169 results about "Financial data processing" patented technology

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention discloses a personal credit score real-time early warning system based on behavior chain mining, which relates to the technical field of financial data processing and comprises a data acquisition module, a behavior chain atlas module, a behavior mining module, a collaborative modeling module, a score fusion module, an early warning interpretation module and a safety optimization module. All the modules interact through an end-to-end data flow and a real-time message mechanism, and dynamic evaluation and intelligent early warning of personal credit risks are achieved. According to the method, diversified normal and high-risk virtual behavior chains are automatically simulated and generated through a generative AI technology, a federated learning mechanism is combined, multiple mechanisms are enabled to jointly confront novel fraud and complex risk behaviors, original data does not need to be transmitted during model training, privacy security and model generalization ability are greatly improved, unknown risk behaviors are virtualized through AI, and the method is high in practicability and easy to popularize. The capability of identifying unprecedented risks is improved, and the problems of data islands and privacy leakage are avoided through federal learning.

The invention provides a multi-source financial data intelligent classification and automatic accounting optimization processing method and system, and relates to the technical field of enterprise financial management, and the method comprises the steps: carrying out the textualized preprocessing of multi-source enterprise financial data, constructing a transaction scene map, carrying out the scene clustering, forming a transaction classification rule set, and carrying out the classification of transactions, according to the method, labels and accounting subject codes are matched for conventional transaction data, a transaction traceability graph is constructed to analyze the integrity of a transaction link, and an accounting voucher is generated to realize automatic accounting, so that the accuracy and efficiency of financial data processing are improved, the manual intervention cost is reduced, and the traceability of financial data is enhanced.

The invention relates to the technical field of data processing, in particular to an enterprise financial data processing system and method. The method comprises the following steps: obtaining multi-source enterprise financial data, and carrying out dynamic data type identification and structure transcoding to obtain preprocessed enterprise financial data; performing graph embedded structure conversion on the preprocessed enterprise financial data to obtain enterprise financial semantic fusion data; constructing a financial data auditing path according to the enterprise financial semantic fusion data; tense version mapping is carried out on the data format fluctuation characteristics in the financial data auditing path to record the structure change process of various vouchers and asset and liability data in the financial data auditing path, hierarchical file compression is carried out on a tense version mapping result, and a structured financial data auditing path is obtained; and time sequence causal link construction is carried out according to the structured financial data auditing path, and a financial cognitive inference model is obtained. The auditing efficiency and the risk monitoring capability can be improved.

The invention discloses a financial data processing method and system based on machine learning, and a storage medium. The method comprises the following steps: encrypting customer credit data of a financial institution through Paillier homomorphic encryption and extracting a risk feature vector; performing secret sharing segmentation on the local gradient parameters and then safely aggregating the local gradient parameters into global gradient parameters through a BGW protocol; on the basis of the privacy level, global gradient parameter differential privacy noise is injected and subjected to federal training to obtain a cross-institution credit evaluation result; calculating risk characteristics and evaluation results through homomorphic encryption to obtain an encrypted credit score, and performing secure multi-party calculation and decryption to generate a customer credit report; and block chain supervision compliance verification is carried out to generate an audit record, and a privacy budget optimization scheme is dynamically adjusted according to the business emergency degree. The technical problems that in an existing financial data processing method, information is incomplete due to data islands, data leakage risks are caused by insufficient privacy protection mechanisms, and an effective supervision compliance verification mechanism is lacked are solved.

The invention discloses a cross-institution financial data federated learning modeling system and a privacy compliance verification method, and relates to the technical field of financial data processing, the system comprises a federated aggregation module used for a server to align financial time sequence characteristics of each institution through a timestamp hash alignment strategy, and a privacy verification module used for verifying the privacy compliance of each institution; aggregating the aligned financial time sequence characteristics by using a federated time convolutional network, and generating time sequence model parameters for capturing cross-mechanism global time sequence dependence; the federation reasoning module is used for generating a minimum spanning tree representation of a global enterprise association relationship based on a security multi-party computing strategy by the server side on the basis of a local sub-graph of the enterprise association relationship constructed by each institution client side; the server performs cross-mechanism graph reasoning by using a federated graph attention network and an aggregation graph embedding vector and combining minimum spanning tree representation, and constructs graph model parameters for identifying enterprise associated risks; and the model joint training module is used for the server to fuse the time sequence model parameters and the graph model parameters so as to determine global model parameters.

The invention provides a multi-source financial data intelligent classification and automatic accounting optimization processing method and system, and relates to the technical field of enterprise financial management, and the method comprises the steps: carrying out the textualized preprocessing of multi-source enterprise financial data, constructing a transaction scene map, carrying out the scene clustering, forming a transaction classification rule set, and carrying out the classification of transactions, according to the method, labels and accounting subject codes are matched for conventional transaction data, a transaction traceability graph is constructed to analyze the integrity of a transaction link, and an accounting voucher is generated to realize automatic accounting, so that the accuracy and efficiency of financial data processing are improved, the manual intervention cost is reduced, and the traceability of financial data is enhanced.

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 method and system for enhanced financial data processing using large language models (LLMs) to transform PDF statements into structured accounting data. The invention preprocesses statements with line numbering, character position tagging, and data partitioning to overcome LLM context limitations. The system extracts transactions, reconciles them with statement balances, normalizes vendor information, and generates enhanced output files compatible with standard accounting platforms. This enhanced transaction data collapses reconciliation and categorization times in downstream accounting systems. The system creates a robust vendor-transaction database supporting direct analysis or simultaneous application of multiple accounting models.

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 data processing method, electronic equipment and a computer readable medium, and the method comprises the steps: carrying out the data cleaning and safety verification of received original financial data, and obtaining pure financial data; semantic analysis is carried out on the pure financial data, and service information, service scenes, sensitive information and semantic relations in the pure financial data are recognized; determining a prompt template matched with the service scene, and generating a basic processing instruction according to the prompt template, the service information, the sensitive information and the semantic relationship; performing transformation processing on sensitive information in the basic processing instruction to obtain a desensitization processing instruction; inputting the desensitization processing instruction into a large-scale language model to obtain a response result output by the large-scale language model; and carrying out restoration processing on the response result to obtain a processing result of the original financial data. The contradictory triangle problem of data privacy, analysis efficiency and system security existing when a large language model is applied in the financial field is solved.

The invention relates to the technical field of financial information, in particular to an intelligent financial statement automatic generation and auditing method based on an AI learning model, and the method comprises the steps: analyzing an original financial voucher image through a multi-modal AI extraction module, extracting and structuring the sum, a transaction party, a timestamp and voucher type label information, and carrying out the verification of the information; the method comprises the following steps: uniformly correcting time data of different voucher sources based on a timestamp conflict detection model, generating a time alignment data unit set, inputting aligned data into a report to generate an AI model, constructing an amount-subject mapping relation through a long-short-term memory neural network, filling the amount-subject mapping relation into a debt sheet and profit sheet template, and automatically generating a to-be-audited report. Performing node-level compliance verification on the report through the dynamic rule base, marking rule conflict codes and generating an audit mark report; according to the method, automation of complex financial data processing, intelligence of report generation and traceability of audit verification can be realized, and financial efficiency and compliance management capability are remarkably improved.

The invention provides a financial data processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring transaction data generated by a financial service system from a block chain node in real time; performing tagging processing on the transaction data according to a preset rule, wherein the tagging dimension comprises a transaction type, a risk level and a data flow direction; based on a graph data model, converting the tagged transaction data into a structured relationship including nodes and edges, the nodes representing a transaction subject, and the edges including a transaction amount, transaction time and a data flow direction; and displaying the structured relationship in a dynamic graph form. The method is used for achieving the effect of improving financial data processing efficiency and accuracy.

The invention discloses an RAG-based cross-document table generation system and method.The system comprises a cross-document information retrieval and fusion module, an intermediate Json Schema building module and a large language model.The cross-document information retrieval and fusion module preliminarily retrieves corresponding semantic information from a financial document of a to-be-generated table, and the intermediate Json Schema building module stores the semantic information in the financial document; according to a retrieval result and user feedback, a new query statement is dynamically generated by adopting a multi-round query generation mechanism, retrieval is performed again from the financial document, and results of two times of retrieval are sorted and filtered and then integrated to form structured input data; the intermediate Json Schema construction module is used for constructing a Json Schema format, and the intermediate Json Schema construction module is used for constructing a Json Schema format; the large language model generates a customized table. According to the method, multi-source heterogeneous financial data can be efficiently integrated, structured financial tables such as financial statements, risk assessment tables and investment analysis tables can be quickly generated, and the financial data processing efficiency is remarkably improved.

The invention discloses a financial knowledge graph data fusion and retrieval platform system for investment suggestions, and belongs to the technical field of financial data processing. A multi-scale time sequence convolutional network is adopted to dynamically calculate and add investment analysis coefficients for entities and relationships in the knowledge graph, wherein the investment analysis coefficients comprise a relationship conduction coefficient and an Alpha emotion token. The module dynamically calculates an investment analysis coefficient of an entity and a relationship through a multi-scale time sequence convolutional network, and realizes accurate description of market risk and income characteristics. The risk conduction strength between the associated assets can be quantified through the relation conduction coefficient, and dynamic hedging and risk control of the investment portfolio are assisted; the emotion token can capture market emotion fluctuations in real time and generate differentiated signals for a quantification strategy. In combination with efficient interaction of a distributed message queue, the module significantly improves the real-time performance and accuracy of investment decision, and is especially suitable for high-frequency transaction and multi-factor strategy optimization.

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 invention discloses a mathematical modeling AI prediction system in financial risk assessment, which relates to the field of financial data processing and comprises a feature principal component extraction module, a compensation alignment execution module, a momentum regulation and control decision module, a gradient direction optimization module and a risk assessment output module. According to the method, principal components are extracted through covariance matrix decomposition of the qualification-debt ratio and the cash flow fluctuation ratio, feature redundancy is eliminated, the core risk characterization efficiency is improved, a cross-market compensation matrix of transaction frequency and price fluctuation is constructed in combination with SVD and cosine similarity, and the model environment adaptability is enhanced. And analyzing momentum attenuation by adopting a dynamic sliding window of a second derivative matrix eigenvalue ratio, and establishing a regulation and control mechanism to suppress gradient oscillation. Orthogonal interference of historical momentum vectors is eliminated through Schmidt orthogonalization projection, and the accuracy of the gradient optimization direction is guaranteed. And converting abstract numerical values into interpretable risk level classification by using a hyperbolic tangent function in combination with credit rating interval mapping.

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 provides a multi-source financial data intelligent classification and automatic accounting optimization processing method and system, relates to the technical field of enterprise financial management, and comprises the following steps: text preprocessing of multi-source enterprise financial data, construction of a transaction scene graph for scene clustering, formation of a transaction classification rule set for transaction classification, matching of labels and accounting subject codes for regular transaction data, construction of a transaction traceability graph for analysis of transaction link integrity, generation of an accounting voucher for automatic accounting, improvement of the accuracy and efficiency of financial data processing, reduction of manual intervention cost, and enhancement of the traceability of financial data.

Disclosed are various data processing systems, user interfaces, application programming interfaces, and related operations for commodity financial data processing scenarios. In some implementations, a system may provide a graphical user interface (GUI) to customize a financial strategy associated with a commodity delivery contract. The GUI may include an interactive graph representation having a vertical axis and a horizontal axis, with the vertical axis corresponding to price values and the horizontal axis corresponding to time values of the financial strategy. The interactive graph representation may provide a vertical user interface control which is capable of horizontal movement to select a time value, and a horizontal user interface control which is capable of vertical movement to select a price value of the financial strategy. An accompanying information display of the GUI may display the time value and the price value and related information obtained for the strategy.

The invention discloses a multi-modal data processing method, a related device and a system, and relates to the field of financial data processing and artificial intelligence combination. The dynamic evolution characteristics of the modal relationship between the multi-modal financial data in the process of updating the initial dynamic graph structure into the current dynamic graph structure are captured, the fusion weight of each modal data is adjusted based on the dynamic evolution characteristics, and the multi-modal financial data are deeply fused. The means of capturing the modal relationship among different modal financial data in real time and adjusting the financial data fusion weight can better fit the frequent fluctuation condition of the financial market; and a cross-modal attention interaction layer in the financial data processing model is adopted to further capture complementary information among modal data, and complementarity and difference among different modal data are deeply mined. In conclusion, the association relationship between the multi-modal financial data is deeply mined, and based on the association relationship, efficient and accurate processing of the multi-modal financial data is realized.

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.