551 results about "Financial trading" patented technology

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.

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.

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.

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.

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 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.

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.

A system and method are disclosed for enabling secure and low-friction peer-to-peer financial transactions using email communication without requiring user login. A payment server receives an email message from a responder that includes a token embedded in a mailto hyperlink. The server extracts and decodes the token to retrieve transaction data, including the responder's email address and a transaction amount. The server authenticates the responder by comparing the email address extracted from the token with the sender address of the email. Upon successful validation, the server initiates a financial transaction with a payment processing system—without requiring the responder to access a login page, enter credentials, or navigate to a website. The invention improves upon conventional browser-based payment workflows by reducing user interaction and enhancing transaction efficiency and security.

An improved electronic wager-based gaming machine (EGM) is disclosed. The EGM supports multiple concurrent players and maintains a plurality of distinct virtual meters, each exclusively associated with a distinct, concurrent game session on a single EGM. The server processes financial transactions, such as deposits and withdrawals using the EGM's cash handling peripherals, against a specific virtual meter and a corresponding player's casino account, which is maintained separate from the EGM's operational funds. This enables the EGM to act as a financial terminal for the player's account. Concurrently, data derived from the virtual meters is transformed and aggregated into a single, unified regulatory meter for compliance reporting, which is distinct from the virtual meters. The EGM provides a shared multiplayer interactive control interface to solve the technical problem of supporting multiple, independent player sessions concurrently on a single EGM.

A system and associated method and service for providing a platform that enables members to transfer, receive, or otherwise exchange cash in various international denominations and commodities, such as precious metals (e.g., gold, silver, etc.), in a mobile highly secured telecommunications environment. The system is capable of delivering cash instantly between two or more users by allowing the exchange of secure transactions between system and cash dispensing machines (ATMs). Users may pay, receive, exchange, deposit, transfer, pay bills, exchange currency, retrieve cash, deposit cash and create accounts based on the mobile phone number or a system generated code.

The invention provides an anti-fraud method and system based on multi-modal perception and cross-time feature modeling. The method comprises the following steps: receiving a multi-modal financial transaction graph sent by an edge computing node; inputting the multi-modal financial transaction graph into a risk control model for risk analysis to obtain a node risk score of each node, an edge risk score of each edge and a gang risk score of each sub-graph; the risk control model is a multi-scale ST-Transform-GCN framework, and the risk control model is a multi-scale ST-Transform-GCN framework; processing the node risk score, the edge risk score and the gang risk score through a gating fusion mechanism to obtain a comprehensive risk score; based on the node risk score, the edge risk score and the gang risk score, extracting a suspicious transaction sub-graph from the multi-modal financial transaction graph; and inputting the comprehensive risk score and the suspicious transaction sub-graph into a preset reinforcement learning decision maker to obtain an anti-fraud strategy, and executing the anti-fraud strategy to achieve the purpose of accurately identifying the complex collaborative fraud behavior.

The invention discloses a cross-transaction system accounting function separation method based on a rule engine, and belongs to the technical field of financial accounting, in the method, the accounting function of a traditional transaction system is decoupled through the rule engine, and the agility and compliance capability of financial transaction processing are effectively improved. A core accounting rule is abstracted as an independent execution unit, so that a bottom layer system does not need to be reconstructed in business operations such as rate adjustment and account division strategy change, and the new rule online period is compressed from a week level to an hour level. The multi-level rule architecture supports a mixed processing mode of real-time transaction and batch correction, can meet millisecond-level liquidation requirements in a cross-border payment scene, can automatically adapt to dynamic compliance strategies of different supervision jurisdictions, and realizes parallel computing and accurate matching of global regional transaction rules. The continuity of key transactions is guaranteed by a geo-fence routing and intelligent traffic segmentation mechanism.

The invention discloses an encryption transmission method and system for financial transaction data and a storage medium, and the method comprises the steps: carrying out the grading preprocessing of original financial transaction data through an amount sensitivity grading algorithm, and obtaining a four-stage transaction amount data set and an account feature code; initializing 8D financial chaotic parameters for the numerical values and the account feature codes to obtain an eight-dimensional chaotic parameter sequence; performing frequency domain decomposition on the fourth-level transaction amount data set to obtain four pieces of frequency domain sub-band data; dynamically generating a DNA coding rule according to the account feature code and the eight-dimensional chaos parameter sequence to obtain a dynamic DNA coding mapping table; and performing adaptive block scrambling on the four frequency domain sub-band data according to the amount level, and performing fusion processing through a dynamic DNA coding mapping table to obtain an encrypted data packet. The technical problem that an existing financial transaction data encryption transmission method cannot perform differential security protection according to transaction amount sensitivity and lacks anti-quantum computing capability is solved.

The invention discloses a financial transaction intelligent analysis system, and belongs to the technical field of financial transactions. The system comprises a data acquisition module, a data preprocessing and feature engineering module, an intelligent analysis and decision module, a user preference and strategy configuration module, a signal matching and reminding generation module and a notification pushing module. According to the invention, multi-dimensional data related to financial transactions are collected, after preprocessing and feature extraction, a transaction signal is generated by using an algorithm model in the intelligent analysis and decision module, and a precise transaction prompt is generated and pushed to a user in time in combination with personalized preferences and strategies set by the user. The method can help the user to overcome the dilemma of information asymmetry and irrational decision, improves the decision efficiency and potential return rate of financial transactions, and has the characteristics of intelligence, individuation, initiative and timeliness.

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 relates to the technical field of financial transactions, in particular to a financial transaction real-time anti-fraud monitoring system which comprises a data processing module, a graph construction module, a graph traversal module, a risk estimation module, a risk level determination module and a transaction management and control module. Performing feature quantization on the historical transaction sequence to obtain an intermediate state feature vector; taking the transaction subject identifier and the transaction counterparty information as nodes, taking the transaction behavior as an edge, and constructing a transaction association graph; on the basis of a preset fraud mode rule set, performing graph traversal on the transaction association graph, and identifying abnormal transaction sub-graphs; generating a comprehensive risk index according to the deviation degree of the abnormal transaction sub-graph and the intermediate state feature vector; performing grade judgment on the abnormal transaction sub-graph to obtain a risk disposal grade, and managing and controlling a corresponding transaction behavior of the transaction platform; according to the invention, the real-time performance of real-time anti-fraud monitoring of financial transactions can be improved.

The invention provides a real-time fraud detection method and system based on multi-modal fusion, and belongs to the technical field of financial transaction fraud detection.The method comprises the steps that firstly, payment behaviors, transaction texts and account behavior data are connected in series through a transaction event unique identifier, and a real-time multi-modal transaction data link is formed; generating an inter-modal fraud association link based on the inter-modal fraud association link; performing interactive operation to obtain cross-modal fraud intermediate features; constructing a transaction fraud tendency path according to a transaction link time sequence; and finally, analyzing the transaction fraud tendency path to determine a fraud association level, generating a fraud response instruction containing a transaction blocking instruction and risk notification content, and transmitting the fraud response instruction to a transaction processing system to terminate the transaction, so that multi-modal data can be comprehensively integrated, a transaction time sequence is considered, and the fraud detection accuracy and real-time performance are improved.

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

The invention is suitable for the technical field of financial security, and provides a financial transaction real-time anti-fraud monitoring method and system, and the method comprises the steps: collecting user equipment interaction data in real time, extracting an operation curvature change spectrum feature vector, and recognizing a conventional verification step missing time period and an equipment silent state interval; calculating a local entropy speed increase rate and capital network temperature gradient space distribution, and generating an entropy change index; setting a curvature detection threshold value according to the entropy change index, executing multi-scale mode matching on the equipment silent state interval, and obtaining equipment peripheral signal data; performing space-time convolution fusion on the peripheral signal data and the pressure value sequence, and executing a pressure test; and carrying out space-time correlation analysis, identifying a multi-dimensional abnormal signal, and generating a cross-scale risk early warning instruction. Through multi-dimensional data fusion and dynamic risk assessment, the accuracy and scene adaptability of financial transaction real-time anti-fraud monitoring are improved, and an effective guarantee is provided for financial transaction security.

The invention discloses a financial transaction risk assessment method and device based on federal forgetting learning, equipment and a medium, relates to the technical field of federal forgetting learning, and is applied to a central server. Comprising the following steps: determining an original model of a central server and each financial institution client through federal learning training, to-be-removed clients corresponding to federal forgetting learning and remaining clients; constructing a Stackelberg game model according to the compensation decision of the central server for each client and the participation decision of each client; solving the to-be-solved function based on the Stackelberg game model to obtain a target compensation decision and a target participation decision; and training by using the data set of the client corresponding to the target participation decision to obtain a target forgetting model, and performing risk assessment on the financial transaction. Therefore, the Stackelberg game model, the balance compensation decision and the client participation decision can be combined, the enthusiasm of the client participating in federal forgetting learning is improved, and the stability and fairness of the final forgetting model are improved.

The invention provides a financial transaction risk assessment method and system based on machine learning, and relates to the technical field of financial transaction risk assessment, and the method comprises the steps: carrying out the parallel deployment of an acquisition module at a transaction system, a behavior log and a market information interface, triggering a block chain smart contract according to a dynamic threshold value, constructing an original time sequence transaction graph, and registering topological hash. Node attributes are normalized through a self-adaptive sliding window, and edge attributes are segmented and standardized according to types and then are split into slices. Performing random walk sampling on each slice by using a standardized edge weight and expanding neighbors to form an observation sub-image set; the sub-graphs are input into the multi-layer graph neural network to sequentially aggregate neighborhood messages, time sequence codes are injected, closed-loop path features are fused, and representation vectors are output. And the contract counts the latest representation vector batch, dynamically updates a threshold value through clustering and mahalanobis distance, marks a high-risk sub-graph, writes back scores and sub-graph hash after under-chain model prediction, and realizes a transparent and traceable risk assessment closed loop.

The invention provides a bank financial transaction real-time risk assessment method based on machine learning, and relates to the technical field of risk control, which can effectively identify fast-in and fast-out fund abnormal flow by analyzing a time interval coefficient and fund retention duration between payment path nodes, and can effectively identify the fund abnormal flow by calculating a fund circulation path and account activation degree. The method can accurately identify the fund exchange relation among multiple accounts, and discovers the sleeve behavior of fund dispersed transfer. Therefore, in a bank cross-border transaction scene, the transaction fraud that the transaction time-space characteristics and the transaction opponent behavior characteristics cannot be associated and analyzed in real time, so that fund fast-in and fast-out by using multiple cross-border accounts is difficult to identify in time can be reduced.

The invention provides a financial risk control and anomaly detection method and system based on a graph neural network, and relates to the field of financial risk control, and the method comprises the steps: constructing a financial transaction graph network, constructing a feature graph in combination with transaction time sequence information, extracting a time sequence feature vector, calculating a risk propagation feature vector, and determining a node embedding vector and a fusion node vector. And constructing a transaction flow diagram and carrying out community division, and finally determining an abnormal transaction community through fuzzy clustering and carrying out early warning.

Application of multi-task learning technique(s) to machine logic (for example, software) used to detect financial transactions that are fraudulent or at least considered likely to be fraudulent. Some embodiments include adjustments and / or additions to conventional multi-task learning techniques in order to make the multi-task learning techniques more suitable for use in fraud detection software. One example of this is compensation for class imbalances that are to be expected as between the likely-fraud and not-likely-fraud classes of data sets (for example, training data sets, runtime data sets).

The invention discloses a financial transaction fraud identification method and system based on a graph neural network, and the method comprises the following steps: S1, collecting financial transaction data, and carrying out the preprocessing of the data; s2, a heterogeneous graph structure is constructed, the heterogeneous graph structure comprises account nodes, equipment nodes and position nodes, and a heterogeneous transaction graph is formed; s3, embedding path-level context information into each edge in the heterogeneous transaction graph, and introducing a node type and edge type joint position index to form a multi-hop edge coding matrix; s4, constructing a graph neural network model, and outputting a node representation vector; s5, fusing node representations of adjacent layers by adopting a residual fusion strategy, and constructing a jump connection structure to form a node feature matrix; and S6, constructing a joint training task, carrying out joint training on the graph neural network model by adopting a multi-task loss function, and outputting a fraud identification and classification result. According to the invention, the accuracy and real-time response capability of transaction fraud detection are effectively improved.