9 results about "Risk distribution" patented technology

The invention provides an insurance rate calculation method and system based on multi-source data analysis, and relates to the technical field of data processing, and the method comprises the steps: integrating a plurality of data sources through a big data analysis platform, including user behavior data, disease trend data and policy change data; performing association rule mining on the user behavior data, the disease trend data and the policy change data by taking the premium rate as guidance, and establishing an association matrix; performing risk prediction based on the incidence matrix in combination with historical data and an existing trend to obtain a risk distribution probability; and according to the risk distribution probability, performing insurance premium assessment to generate an insurance premium rate. The technical problem that the insurance rate calculation in the prior art often depends on a fixed data source and cannot cover constantly changing external factors, so that the insurance rate calculation cannot reflect a real risk state, and the risk control and pricing accuracy of an insurance company are affected is solved.

The invention relates to the technical field of artificial intelligence and enterprise financial management, and discloses an AI financial intelligent analysis system. The method comprises the following steps: firstly, extracting transaction term characteristics, an operational cash flow net amount change value and an enterprise overall risk state change value from enterprise historical transaction data; secondly, quantifying a net influence coefficient of transaction terms on the cash flow by adopting a dual machine learning method, and calibrating a conversion coefficient of cash flow change on the overall risk through median regression; then, fusing the coefficients, and calculating a theoretical system risk value for historical transactions to construct a training data set; then, training a transaction risk prediction model by using a gradient boosting decision tree; and finally, inputting the new transaction terms into the model to obtain a risk prediction value, and automatically generating a risk assessment report by combining a historical risk distribution set threshold value. According to the invention, end-to-end, automatic and explainable quantitative evaluation of transaction risks from microscopic terms to macroscopic influences is realized.

The application provides an artificial intelligence-based financing guarantee anti-fraud method and system, relates to the technical field of artificial intelligence, and comprises the following steps: constructing a feature vector by acquiring enterprise multi-source heterogeneous data, establishing a bidirectional causal graph structure to represent the risk transmission relationship between enterprises, acquiring a risk transmission adjacency matrix by using a NOTEARS algorithm, constructing a risk distribution metric space by using a Wasserstein distance to perform distribution robustness optimization, training to obtain an enterprise risk representation vector, and combining an ensemble learning framework to evaluate fraud risk. The application can effectively identify fraud in a complex network and improve the accuracy and reliability of financing guarantee risk assessment.

The present application relates to the technical field of security risk classification, in particular to a kind of city security risk classification and evaluation early warning system, comprising: risk sampling module, region division module, risk load module, risk progression module and risk update module;Through the risk data and risk type in the risk sampling area are listed;Using the risk type and the risk load capacity of each sampling area is classified, determine the regional risk distribution map of each sampling area;From the regional risk distribution map, extract risk point, with the data proportion of each risk point and the risk load capacity of region, generate regional risk load probability;Set the risk point load capacity of each risk point, and extract the progression probability of risk point load capacity, obtain the relative risk state of each sampling area;According to the relative risk state of each sampling area, identify the risk entity and the update range of risk entity of each sampling area, and iteratively update the risk level of each sampling area;The efficiency of risk level division is realized.

The invention discloses a full-dimension asset liquidation method and system, and the method comprises the steps: building an enterprise group topological relation graph, comprehensively integrating the equity control and non-equity association relation, and carrying out the layer-by-layer penetration verification of the internal transaction flow, quantification of the guarantee responsibility expected present value, and restoration of the real contribution structure of a fund pool based on the equity control and non-equity association relation. A multi-level debt conduction path model is established, and the asset and liability conditions of the individual enterprises are calibrated and reconstructed; on this basis, a value backtracking and responsibility fulfillment program is executed, and finally an assignable net value result reflecting real risk distribution is generated; the problem of unclear hidden risk open identification is effectively solved, the authenticity and traceability of the liquidation result are significantly improved, and it is ensured that the calculation of the whole net assets of the group is established on the basis of fully revealing the internal economic relation.

The invention relates to the technical field of financial risk intelligent control, particularly discloses a logistics supply chain financial risk control method based on the Internet, and solves the fuzzification problem of supply chain financial risk dispersion and risk transfer through a risk early warning mechanism. The method comprises the following steps: S1, accessing multi-source data of a platform in real time through the Internet, and constructing a supply chain real-time data view according to a timestamp alignment algorithm; s2, running a preset risk early warning model based on the supply chain real-time data view, dynamically monitoring and analyzing to obtain a risk factor, and generating a risk early warning event when judging that the risk factor meets a preset early warning threshold value; s3, carrying out transferability evaluation analysis on the risk early warning event, judging whether risk dispersion or transfer can be carried out through a financial tool, if so, entering step S4, and if not, entering step S3; if not, continuing monitoring; and S4, automatically matching a marketization risk distribution tool adaptive to the type, the target and the scale of the risk early warning event from a pre-connected financial product interface library.

The invention provides a financing guarantee anti-fraud method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a feature vector through obtaining enterprise multi-source heterogeneous data, building a bidirectional causal graph structure to represent a risk propagation relationship between enterprises, obtaining a risk propagation adjacency matrix through employing a NOTEARS algorithm, and obtaining a risk propagation adjacency matrix; and constructing a risk distribution measurement space by adopting a Wasserstein distance to carry out distribution robustness optimization, training to obtain an enterprise risk representation vector, and evaluating a fraud risk in combination with an integrated learning framework. According to the invention, fraudulent behaviors in a complex network can be effectively identified, and the accuracy and reliability of financing guarantee risk assessment are improved.

The application provides a kind of financial data intelligent management method and system, the system includes multi-source heterogeneous data integration module, collects and standardizes fund flow data, bill information and operation index data;Intelligent analysis decision module, through time convolution network analysis data, generates fund dynamic prediction value, calculates risk coefficient and generates transaction analysis report;Risk visualization module, based on standardized data set, constructs fund correlation network, and generates interactive fund chain atlas, shows risk distribution;Audit tracking module, priority audit list is generated using path optimization algorithm, to improve audit efficiency;Data hub module, using blockchain technology generates a unique identification code for financial data, to ensure the security and traceability of data. The application provides an intelligent, real-time and secure solution for financial data management, effectively improving the transparency and decision support capability of financial management.

The invention relates to the technical field of financing decision analysis, and discloses an artificial intelligence assisted financing decision analysis method and system. According to the method, multi-dimensional data streams related to a target financing project are obtained in real time through a cloud data interface, wherein the multi-dimensional data streams comprise stock market data, an enterprise asset debt table and an international trade index. The method comprises the following steps: denoising and aligning a multi-dimensional data stream to form a regular time sequence, and learning joint feature representation from the regular time sequence by adopting an adaptive feature extraction engine; based on joint feature representation, a multi-level decision spectrum is constructed according to data sampling rate differences, a high-sampling-rate area corresponds to denser spectrum nodes, and a risk fluctuation spectrum is generated by performing mode recognition on time evolution of the multi-level decision spectrum. The risk exposure degrees of a plurality of sensitive elements in a financing framework are simulated by applying an intelligent computing unit, a risk distribution diagram is obtained, the occurrence frequency of risk events of the sensitive elements is tracked according to the diagram, and a scientific basis is provided for financing decision making.