11 results about "Reduced risk" patented technology

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.

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 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.

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.

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.