678 results about "Financial trading" patented technology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 relates to a financial case fund flow traceability analysis method, and belongs to the technical field of computer networks. According to the method, the social network analysis technology and the graph neural network technology are combined, and the advantages of the social network analysis technology and the graph neural network technology are comprehensively utilized to analyze and track the fund flow direction in the financial transaction network. According to social network analysis, key nodes and group structures in a transaction network are identified by calculating node centrality and community discovery; the graph neural network analysis further extracts complex transaction features and modes through a deep learning model, and identifies abnormal transaction behaviors. Besides, advanced graph neural network models such as a graph convolutional network and a graph attention network are utilized, parallel computing and distributed training technologies are combined, the model training process is efficient and has good expansibility, and the method adapts to processing requirements of large-scale financial transaction data.

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 field of financial data analysis, in particular to a financial transaction system anomaly identification method based on an isolated forest algorithm, and relates to big data analysis and machine learning. The method comprises the steps of obtaining multi-dimensional transaction data, executing data cleaning and feature engineering, constructing an isolated forest model, and calculating an anomaly score. The method has the remarkable advantages in the aspects of accuracy, real-time performance, adaptability and interpretability, abnormal transactions can be effectively recognized, financial environment changes can be rapidly adapted, and a powerful and flexible risk management tool is provided for financial institutions.

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 a digital financial fraud identification method and system based on an alliance chain and deep learning, and the method provides a safe, transparent and non-tampering financial transaction and data sharing platform based on the alliance chain, guarantees the authenticity and traceability of all transaction records, reduces the risk of data counterfeiting and tampering, and improves the safety and reliability of the system. Meanwhile, through strong data processing and feature extraction capabilities of deep learning, the system can analyze mass and unstructured data from different industries and scenes, and capture subtle modes and abnormal signs hidden behind various transaction behaviors, so that digital financial fraudulent behaviors in two financial business modes can be accurately identified, and the system can be widely applied to the field of digital financial fraudulent behaviors. And the generalization ability and robustness of the financial fraud identification algorithm are improved.

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.

A system and method for authenticating identity during a financial transaction based, in part, on dynamic biometric factors such as body temperature, heart rhythm, blood pressure, blood sugar, vein pattern, skin pore pattern, and voice recognition. The biometric values are captured, aggregated, and patterned over time to create a unique biometric profile. When a transaction is initiated, current biometric values are captured and compared to the biometric patterns, and a biometric authentication determination is made based on the results. An overall identity authentication score is generated based on a combination of the biometric authentication determination and other identity authentication determinations such as a purchase pattern authentication determination or a location authentication determination. An identity authentication determination is made based on the overall identity authentication score and communicated to the merchant. Further, the transaction may be rejected when the overall identity authentication score violates a pre-established fraud threshold value.

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.