37 results about "Risk profiling" patented technology

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.

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.

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.

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.

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 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.

This invention discloses a privacy-preserving analysis terminal and method for financial transaction records based on an edge computing gateway. Addressing the lack of logical error correction and data leakage privacy risks in existing financial bill recognition systems, this invention leverages a general heterogeneous computing architecture to construct an integrated NPU and TEE, and performs hardware-software collaborative processing logic through a high-speed collaborative channel. The method includes: the NPU performing OCR to extract text; the TEE replacing sensitive entities with encryption masks and mapping the text to SMT constraint expressions for logical verification; if a verification conflict occurs, a logical penalty term is calculated, triggering an NPU interrupt via the collaborative channel to call the operator dynamic update interface. While preventing redundant calculation of visual context features, the penalty term is injected as a bias into the decoding layer, dynamically adjusting the decoding weights and reconstructing the recognition result; finally, the desensitized data, based on homomorphic encryption to maintain the topology structure, is transmitted to the cloud for risk analysis and then restored locally. This invention achieves a closed loop between perception and deterministic logic, significantly improving feature extraction accuracy while ensuring privacy data remains within the data domain.

The invention relates to the technical field of machine learning, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a risk data feature value analysis method, device and equipment and a medium, and the method comprises the steps: obtaining multi-dimensional data of a preset risk type, carrying out the basic feature extraction of the multi-dimensional data, and obtaining data basic features; performing basic risk analysis on the data basic features to obtain initial risk feature values; obtaining a residual feature value according to a preset residual, and combining the initial risk feature value with the residual feature value to generate a risk feature value; extracting key features of the multi-dimensional data by adopting a preset residual analysis model, and performing interval score assignment on the key features to obtain feature interval scores; and constructing an initial risk analysis model according to the feature interval scores and the key features. By integrating the residual error correction logic into the initial risk analysis model, the initial risk analysis model is optimized in a targeted manner to eliminate the deviation of the initial model, and finally the risk scoring accuracy is improved.

The invention relates to the technical field of computer data processing and project management, and discloses an industrial-grade business and financial fusion method and system based on project management and a storage medium, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data in a project, and mapping the data into business and financial entities; s2, abstracting the state of the business and financial entity into a system state vector, and establishing a state transition matrix for defining a time evolution rule of the system state vector; s3, performing prediction or risk analysis on future financial indexes of the project based on evolution of the system state vector driven by the state transition matrix; and S4, according to the prediction or risk analysis result, generating a decision scheme for intervening the project execution, the real-time business operation of the project and the financial state change are deeply fused, and through establishing the dynamic state space evolution model, the prospective and quantitative prediction of future financial indexes of the project is realized.

The invention relates to the technical field of data analysis, in particular to a risk analysis method and system based on financial institution data. A risk analysis system based on financial institution data comprises a transaction account screening module and a transaction record screening module. According to the method, the financial institution data is mapped into the transaction thermodynamic diagram in the preset period, and attention enhancement is performed in combination with the transaction adjacency matrix, so that account information associated with risks can be quickly positioned in a diagram recognition mode, and first screening of large-scale transaction data is realized; furthermore, for the screened risk transaction pairs, transaction feature distribution values and joint distribution values are extracted to construct transaction feature distribution vectors, the transaction feature distribution vectors are input into a transaction risk analysis model for secondary screening, and high-risk transaction records are output, so that through the double-layer screening mechanism, the transaction data volume needing manual auditing can be effectively reduced, and the transaction efficiency is improved. The workload of transaction risk analysis is significantly reduced, and the efficiency of risk identification is improved.

This invention discloses a knowledge graph-based financial data analysis method, comprising the following steps: collecting and preprocessing multi-source financial data; performing semantic unification and hierarchical element extraction on standardized financial datasets; constructing a set of financial semantic subgraphs based on the hierarchical financial semantic element set; determining target cross-layer connection relationships and constructing a hierarchical financial knowledge graph based on the set of financial semantic subgraphs; comprehensively analyzing cross-layer association paths between different semantic layers based on the hierarchical financial knowledge graph; constructing a filtering sequence and performing continuous coherence analysis to extract hierarchical graph structural features and generate a set of hierarchical graph structural features; determining potential association relationships, association strength, and risk transmission relationships between financial entities, and generating financial data analysis results. This invention employs knowledge graphs and cross-layer semantic analysis methods to achieve multi-source financial data association modeling, possessing the advantages of accurate association identification and comprehensive risk analysis.

This application provides a method, apparatus, device, medium, and program product for security detection of application behavior, relating to the financial technology field. It includes: based on the startup status of a banking application installed on a terminal device under test, in a trusted execution environment corresponding to the security chip of the terminal device, real-time collection of sensitive behavior data of the banking application; in the trusted execution environment, risk analysis processing of the sensitive behavior data is performed based on the historical usage data of the banking application corresponding to the terminal device to determine the risk category of the sensitive behavior data; based on the risk category, a security report for the banking application is generated, and a security report generation prompt is displayed; in response to the user's viewing operation of the prompt, abnormal behavior verification is performed on the terminal device and the banking application in the trusted execution environment; if the verification is successful, the security report is de-identified to obtain and display a de-identified security report. The method of this application improves the security of application behavior detection.

The application provides a method for aggregating and analyzing electronic invoices based on direct connection of industry, finance and tax, comprising: S101, configuring a secure authentication gateway, obtaining business data, financial data and tax data of a target enterprise from a business management system, a financial management system and a tax platform through the secure authentication gateway, and collecting the data into original industry, finance and tax data; S102, storing the original industry, finance and tax data into a data lake, constructing a cross-domain unified data model, and associating and mapping the electronic invoices of the target enterprise with the original industry, finance and tax data through the cross-domain unified data model; S103, monitoring key operations of the electronic invoices initiated by the target enterprise through an intelligent agent, analyzing potential risks of the electronic invoices based on the cross-domain unified data model, combining a rule engine and AI analysis, generating a risk analysis report and outputting. The method combines the rule engine and the artificial intelligence technology, aggregates and analyzes the potential risks of the electronic invoices based on the direct connection of the industry, finance and tax data, and can identify and block the risks in time.

The invention relates to the technical field of data analysis, and discloses a patent value and risk association analysis method and system fusing commercial data, and the method comprises the steps: carrying out the fusion of multi-dimensional feature vectors in a data source, and obtaining a multi-dimensional feature matrix of the data source; performing weighted fusion on the technical feature vector and the financial index vector in the multi-dimensional feature matrix to obtain an initial patent value index, and introducing a time decay factor for the initial patent value index to obtain a target patent value index; predicting the risk probability to obtain a patent risk level; constructing a patent risk association rule base based on historical cases and domain knowledge, and matching numerical value modes in the patent risk association rule base; generating a value-risk association map, converting the value-risk association map into a risk report, and transmitting the risk report to the user terminal; according to the invention, the value and risk analysis accuracy can be improved.

The invention relates to the technical field of internet financial enterprise credit risk analysis, and discloses an internet financial enterprise credit risk analysis method based on big data, which performs hierarchical data acquisition, constructs big data features and risk features, and constructs a multi-level dynamic credit risk assessment model. According to the internet financial enterprise credit risk analysis method based on the big data, enterprise credit risks are comprehensively described through multi-source data fusion and a multi-level model, risk points missed by a traditional method are effectively recognized, bad debt losses are remarkably reduced, and the enterprise credit risk analysis efficiency is improved. Through a big data cleaning and verification mechanism, the problems of non-uniform data formats and much false data are effectively solved, the data missing rate is reduced, the false data recognition rate is improved, reliable data support and a real-time data acquisition and model updating mechanism are provided for risk analysis, and the risk analysis efficiency is improved. Risk monitoring is upgraded from static one-time evaluation to dynamic continuous monitoring, risks are recognized in advance, unused quotas are frozen in time, and overdue occurrence is avoided.

The system according to this embodiment aims to provide appropriate financial products to a specific demographic group. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit to identify the user's risk profile and needs. The generation unit generates financial products based on the risk profile and needs identified by the analysis unit. The provision unit provides the financial products generated by the generation unit.

The invention, which relates to the technical field of financial risk analysis, discloses a dynamic assessment and risk early warning method and system based on a non-performing asset repayment capability, comprising the following steps: performing real-time dynamic acquisition and standardized adaptation to obtain cross-domain heterogeneous data; executing deep analysis on the obtained multi-source heterogeneous data, and performing structured fusion processing; constructing a repayment capability evaluation model based on the dynamic characteristics and the adaptive weight and optimization iteration thereof; scene risk early warning and dynamic threshold optimization are carried out based on the subdivided customer groups; and executing data security and compliance auditing guarantee throughout the whole process. The problems that in the prior art, evaluation results are lagged due to the fact that the repayment capacity of the unfavorable assets depends on historical static data, early warning accuracy is low due to the fact that a unified risk threshold value is adopted, risk disposal efficiency is low due to system architecture splitting, and overall limitation is high are solved.

The application discloses a financial data risk analysis method and device based on reinforcement learning and a medium, relates to the technical field of data risk analysis, and comprises the following steps: performing time sequence processing on splicing indexes of financial related data to determine a financial time sequence vector; performing time period division feature extraction on the financial time sequence vector to obtain a financial feature vector; determining a financial plan risk value based on the financial feature vector by learning the financial plan risk analysis of an intelligent agent; obtaining a trained reinforcement learning intelligent agent through parameterized learning of a stochastic gradient descent update according to the financial plan risk value; inputting the financial time sequence vector into the reinforcement learning intelligent agent to determine a risk value function, and determining a financial data risk analysis report through plan feature analysis based on the risk value function. The application solves the technical problems of insufficient timeliness and adaptability and dependence on manual processing in the prior art by using the above method.

The invention discloses a financial service credit risk analysis method and system based on artificial intelligence, and particularly relates to the field of artificial intelligence and financial service cross technology, and the method comprises the steps: collecting first-class static data, second-class dynamic behavior data and third-class external environment data of a target user; preprocessing the three types of data to remove abnormal values, extracting a debt income ratio, an overdue behavior gradient index, a digital behavior stability index and a regional economic sensitive factor to construct a four-dimensional feature set, performing feature extraction by using user data of a known risk level to generate a verification set, training and verifying an improved deep belief network model, and obtaining a debt risk index; and inputting real-time data of the target user, outputting a risk score and a grade, and matching a credit line with an interest rate. The system comprises a data acquisition module, a preprocessing module, a feature engineering module, a model establishment module and a risk assessment module, and improves the accuracy and dynamic adaptability of credit risk analysis.

The embodiment of the invention provides a financial risk analysis method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of performing data fusion processing on unstructured data and structured data to obtain fused data; updating the knowledge graph based on the fused data; and performing risk analysis on the updated knowledge graph through a graph calculation algorithm to obtain a visual risk analysis result, the risk analysis result comprising a risk conduction path. According to the method, the analysis and evaluation capability of financial risks is enhanced.

This invention relates to the fields of financial data processing and intelligent investment advisory technology, and discloses a method and system for generating structured investment inquiry solutions. The method includes receiving and preprocessing user investment inquiry information; selecting target investment strategy models from a pre-set investment strategy library based on the preprocessing results; performing knowledge enhancement using investment inquiry semantic vectors based on the target investment strategy models to generate an initial investment advisory report, which is then verified to obtain a target investment advisory report; acquiring user risk profile features; performing convex optimization on the target investment strategy models to obtain optimal asset allocation weights; performing compliance verification on the optimal asset allocation weights; and generating an investment inquiry solution by combining the target investment advisory report and the optimal asset allocation weights when the compliance verification result is passed. This invention addresses the shortcomings of existing technologies in simultaneously considering user needs, solution adaptability, and compliance from three perspectives: demand matching, adaptation optimization, and compliance assurance.

This specification discloses a method, apparatus, and device for processing blockchain data. The method includes: acquiring on-chain and off-chain data of a target blockchain applied to a preset financial transaction business; then, extracting features from the on-chain and off-chain data to determine financial transaction risk characteristics, which include one or more of graph structure topology features, temporal behavior features, and transaction resource quantity and frequency features; subsequently, based on the financial transaction risk characteristics, performing subgraph mining processing on the graph structure data of the account transaction network corresponding to the preset financial transaction business to obtain account groups with preset risks, and determining the resource link traceability information corresponding to the account groups; finally, based on the financial transaction risk characteristics and the resource link traceability information corresponding to the account groups, generating a risk analysis report for the target blockchain through a preset large language model.

The invention relates to a system that enables accurate and reliable calculation of risk scores for immovable properties (20) subject to insurance policies (10) by using geographical database–based analyses The system operates in an integrated manner with a location determination module (30) for identifying the location of the property, a geographical database (40) and an environmental proximity analysis module (50) for analyzing historical loss and natural disaster risks, a cumul calculation module (60) for evaluating regional risk accumulation, and a user interface (90) for visualizing the analysis results. The user (K) initiates the risk analysis process by entering details such as the activity type, construction type, and floor information of the property. The resulting risk scores calculated by the risk score calculation module (80), together with cumul assessments, enable accurate pricing of insurance policies (10). By improving analytical precision and optimizing operational costs, the system contributes to the financial sustainability of insurance companies. This innovative structure provides a fast and reliable solution for the insurance sector.

The invention discloses a derivative pricing method based on a multi-asset economic scene generator, and relates to the field of financial risk analysis, and the method comprises the following steps: obtaining macroeconomic data, financial market data and to-be-priced derivative information of a data source, and carrying out the preprocessing, and obtaining modeling data; constructing a joint dynamic model according to the modeling data, and performing parameter calibration on the joint dynamic model to obtain a multi-asset economic scene generator; based on the multi-asset economic scene generator, generating a plurality of asset simulation paths through a Monte Carlo simulation method; and calculating a cash flow of the to-be-priced derivative at each time node on each asset simulation path, discounting the cash flow as a present value, and averaging the present values of all asset simulation paths to obtain a derivative pricing result. The problems that in the prior art, a calculation framework is single, only a single asset model can be processed, and complex multiple asset models are difficult to process can be solved.

A system is provided.SOLUTION: A system, comprising: means for acquiring a risk profile of an investment product; means for acquiring basic user information; a AI engine for calculating an optimal insurance amount and period based on the risk profile of the investment product and the basic user information; means for generating an insurance plan based on the insurance amount and period calculated by the AI engine; and means for displaying the insurance plan to a user.SELECTED DRAWING: Figure 1

The application discloses a financial activity public opinion analysis method and system based on multi-modal data analysis, relates to the technical field of multi-modal financial public opinion monitoring, and comprises the following steps: collecting a multi-modal public opinion data set related to a target financial activity, extracting four types of financial behavior structural features, namely, income description, risk expression, fund path and behavior guidance, through structured analysis; identifying invariant and variable features through a pre-constructed illegal financial activity template; respectively carrying out similarity and migration risk analysis to obtain two types of risk analysis results; and finally fusing the public opinion risk analysis results. The application solves the technical problem that traditional public opinion monitoring is difficult to cope with the evasion means of illegal financial activities, such as rhetoric migration, concept replacement and scene packaging, and the accuracy and timeliness of the analysis results are significantly insufficient, effectively breaks the evasion and identification behaviors of illegal financial activities, and improves the accuracy and timeliness of financial public opinion risk analysis.

The invention relates to the technical field of financial data processing, and discloses a financial data processing method and device, and the method comprises the steps: obtaining multi-source financial data which at least comprises financial market data and financial information report data; semantic understanding is conducted on a data query problem input by a user through a generative model, structured data of the data query problem is obtained, and the generative model is constructed based on cue word engineering; and querying the multi-source financial data according to the structured data to obtain target financial data. According to the method, accurate query of the multi-source financial data can be realized based on semantic understanding information, accurate query of the financial data can be realized through semantic understanding of the generative model, meanwhile, data query and processing can be performed in combination with non-statistical information such as financial information report data, and the data query efficiency is improved. And an accurate decision basis is provided for subsequent financial risk analysis.

This invention relates to the interdisciplinary field of fintech and artificial intelligence, and particularly to a method for constructing investor behavior risk profiles based on knowledge graphs. The method includes: synchronously collecting investor behavior data, public opinion data, and related market data via the PTP protocol; processing investor identity information using k-anonymization; constructing a knowledge graph comprising a three-layer entity system, establishing behavioral relationship networks and risk transmission relationship networks; fusing multimodal risk features to generate knowledge graph embedding vectors; constructing a knowledge graph-neural network joint model, achieving feature interaction through cross-compression units, and outputting four risk levels; generating a risk heatmap and executing a three-level early warning mechanism. This invention can improve the accuracy of high-risk account identification, reduce response latency, and can be applied to financial regulatory scenarios such as securities and banking to achieve real-time risk warnings.