105 results about "Financial data processing" patented technology

The invention is suitable for the technical field of data processing, and particularly relates to a financial data processing and classification method, system and equipment based on verifiable computing, and the method comprises the steps: obtaining on-chain transaction data verified by a block chain consensus node storage and risk-related index data verified by an under-chain oracle machine; the problems that the authenticity of traditional financial data is difficult to verify and the data is split are solved; calling a risk assessment model through an intelligent contract deployed on a block chain, and placing an assessment process in a decentralized block chain network; performing verifiable calculation through an intelligent contract to determine a total performance score and a risk index, and realizing whole-course tracing and verification of a calculation process by utilizing the characteristics of verifiable calculation of a block chain; the total performance score and the risk index are stored in the block chain as non-tampering records, and the risk rating groups are automatically divided according to the fixed rating rule on the chain through the smart contract, so that the authenticity, objectivity, verifiability and fairness of financial data processing are guaranteed.

A financial report analysis system having an intelligent automation unit for selecting from stored financial data the financial data to be processed and automatically scheduling for processing the selected financial data, a conversion unit for converting the selected financial data stored in a storage unit into a selected processing format to form converted financial data, a financial data processing unit for automatically processing the converted financial data to form processed financial data and for generating marked financial data from the processed financial data that includes one or more markings, and a verification unit for verifying an accuracy of the processed financial data by comparing the processed financial data to other financial data, and then highlighting or marking portions of the processed data if the data is inaccurate.

The invention discloses a financial heterogeneous data real-time cleaning and fusion method, and the method comprises the following steps: S1, obtaining multi-source, multi-time-dimension and multi-granularity financial heterogeneous original data, building a data access channel, and executing format standardization processing; and S2, constructing a cross-source, cross-time and cross-granularity financial index consistency constraint space, dynamically learning systematic deviation distribution of each data source in different market states based on the constraint space, and generating an implicit consistency deviation field. According to the financial heterogeneous data real-time cleaning and fusion method, a cross-source, cross-time and cross-granularity financial index consistency constraint space is constructed, systematic deviation distribution of data sources in different market states is dynamically learned based on the space, and an implicit consistency deviation field is generated; the problem of consistency among multi-source heterogeneous data is effectively solved, and the accuracy and reliability of the data are improved.

The invention relates to the technical field of data processing, and discloses a business financial data processing method and system based on a large model and a storage medium, and a converged communication method comprises the following steps: S1, data acquisition and real-time preprocessing; s2, unified semantic space mapping; s3, performing causal analysis and knowledge graph construction; s4, automatically creating and dynamically updating a business-finance mapping rule; s5, risk identification, decision quantification and ROI optimization strategy generation: carrying out risk identification and decision quantification on the industry and financial multi-source data according to the business-financial mapping rule through a decision maker of the industry large model in combination with an expert voting mechanism, identifying a potential loss chain and generating an executable scheme; through cooperative work of the data acquisition module, the cloud computing module and the business and financial decision module, full-process intelligence of business and financial data from acquisition and processing to decision support is realized, and the efficiency and effect of business and financial cooperative management are significantly improved.

The invention discloses a financial general ledger management method and system based on wealth management and consumption capital driving, and belongs to the technical field of financial data processing and asset management. And according to real-time consumption behaviors and external financial fluctuation information of the user monitored in real time, performing real-time dynamic updating on the total financial account of the user, and introducing a capital proportion algorithm to calculate a corresponding long-term incentive fund for the consumption behaviors, thereby ensuring that the user can actually obtain long-term earnings, and improving the user experience. Therefore, sustainable consumer asset transformation and consumer asset accumulation are carried out, the discrete relationship between consumption and storage is changed, and a unified, long-term and sustainable income updating and asset management mechanism is formed. The consumption investment proportion of the user in the current period is calculated through the double-account balance algorithm, the corresponding financial general account management suggestion is generated, and accurate and reasonable asset management suggestions are provided for the user.

The application discloses a kind of financial data full-link monitoring method and system, mainly related to financial data processing technical field. Including the following steps: collecting financial data from financial business system;The financial data collected is cleaned, including: data format conversion, missing value processing and outlier processing;According to the correlation rule of pre-setting, the financial data of different sources is associated, and the financial data full-link graph is constructed;Based on the financial data full-link graph constructed, the financial data is monitored in real time;According to the risk early warning rule of pre-setting, the abnormal data monitored is risk early warning, and generates early warning report.The beneficial effects of the application are that it realizes the full-link monitoring of financial data by constructing the financial data full-link graph, breaks the data island, and improves the data transparency.

The invention provides a financial data processing method and system, and relates to the technical field of financial data processing, and the method comprises the steps: employing a recommendation model to determine a plurality of pieces of recommended hotel information and a plurality of pieces of recommended restaurant information based on a business trip destination and a reimbursement amount interval of the destination; based on the reimbursement amount interval of the destination, the multiple pieces of recommended hotel information and the multiple pieces of recommended hotel information, using a generative adversarial network to generate multiple groups of primary selection recommendation schemes; using a graph neural network model to determine a target recommendation scheme based on the multiple groups of primary selection recommendation schemes; and sending the target recommendation scheme as a financial reimbursement scheme to the user. The method can efficiently and accurately generate the financial reimbursement scheme meeting user preferences and reimbursement standards.

The invention relates to the technical field of data processing, is suitable for the financial field, and provides a financial data processing method for lease business management, and the method comprises the steps: obtaining a revocation request for a lease business contract; in response to the acquisition of the revocation request, determining whether the type of the lease business contract is a revocable type; if the type of the lease service contract is the revocable type, determining revocation associated information of the lease service contract based on the revocation request; determining a target version matched with the revocation association information in a plurality of historical versions of the rental business contract; and rolling back contract associated data of the leasing business contract to the target version. According to the technical scheme, the accuracy of revocation processing of the rental business contract is remarkably improved.

The invention discloses an intelligent bill processing method and system based on multi-source data cross validation, and belongs to the technical field of financial data processing. The method comprises the following steps: obtaining and standardizing and aligning heterogeneous bill data in a plurality of data sources associated with the same transaction event to form a to-be-verified data unit; for a preset key field in the to-be-verified data unit, querying and obtaining a dynamic credibility score of each data source on the key field; grouping the data sources based on the numerical values of the key fields, and calculating the sum of weighted credibility of each group; based on the weighted creditworthiness sum, automatically deciding or triggering artificial arbitration to determine a credible value of the key field; and performing feedback updating on the dynamic credibility score of the data source on the key field by adopting a preset excitation and punishment rule according to a judgment or arbitration result. The technical problems that in the prior art, bill processing depends on a complex model, the decision-making process is opaque, and the adaptive learning ability is poor are solved.

The invention relates to the technical field of financial data processing, in particular to a DPU-based index analytic calculation and release acceleration system, which comprises a flow controller, a finite state machine (FSM), a hierarchical storage module, a parallel ALU calculation channel, a coding transmission module and an upper computer interaction module, the hierarchical storage module comprises a local storage unit and a global storage unit, and the encoding transmission module covers an encoder, an output buffer area, a DMA transmission unit and a network card. The system carries out four-stage propulsion processing, an FSM analyzes control words and distributes channels, a hierarchical storage module efficiently manages data, a parallel ALU calculation channel completes index correlation calculation, an encoder carries out preprocessing and then packages the data and sends the data through DMA, the system is provided with a software and hardware collaborative optimization module to support dynamic parameter updating, and whole-process hardware acceleration is achieved. The method has the characteristics of high certainty, high throughput and flexible configuration, and is suitable for financial scenes with strict requirements on real-time performance and certainty.

The embodiments of this application relate to the field of data processing technology, specifically to a financial data processing method, apparatus, computer equipment, and storage medium. The main steps of the aforementioned method include: storing reference data sequences in vector form in a distributed database; for each moment in each reference data sequence, associating and storing the sequence elements of the current moment with those of the previous moment in a wide table to generate fused data records, and dividing them into multiple data shards according to the time dimension; allocating target data to multiple computing nodes, calculating the differences between the target data and each data shard in parallel to obtain a difference array; and determining the reference result of the target data in each reference data sequence based on the minimum difference value in each difference array, used to quantify the valuation status of the object to be evaluated. Through the above method, the processing efficiency of massive amounts of financial data can be significantly improved, meeting the timeliness requirements of high-frequency trading in data processing.

The invention relates to the technical field of financial data privacy protection, and discloses a financial data privacy protection method based on a reversible desensitization technology. The method comprises the following steps of: acquiring a multi-source financial data stream, wherein the multi-source financial data stream comprises a transaction data stream and a user data stream; performing feature extraction and privacy risk estimation on the multi-source financial data stream to determine a financial sensitive position and desensitization intensity estimation; setting a reversible desensitization processing mechanism, and generating desensitization financial data based on the desensitization intensity estimation; constructing a distributed secure transmission architecture, and adaptively selecting a data transmission strategy according to a real-time security condition; and transmitting the desensitized financial data through the distributed secure transmission architecture, and realizing reversible desensitization recovery. According to the method, while the privacy of the financial data is effectively protected, the availability and transmission security of the data are guaranteed, and the method is suitable for financial data processing and sharing in multiple scenes.

The application relates to a data matching method between multiple types of financial bills, and belongs to the technical field of financial data matching. The method obtains multiple original business orders, and generates corresponding monthly bills by summarizing the multiple original business orders according to preset summary rules. According to a preset splitting and combining logic, the monthly bills are split and combined based on the total application bill amount in the monthly bills, corresponding settlement bills are generated, and the settlement bills are used for bill opening to obtain corresponding invoices. A to-be-matched payment order is obtained, and data matching is performed on the original business order, the settlement bill and the to-be-matched payment order according to preset filtering conditions, so that the matching relationship between the original business order, the settlement bill and the to-be-matched payment order is determined. The application can associate the business order, the invoice content and the payment certificate, and improves the business processing efficiency of financial data processing.

The application discloses a cross-agency financial data federated learning modeling system and a privacy compliance verification method, relates to the technical field of financial data processing, and comprises the following: a federated aggregation module, which is used for aligning financial time sequence characteristics of each agency by a timestamp hashing alignment strategy on a server side, and aggregating the aligned financial time sequence characteristics by using a federated time convolution network to generate time sequence model parameters for capturing cross-agency global time sequence dependence; a federated reasoning module, which is used for generating a minimum spanning tree representation of global enterprise correlation relationship based on a local subgraph of enterprise correlation relationship constructed by each agency client on the server side based on a secure multi-party computing strategy; the server side aggregates graph embedding vectors by using a federated graph attention network and performs cross-agency graph reasoning in combination with the minimum spanning tree representation to construct graph model parameters for identifying enterprise correlation risks; and a model joint training module, which is used for fusing the time sequence model parameters and the graph model parameters on the server side to determine global model parameters.

The application belongs to the field of big data, and relates to a financial data processing method, comprising the following steps: obtaining enterprise-side financial data and government-side financial data of a target project; adopting a preset data warehouse to perform data integration on the enterprise-side financial data and the government-side financial data, and obtaining a comprehensive financial data set; receiving a report request of a user, determining accounting standards based on the report request, loading accounting processing logic corresponding to the accounting standards from a pre-established accounting module; and performing financial analysis and processing on the comprehensive financial data set based on the accounting processing logic, and generating a financial report corresponding to the target project. The application also provides an apparatus, a device and a medium. The application can improve the financial data processing efficiency.

The invention discloses an enterprise financial examination system based on big data. The enterprise financial examination system comprises a financial data processing module, an intelligent analysis module and an application service module, the financial data processing module constructs a real-time data pipeline through Apache Kafka or Flink, gets through data barriers of ERP, CRM, a supply chain system and a financial system, realizes real-time synchronization and seamless circulation of full-link business data, and guarantees compliance and quality of financial data through data governance. According to the financial data processing module, data barriers such as ERP, CRM, a supply chain system and a financial system are broken through, and a real-time data pipeline is constructed by using Kafka and Flink, so that real-time synchronization and seamless circulation of full-link business data are realized, the consistency of financial and business data is effectively guaranteed, and the business efficiency is improved. And meanwhile, a data governance mechanism effectively ensures compliance and quality of financial data.

The invention provides a financial data processing method and device and electronic equipment. A reimbursement system cleans multi-source flow data pushed by a business and financial platform, and generates a deduction prepayment order based on the cleaned data and a preset field mapping relation; the reimbursement system performs red punching verification on the deduction prepayment order, and generates a deduction refund order based on the deduction prepayment order, a preset field mapping relation and a flow claiming red punching order pushed by the business and financial platform; the reimbursement system performs first examination and approval on the generated deducted document based on a preset examination and approval rule so as to push the deducted document to a financial system for second examination and approval when the first examination and approval of the deducted document are passed; and the reimbursement system synchronizes the first state of the deducted document in the first approval process and the second state of the deducted document in the second approval process to the business and financial platform. According to the invention, the document generation efficiency and accuracy and the abnormal business processing efficiency can be improved, and the business process integrity is guaranteed.

The invention relates to the technical field of financial data processing, and particularly discloses an AI-based financial text data auxiliary processing system, which comprises a data acquisition module, a data preprocessing module, an AI intelligent analysis module, a data verification module, an intelligent decision-making auxiliary module, a data storage and management module and a safety management and control module. The system can automatically collect multi-format and multi-source financial text data in batches, after the data quality is optimized through preprocessing, intelligent text classification, key information extraction and structured conversion are achieved through an improved deep learning algorithm, the accuracy and compliance of the data are guaranteed in combination with multi-dimensional verification, then decision support such as financial analysis and risk early warning is provided, and the accuracy and the reliability of the system are improved. And data management and security are guaranteed through distributed storage and an omnibearing security mechanism. According to the method, full-process automation of financial text processing is realized, the processing efficiency and data accuracy are improved, the problem of data dispersion of an existing system is solved, the intelligent level of financial decision is improved, and compliance requirements are met.

The application belongs to the field of financial data processing, and discloses a kind of financial data processing system and method, comprising: data acquisition module, for collecting the multi-source multi-modal data of listed company, multi-source multi-modal data includes enterprise internal financial data, enterprise internal non-financial data, social external index and macroeconomic variable;Pretreatment module;Characteristic screening module;Model construction and optimization module;Interpretability analysis module;Output module.This scheme constructs a set of multi-source data integration, automation processing, intelligent modeling and decision explanation in one financial early warning system.Through the standardization of module pipeline, the whole process closed loop processing is realized;At the same time, the high precision prediction and SHAP interpretability technology are fused, so that the model can output accurate risk probability, and the influence degree and direction of each risk factor can be clearly quantified, the traditional "black box" prediction is transformed into transparent, reliable and operable decision basis, and the unity of early warning accuracy and decision support is realized.

The application relates to the technical field of intelligent report forms, in particular to a financial report form intelligent generation method and system fusing a variational Gaussian process and reinforcement learning. The method establishes an initial report form template by collecting original business data, completes business feature clustering and semantic mapping to identify a business type by using hierarchical fuzzy clustering, constructs a financial relationship graph based on sparse representation graph embedding and performs abnormality identification to generate a financial data set, fuses variational Gaussian process regression and reinforcement learning to complete budget prediction, finally fills a template with budget results and performs structure reconstruction by a graph regularization autoencoder to generate a final available financial report form, and realizes intelligent report form automatic generation under digital finance. The application significantly improves the automation degree, prediction accuracy and report form generation efficiency of financial data processing.

The application discloses a financial knowledge graph data fusion and retrieval middle platform system for investment suggestions, and belongs to the technical field of financial data processing. The system comprises an investment analysis enhancement module and a knowledge graph construction module which interact through a distributed message queue. A multi-scale time series convolution network is adopted to dynamically calculate and add investment analysis coefficients, including a relationship transmission coefficient and an Alpha sentiment token, to entities and relationships in the knowledge graph. The module dynamically calculates the investment analysis coefficients of entities and relationships through the multi-scale time series convolution network, thereby accurately depicting market risk and yield characteristics. The relationship transmission coefficient can quantize the risk transmission strength between associated assets, thereby assisting dynamic hedging and risk control of investment portfolios. The sentiment token can capture market emotion fluctuations in real time, thereby generating differentiated signals for quantitative strategies. In combination with the efficient interaction of the distributed message queue, the module significantly improves the real-time performance and accuracy of investment decisions, and is particularly suitable for high-frequency trading and multi-factor strategy optimization.

The invention discloses an intelligent decision support method and system fusing multi-source financial data and a security fence, and relates to the field of financial data processing, and the method comprises the steps: analyzing a customer portrait generation request, determining a portrait task scene, and converting the portrait task scene into a task context vector; according to the target customer identifier, recalling the original data, and decomposing the original data into a plurality of feature atoms; labeling metadata for each feature atom; constructing a financial feature topology network based on the plurality of feature atoms; calculating a dynamic confidence score of each feature atom; according to the dynamic confidence score, distributing the plurality of feature atoms to different confidence regions of a structured cue word template to generate a structured cue word; inputting the structured cue word into the large language model to generate a customer portrait report; and based on the customer portrait report, customer insight analysis and personalized verbal skills are generated and displayed. According to the method, effective credibility evaluation can be carried out on the multi-source financial data, so that the fact consistency is ensured.

The invention discloses a financial time series data multi-scale feature analysis and prediction method and system, and belongs to the technical field of financial data processing and deep learning, and the method comprises the steps: obtaining and preprocessing multi-source financial time series data; performing multi-scale decomposition by adopting discrete wavelet transform and empirical mode decomposition to generate a trend component, a periodic component and a noise component; respectively extracting time dependence characteristics of each component through a time sequence Transform coding module; multi-scale features are subjected to adaptive weighted fusion through a multi-scale attention fusion module; outputting a predicted value and a confidence interval through a probability prediction module; and the closed-loop optimization module adjusts decomposition parameters and attention weights according to prediction error feedback, so that different frequency components of financial time series data can be effectively separated, a multilevel time dependency relationship is captured, and adaptive feature fusion and online optimization are realized.

The invention provides a financial data processing method and system, and relates to the technical field of financial data processing. Comprising a data acquisition module, a data storage module, a data security extraction module, a data processing module, a trend analysis module, a risk prediction module, a user side storage module, a user side display module, a security login module and a cloud data storage recovery module, comprising transaction records, market quotation data, customer information and the like; and the data storage module is responsible for safely and reliably storing the collected data into a database. The system has the advantages that the dynamic code is generated in real time through the dynamic verification code generation module in the working process, so that the data extraction module can perform data extraction only when the data in the data storage module is extracted according to the verification code, and data leakage caused by stealing the data in the system by viruses such as crawlers and Trojan horses is avoided.

The application discloses a kind of financial data processing methods, obtain policy data set;Policy data set includes at least one initial policy;Initial policy is classified, and target complete policy, first missing policy and second missing policy are obtained;With the target complete policy that first missing policy matches is recorded as first control policy, with the target complete policy that second missing policy matches is recorded as second control policy;According to first allocation data and the target allocation data of first control policy, determine the first allocation result of first missing policy, according to second allocation data and the target allocation data of second control policy, determine the second allocation result of second missing policy.The application classifies initial policy, so as to distinguish the policy of control group and the policy of missing group.Determine first control policy and second control policy, calculate first allocation data, second allocation data and target allocation data, improve the efficiency and accuracy of financial data processing.

This invention discloses a method for improving the confidentiality of financial data processing. In the field of information security technology, the method automatically identifies the source department and data type based on the submitter information of the financial data, determines the confidentiality level of the data using a confidentiality level rating table, and dynamically parses the key length and arrangement rules accordingly. It uses the characterization conversion results of the submitter's account and submission time, along with a random string, to perform multi-source fusion to construct a high-entropy basic key. After being hosted by a hardware security module, this key is intercepted to form the final encryption key, ensuring its unpredictability and security. An encryption algorithm combined with a random initialization vector is used to encrypt the original data text, ensuring the confidentiality and integrity of the data. The encrypted data is segmented according to the key length, generating a random number sequence and binding it to the data block. The number mapping relationship is stored on the blockchain. After being encapsulated with a unique identifier, the data block is randomly shuffled and further divided, and finally distributed and stored in the cloud.

The invention relates to a quantitative strategy generation method and system based on a hybrid development architecture, and relates to the technical field of financial data processing. The method mainly comprises the steps of obtaining attribute information of a historical stock market, and determining a market state tag sequence according to the attribute information of the historical stock market; grouping the market state label sequence according to the daily yield sequence of each quantitative strategy type to obtain a yield sub-sequence corresponding to each quantitative strategy type under all market state labels; calculating an average performance value corresponding to each quantitative strategy type under the same type of market state label according to the return rate sub-sequence; and in response to a quantization strategy generation instruction, generating a quantization strategy type of the current stock market according to the average performance value corresponding to each quantization strategy type under the same type of market state tag and the attribute information of the current stock market.

This invention relates to the field of financial data processing technology, specifically to a machine learning-based anti-money laundering risk identification method and system. The invention collects positive and negative sample data for anti-money laundering as training and validation data. The collected data is preprocessed, and general and deep features are extracted and evaluated to form a feature library. Based on data from each account in the feature library, a COPOD model is trained and validated using positive and negative samples to generate an anti-money laundering risk identification model. This model scores the input data for anti-money laundering risk and outputs the risk level and processing method based on threshold comparisons. While maintaining a high confirmation rate, the invention reduces the workload of manual screening of suspicious transactions, improves the quality of anti-money laundering reporting, and constructs a technology-driven, precise monitoring, and agile response anti-money laundering prevention and control system.

The invention provides a financial data processing method which can be applied to the technical field of big data. The financial data processing method comprises the steps that in response to a received problem proposed by a user for financial data, the problem is input into a feature recognition module of an intention and entity combined recognition model to determine a semantic feature vector of the problem, and the intention and entity combined recognition model further comprises an intention recognition module and an entity recognition module; inputting the semantic feature vector into an intention recognition module to obtain the intention of the question; inputting the semantic feature vector into an entity recognition module to obtain at least one entity in the question; determining a processing action for the financial data according to the intention and the at least one entity; and executing a processing action on the financial data to obtain a processing result. The invention further provides a financial data processing device and equipment, a storage medium and a program product.

The invention discloses a multi-modal investment unified information standard model construction method and device, and relates to the technical field of financial data processing, and the method comprises the steps: constructing a standardized data system through unifying the format, the standard and the coding specification of multi-source investment information data, and training a prediction model in combination with a targeted time sequence analysis method; according to the invention, the data quality and management efficiency are significantly improved, the influence of data errors on services and downstream systems is greatly reduced, the cost and error of manual data processing are effectively reduced, the prediction and risk early warning of investment market fluctuation are realized, and the effects of unifying industry data management standards, reducing investment risks and meeting supervision requirements are achieved.