4587results about "Protocol authorisation" patented technology

The invention relates to the technical field of financial risk control, and discloses a telecommunication fraud risk identification method based on a bank card transfer scene. The method comprises the following steps: a basic feature construction stage: acquiring multi-source data of historical victim users, extracting multi-dimensional features, and processing the multi-source data into standardized time sequence data through feature coding and a DTW algorithm; in the multi-modal fusion and adversarial learning stage, a core layer containing a multi-modal analysis engine, a risk reasoning model and an adversarial generation model is constructed, and multi-modal feature fusion, risk probability output under an RLHF framework and simulated fraud feature generation driven by WGAN-GP are achieved; in the strategy output stage, risk scores are mapped through a double-layer scoring system, and three-level interception is triggered; in the model management stage, model iteration is achieved by means of a monitoring instrument panel and a rolling time window, the online effect is guaranteed by combining gray release and A / B testing, the telecommunication fraud in the transfer scene can be accurately recognized, and the risk control efficiency is improved.

A method of provisioning a credential for a transaction between a payment device of a user and a transaction processing device is described. The method is implemented a third-party server remote from the payment device and the transaction processing device. The method comprises assigning the credential to the transaction and the user and provisioning the credential into the payment device for subsequent retrieval by the payment device. Assigning the credential to the transaction comprises: determining unique identifying information of the user; allocating a first user-identifying portion of the credential to correspond to the unique identifying information; determining a plurality of properties intrinsic to the transaction and encoding the plurality of properties into transaction-identifying information; and allocating a second transaction-identifying portion of the credential to correspond to the transaction-identifying information. The credential is associated with encrypted authentication data that is generated by the payment device during the transaction, and used for subsequent authentication of the transaction by the remote third-party server.

A system for self-executing enhanced interaction with a node-based logistics receptacle. The system includes a wireless accessory sensor node disposed on the node-based logistics receptacle to monitor storage receptacle components of the node-based logistics receptacle to generate sensor data. The system includes a bridge node disposed on the node-based logistics receptacle that uploads information related the sensor data, detects an external device separate from the node-based logistics receptacle, communicates with the external device to establish a smart contract based connection that provides an interaction privilege, interfaces with the external device according to the interaction privilege, and transmits an update message to the backend server that corresponds to at least a portion of the uploaded information related to the sensor data and information related to interfacing with the external device.

A dynamic learning server-based logistics system includes a node-based logistics receptacle operative to receive a delivery item as part of a logistics transaction and also includes a backend server maintaining a management profile related to operation of the node-based logistics receptacle. The node-based logistics receptacle includes a plurality of monitored receptacle components, a wireless accessory sensor node having a plurality of sensors, and a bridge node operative to retrieve event information from the wireless accessory sensor node. The backend server receives the retrieved event information, compares the retrieved event information with the management profile, identifies a threshold change condition from the comparison, dynamically revises the management profile when the threshold change condition is identified, and transmits an adjustment message to the bridge node. The adjustment message is based upon the revised management profile, and the adjustment message initiates a timing change to operation of the bridge node.

Aspects of the present disclosure describe a system comprising a processor and memory storing instructions, which when executed by the processor, enable the system to detect a payment-related event using vehicle sensors, such as a camera or GPS unit. The detection can involve a machine learning model trained to recognize objects associated with the payment-related event while minimizing false positives. Upon detecting the event, the system can handle the electronic payment by utilizing an in-vehicle digital wallet to execute the payment and recording the transaction on a blockchain-based ledger.

The invention relates to the technical field of risk control and data analysis, in particular to a risk control real-time feature platform calculation method and system based on a big data technology. The method comprises the following steps: firstly, acquiring multi-source data in real time through a parallelization mechanism; executing three-layer cleaning based on a streaming engine; dynamically generating numeric and associated feature vectors by adopting a sliding window; loading a pre-training model to carry out risk assessment on the feature vector and outputting a third-level risk event; executing a hierarchical response strategy: transaction blocking, manual auditing and auditing release; a layered framework is adopted, and incremental model training and deployment are supported; and real-time monitoring and dynamic configuration functions are provided. According to the invention, millisecond risk identification and response are realized, and risk control timeliness and accuracy are significantly improved.

The invention provides a quantitative transaction strategy dynamic generation method and system based on a large model, and a storage medium. The method comprises the following steps: S1, collecting market data, related financial news and information of financial products; s2, inputting market data, financial news and information into a large language model, analyzing current market environment characteristics, and intelligently selecting a matched transaction strategy type through a strategy generator; s3, performing simulated transaction verification on the generated strategies by using historical market data, and selecting the strategy with the optimal performance from a plurality of candidate strategies through risk assessment, parameter optimization and strategy screening; s4, deploying the strategy passing the verification of the large model to a transaction execution system, monitoring the execution effect, the market environment and the risk index of the strategy, and dynamically adjusting the strategy parameters or regenerating the strategy according to the monitoring result; and S5, a feedback learning step. The method has the advantages that multiple strategy types are integrated, and diversity and innovativeness of strategies are improved.

Backup and recovery of multi-party computation (MPC) security data utilized to secure digital assets and transactions thereof can enhance user confidence and user experience in digital asset transactions. Example MPC security data can include cryptographic keys and key shares utilized with a N×M MPC signature and validation framework. A computing device participating in generation of MPC secure data can retain a segment of the MPC secure data and can encrypt and store an encrypted segment at a second device. A recovery service or recovery application at the second device can facilitate recovery of the MPC secure data segment at the computing device, or at an additional device not involved in generation of the MPC secure data. The recovery service or application can facilitate recovery of the MPC secure data segment even in the event the computing device is lost.

Determining user types from behavior is described. An artificial intelligence (AI) model is trained to classify user accounts of a payment service into different user types using contextual data associated with processed payments between the user accounts. The AI model is used to analyze additional contextual data associated with additional payments between additional user accounts to classify the additional user accounts, and, if a particular user account of the additional user accounts is associated with a user type of the different user types that requires an action to be performed, an instruction is sent to a user device associated with the particular user account to cause a payment application to present a user interface element prompting a user to perform the action, and, based on whether the action was performed, account data indicating whether the particular user account is an authorized account is stored in a datastore.

The invention relates to the technical field of artificial intelligence, and discloses an account bill abnormal transaction identification and analysis method based on deep learning. The method comprises the following steps: fusing user historical account bill time series data and a static portrait, and constructing a multi-modal feature vector; using a converter-based conditional variation auto-encoder to learn personalized normal behavior distribution; calculating an abnormal score through the reconstruction error; dynamically generating a judgment threshold in combination with an extreme value theory to identify an abnormal transaction; and carrying out feature attribution analysis on the abnormity. The method does not need abnormal sample training, and has the advantages of strong generalization ability, low false alarm rate and high interpretability.

A node-enabled logistics receptacle system with a deployable storage element for receiving and temporarily maintaining a delivery item. The system includes a storage receptacle with an entrance opening for receiving the delivery item and a deployable autonomous mobile container as the deployable storage element. A first node is mounted to the storage receptacle and includes a first node processor, a first node memory, a communication interface, and a plurality of sensors including an interaction sensor and a storage sensor. The first node detects a change in the state of the entrance opening, determines a current condition of what is presently maintained within the deployable autonomous mobile container, generates a deploy activation signal, and transmits the deploy activation signal to the deployable autonomous mobile container to initiate movement by the deployable autonomous mobile container from the host position within the storage receptacle to a position external to the storage receptacle.

Methods, systems, and machine-readable mediums that enhance transaction authentication of a transaction of a first application by checking that a state of one or more other applications matches prespecified states or that one or more of those applications transition states within a prespecified period of time. For example, the prespecified states may correspond to the application being installed on a specified device (e.g., of the user) and having an authenticated session with a specified user.

A device, system and method for machine-generated automatic fraud detection using a large language model to generate a human-readable summary to detect anomalies in a user's transaction history behavior. A prompt may be input into a large language model comprising a set of features of the user's current and past transactions and instructions to generate a summary explaining deviation in the user's behavior between the current and past transactions. The summary may be analyzed to detect if the deviation in the user's behavior is anomalous. When the analysis detects deviant behavior patterns between the user's current and past transactions, fraud may be suspected to automatically trigger a preventative anti-fraud action, e.g., to pre-emptive cancel, delay execution or escalate interrogation, of the current transaction.

A computer-security architecture removes activation authority from client-facing purchase flows. A server records a digital entitlement in an INACTIVE state and issues a presentation artifact. A verification service accepts messages only from trusted origins carrying freshness-bounded evidence and validates using (i) stateless keyed recomputation over a time-windowed payload, (ii) confirmation that token-rail authorization data maps to a registered association within a policy freshness bound, or (iii) a server-maintained session. Activation occurs only on an authoritative commit path that enforces no activation without a durable audit record and effective-once behavior: either a single ACID transaction that conditionally updates state and writes an insert-only audit in the same commit, or an append-only activation ledger with idempotent materialization. Access controls deny issuance-facing code permission to set ACTIVE. Expiration is timer-free via batch or TTL evaluation.

Systems and methods for purchasing (typically under a factoring model but other models are still possible), on a selected basis, declined payment transactions; receive, for assessment, the failed payment transaction via API; enrich the data received for the failed transaction with external and internal data; define, via one or more predictive models, the expected cause of payment failure; define, via the one or more predictive models the probability of successfully process in the following days the transactions successfully, calculating the associated cost of risk; interact in real-time with the customer where needed; confirm to the party invoking the service, for each transaction sent for assessment, the purchase or not of the failed payment transaction.

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-border e-commerce abnormal transaction behavior risk prediction method based on a graph neural network, and the method comprises the steps: synchronously collecting multi-source data of a cross-border e-commerce platform, and writing the multi-source data into a data buffer region according to a unified event timestamp; mapping the multi-source data into a heterogeneous dynamic transaction graph; in the current time window, performing structure comparison on the historical transaction graph and the heterogeneous dynamic transaction graph to generate a structure difference sub-graph, and performing normalization processing to obtain a residual graph; splicing the heterogeneous dynamic transaction graph and the residual graph, inputting the spliced graph into a time sequence graph neural network, obtaining a risk embedding vector of a target transaction sub-graph, and calculating an abnormal score through a mapping function; and comparing the abnormal score with a first threshold value, outputting a risk level of the corresponding transaction and triggering early warning. The cross-border e-commerce abnormal transaction behavior risk can be quickly and accurately identified and predicted, the active identification and real-time early warning of the abnormal transaction risk can be realized, and the real-time performance and accuracy of the risk management of the cross-border e-commerce platform can be greatly improved.

Embodiments provide artificial intelligence methods and systems for evaluating vulnerability risks of issuer authorization system. Method performed by a server system includes accessing a set of payment transaction data including subset of fraudulent transaction data. Method includes generating via a machine learning model, set of synthetic transaction data based on the subset of fraudulent transaction data. Method includes accessing set of historical card velocity features and collating the set of synthetic transaction data and the set of historical card velocity features to generate set of enriched synthetic transaction data. Method includes extracting via a classifier, subset of feasible fraudulent transaction data from the set of enriched synthetic transaction data. Method includes generating simulated authorization model based on the set of payment transaction data. Method includes classifying via simulated authorization model, each enriched synthetic transaction from the subset of feasible fraudulent transaction data as one of fraudulent transaction and non-fraudulent transaction.

A computer-readable recording medium having stored therein a fraud detection program causing a computer to execute a process including obtaining a result of object detection by inputting a target image group including a self-checkout-apparatus in an imaging range, into a model trained using a target image and an annotation, and performing fraud detection at the self-checkout-apparatus based on information about an item registered thereto and the result. The target image is identified by calculating statistical information of a position of a detection region of an object in each image in a first group based on positions by inputting the first group into the model, obtaining a position in each image in a second group using the model, and identifying the target image in which a region having an appearance probability equal to or less than a threshold is present, from the second group, based on the statistical information.

The invention discloses an anomaly detection method and system for transaction flow data, and relates to the technical field of big data analysis. The method comprises the following steps: based on transaction flow data, acquiring operation behavior data of a target user in a transaction process and interaction behavior data after the transaction is completed, and sorting according to timestamps to form a user operation behavior sequence and a user interaction behavior sequence; inputting the sequence into a pre-trained abnormal risk prediction model, and outputting an initial risk coefficient, wherein the model integrates an individual behavior baseline and an adaptive weight module; acquiring a group behavior baseline of the similar user group, and correcting the initial risk coefficient in combination with a matching result of the sequence and the group baseline to obtain a final risk coefficient; and performing classification abnormity early warning operation on the final risk coefficient based on a preset risk threshold. According to the method, through whole-process behavior sequence analysis, individual and group baseline dual calibration and dynamic weight adjustment, the accuracy and timeliness of transaction flow data anomaly detection are effectively improved.

The invention relates to the field of cloud computing and cross-border supply chain management, and discloses a cross-border supply chain real-time monitoring method in a cloud computing environment. Comprising the following steps: deploying data acquisition and access modules for supply chain nodes of different countries or regions on a cloud computing platform, and establishing a global time index based on node reporting time to realize time sequence correction of multi-source data; performing multi-modal fusion processing on the corrected data set to form a comprehensive feature vector with a unified dimension; constructing a supply chain digital twin map to associate logistics, information flow and fund flow, and realizing node state prediction and risk identification; executing cross-border data synchronization and privacy protection according to compliance requirements of each country; after the risk is identified, risk traceability and feedback adjustment are carried out through graph structure simulation, so that self-adaptive updating and periodic self-correction of system parameters are realized. According to the invention, real-time monitoring, risk prediction and compliance data synchronization of the cross-border supply chain can be realized, and the safety and stability of the system are improved.

Systems, methods, and other embodiments associated with a framework for multi-entity detection, data sourcing, and processing are described. In one embodiment, a multi-entity processing method includes parsing an electronic document being processed in a processing flow for a first entity to detect that the document also concerns a second entity based on entity identifiers in the document. In response to the detection, the method determines an inter-entity agreement that governs processing between the first entity and second entity by matching attributes of the document against stored agreement data structures. The method automatically orchestrates processing of an inter-entity transaction between the first entity and the second entity by generating execution steps according to sequential and parallel dependencies specified by the inter-entity agreement. And, the method generates electronic records in a system of the first entity and a system of the second entity that reflect execution of the inter-entity transaction.

The invention discloses a cross-border payment path selection method and system based on payment channel dynamic adjustment. The method comprises the following steps: collecting real-time state information of a plurality of cross-border payment channels; generating a comprehensive score based on the state information and the transaction context, and punishing time delay fluctuation and channel congestion; generating an initial path plan, splitting the target amount into at least two sub-amounts, distributing the sub-amounts to different channels, and generating a settlement instruction; in the payment execution process, channel states are continuously monitored, comprehensive scores are dynamically updated, if the score advantage of a standby channel exceeds a preset threshold value and is continuously kept, or the default probability of a current channel exceeds a threshold value, path adjustment is triggered, only unsubmitted or revocable sub-amounts are switched, and the sequence of submitted sub-amounts is kept; and after settlement is completed, account checking and difference marking are performed on the channel, and the score and path parameters are backtracked and updated based on an execution result. According to the method, multi-dimensional dynamic routing can be realized in cross-border payment, the cost is reduced, and the success rate and the stability are improved.

The invention discloses a block chain cross-domain service method and system based on sharing verification and a smart contract, and the method comprises the steps: building a cross-domain sharing channel between a first business domain of a first block chain and a second business domain of a second block chain through a main node on a relay chain; performing hash operation on the original data to obtain a data fingerprint, packaging the data fingerprint, the data metadata and the digital signature of the data holding node into a first message, and submitting the first message to a relay chain through a sharing channel; after receiving the first message, performing consensus verification on the data fingerprint based on a threshold signature mechanism, and writing a verification result into a shared state table; querying the verification state of the data fingerprint from the sharing state table through the data use node in the second service domain, and requesting to download the encrypted data from the intelligent contract of the relay chain; executing permission judgment, and triggering a key distribution contract to transmit a decryption key to a data use node; and downloading the encrypted data, and writing the cross-domain data sharing transaction record into a block chain account book of the second business domain.

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 data security, and discloses a risk prediction method based on mobile terminal equipment. The method comprises the following steps: acquiring a historical behavior data set of mobile terminal equipment; a dynamic input feature set and a static interference feature set are divided according to the data set, the dynamic input feature set is derived from a user operation behavior sequence updated in real time, and the static interference feature set is generated by fusing an equipment state change record and an environment parameter change track. And establishing a dynamic risk prediction model, taking the dynamic input feature set as a model input layer, taking the static interference feature set as a model hidden layer adjustment parameter, and outputting a risk probability value by means of a bidirectional feature coupling mechanism. Segmenting and intercepting the static interference feature set by adopting a sliding time window, extracting a fluctuation period and a frequency distribution rule of the interference feature, and generating an interference feature fluctuation map. And matching the current interference characteristic state based on the map, and generating a real-time risk early warning signal in combination with the risk probability value.

The invention relates to the technical field of supply chain technology, in particular to an aggregation supply chain multi-agent profit clearing system and method based on an intelligent contract, and the system comprises a rule contract module, a distributed account book module, a transaction verification and triggering module, a contract execution and clearing module, and an automatic settlement module. The invention aims to provide the automatic profit clearing and settlement system and method based on the distributed account book technology, contract of profit distribution rules, transaction triggering, contract automatic execution, funds atomization and real-time clearing can be realized, and the problems of trust and settlement efficiency in multi-party cooperation are solved.

The embodiment of the invention provides a risk detection method and system and a storage medium. According to the risk detection method, a target event can be obtained, initial event description information corresponding to the target event is generated, and then a large model is guided to carry out risk detection on the target event from M detection dimensions based on the initial event description information so as to obtain detection results corresponding to the M detection dimensions, and generating a first risk detection report of the target event based on the detection results corresponding to the M detection dimensions. Wherein the initial event description information is expressed in a natural language, the M detection dimensions comprise at least one detection dimension which is identified by the large model based on the initial event description information and is suitable for the target event, and M is an integer greater than or equal to 1.

The disclosed computer-implemented method may include generating an interface code configured to connect to a payment server and generating, in response to loading a merchant website hosted on a merchant server that integrates the interface code, a payment interface with the interface code. The interface code may bypass the merchant server to connect to the payment server. The method may also include completing a payment using the payment interface that bypasses the merchant server to connect to the payment server. Various other methods, systems, and computer-readable media are also disclosed.