data and artificial intelligence. The method comprises the following steps: constructing an internal data set and an external data set, and preprocessing the internal data set and the external data set to obtain a preprocessed data set; constructing an enterprise financial risk scoring model composed of a random forest model and an XGBoost model, inputting the preprocessed data into the model to obtain a prediction result, calculating an optimal weight vector of the model through a Lasso method according to the prediction result and an actual result, and embedding the obtained optimal weight vector into the model; training and optimizing the enterprise financial risk scoring model, and predicting a final result; and carrying out risk grade division according to the final prediction result of the enterprise, generating risk early warning according to the risk grade, and generating a detailed risk analysis report. The method can improve the precision and stability of enterprise financial risk prediction.

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 invention relates to the field of management systems, and discloses an internal compliance management system based on a financial compliance large model, which comprises an information import module, a compliance examination module, a risk monitoring module, an information consultation module and a training education module, the information import module is used for importing business application information submitted by business personnel; the compliance review module is used for performing compliance review on the service application information, and entering the next step after the compliance review is passed; the risk monitoring module is used for carrying out risk analysis on the business application information passing the compliance examination; the information consultation module is used for providing consultation service for business personnel; and the training education module is used for performing compliance education training for business personnel. According to the invention, more comprehensive and intelligent financial industry internal compliance management can be realized.