61 results about "Risk model" patented technology

The invention discloses a financial deep counterfeiting real-time detection and defense method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal data in a financial transaction scene, and carrying out the desensitization of an edge end; performing dynamic time sequence alignment on the multi-modal data, and calculating a synchronous error of lip motion and voice by adopting a dynamic time warping algorithm; inputting the features into a dynamic risk modeling layer, and generating dynamic risk features in combination with the updated risk feature library; analyzing the features through a double-flow GAN detector, and outputting a forgery probability; and a detection result is input into a compliance verification layer, the supervision file is analyzed through a legal BERT, a structured rule is generated, and real-time transaction interception and block chain log recording are executed. The method protects user privacy and data security, combines the risk feature library updated in real time, and has high flexibility and adaptability. According to the design of the double-flow GAN detector, image and video stream information is fully utilized, and the accuracy and reliability of detection are further improved.

The invention relates to a credit line dynamic management system and method based on an AI large model, and the system comprises the following modules: a data interface module which is used for collecting the real-time financial data, transaction records, credit records and other related information of an individual or an enterprise; the multi-dimensional data analysis engine inputs the data into an analysis engine based on an AI large model; according to the analysis result, the AI model can automatically generate a personalized credit line and repayment deadline strategy for each borrower; and the market risk prediction model integrates the market risk model and predicts the influence of economic fluctuation on the borrowers. According to the invention, banks and financial institutions can provide more personalized and flexible credit services, the credit safety is ensured, and the service quality and the customer experience are improved.

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

The invention provides a risk estimation method based on a knowledge graph, and relates to the technical field of risk identification, and the method comprises the following steps: S1, data collection; s2, performing entity identification; s3, constructing an atlas; s4, feature extraction; s5, risk model training; s6, risk prediction; according to the method, dynamic interaction key feature information is extracted from time sequence data contained in an existing knowledge graph based on time-frequency analysis, new features serve as additional information to be added into corresponding entities or relations, conversion from static data analysis to dynamic behavior pattern mining is achieved, and the dynamic behavior pattern mining efficiency is improved. And the situation that the meaning of some risk indexes may change and the concept drifts as time goes on is avoided, and a new view angle and technical support are provided for risk management of the financial industry.

The invention relates to the technical field of enterprise system construction, in particular to an enterprise standard system construction system based on dynamic classification and strategic adaptation, which comprises a dynamic classification module, a strategic adaptation verification module and a feedback optimization channel module, wherein strategic document semantic analysis and industry benchmark data are fused in real time through a dynamic classification module to generate a strategic weight coefficient, an environment sensitivity coefficient is calculated by combining compliance dynamics and a market risk model, and the fusion weight ratio of the strategic document semantic analysis and the industry benchmark data is dynamically adjusted based on threshold judgment, so that standard label weight distribution and hierarchical division are realized; the strategic adaptation verification module tracks the dynamic contribution degree of a standard node by using a node influence attenuation model of the association map, and detects strategic deviation by executing the deviation between an index and an expected value in real time; and after the feedback optimization channel module analyzes the deviation signal, the fusion weight is adjusted according to the deviation direction and amplitude, and standard level elastic lifting is triggered through sliding window monitoring, so that strategic suitability real-time verification and quantitative response to external environment change are realized.

The invention relates to the technical field of invoice risk control management, and discloses an invoice risk control management method and system based on multi-source data linkage. According to the method, multi-source data of the whole invoice business process is collected in real time through a multi-source data fusion gateway. And performing streaming ETL processing and encryption integration on the multi-source data to generate an encrypted unified data view. And based on the view, constructing an association hypergraph by utilizing a dynamic graph association technology, analyzing an entity relationship by virtue of a graph neural network, and identifying false invoice making gang and repeated financing risks. And calculating a dynamic risk score by adopting a risk quantification and traceability technology, and outputting a risk level, a responsibility node identifier and a capital flow thermodynamic diagram. When the risk score exceeds a threshold value, risk gradient information is safely shared among the group headquarters, the molecular companies and the tax-bank institutions, and linkage processing actions such as invoice freezing, red character notification or credit adjustment are triggered. According to a manual reexamination result, the risk model is finely adjusted through a knowledge self-updating technology, and the rule base is hot-updated.

The invention discloses a multi-risk management method based on a conditional value-at-risk model in a worst scene, and the method comprises the steps: setting an operation mode of a virtual power plant in a single power market, and analyzing the bidding profit and constraint of the virtual power plant; a typical distribution scene of uncertain variables which can bring risks is constructed by adopting a Monte Carlo simulation method and a fast preceding elimination technology based on probability distance, and then a conditional value-at-risk model under the worst scene is constructed and the maximization of the conditional value-at-risk under the worst scene is taken as a target. And converting a max-min problem into a semi-definite programming problem by adopting a duality theory, and solving to obtain an optimal bidding strategy of the virtual power plant after the management risk. The method can be used as a powerful tool for assisting decision making, theoretical support is provided for a virtual power plant to make a scheme for improving random variable prediction precision, market risks caused by prediction errors are minimized, and a transaction strategy is closer to an optimal strategy in an accurate situation.

The invention discloses a bank credit evaluation system and method based on deep learning, and relates to the technical field of data analysis and evaluation, and the system comprises a data acquisition module, a credit evaluation module, a credit analysis module, and a decision output module. According to the method, customer groups are divided in a layered and detailed manner through data acquisition, a dynamic updating and exception rechecking mechanism is applied, a data set of key information is constructed, potential risks of daily demand expenditure of customers on credit are evaluated, and meanwhile income stability analysis is performed according to work to judge the stability of the work development trend of the customers; considering the influence of work on customer credit conditions, establishing a skill equivalent replacement risk model, simulating equivalent work replacement execution, performing customer economic risk evaluation, predicting credit risks generated by work, and integrating a historical credit data set and the work stability and development trend reflected by a work trend change coefficient to obtain a credit risk evaluation result. And a comprehensive customer credit assessment value is generated according to the fund demand and the risk condition embodied by the daily demand prediction risk value.

The invention provides a financial risk monitoring system based on distributed setting, and the system comprises a data collection layer which is used for collecting data from a plurality of data sources inside and outside a financial institution, and carrying out the preprocessing of the collected data; the data storage layer is used for storing the collected and preprocessed data; the calculation and analysis layer is built based on a distributed calculation framework, integrates various financial risk assessment models and is used for analyzing the preprocessed data and calculating comprehensive risk indexes in real time; the risk early warning and decision-making layer is used for monitoring the calculated risk indexes according to a preset risk index threshold value, triggering a risk early warning mechanism when the risk indexes exceed the threshold value, and providing decision-making support for the management layer of the financial institution; and the user interaction layer is used for providing risk monitoring data query, risk index report checking, risk early warning information acquisition, risk parameter setting and risk model adjustment functions for users. According to the invention, the efficiency and level of financial institution risk management are improved.

The invention provides an engine configuration method which can be applied to the technical field of big data. The engine configuration method comprises the following steps: constructing a risk model in a risk assessment engine based on business information related to risk assessment and a rule engine integrated with different decision tools; according to service logic in the service information, integrating a plurality of index items defined in the risk model and calculation rules of the index items in the decision tool to generate a decision flow corresponding to the risk model, the decision flow comprising a plurality of decision nodes, and the decision nodes comprising a plurality of decision nodes; each decision node is correspondingly provided with an evaluation strategy determined according to the index item and the calculation rule; and configuring the decision flow into a risk assessment engine so as to perform risk assessment by using the configured risk assessment engine. The invention further provides a risk assessment method, an engine configuration device, equipment, a storage medium and a program product.

The invention discloses a financial credit risk identification method and system. The method comprises the following steps: acquiring information of borrowers from a plurality of data sources; cleaning, duplicate removal, missing value filling and standardization processing are carried out on the collected data; constructing a multi-dimensional feature set based on the preprocessed data; training the feature set by using a machine learning algorithm in combination with historical credit default cases to generate a credit risk prediction model; inputting data of a to-be-evaluated borrower into the trained credit risk prediction model, and outputting a credit risk score and a potential risk point prompt; credit behavior changes of borrowers are tracked continuously, and the risk model is updated regularly to adapt to market and economic environment changes. According to the method, comprehensive data is obtained through multi-data source fusion, the limitation of traditional single data source assessment is broken, and risk assessment is more comprehensive and accurate; through refined data preprocessing and scientific feature construction, deep information of data can be mined, and high-quality input is provided for model training.

A fraud detection system preemptively identifies and mitigates fraudulent financial transactions in real-time across a diverse range of digital and physical transaction sources. Transaction data may be acquired from various platforms and categorized into rule sets specific to transaction types. Categorized data is aligned with existing risk profiles to construct a dynamic hysteresis model. This model, evaluated by a decision tree algorithm, identifies potential fraud by integrating immediate transaction details with a comprehensive historical data analysis, thus enabling advanced trend analysis and pattern recognition. Key features identified by the decision tree are used to form a heuristics model, which is then analyzed by a transformer network risk model. A feedback loop enhances the system's effectiveness by incorporating decision outcomes back into the model training server, thus refining the training dataset and continuously improving the accuracy of the risk model.

The invention discloses a camera gun video resource positioning method and related equipment. The method comprises the following steps: acquiring camera gun information of each financial network; obtaining structured risk model data for the daily recovery high-risk model troubleshooting index; determining a target financial network point corresponding to each piece of risk model data in the structured risk model data; according to the structured risk model data, the target financial network point corresponding to each piece of risk model data, the camera gun information of each financial network point and the current popularity value of each camera gun, determining a plurality of current retrieval camera guns; according to the current user operation log, updating the current popularity value corresponding to each camera gun; and according to the updated current popularity value corresponding to each camera gun, determining a plurality of updated current retrieval camera guns. The method can achieve the precise pushing of the video resources needed by the daily redisk of the website, improves the video verification efficiency, and can be widely applied to the technical field of artificial intelligence.

The invention discloses an enterprise financial risk real-time early warning and visualization method based on stream-oriented computing, and the method comprises the following steps: 1) multi-source heterogeneous data real-time collection and access: collecting stream-oriented data from a plurality of data sources in real time, the data sources comprising internal business data and external financial data; 2) stream data cleaning and standardization: preprocessing the data accessed in real time to ensure that the formats of the data from different sources are uniform and can be used for subsequent calculation; 3) real-time financial index calculation and risk model evaluation: 3.1) real-time index calculation; (3.2) carrying out real-time risk assessment; 4) risk early warning and graded pushing; 5) performing real-time visualization and interactive analysis; according to the method, efficient analysis, visual presentation and man-machine interaction of complex data are achieved in an automatic and intelligent mode, and therefore support is provided for decision making.

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 embodiment of the invention relates to the technical field of artificial intelligence, and provides an apartment financial management intelligent auxiliary method and system based on artificial intelligence, and the method comprises the steps: obtaining apartment management data, hotel management data and transaction data disclosed by a market, and obtaining multi-source data; storing the multi-source data to a cloud financial management center and encrypting the multi-source data; outputting a prediction result of the multi-source data through a financial analysis prediction network; the financial analysis and prediction network comprises an income prediction model and a component analysis optimization model which run in parallel; constructing a visual report form and an analysis report based on the prediction result, and dynamically displaying the report form and the analysis report to the user; and inputting the multi-source data into the financial management model, performing analysis through an auditing rule in the financial management model to generate a financial auditing report, calling the financial risk model to monitor the financial auditing report, and synchronously pushing the financial auditing report to a manual auditing terminal for verification and approval processing. According to the method, the data utilization rate is increased, and the cost optimization accuracy, the financial management efficiency and the safety are improved.

The invention provides a risk list supervision system and method, the risk list supervision system comprises a database, a risk analysis unit, a risk list generation unit and a server, the database is used for storing account data of accounts and transaction cash flow data; a transaction risk model of the risk analysis unit is used for analyzing transaction cash flow data to generate at least one risk factor corresponding to the transaction cash flow data and calculating the weight of the at least one risk factor to generate a risk score, and when the risk score is larger than a risk standard threshold value, the risk list generation unit generates and sends a risk account list, and the risk account list is used for generating a risk account. The server marks the account corresponding to the account data in the risk account list as a temporary forbidden state; therefore, the risk list supervision system provided by the invention can identify the high-risk account timely, efficiently, quickly and automatically, so as to improve the efficiency and reduce the labor cost.

Methods and systems are presented for dynamically modifying a risk model based on detected shifts of input features. Input values corresponding to an input feature and associated with transactions may be obtained. A distribution of the input values is determined, and compared against a benchmark distribution. An anomaly is detected when a difference between the distribution of the input values and the benchmark distribution exceeds a threshold. Based on the detected anomaly, a risk model configured to perform risk predictions for incoming transaction requests may be modified. Input values corresponding to the input feature may be monitored to determine if the anomaly is sustained or receded. The modified risk model may be reverted back to the original risk model when the anomaly no longer exists.

A kind of multi-risk management method based on worst-case scenario conditional value at risk model, by setting the operation mode of virtual power plant in single electricity market, and after analyzing its bidding profit and constraint, the typical distribution scene of uncertain variable that will bring risk is constructed by using Monte Carlo simulation method and fast pre-generation elimination technology based on probability distance, then the conditional value at risk model under worst-case scenario is constructed, and the maximum conditional value at risk under worst-case scenario is taken as the goal, max-min problem is converted into semi-definite programming problem by using dual theory to solve, and the optimal bidding strategy of virtual power plant after management risk is solved.The present application can be used as a powerful tool for decision-making, provide theoretical support for virtual power plant to formulate scheme to improve the prediction accuracy of random variable, so as to minimize the market risk caused by prediction error, and make the transaction strategy closer to the optimal strategy under accurate situation.

The invention discloses a risk transaction processing method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: inputting the risk data of a current risk transaction into a pre-trained risk classification model, so as to enable the risk classification model to determine the risk channel of the current risk transaction; determining a risk model according to the risk data of the current risk transaction; and inputting the risk channel and the risk model of the current risk transaction into a pre-trained risk processing model, so that the risk processing model determines a processing mode of the current risk transaction. According to the technical scheme, after the risk channel and the risk model of the current risk transaction are determined, the risk channel and the risk model of the current risk transaction are input into the risk processing model, and the processing mode of the current risk transaction is determined based on the risk processing model, so that the processing mode of the risk transaction is rapidly and accurately determined.

The invention relates to the technical field of financial credit risk control, in particular to a risk control model construction method and system for identifying customer credit risks, and the method comprises the steps: constructing a cross-product dichotomy task data set; training a sample similarity screening model; outputting a probability score; performing equal-frequency division according to probability scores and calculating the concentration and multiple of new product positive samples in each subgroup; selecting the group with the highest multiple as a high-similarity candidate region, carrying out multi-round equal-frequency division, judging whether equal-frequency division is stopped or not by combining a convergence judgment mechanism, and determining to supplement positive samples; and integrating the new product positive sample and the supplementary positive sample to carry out weighted training. According to the method and the device, the sample similarity screening model is constructed, and multiple rounds of equal-frequency segmentation, KS index monitoring and a target sample multiple evaluation mechanism are combined, so that old product samples highly similar to target positive sample features are dynamically identified, high-quality expansion of an original positive sample set is realized, and the training effect and generalization ability of a subsequent risk model are improved.

The invention relates to the field of data risk assessment, and discloses a project information data assessment method and system based on big data, and the method comprises the steps: obtaining a calculation budget execution rate, a cost deviation and an unallocated cost, substituting the calculation budget execution rate, the cost deviation and the unallocated cost into a financial risk model to calculate a project financial risk, and then obtaining a milestone achievement rate, a progress deviation and floating time; substituting into a progress risk model, calculating a project progress risk, then obtaining a professional skill matching degree, average working experience, a personnel loss rate and average performance, substituting into a personnel risk model, selecting a project personnel risk, if financial, progress or personnel risk coefficients exceed a threshold value, triggering a high-risk alarm, and if all risk coefficients are lower than the threshold value, judging that the project is abnormal. And calculating a potential risk coefficient, if the potential risk coefficient exceeds a threshold value, prompting a potential risk, evaluating the risk from multiple dimensions of finance, progress and personnel, covering key risk points of a project, introducing the potential risk coefficient, and capturing a hidden risk.

This application discloses a risk model iteration method, apparatus, device, and product, relating to the field of model iteration technology. The method includes: collecting operational data of an initial risk model in a production environment; detecting the operational data using detection indicators; when the operational data reaches the iteration trigger condition, creating a model iteration task and loading a pre-configured business dataset; collaboratively optimizing the hyperparameters of the initial risk model using the business dataset to obtain a globally optimal feature subset and an optimal hyperparameter combination; and iterating the initial risk model using the globally optimal feature subset and the optimal hyperparameter combination to obtain a final risk model. This solves the problems of lacking automated closed-loop iteration, delayed response, and the need for professional personnel to operate the risk model, thus failing to quickly adapt to dynamic business changes and improving the efficiency of risk model iteration.

The present disclosure provides a risk management method, which can be applied to the fields of artificial intelligence technology, big data technology or finance. The method includes: obtaining credit request data, where the credit request data includes a user identifier; obtaining corresponding user feature data based on the user identifier; inputting the user feature data into a pre-trained first risk federated model to obtain a risk assessment result; and generating credit request feedback data and a credit whitelist based on the risk assessment result, where the first risk federated model is trained based on a federated learning algorithm, and the training is jointly executed by a bank server and a big data bureau server, and the data used for training includes collateral sample data, and the collateral sample data is obtained from the big data bureau server. The present disclosure also provides a risk management device, a risk model training method and device, a device, a medium and a program product.

The invention relates to the technical field of probability statistical modeling, in particular to a risk management system based on the probability theory and mathematical statistics, and the system comprises a data preprocessing module, a tail distribution estimation module, a tail extreme value fitting module, a dynamic risk assessment module, and a risk early warning and quantification module. According to the method, by extracting the extreme income points in the time sequence data and the market data with remarkable tail characteristics, the accurate capture capability of the tail risk events is improved, distribution analysis of the tail risks is refined by combining quantile interval division and conditional density calculation of the tail data, the quantification capability of risk distribution characteristics is enhanced, and the accuracy of the tail risk events is improved. And furthermore, local extreme value calculation and dynamic analysis of the tail revenue migration rate are combined, so that real-time adjustment of the risk model is realized, the sensitivity and adaptability of risk assessment are improved, and the accuracy and pertinence of risk early warning are enhanced by accurately calculating the interval range of triggering the market risk and the tail risk characteristics.

The invention relates to the field of artificial intelligence, and provides a distributed training method and device of a risk control model, equipment and a medium, which are applied to the financial field, and the method comprises the following steps: separately fragmenting risk control data of each mechanism node to obtain a plurality of pieces of target fragmented data; inputting the plurality of pieces of target fragment data and the global parameters into a risk control model to carry out distributed training and output first target parameters; aggregating, adjusting and collecting the first target parameter to obtain feedback data; and predicting the feedback data to obtain a prediction error value, updating the training parameters by using the prediction error value to obtain updated training parameters, and re-initializing the global parameters according to the updated training parameters to start a new round of distributed training. By implementing the embodiment of the invention, distributed training and parallel computing of each node of the risk model are realized, the model training time is shortened, the risk control model can quickly respond to the risk change of the current insurance market, the timeliness and accuracy of risk early warning are improved, and thus the efficiency is improved.

Risk element information indicating risk elements is acquired. An asset capable of becoming a fault due to a risk element indicated in the risk element information, and a fault probability that the asset becomes the fault are specified based on static configuration information. A risk model in which the asset capable of becoming the fault and the fault probability are associated is generated in advance. In response to a designated input, an one asset to be evaluated is specified as an evaluation target asset, based on the designated input. A risk model related to the evaluation target asset is specified. A risk evaluation value being an index indicating a risk of the evaluation target asset is calculated based on the fault probability of the evaluation target asset and the static configuration information. The risk evaluation value of the evaluation target asset is associated with the asset of the risk model.

The present application provides a method for training an account risk model and a method for determining a risk user group. The method for training an account risk model includes: generating at least one sample connectivity graph based on the transaction data of multiple sample user accounts, generating a sample weighted adjacency matrix for any sample connectivity graph based on the preset transaction amount and the transaction amount corresponding to each path in the sample connectivity graph, generating a sample training matrix set based on at least one sample weighted adjacency matrix, and training the account risk model using the sample training matrix set to obtain a trained account risk model. In this technical solution: supervised training of the account risk model is performed using the sample training matrix set generated based on the sample weighted adjacency matrix, and the sample weighted adjacency matrix contains the transaction relationship between the sample user accounts, thereby improving the recognition accuracy of the trained account risk model.

The embodiment of the invention relates to the technical field of financial science and technology, and discloses a risk feature management method and device in risk business, equipment and a medium. The method comprises the following steps: classifying risk features according to service lines, service classifications and data sources in risk services to obtain a risk feature directory of a service perspective; according to the root features in the risk feature directory and the application scene information of the risk features, determining corresponding derivative features; pushing the root features and the corresponding derivative features to a risk view and / or a risk model; and obtaining calling information of each risk feature in the risk view and / or the risk model, and generating a risk feature operation result. According to the method, the risk features dispersed in each business system can be managed in a unified manner, repeated development of the risk features is avoided, and the use condition of the risk features can be mastered.

The invention belongs to the technical field of artificial intelligence, and relates to a data generation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining disaster intensity data, and calculating corresponding disaster intensity change data based on the disaster intensity data; judging whether the disaster intensity change data meets a dynamic optimization condition or not; if yes, updating the initial loss rate function based on the disaster intensity data to obtain a target loss rate function; performing function updating processing on the risk model based on the target loss rate function to obtain a target risk model; solving the target risk model based on a quantum annealing strategy to obtain target reinsurance proportion data; generating a target reinsurance scheme based on the target reinsurance proportion data and outputting the target reinsurance scheme; in addition, the invention also relates to a block chain technology, and the target reinsurance scheme can be stored in a block chain. The method can be applied to an insurance risk assessment scene in the field of financial science and technology, and the generation efficiency and accuracy of the target reinsurance scheme are effectively improved.