202 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 discloses a financial data generation method and system based on a rule configuration engine, and the method comprises the steps: converting a business demand into structured rule description through a semantic analysis engine according to the financial business demand, dynamically configuring a financial data processing rule through a context-aware rule template library, and generating financial data according to the financial data processing rule. Generating an executable rule configuration set; extracting related data from a plurality of heterogeneous financial data sources according to the rule configuration set, and mapping features of different data sources to a unified high-dimensional vector space by adopting a multi-source data fusion algorithm to generate a fused financial feature matrix; and inputting the fused financial feature matrix into a rule configuration engine, adopting a rule execution optimization model, dynamically adjusting a rule execution sequence and parameters, generating financial data, and performing noise addition processing on the financial data to ensure data privacy security. According to the embodiment of the invention, the financial data can be flexibly and efficiently generated, and the safety and efficiency of overall data processing are improved.

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 discloses a financial data processing method which is applied to processing financial data in the field of finance. In the method, a computational graph comprising nodes and edges is adopted to represent a processing flow of financial data, and the nodes in the computational graph can indicate input financial data, an algorithm model function for processing the financial data and an operation rule function. When the computational graph is executed, a series of processing processes of the financial data can be realized by calling the data or functions indicated by the nodes in sequence, so that the data, an algorithm model and an operation rule are visually combined together, and the interpretability of the financial data processing flow is improved. Moreover, when the financial data processing flow is visually presented on the basis of the computational graph, the financial data processing flow can be changed only by adjusting nodes in the computational graph, and the financial data processing flow can be analyzed and adjusted conveniently.

The invention provides an account reimbursement method and device based on an OCR and a Transform decoder and electronic equipment, and belongs to the technical field of financial data processing. The method comprises the following steps: scanning a bill to obtain a bill image, preprocessing the bill image, performing text recognition on the preprocessed bill image by adopting OCR (Optical Character Recognition), and outputting a text recognition result; classifying the text recognition results based on a preset financial business rule, generating financial data information and storing the financial data information; converting the financial data information into an input format corresponding to a Transform decoder, inputting the financial data information into the Transform decoder, and outputting structured reimbursement information; performing compliance verification on the structured reimbursement information based on financial business rules; and filling the verified structured reimbursement information into a reimbursement bill template in the financial reimbursement system, and generating a reimbursement bill. The full-process automatic processing from bill scanning to bill generation can be realized, the reimbursement efficiency and accuracy are obviously improved, and meanwhile, the requirement of manual intervention is reduced.

The invention discloses a financial data anomaly analysis system and method based on artificial intelligence, and relates to the technical field of financial data. The method comprises the following steps: collecting and cleaning various financial data in real time through docking with an enterprise financial system; processing data extraction features; analyzing the feature data by using the trained deep neural network model, marking abnormal data, and setting a reasonable index threshold to further confirm an abnormal condition; deeply analyzing the financial items marked as abnormal by using an optimization algorithm, and obtaining financial optimization target data to generate specific adjustment suggestions; and financial data are collected again for monitoring again and abnormal type analysis, and a project set is constructed to calculate an association index to find potential problems in the business process, and an early warning report is generated. According to the method, automation and intelligentization of financial data analysis are realized, the detection precision and efficiency are improved, optimization suggestions with strong pertinence are provided, and enterprises are helped to deal with financial risks in time.

The invention relates to the technical field of data processing, in particular to a financial data processing system based on a block chain, which comprises an access chain authorization module, a write-in arbitration scheduling module, a voucher path identification module, a structure back adjustment point module and a chain type result output module. According to the method, the stability score is calculated according to the field calling sequence difference, the calling interval time difference and the calling return content consistency of the node access behavior log, the nodes with stable access behaviors are screened, and block access permission mapping is formed; a sorting label is constructed based on a transaction timestamp and a fund transaction amount to realize sorting of concurrent transaction priorities, dynamic matching judgment is performed through a public key address list and a multi-signature access threshold, and link access path redirection is formed, so that the possibility that a node tampers data is reduced, and the transaction efficiency is improved. And according to the chain segment block height, the relay node confirmation state and the tail end data field variation value, sequence verification and index tracing are carried out, so that the tracing capability of the financial transaction data is obviously improved.

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.

The invention relates to the technical field of financial data processing, in particular to a payment and settlement service system based on a big data technology, and the system comprises a data collection module, a user payment behavior recognition module, a payment channel evaluation module, and a payment execution and monitoring module. According to the invention, through deep analysis of transaction data and dynamic user portrait construction, consumption modes and preference trends of users can be captured and analyzed more accurately, and through extraction of periodic purchase behaviors and sudden change preferences, payment experience is highly personalized, customer service quality is greatly improved, payment channel assessment is comprehensive, and user experience is improved. The method includes real-time performance monitoring and cost analysis, guarantees the optimization of the payment process, effectively improves the processing speed and safety, reduces the fraud risk, enhances the transaction safety and transparency through the implementation of full-chain encryption and the real-time tracking of the payment progress, and improves the response speed to potential abnormalities.

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 provides a document type recommendation method based on a large model, and belongs to the technical field of financial data processing, and the method comprises the steps: extracting text content of a document processing rule file, and segmenting the text content to obtain a static processing rule of the document processing rule file; obtaining a dynamic processing rule through a pre-trained rule mining model and a historical audit document type recommendation result; carrying out vectorization storage on the static processing rule and the dynamic processing rule to construct a vector knowledge base; and receiving bill data uploaded by a user, and determining a bill recommendation type corresponding to the bill data based on the vector knowledge base. According to the method, the vector knowledge base containing the implicit processing rule and the dynamic processing rule is constructed, and the bill recommendation type corresponding to the bill data is determined based on the vector knowledge base, so that intelligent recommendation of the bill type is realized.

The invention relates to the technical field of distributed data processing, and discloses a distributed financial data processing system. The system comprises a multi-source data acquisition module, a heterogeneous data integration module, a distributed calculation verification module, a service flow modeling module, a resource scheduling module and the like. The multi-source data acquisition module captures multi-dimensional financial transaction data in real time by using a distributed edge computing node, and distributes an acquisition path through a dynamic load balancing algorithm; the heterogeneous data integration module analyzes the cross-platform data format and generates a standardized data stream; the distributed calculation verification module constructs a credible verification model based on a hybrid consensus mechanism; the business flow modeling module predicts a risk conduction path; and the resource scheduling module optimizes resource allocation. The system effectively solves the problems of a traditional financial data processing system in the aspects of data acquisition, integration, calculation verification and the like, improves the data processing efficiency, the risk prediction capability and the resource utilization efficiency, and guarantees the safety and compliance of financial transactions.

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 present invention relates to the technical field of financial data processing, and discloses an AI financial task allocation method based on an accounting knowledge graph. The method includes obtaining experience relevance data, skill relevance data, professional matching data of financial personnel, and enterprise financial business requirements; generating an accounting form graph according to the enterprise financial business requirements, and performing form integration and summarization operations to obtain a complete set of accounting forms; calculating based on the analytic hierarchy process to obtain a task matching degree; analyzing the task capabilities of financial personnel according to the task matching degree, and performing intelligent allocation operations to obtain a task allocation form; performing data entry and node feedback operations according to the complete set of accounting forms and the task allocation form to obtain a real-time task form; performing progress analysis and task reallocation operations according to the real-time task form to obtain a final task plan form. This method can realize intelligent allocation of tasks, improve the accounting efficiency, and reduce the error rate.

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.