20 results about "Reduced risk" patented technology

The invention relates to the technical field of asset transaction risk monitoring, and proposes an asset transaction risk monitoring system based on data analysis, and the system comprises a data obtaining module which is used for obtaining and integrating multi-source heterogeneous data, including but not limited to transaction records, market quotation data, user behavior data, legal contract texts and financial indexes, the data is preprocessed; and the risk assessment module is connected with the data acquisition module through a data transmission technology, and assesses the risk in real time based on a preset rule and a machine learning model. Through timely acquisition and analysis of latest transaction information, market dynamics and user behavior changes, a reinforcement learning technology is utilized, and according to a real-time risk assessment result and a disposal effect, risk monitoring is ensured to be always matched with a market actual condition, so that a financial institution can timely capture a risk signal and take precautionary measures in advance, and the risk monitoring efficiency is improved. And the risk loss is effectively reduced.

The invention is suitable for the field of financial engineering, and provides a data analysis model-based unhealthy asset package valuation method and system, and the system comprises a comprehensive data collection module, a cost income analysis module, a decline trend analysis module, an asset present value analysis module, an expected value adjustment module, a potential risk identification module, and a result generation module. According to the method, the decline and disposal stages are analyzed through the life cycle, the future expected value can be adjusted in time, the asset depreciation trend is reflected, and the risk is reduced. By flexibly constructing the discount cash flow model, the discount rate can be adjusted according to market conditions, and a more accurate asset present value is provided. Finally, according to the method, evaluation results of all stages are integrated, a comprehensive asset package evaluation value report is generated, an effective management strategy is provided, and the practicability and perspectiveness of the report are enhanced. Due to the characteristics, the evaluation accuracy and decision support capability of asset life cycle analysis in the non-performing asset management are remarkably improved.

Integrated dynamic impact or risk maps can be generated using borehole operation parameters, with associated impact parameters (representing a severity of an event), softness parameters (representing a range or a percentage of the impact while not exceeding a critical threshold or an impact transition mechanism), and trust parameters (representing the accuracy of the applied advisor model). As borehole operations are in progress, factors change over time, such as depth, temperature, fluid composition, number of rotations, and other operational and environmental factors. As these factors change, the associated impact, softness, and trust parameters can be dynamically updated with the current information. The resulting generation of dynamic impact or risk maps using this information is therefore updated to represent the most current conditions. The dynamic impact or risk maps can be utilized to determine modifications to borehole operations to reduce risk, costs, maintenance downtime, and improve return on investment of the borehole.

The invention relates to the technical field of data processing, in particular to a financial quantitative transaction method for reducing risk protection profit, accurately stopping loss and stabilizing emotion, which comprises the following steps of: S1, determining a loss stopping price corresponding to an investment subject matter according to an entrance price of the investment subject matter and historical transaction data; s2, according to the loss stop price, generating a loss stop condition and at least one warehouse reduction price; s3, configuring and storing corresponding transaction triggering conditions; and S4, when the real-time market price meets a transaction triggering condition, triggering a transaction response behavior. In order to solve the problem that a user cannot accurately measure a departure price in a transaction process in the prior art, a loss stop price determined based on historical transaction data is introduced, and a corresponding loss stop condition and at least one warehouse reduction price are set before the loss stop price. In the system operation process, the real-time market price can be automatically monitored, automatic order hanging and selling are carried out after the corresponding transaction triggering condition is triggered, and the user loss is reduced.

The invention discloses an insurance policy pricing method under a market simulation scene and a related device, and belongs to the technical field of data analysis. The method comprises the following steps: configuring economic scene environment parameters, obtaining insurance policy information of an insurance policy to be priced, and analyzing the insurance policy information to obtain risk factor information related to a product; based on the insurance policy information and the risk factor information, performing matching in a data source to obtain related scene data; constructing an economic scene generation model based on the related scene data, and generating a plurality of economic scene paths representing future market conditions through the economic scene generation model; adopting a customer behavior model to simulate and generate customer behavior data of each time node under each economic scene path according to the economic scene paths; and according to the customer behavior data, calculating a cash flow under each economic scene path, and summarizing to obtain an expected price of an insurance policy. According to the invention, more accurate insurance policy pricing is realized, and the risk loss is effectively reduced.

The invention discloses a project risk determination method and device, equipment, a medium and a product, which can be applied to the field of financial science and technology, and the method comprises the steps: determining a to-be-monitored project and risk monitoring information of the to-be-monitored project based on a risk monitoring demand in a risk management instruction, the risk monitoring information comprises at least one to-be-monitored risk index and generation logic of the at least one to-be-monitored risk index; based on the generation logic of each to-be-monitored risk indicator and a predetermined general operator library, risk assessment information of the to-be-monitored project is determined, the risk assessment information comprises at least one target risk indicator, and the number of the target risk indicators is not greater than the number of the to-be-monitored risk indicators; and determining project risk information of the to-be-monitored project based on the calculation result of each target risk index. According to the invention, the target risk index for determining the risk condition of the to-be-monitored project can be quickly obtained, the generation efficiency of the risk index is improved, and the risk management workload and the risk management cost are reduced.

This invention relates to the field of financial risk assessment technology, specifically to a case risk assessment system and method based on machine learning algorithms. The system mainly includes a pre-training module and a prediction module. This invention is designed with the application of artificial intelligence algorithms in the risk assessment of suspicious cases as its core concept. Guided by theories such as supervised learning and unsupervised learning, it analyzes the performance of various features of cases within a fine-grained feature system through classification algorithms, anomaly detection algorithms, and statistical analysis methods. This process uncovers hidden and anomalous information, assesses the risk of cases, and assigns scores and rankings them. Through the application of AI technology, financial institutions can more quickly and accurately identify and handle high-risk cases, rationally allocate the time and effort of business personnel in handling cases, and promptly reduce risks and losses.

The invention discloses a credit assessment method, system and device based on enterprise information, and a storage medium. The method comprises the steps of obtaining enterprise industry and commerce information, operation risk information, legal risk information and financial information; performing data preprocessing on the enterprise industry and commerce information, the operation risk information, the legal risk information and the financial information to obtain industry and commerce data, operation risk data, legal risk data and financial data; extracting enterprise credit evaluation indexes from the industry and commerce data, the operation risk data, the legal risk data and the financial data; performing quantitative scoring on the enterprise credit evaluation indexes through a credit evaluation model to obtain an enterprise comprehensive credit score and an enterprise credit grade; and credit evaluation of the enterprise information is realized based on the enterprise comprehensive credit score and the enterprise credit rating. According to the method and the system, various data analysis and machine learning models are utilized, automatic, high-precision and comprehensive-coverage-dimension enterprise credit assessment is realized, and the cooperation efficiency is improved while the risk is reduced.

The invention provides a throwing and research method and system based on agent reflection and debate, and the method comprises the following steps: S1, simulating an opposition field through an agent: generating viewpoints of'square 'and'reverse' through presetting an agent role, and forcing the positive and negative agents to sound; s2, searching for an expected difference: constructing a heterogeneous data fusion engine, integrating data sources, and discovering non-dominant association by applying a causal inference model; s3, decision weight decentralization: dynamically adjusting member viewpoint weights according to the historical win rate of each agent, wherein a leader viewpoint is only used as one of inputs; and S4, human traders participate in, wherein human researchers check the analysis logic chain of the intelligent agent, and wrong logic is eliminated. According to the method, a multi-agent debate mechanism is introduced, and multi-view debate is forcibly carried out, so that the problems in a traditional investment and research mode are solved, investors are helped to mine market opportunities, and risks are reduced.

The system according to this embodiment aims to effectively audit the control status of group companies and reduce risks. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a checking unit, a countermeasures unit, and a coordination unit. The reception unit receives answers to questionnaires and data submissions. The analysis unit describes specific methods for conducting internal control audits and data analysis based on the information received by the reception unit. The checking unit self-checks the effectiveness of the control status based on the results obtained by the analysis unit. The countermeasures unit takes necessary countermeasures based on the results obtained by the checking unit. The coordination unit implements the countermeasures taken by the countermeasures unit in cooperation with the internal audit office and second-line departments.

The invention relates to a financial institution post-loan risk control method and risk control system based on fused multi-source dynamic data, and the method comprises the steps: fusing the multi-source dynamic data, carrying out the classification and feature extraction of transaction / sales / production risk monitoring data and dynamic feedback data, and updating a transaction / sales / production risk assessment index and dynamic feedback driving data. Generating a transaction / sales / production risk score and a transaction / sales / production risk weight based on the transaction / sales / production risk assessment index; the transaction / sales / production risk weight is dynamically adjusted according to the dynamic feedback driving data; and a comprehensive risk score is obtained through weighted calculation, so that a risk level and a corresponding risk control measure are obtained. According to the method, multi-dimensional data values of loan enterprise operation, market environment and the like are fully mined, more accurate risk assessment on the loan repayment risk of the loan enterprise is realized, the loan repayment risk of the loan enterprise can be comprehensively measured more reasonably and scientifically, the risk early warning accuracy is improved, and the risk early warning false alarm rate is reduced.

The disclosed systems and methods relate to allowing trading of over the counter (“OTC”) foreign exchange (“FX”) contracts on a centralized matching and clearing mechanism, such as that of the Chicago Mercantile Exchange's (“CME”'s) futures exchange system (the “Exchange”). The disclosed systems and methods allow for anonymous transactions, centralized clearing, efficient settlement and the provision of risk management / credit screening mechanisms to lower risk, reduce transaction costs and improve the liquidity in the FX market place. In particular, the disclosed embodiments increase speed of execution facilitating growing demand for algorithmic trading, increased price transparency, lower cost of trading, customer to customer trading, and automated asset allocations, recurring trades as well as clearing and settlement efficiencies.

Integrated dynamic impact or risk maps can be generated using borehole operation parameters, with associated impact parameters (representing a severity of an event), softness parameters (representing a range or a percentage of the impact while not exceeding a critical threshold or an impact transition mechanism), and trust parameters (representing the accuracy of the applied advisor model). As borehole operations are in progress, factors change over time, such as depth, temperature, fluid composition, number of rotations, and other operational and environmental factors. As these factors change, the associated impact, softness, and trust parameters can be dynamically updated with the current information. The resulting generation of dynamic impact or risk maps using this information is therefore updated to represent the most current conditions. The dynamic impact or risk maps can be utilized to determine modifications to borehole operations to reduce risk, costs, maintenance downtime, and improve return on investment of the borehole.

The application discloses a financial product checking method and device based on a process robot, relates to the technical field of calculation, and can also be applied to the financial field. The method comprises the following steps: traversing a specified financial product storage address according to a preset configuration file; analyzing an evaluation table of a financial product in the financial product storage address to generate a first data set; analyzing the custody information of a user-specified financial product to generate a second data set; wherein the second data set is stored in the financial product storage address; and checking the key field value extracted from the second data set in the first data set. The method provided by the application realizes the function of automatically matching the management report and the custody evaluation table data, thereby achieving the data connection between the external management report and the internal custody system, improving the work efficiency, reducing the work error rate, timely feeding back the management report data problems, and reducing the risk.

The invention relates to the technical field of data analysis, in particular to a financial data analysis system based on machine learning, and the system comprises a data set generation module which is used for obtaining historical financial data; the model evaluation module is used for evaluating the performance index of each pre-stored machine learning model according to the historical financial data and generating a performance evaluation result; the data acquisition module is used for acquiring financial data to be processed and corresponding business risk preferences; the model screening module is used for generating the weight of each performance index according to the business risk preference corresponding to the financial data to be processed, and screening a target model according to the performance evaluation result and the weight corresponding to each performance index; the model training module is used for training a target model; and the risk assessment module is used for performing data analysis on the to-be-processed financial data by adopting the trained target model. By adopting the scheme, the model most conforming to the current risk decision target can be screened out for training and deployment according to the actual demand, so that the deviation of the risk assessment result is reduced.

The invention relates to the technical field of enterprise asset management, and provides an enterprise asset digital integrated management platform based on deep learning, which comprises a DBN-RL financial analysis decision optimization module, an LSTM-GCN investment high-risk early warning module and an SOM-GAN fund flow cloud monitoring module. According to the enterprise asset digital integrated management platform based on deep learning, all-around improvement is brought to enterprise asset management through cooperative work of all the modules. In the aspect of financial analysis and decision, the DBN-RL module provides the capabilities of deeply mining data and optimizing decision, so that an enterprise can make a more intelligent decision in a complex financial environment, and the financial stability and profitability of the enterprise are enhanced. The LSTM-GCN module plays a key role in investment management, and through accurate trend prediction and risk early warning, the enterprise is helped to reduce the risk in the investment activity, improve the return on investment, and guarantee the safety and appreciation of enterprise investment assets. And the SOM-GAN module effectively monitors the fund flow, so that the abnormal fund condition can be found in time.

An underground storage system (1) for storing a fluid, comprising: a hole (2) located on a mass (3), the hole (2) having a bottom (7); a support element (4) comprising at least one opening (9); a connecting element (6) inserted into the opening (9) of the support element (4), said connecting element (6) comprising a vent to allow the fluid contained in the reservoir (5) to be discharged in the event of a leak; and at least one reservoir (5) having a longitudinal axis, a lower end (11) closed by a first closure device (10) and an upper end (12) closed by a second closure device (13).

The invention relates to the technical field of financial risk measurement and calculation, in particular to a case risk measurement and calculation system and method based on a machine learning algorithm analysis case, and the system mainly comprises a pre-training module and a prediction module. According to the method, the application of an artificial intelligence algorithm in suspicious case risk measurement and calculation is taken as a design concept, under the theoretical guidance of supervised learning, unsupervised learning and the like, the performance condition of each feature of a case in a fine-grained feature system is analyzed through a classification algorithm, an anomaly recognition algorithm, a statistical analysis method and the like, hidden information and anomaly information are mined, and the risk of the case is measured and calculated. And performing risk measurement and calculation on the cases, and performing scoring, sorting and grading. Through the application of the AI technology, a financial institution can identify and process high-risk cases more quickly and accurately, the time and energy of business personnel for processing the cases are reasonably allocated, and the risk and loss are reduced in time.

The invention provides a financial reimbursement risk identification method, system and device and a storage medium, and relates to the technical field of financial risk management. According to the method, a dual risk identification mechanism is formed by combining a preset business rule with first risk identification of a rule risk threshold value and utilizing a pre-trained risk assessment model and second risk identification of a preset model risk threshold value. On one hand, the risk condition of the real-time to-be-reimbursed bills can be evaluated more comprehensively and accurately through the multi-dimensional and multi-level identification mode, the probability of risk missed judgment and misjudgment is effectively reduced, and the accuracy and reliability of financial reimbursement risk identification are remarkably improved. And on the other hand, through a dual intelligent identification mechanism of a preset business rule and a risk threshold value and a risk assessment model and a model threshold value, risk identification can be automatically carried out, the participation degree of manual auditing in the financial reimbursement process is significantly reduced, and automatic identification of the risk of the real-time to-be-reimbursed bills is realized.