354 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.

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.

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.

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.

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 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 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 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 relates to the technical field of financial transactions, and discloses an artificial intelligence-based transaction execution and risk control management system, which comprises a heterogeneous data acquisition module, a conduction prediction module, a collaborative execution strategy module, a risk coupling and evaluation module, a signal monitoring and compensation module and a risk control collaborative optimization module. According to the method, heterogeneous data such as an order book, time sequence transactions, macroeconomy and news texts are fused through a multi-modal neural network, a dynamic asset association graph is constructed by adopting a graph neural network to predict a market impact conduction path, and a cross-market multi-leg transaction execution problem is modeled by applying Actor-Critic deep reinforcement learning; a Copula function and a Bayesian network are utilized to capture a nonlinear coupling relation of multi-dimensional risks, strategy signal attenuation is monitored in real time and dynamically compensated based on an LSTM neural network, and cooperative balance of execution efficiency and risk control is realized through a method of combining multi-objective optimization with a game theory.

Technologies for initiating payment processing include a compute device. The compute device includes circuitry configured to obtain, at a merchant location, a notification transmitted from a merchant point of sale device, to facilitate a financial transaction. The notification may be indicative of a financial transaction that was initiated between the merchant point of sale device that is at the merchant location and a remote gateway compute device that is not at the merchant location. The circuitry may also be configured to send, to the remote gateway compute device, payor identification data that identifies the payor associated with the financial transaction. Further, the circuitry may be configured to send, to the remote gateway compute device, payment method selection data indicative of a payment method selected by the payor for the financial transaction.

A fraud detection system preemptively identifies and mitigates fraudulent financial transactions in real-time across a diverse range of digital and physical transaction sources. Transaction data may be acquired from various platforms and categorized into rule sets specific to transaction types. Categorized data is aligned with existing risk profiles to construct a dynamic hysteresis model. This model, evaluated by a decision tree algorithm, identifies potential fraud by integrating immediate transaction details with a comprehensive historical data analysis, thus enabling advanced trend analysis and pattern recognition. Key features identified by the decision tree are used to form a heuristics model, which is then analyzed by a transformer network risk model. A feedback loop enhances the system's effectiveness by incorporating decision outcomes back into the model training server, thus refining the training dataset and continuously improving the accuracy of the risk model.

Payment platforms such as point of sale terminals can send test transactions to an acquirer processor certification and analytics system for certification of the payment platform's readiness to perform financial transaction processing with payment networks. A payment platform sends a test transaction to an acquirer processor and receives a unique request identifier. The request identifier and a test identifier associated with the test transaction are sent to an analytics engine that retrieves data associated with the test transaction from the acquirer processor, and expected values based on the test identifier. The analytics engine performs a comparison and determines a certification result for the payment platform based on the comparison. Based on certification results, authorization or licenses can be granted to payment platforms to use payment networks of the acquirer processor.

The invention discloses a complex fund chain intelligent management and anomaly detection method based on a deep learning model, and the method comprises the steps: capturing a fund flow network structure between accounts through building a fund topological relation, extracting the periodicity and trend characteristics of a transaction sequence through combining a time sequence neural network, and calculating a reconstruction error through an anomaly detection model, thereby achieving the intelligent management of a fund chain. Quantifying transaction deviations and identifying anomalies; if the abnormal score exceeds a threshold value, backtracking a risk conduction chain through a path tracking method, revealing an abnormal fund propagation path, simulating future fund transfer scale and direction by using a flow prediction model, comparing with a historical risk threshold value, judging potential fluctuation, and generating a real-time early warning signal; according to the method, full-chain analysis from anomaly detection to risk prediction is realized through multi-model fusion, the accuracy and timeliness of risk identification in financial transactions are remarkably improved, and effective technical support is provided for prevention of systematic financial risks.

Approaches are described for contingent transfers of value. A configuration record for an account associated with a virtual wallet is obtained. The configuration record is used to evaluate at least one virtual wallet of an owner account. Based on the configuration record, keys or other secret data and an authorization scheme can be determined and applied to virtual wallets that are part of a contingent contract. Thereafter, the virtual wallets can be utilized to exchange tangible and virtual digital currencies in various financial transactions, banking operations, and other asset exchanges and / or utilized for another purpose such as exchanging an irreversible transfer of value as in a virtual currency to a reversible transfer of value as in fiat currency or a financial instrument.

In one aspect, a computer-implemented method includes: receiving, by an interface to an AI-assistant service, a request to carry out a financial transaction pertaining to a particular account; receiving, from the AI-assistant service, a text response to the request to carry out the financial transaction, wherein the text response is an instruction to carry out the financial transaction; and performing, by a financial service, the financial transaction as instructed in the text response, whereby the financial transaction is semi-autonomously performed in response to the request.

The invention discloses a short message fraud early warning method and system based on abnormal behavior detection, and relates to the field of short message fraud early warning, and the method comprises the steps: obtaining user terminal equipment behavior data and financial transaction request behavior data, and constructing a continuous behavior sequence after a user short message is received; constructing a dynamic baseline based on user historical behavior data, calculating a deviation degree between single behavior data and the dynamic baseline, and generating a suspicious behavior set; performing cross-platform behavior tracking to obtain cross-platform behavior data; inputting the deviation degree corresponding to the suspicious behavior, the sequence context information of the occurrence time point of the suspicious behavior and the cross-platform behavior data into an AI fusion analysis model, outputting a fraud risk confidence coefficient, and calculating a fluctuation ratio; and performing fraud behavior identification based on the fraud risk confidence and the fluctuation ratio, and triggering real-time early warning when fraud is confirmed. According to the invention, high-precision and self-adaptive real-time detection and early warning of short message fraud behaviors are realized.

The invention discloses a message encryption method and device based on an improved digital signature algorithm, and electronic equipment, and relates to the field of privacy computing or financial science and technology, and the method comprises the steps: carrying out the calculation through employing a hash function and a pre-generated random bit string, and obtaining a preprocessed message, the sum of chain length parameters of positions of all chains in the signature is equal to a natural number parameter, constructing a Hash forest by using an FORS algorithm, generating a one-time signature for each tree, and combining two signatures by using an upper-layer key to obtain a WOTS + signature; and encrypting the financial transaction message and the preprocessed message based on the final signature result, and transmitting an encryption result and the final signature result to the target terminal for verification. According to the method and the device, the technical problems that the encryption speed is low and the encryption operation cannot be completed in time due to the complex algorithm when the financial transaction message is encrypted by adopting a post quantum cryptography encryption mode in the related technology are solved.

A computerized-method for detecting market sentiment manipulation that is related to financial-instruments trading. The computerized-method includes: (i) monitoring incoming alerts in an analytics-engine. (ii) retrieving data from each alert, by operating a context-extraction module; (iii) for each alert: a. collecting feeds from social-media servers based on the retrieved data by operating a social-media feeds-extraction module; b. for each feed, generating a summary by using Gen AI; and c. analyzing a social-media sentiment by providing the generated summary of each feed to the Gen-AI; (iv) for each financial instrument that has been traded in the preconfigured period, searching an anomaly between a sentiment from traditional news source and the analyzed social-media sentiment; (v) storing each anomaly in an anomalies database; and (vi) pausing each financial-transaction that has been processed and related to the financial-instrument in the anomalies database.

A method is provided for processing micropayments. Micropayments are financial transactions involving relatively small financial amounts that can be difficult for conventional transaction processing systems to handle in a cost-effective and expeditious manner. The improved method uses a virtual micropayment account managed by a transaction processor. The limit of the virtual micropayment account may be adjusted with credit reduction requests and credit reset notices that are sent from the transaction processor to the issuer bank. The issuer bank may use the credit reduction requests and credit reset notices to adjust a temporary credit limit of the credit account from which the micropayments are to be made.

The application relates to the technical field of security protection, and discloses a financial transaction gateway routing method, device, equipment and medium, which comprises the following steps: obtaining an external request and generating an authentication combination key and a flow limiting dimension based on the external request; performing multi-level authentication verification on the authentication combination key; after the multi-level authentication verification is passed, performing dynamic flow limiting inspection on the external request according to the flow limiting dimension; after the dynamic flow limiting inspection is passed, analyzing the external request to obtain a transaction feature vector and a transaction message, and matching a preset test case library according to the transaction feature vector; if the transaction feature vector hits a test case in the preset test case library, a simulation response is returned; and if the transaction feature vector does not hit a test case in the preset test case library, the transaction message is forwarded according to a target routing rule. The application can be applied to a financial scene, can improve multi-level protection, and meets the transaction circulation demand of high security and low risk.

The invention relates to a method. Any information (instructions, events, occurrences, invitations, social interactions, histories, relationships, associations, membership, affiliations, hierarchies, rights, privileges, requests, ownership, obligations, responsibilities, contracts, commitments, identities, attributes, statuses, endorsement, qualification, authentication, credentials, etc.) worthy of being recorded for future reference, transactions, verification, calculation, determination, and execution can be made through a law enforcement agency. Electronic communication records sent to block chain operators and guardians (nodes, miners and the like) by supervision organizations and other entities or individuals are recorded into the block chain system. Then, the block chain system can decide whether to approve the transaction, whether to unfreeze the frozen account, how to perform and settle the smart contract, and the like. The external system can also query information such as facts, verification and guidance from the block chain system. According to the method, law enforcement, supervision and social, economic and financial transactions and verification can be performed on the block chain, and the method is not only suitable for an unlicensed block chain system, but also suitable for a licensed block chain system.

The invention discloses an anomaly detection method and device based on financial transaction behavior attribute graph data, and the method comprises the steps: determining a positive subgraph and a negative subgraph corresponding to a node for each node in a constructed static attribute graph; inputting the sub-graph corresponding to the node into a transaction anomaly detection model, and determining a curvature space weight corresponding to the node based on a weight network; hybrid curvature embedding corresponding to the nodes is determined based on a hybrid curvature graph encoder; performing feature extraction on the mixed curvature embedding corresponding to the nodes based on the comparison network to obtain feature representations corresponding to the nodes, and obtaining positive pair scores and negative pair scores of the nodes in various curvature spaces through discriminators corresponding to the various curvature spaces by the feature representations; according to the curvature space weights corresponding to the nodes and the positive pair scores and the negative pair scores of the nodes in various curvature spaces, determining transaction anomaly probability values of the nodes; and determining whether the node is an abnormal transaction node according to the abnormal transaction probability value of the node. And the accuracy of transaction anomaly detection is improved.

1. The name of the design product: bank card (ocean card (shark)). 2. The use of the design product: the design product is used for storing financial data or conducting financial transactions. 3. The design points of the design product: in the pattern. 4. The picture or photo that best indicates the design points: design 1 front view. 5. The design product is a thin product, and other views of the design are omitted. 6. Design 1 is designated as the basic design.

The invention discloses a financial load balancing method and system based on cooperation of an application layer and a network layer, and belongs to the technical field of networks. The method comprises the following steps: deploying an application layer load balancing node, an SDN controller, a collaborative decision module, a traffic prediction module and a state acquisition module, and establishing communication connection; collecting an application layer server state index, a network layer link state index, a financial transaction priority, a protocol type and whole network flow data in real time, and predicting a flow change trend; the collaborative decision-making module performs linkage analysis on the multi-dimensional indexes to generate an application layer scheduling instruction and a network layer scheduling instruction which are synchronous; and the instruction is issued to an application layer load balancing node and an SDN controller for execution, indexes after execution are collected and fed back to the collaborative decision module, and the instruction is dynamically adjusted according to the index difference. According to the invention, index intercommunication and instruction synchronization of the application layer and the network layer are realized, closed-loop control is formed, and the service quality and operation stability of the financial SDN network are improved.

The application belongs to the technical field of financial transaction security, and particularly relates to a financial behavior mode analysis and prediction method based on time sequence chart representation learning, which comprises the following steps: obtaining transaction flow data; obtaining flow embedding vectors based on an event encoder and a time encoder; learning the flow embedding vectors based on an autoregressive model, and obtaining a representation matrix through self-supervised contrast learning training; constructing an interaction graph, modeling node interaction on graph structure features using a graph neural network to form a graph enhancement module, and enhancing the representation matrix; and constructing a machine learning classification model to realize financial behavior mode analysis and prediction. Compared with the prior art, the application can automatically capture more complex and abstract information from massive information, mine the time sequence, interaction and business correlation between account flow and labels, and realize rapid and wide application of the model in the absence of business expert experience analysis.

The invention discloses a transaction anomaly detection method, device and equipment, a medium and a product, and belongs to the field of finance. The method is applied to a first model generator, and comprises the following steps: in response to a model parameter optimization request for a financial transaction model, obtaining historical financial transaction data, and generating a transaction feature tag of the historical financial transaction data; determining a target model configuration parameter of an initial financial transaction model of a first model generator; performing parameter configuration on the initial financial transaction model by adopting the target model configuration parameters to obtain an intermediate financial transaction model; performing model training on an intermediate financial transaction model of a first model generator according to the historical financial transaction data and the transaction feature tag thereof to obtain a target financial transaction model; and carrying out transaction anomaly detection on the to-be-detected transaction by adopting the target financial transaction model. According to the scheme of the embodiment, the optimization efficiency of the financial transaction model and the accuracy of the prediction result are improved, and the protection capability of financial privacy data is improved.