1005 results about "Financial trading" patented technology

The invention relates to the technical field of data processing, and discloses a financial transaction anomaly detection and risk assessment method and device based on artificial intelligence. The method comprises the following steps: collecting and preprocessing multi-source financial transaction related data to obtain a multi-dimensional transaction data set; performing feature engineering and abnormal mode recognition on the multi-dimensional transaction data set to obtain an abnormal transaction recognition result; based on the abnormal transaction identification result and the multi-dimensional transaction data, constructing a financial domain knowledge graph to obtain a relation network between transaction entities; performing multi-dimensional risk assessment on the relation network among the transaction entities to obtain a risk score and risk description data; based on the risk score, performing time sequence analysis and trend extrapolation on the risk description data to obtain a dynamic risk early warning signal; and performing scene simulation and multi-factor analysis on the dynamic risk early warning signal to obtain a risk management strategy suggestion. The efficiency and accuracy of financial transaction anomaly detection and risk assessment based on artificial intelligence are improved.

In accordance with embodiments of the present disclosure, a method for conducting secure financial transactions uses a model-based reflex agent to ensure real-time authorization, anomaly detection, and risk assessment. The agent receives transaction-related data, maintains an internal state reflecting observed and inferred conditions, and applies condition-action rules to determine appropriate actions, such as authorizing payments, triggering multi-factor authentication, or halting transactions. The agent dynamically updates its internal state based on feedback, adapts to changes using machine learning, and ensures compliance through audit logs. This approach enhances security, minimizes fraud, and provides seamless, adaptive processing for financial transactions in dynamic environments.

The invention provides a domain data management method and system based on an artificial intelligence platform, and the method comprises the steps: obtaining financial transaction data through a data warehouse, carrying out the preprocessing, and extracting transaction metadata and semantic association thereof through a natural language technology; and classifying the metadata by adopting a hierarchical clustering algorithm, and constructing a transaction data consanguinity map. And extracting time sequence and spatial features from the atlas, calculating a node interaction index by using a pre-configured data interaction prediction model, and marking a high-heat node. And extracting high-dimensional transaction association features based on the graph neural network model, and identifying abnormal transaction data. And marking an abnormal node in the blood relationship map, and tracking and positioning an origin node of abnormal data. According to the method, multiple advanced technologies are comprehensively utilized, and rapid and accurate risk identification can be realized in the face of massive financial transaction data.

The invention relates to the technical field of financial services, in particular to a testing method and system based on financial environment stability. The method comprises the following steps: carrying out real-time state monitoring on a financial transaction system, and generating system state basic data; constructing a virtual test environment according to the system state basic data, and performing test scene adaptive configuration to generate test environment parameter data; performing multi-dimensional pressure test analysis on the test environment parameter data, calculating a system stability index, and generating stability reference data; performing dynamic load gradient processing according to the stability reference data to obtain system pressure gradient data; and performing risk level mapping on the system pressure gradient data to generate risk assessment mapping data. According to the invention, the virtual test environment is constructed and self-adaptive configuration is carried out, so that a real transaction scene can be simulated, the correlation and effectiveness of the test are improved, and the stable operation of the system under different pressure conditions is ensured.

A machine learning-based fraud detection system (100) for financial transactions, comprising the following: a data collection and preprocessing module configured to capture transaction data from financial institutions, payment gateways and bank modules, and to perform data cleansing, normalization and format conversion; a module for creating user profiles and behavioral analyses, configured to create dynamic user profiles based on historical transaction patterns, spending habits, device usage, and geographic location; an anomaly detection and risk assessment module configured to use machine learning algorithms to detect deviations from normal transaction patterns and assign a dynamic risk rating to each transaction; a fraud detection module configured to classify transactions as legitimate or fraudulent based on supervised and unsupervised learning techniques, including decision trees, support vector machines (SVMs), and deep learning models; a rule-based and hybrid fraud detection module configured to integrate predefined rule-based fraud detection mechanisms with AI-driven anomaly detection to improve fraud detection accuracy; a real-time fraud detection and warning module configured to monitor transactions in real time and generate security alerts via SMS, email or mobile app when suspicious activity is detected; a transaction verification and multi-factor authentication (MFA) module configured to implement adaptive authentication measures, including biometric verification, one-time passwords (OTP), and challenge-response authentication for high-risk transactions; a self-learning and model-updating module configured to dynamically update fraud detection models using reinforcement learning and federated learning techniques; a data encryption and security module configured to protect transaction data using encryption techniques, including AES-256 and RSA, to prevent unauthorized access and data breaches; and A dashboard and reporting module configured to provide real-time analytics, visualizations, and fraud detection reports for financial institutions to monitor, track, and mitigate fraudulent activity.

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

A method, system, computer program product of processing early payment financing request, enabling dynamic discounting (DD) or reverse factoring (RF) financing, via SmartHub Finance Processor (SHFP), including receiving request for Early Payment Financing Request (EPFR) from supplier; accessing asset SmartPass Non-FungibleToken (NFT) Repository; receiving risk management model; notarizing NFT ProxyPackage (NFTPP) to local NFTBlockchain Repository (NFTBCR); notarizing / collateralizing NFTPP to restricted access public NFTBCR; securely binding DynamicDiscounting (DD) NFT Cryptographic Token embodying DD Rights (DDRightsNFT), establishing Secure Unbreakable DigitalLinkage; notify EarlyPaymentNetwork (EPN), may notify FintechFinanceMarket; ValueChainFinanceExchange; DeFiMarket; or buyer; verify NFT to local NFTBCR; or respond to EPFR. Borrower requests funds presenting SmartPassDigitalCollateral, supplychainDigitalTrackTraceArtifact; Financer / Lender verify collateral process, provide bids for acceptance with a DigitalFundsTransferAgreement; register financers / lenders, provide secured access, verify DigitalCollateralBlockchainTransactions, secure collateral, ensure authenticity by Digital Signatures / Blockchain, process loan agreements, transfer electronic / cryptocurrency funds financer-to-borrower, assess fees / interest, process payments, Forward / ReverseFactoring (RF) or DD loan terms, DDRightsNFT, participate in DD making independent early payment arrangements with supplier not involving buyer.

A method for detecting fraudulent financial transactions in information technology networks involves obtaining a multitude of features associated with a financial transaction conducted over an information technology network by an unknown transaction party. The multitude of features includes clickstream data obtained from the unknown transaction party. The clickstream data is associated with data of the financial transaction being entered by the unknown transaction party. The method further involves obtaining a first fraud indicator using a machine learning classifier operating on the multitude of features, obtaining a second fraud indicator using a rule-based classifier operating on the multitude of features, obtaining a fraud prediction for the financial transaction, using the first fraud indicator and the second fraud indicator, and taking an action, in response to the fraud prediction.

An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.

The invention discloses a financial deep counterfeiting real-time detection and defense method and system based on a multi-modal large model, and the method comprises the steps: obtaining multi-modal data in a financial transaction scene, and carrying out the desensitization of an edge end; performing dynamic time sequence alignment on the multi-modal data, and calculating a synchronous error of lip motion and voice by adopting a dynamic time warping algorithm; inputting the features into a dynamic risk modeling layer, and generating dynamic risk features in combination with the updated risk feature library; analyzing the features through a double-flow GAN detector, and outputting a forgery probability; and a detection result is input into a compliance verification layer, the supervision file is analyzed through a legal BERT, a structured rule is generated, and real-time transaction interception and block chain log recording are executed. The method protects user privacy and data security, combines the risk feature library updated in real time, and has high flexibility and adaptability. According to the design of the double-flow GAN detector, image and video stream information is fully utilized, and the accuracy and reliability of detection are further improved.

The invention provides a financial data intelligent quality inspection method and system, and the method comprises the steps: S1, accessing a real-time transaction data flow through a dynamic rule engine, and enabling the dynamic rule engine to dynamically adjust the rule weight through a Bayesian network and reinforcement learning hybrid model; s2, calling a multi-modal LLM verification framework, performing joint semantic analysis on the text, the image and the time series data, and generating a risk early warning signal; s3, identifying a cross-entity risk path based on the financial knowledge graph, and converting the identified risk path into a structured risk report; and S4, a closed loop iteration system is formed according to the weight of the early warning feedback optimization rule. According to the method, full-life-cycle quality management and control of financial transactions can be realized through technical collaboration of real-time data stream processing, multi-dimensional semantic verification and cross-entity risk tracking.

An AI-powered system (100) for multimodal data processing, dynamic authentication and secure FinTech and IoT transactions, which includes the following: a multimodal data processing module configured to process structured, semi-structured and unstructured data, including financial transactions, IoT sensor data, voice input and image-based data; an AI-powered dynamic authentication module configured to provide behavioral biometrics, context analysis, and multi-factor authentication (MFA) for continuous user and device verification; an anomaly detection module integrated with machine learning models and configured to provide real-time fraud detection by analyzing transaction patterns, user behavior, and device interactions; a secure encryption module configured to use homomorphic encryption and secure multi-party computations to protect sensitive financial and IoT data; and a blockchain-based transaction validation module configured to create an immutable, tamper-proof ledger to ensure data integrity and transaction transparency.

The invention provides a financial information management method and system based on a block chain technology. The method comprises the following steps: receiving a financial transaction request initiated by a user, and performing hash operation on a data block to generate a hash value of the data block; based on the hash value and identity verification information of the user, generating an encrypted signature by using an improved elliptic curve digital signature algorithm, and binding the transaction time of the preliminary transaction proposal with the encrypted signature by using a timestamp service; broadcasting the data block corresponding to the preliminary transaction proposal and the encrypted signature to a plurality of nodes by using a distributed network so as to determine an effective financial transaction; and linking the data blocks corresponding to the effective financial transactions to a preset block chain according to a time sequence and a logic relationship based on a Merkle tree structure, and regularly checking the consistency and integrity of data on the chain. The financial information management method which is high in safety, cannot be tampered and is traceable can be provided, and the safety of financial transactions is improved.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

The invention relates to the technical field of financial transaction security, and discloses a POS machine transaction risk identification method based on data analysis and a deep learning technology. According to the method, a transaction data stream is collected in real time, after multi-dimensional feature extraction and dynamic time sequence segmentation, the transaction data stream is input into a first deep learning model for preliminary risk scoring, high-risk time windows are screened, transaction data fine-grained features of the high-risk time windows are enhanced, then the transaction data stream is input into a second deep learning model for secondary risk judgment, and transaction risk tags are generated. Based on this, a real-time risk handling instruction is triggered. Wherein the multi-dimensional feature extraction comprehensively depicts transaction data, the deep learning model effectively mines risk features, and the dynamic time sequence segmentation adapts to different transaction frequencies. According to the method, the POS machine transaction risk can be accurately identified, disposal measures are taken in time, financial transaction safety is guaranteed, and the effect of improving risk identification accuracy, timeliness and comprehensiveness is remarkable.

The invention relates to the technical field of transaction verification, in particular to a block chain financial transaction tracing method and system, and the method comprises the following steps: calculating a node interaction proportion, recognizing illegal chain segments and marking nodes, analyzing a field combination sequence matching path structure, judging numbering jump and behavior path pairing, and extracting a field corresponding relation and a matching difference. And identifying a node state offset and a structure jump trend, and generating a state chain segment backtracking section record. According to the method, the interaction frequency and the behavior type proportion are introduced to serve as the node state recognition basis, dynamic credible judgment of boundary nodes is achieved, the recognition precision of chain segment structure interruption is enhanced through the path field combination sequence and the serial number continuity, the serial number jumping behavior and node action offset are combined, the chain jumping path recognition capacity is optimized, and the reliability of the system is improved. The field consistency verification effect is improved by adopting field logic pairing and value domain offset comparison, and the backtracking positioning capability of the broken link node is enhanced by using a path state offset and backtracking pointer association strategy.

The invention discloses a risk analysis method and device for a financial system, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: obtaining multi-source financial data which at least comprises structured data and unstructured text data; performing semantic analysis on the multi-source financial data to obtain an analysis result containing semantic information; extracting risk entities, association relationships among the risk entities, risk events and attribute information of the risk events from the analysis result; constructing a financial risk knowledge graph based on the risk entities, the association relationships, the risk events and the attribute information; and performing association analysis based on the financial risk knowledge graph to obtain a risk analysis result, and executing risk early warning based on the risk analysis result. Through the method and the device, the technical problem of low risk analysis accuracy of the financial transaction system in related technologies is solved.

Various embodiments of the present invention relate to a system for providing financial transaction services in conjunction with a metaverse environment and a method of operating the same. According to various embodiments of the present invention, the present invention may provide financial transaction services that may improve user convenience by providing financial transaction services using an ATM device in the real world and providing financial transaction services using a virtual ATM device within the metaverse environment.

Methods and systems for locational visualization of financial statements are described. In an example, a method may receive, in a financial institution system, a request to display a record associated with a financial transaction, determine a location of a merchant associated with the financial transaction using merchant information, request, from an online mapping service, a customizable map interface associated with the location of the merchant, receive the customizable map interface, and display the customizable map interface integrated into the record associated with the financial transaction.

The invention relates to an abnormal transaction behavior recognition method and system based on artificial intelligence, and belongs to the technical field of financial transaction risk control, and the recognition method comprises the steps: collecting real-time transaction flow data, user behavior time sequence data and association graph data of a user, carrying out the space-time alignment processing, and generating a space-time aligned structured data set; dynamically calculating a transaction time attenuation factor; extracting a composite feature vector, inputting the composite feature vector into a pre-trained dual-channel decision model, and performing confidence weighted fusion on a dual-channel decision result based on a time decay factor to obtain a final risk score; dynamically adjusting a risk judgment threshold, and generating a dynamic decision boundary; judging whether the final risk score exceeds a dynamic decision boundary, if so, outputting an abnormal transaction behavior recognition result, and judging a transaction risk level; and triggering a risk disposal action corresponding to the transaction risk level. The method can improve the understanding depth of complex transaction behaviors, and reduces the false alarm rate of abnormal transaction recognition.

The embodiment of the invention provides a multi-language financial contract intelligent auditing system based on a block chain technology. In a specific implementation mode, the system comprises a first block chain, a second block chain, a third block chain, an auditing module, a backup module, an obtaining module and a supervision module. According to the embodiment, the multi-language financial contract is received and processed, the original text and the translated text are comprehensively audited by constructing the rule base, and the contract execution efficiency and accuracy are improved. Legal affairs, risk control and financial risk auditing are integrated, legal risks are reduced, and financial transaction safety is enhanced. Intelligent automatic auditing is achieved, and the labor burden is relieved. Three block chains are adopted for mutual backup, data integrity and safety are ensured, and faults are responded in time. Translated texts under the national legal system are intelligently recognized and processed, seamless joint of language and legal differences is achieved, and potential risks are reduced. And by utilizing the transparent traceability characteristic of the block chain, the auditing process is ensured to be open and transparent, and the market credibility is improved.

The invention relates to a financial transaction verification method and system based on a block chain and artificial intelligence, and the method comprises the steps: storing financial transaction data through a multi-level block chain network, carrying out the feature extraction and model training of the preprocessed data through an artificial intelligence large model, verifying a new financial transaction, and outputting a result. The method comprises the following specific steps: storing financial transaction data through a multi-level block chain network; performing preprocessing operation on the data in the block chain network; performing feature extraction on the preprocessed data by using an artificial intelligence large model to form a feature vector; taking the feature vectors as input data, and performing training and optimization by using an artificial intelligence large model; verifying the new financial transaction according to the steps and outputting a result; updating and optimizing the model through a feedback mechanism; according to the invention, by combining the block chain and the artificial intelligence technology, the efficiency, accuracy and security of financial transaction verification are improved, and meanwhile, the privacy of data and the robustness of the model are ensured.

The invention discloses a transaction risk adaptive blocking system and method based on large model behavior chain analysis. The system comprises a data acquisition module, a behavior chain construction engine, a large model analysis module, a dynamic time sequence modeling module and a risk assessment and blocking module. According to the system, transaction behaviors of a transaction subject at different time points are connected in series to form a sequence, a time sequence risk feature vector is generated in combination with dynamic time sequence modeling, and real-time accurate assessment of risks is achieved. The system can quickly identify potential risk transactions and give an early warning in advance. Meanwhile, the risk assessment and blocking module dynamically adjusts a risk threshold according to market fluctuation and credit conditions of transaction subjects, adaptively blocks high-risk transactions, and ensures that prevention and control strategies are matched with actual demands. And meanwhile, multi-dimensional data including geographic position and operation behavior information are fused, so that the comprehensiveness and accuracy of risk identification are further improved. An innovative and effective technical means is provided for financial transaction risk prevention and control, and safe and stable operation of financial transactions is powerfully guaranteed.

An apparatus, computer-readable medium, and computer-implemented method to facilitate scalable compliance and issuer governance of decentralized financial transactions especially for the trade and transfer of tokenized securities. The resulting Compliance Aware Tokens contain the rulesets to restrict transactions and facilitate regulatory reporting and oversight. The embodied process, includes of a novel combination of compliance workflows, attribute verification tools, smart contracts and other ledger controls, provides a decentralized Attribute Based Access Control (ABAC) capability. ABAC patterns are extended to govern global financial transactions without the need for an active intermediary.

The invention discloses a financial transaction management and control method and device, and relates to the field of financial science and technology, and the management and control method comprises the steps: obtaining a business identifier, a product identifier and a channel identifier of a target financial transaction based on a financial transaction request, and constructing key value information based on the business identifier, the product identifier and the channel identifier; based on the key value information, determining a target financial interface corresponding to the target financial transaction, and obtaining a management and control strategy set of the target financial interface corresponding to the target financial transaction, the management and control strategy set including M interface authorization management and control strategies, N interface time management and control strategies and K interface flow management and control strategies; and determining the management and control state of the target financial transaction based on the management and control strategies in the management and control strategy set. According to the invention, the technical problem that a current-limiting protection mode based on a leaky bucket algorithm or a token bucket algorithm cannot satisfy financial services in a complex scene in the prior art is solved.

The invention discloses a financial transaction data intelligent protection method and system based on a block chain, and belongs to the technical field of block chains, and the method comprises the steps: collecting financial transaction data from a data source of a financial transaction system, and carrying out the cleaning, format unification and abnormal value detection processing; dynamic encryption strength adjustment is carried out through the smart contract according to the sensitivity and the purpose of the transaction data, compared with a traditional fixed encryption mode, data can be protected more accurately, identity authentication is carried out in combination with a zero-knowledge proof mechanism, specific identity information does not need to be exposed in the identity authentication process, identity information leakage is effectively prevented, and user experience is improved. Encrypted financial transaction data are recorded in a block chain, it is ensured that the data are not maliciously tampered in the storage and transmission process through the non-tampering characteristic of the block chain, each transaction generates a unique hash value as an identity identifier, and decentralized propagation and consensus verification are performed in a block chain network, so that the security of the transaction is improved. And the integrity and credibility of the data are further enhanced.

Systems and methods for computer-implemented commodity transactions using blockchain-based smart contracts. A server with memory executes application to authenticate buyer and seller, receive listing with commodity identifier, quantity, quality, delivery terms, price; generate smart contract specifying participant identifiers, wallet or banking info, order and contract IDs, milestone events, settlement terms; commit contract to blockchain; escrow funds associated with contract; obtain shipment, inspection, and delivery status via oracle; verify milestone satisfaction; release escrowed funds accordingly; record settlement on chain; and update database. Modules may include compliance to compute taxes and fees, identity to perform KYC / AML, and dispute resolver to execute predefined remedies. Optional multisignature escrow and fiat or crypto settlement are supported. Executions store hashes and ledger indices to provide tamper evident audit trails.

The invention discloses a cross-chain-based adaptive identity revocation method and system, and belongs to the technical field of block chains. The cross-chain-based self-adaptive identity revocation system comprises an application chain, a super chain, trusted hardware and an automatic agent node, the application chain provides an initiator and a receiver for a cross-chain transaction, realizes identification of cross-chain operation of a user through an intelligent contract, and transmits the cross-chain operation to the automatic agent node; the superchain periodically issues revocation list information to the automatic proxy node through a heartbeat mechanism; the automatic agent nodes are provided with the trusted hardware, provide a trusted execution environment for an identity revocation mechanism and are used for verifying heartbeat information, synchronizing time and executing identity revocation operation at the same time. The system is suitable for financial transactions, supply chain management and internet of things identity authentication scenes in a multi-chain environment, and the security, transparency and revocation efficiency of a cross-chain system are effectively improved.

The invention provides a multi-dimensional data fusion analysis method for financial transaction anomaly detection, and the method comprises the steps: obtaining time series data of opening an unclosed contract, the time series data of opening the unclosed contract comprises a date, a transaction volume, an opening price, a closing price, a contract type and a row right price, and carrying out the preprocessing of the time series data, removing noise and abnormal values to obtain cleaned time sequence data; and constructing a classifier based on wavelet transform features for the frequency sub-bands judged as the high-frequency transaction behavior mode, and training the wavelet transform features to obtain a classification model for identifying the high-frequency transaction behavior for monitoring the transaction behavior in real time.