32254results about "Finance" patented technology

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

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

The invention provides a procurement supply chain collaborative intelligent management method and a procurement supply chain collaborative intelligent management system. Belongs to the technical field of supply chain management. Constructing a purchase demand prediction model; dynamically adjusting the purchasing strategy according to the prediction result; the record and the evaluation result are stored on the block chain; the system automatically sends order information to a supplier; the supplier confirms the order through the block chain platform and updates the production progress and the logistics information; the system adjusts an inventory strategy in real time; the logistics state is monitored in real time; the system analyzes data of each link of the supply chain in real time and identifies potential risks. Through dynamic path planning and real-time transportation optimization, the logistics path can be adjusted in real time according to traffic conditions, environmental factors and emergencies, the transportation time is shortened, the transportation cost is reduced, and therefore the overall efficiency of a supply chain is remarkably improved.

The invention relates to the technical field of deep learning, and discloses an MES digital collaborative management method and system based on deep learning, and the method comprises the steps: collecting multi-source heterogeneous data, and constructing a dimensionless multi-dimensional data set; analyzing a dynamic association relationship among the data items through node feature embedding and edge relationship learning, and generating a production line operation dependency relationship graph; performing collaborative anomaly detection and root cause positioning in combination with the dynamic association weight between the data items to obtain causal association data between the abnormal event and the data parameters; constructing a digital twinborn simulation environment to simulate an influence path of heterogeneous data parameter intervention production disturbance on collaborative anomaly so as to evaluate an anomaly influence result; constructing an iterative scheduling model according to a deep learning framework, and iteratively generating a multi-objective optimized collaborative scheduling strategy to realize dynamic configuration and exception prevention among heterogeneous data; therefore, collaborative optimization management of production operation parameter configuration and abnormity prevention of multi-source heterogeneous data in large-scale production with high dimension, high complexity and dynamic change can be realized.

The invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

This invention provides a comprehensive maritime shipbroking platform uniting digital twin modeling, AI-driven cargo allocation, blockchain-based contract management, predictive maintenance (AR / VR), route optimization, real-time tracking, and single-window compliance. The digital twin engine continuously simulates vessel performance, enabling data-driven decisions on stowage, scheduling, and maintenance. A cargo-freight matching module optimally allocates shipments, factoring in market rates, vessel metrics, and port congestion. Blockchain-secured smart contracts automate negotiations, ensuring transparency and tamper-proof enforcement. A predictive maintenance system applies advanced analytics to diagnose technical issues early, while the route optimization engine finds cost-effective, emission-compliant routes. Real-time tracking gives stakeholders constant visibility, and the single-window interface integrates Electronic Bill of Lading processes, satisfying IMO and IG P&I standards. Additionally, a dynamic vessel ranking system incorporates SIRE, RightShip, PSC, and user feedback for safer, more efficient chartering decisions. The invention addresses day-to-day operational challenges in maritime logistics, elevating efficiency and compliance.

An example operation may include one or more of receiving content from a conversation between a user on a user device and a second user on a second user device that is connected to the user device via a network, identifying a topic of the conversation based on execution of a generative artificial intelligence (GenAI) model on the received content from the conversation, identifying a call list that is previously stored in memory that is associated with the topic of the conversation based on keywords included in the identified topic of the conversation, and adding an identifier of the user to the call list stored in memory.

A system, device and method are provided for at least in part automating remediation in an enterprise system. An illustrative method includes receiving a request to perform an action, the action requiring at least one of a plurality of subsystems to be completed. The plurality of subsystems include employee and customer subsystems. The method includes transmitting an event, based on the request, to the at least one of a plurality of subsystems via an event subsystem. The method includes with an automated remediation platform: monitoring events sent from the at least one of a plurality of subsystems to the event subsystem to detect a failure; and in response to detecting the failure, generating a trigger event for consumption by the event subsystem. The method includes in response to receiving the trigger event, transmitting a remediation event for consumption by the at least one of a plurality of subsystems.

An example operation may include one or more of storing a current portfolio of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest of the user based on execution of a generative artificial intelligence (GenAI) model based on the received contextual data of the user and the current portfolio of the user stored in memory, predicting a performance of the current portfolio with the identified asset of interest included therein at a future point in time, and displaying the predicted performance of the current portfolio with the identified asset of interest included therein on a user interface.

An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving text from a conversation between the user on a first device and a second user on a second device, identifying an upcoming life event of the user based on execution of a generative artificial intelligence (GenAI) model on the received text from the conversation, determining a change to the portfolio of assets of the user based on the upcoming life event and existing assets within the portfolio of assets, and displaying the change to the portfolio of assets of the user via a user interface.

The invention provides a method for intelligently generating a complete dispatch report for financial unfavorable assets, relates to the field of management systems, and aims at deeply fusing multi-source heterogeneous data such as legal instruments and financial statements and forming a comprehensive context sensing model for target assets through multi-modal feature extraction, semantic alignment and knowledge graph construction technologies; a potential and non-dominant risk factor combination deeply coupled with asset characteristics is automatically mined by applying a genetic algorithm and other evolutionary calculation methods, and a self-adaptive risk assessment network is constructed to dynamically and quantitatively assess and predict the comprehensive risk level; carrying out attribution analysis on a risk assessment result by adopting an interpretable artificial intelligence model, and clearly revealing a key influence path and core data evidence; and according to a report logic framework and a narrative template which can be dynamically adjusted by a user, automatically outputting a financial non-performing asset full-duty survey report including deep analysis, risk early warning, diversified disposal suggestions and compliance review key points.

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

An example operation may include one or more of receiving a current status of a portfolio of a user and previous actions taken on the portfolio of the user over a predetermined period of time, determining a goal for the user based on execution of a generative artificial intelligence (GenAI) model on the current status of the portfolio of the user and the previous actions taken, receiving a request for the portfolio of the user via a user device, and in response to the request, generating a text-based description of the goal and displaying the text-based description of the goal with portfolio content from the portfolio of the user on a user interface of the user device.

An example operation may include one or more of training an artificial intelligence (AI) model to recommend numerical goals based on execution of the AI model on historical profile data and contextual data that is associated with the historical profile data, storing profile data of a user profile within a data store of a software application, receiving a request via a user interface of a user profile page of the software application, wherein the request comprises context of a user of the user profile, determining a numerical goal for the user profile based on execution of the AI model on the profile data of the user profile and the context, and displaying the numerical goal via the user interface of the user profile page within the software application.

An example operation may include one or more of generating a unique image comprising a plurality of image segments via an image generating model and displaying the unique image via a user interface of a software application, installing a smart contract associated with the unique image on a blockchain ledger, detecting a command to move an image segment from among the plurality of image segments based on user inputs via the user interface of the software application, removing the image segment from the unique image and adding the image segment to a different image based on the detected command, and transmitting the different image to a user account of the software application.

An example operation may include one or more of storing a portfolio of assets of a user in memory, receiving contextual data of the user from a user device of the user, identifying an asset of interest that is not included in the portfolio of assets of the user based on execution of a generative artificial intelligence (GenAI) model on the portfolio of assets of the user and the received contextual data of the user, generating a different portfolio of assets based on the asset of interest that is not included in the portfolio of assets of the user, and displaying the different portfolio of assets via a user interface.

A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

An example operation may include one or more of identifying a first topic from a call actively in progress, displaying a dashboard on a user device on the call, wherein the dashboard comprises content related to the first topic, receiving discussion data from the call, determining that a focus of the call has shifted from the first topic to a second topic, executing an artificial intelligence (AI) model on the second topic, dynamically generating dashboard content based on the execution, and displaying the dynamically generated dashboard content to the dashboard on the user device.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

An example method includes for each training iteration in a series of training iterations: receiving, for a first set of unlabeled metrics data, a set of respective labels associated with an account in a set of accounts. The first set of unlabeled metrics data and a second set of unlabeled metrics data can be processed to generate network inputs representing a respective account in the set of accounts. The network inputs can be processed using a teacher neural network that generates an anomaly prediction output for each network input. A student neural network can be trained to optimize a loss function, which can include minimizing a loss term measuring a difference between the anomaly prediction output and the student network's proposed anomaly prediction output and minimizing a loss term measuring a difference between the proposed anomaly prediction output and the account's associated label.

An artificial intelligence driven system of systems may include a layered architecture for providing transaction support to various types of enterprises. A governance layer implements automated governance and policy enforcement through specialized governance modules utilizing generative AI technology. An enterprise layer supports enterprise functions by integrating management and control platforms with digital infrastructure. An offering layer creates and manages system offerings via content generation, personalization, and smart product modules. A transactions layer enables automated transaction orchestration through API integration, execution, and fulfillment modules. An operations layer manages AI systems through generation, training, verification and orchestration modules. A network layer provides adaptive networking capabilities through routing, protocol selection and communication modules. A data layer processes fused data from multiple sources using machine learning and AI systems. A resource layer manages computing, storage, and other resources through specialized resource modules.

An example operation may include one or more of receiving a loan application of a user, extracting a plurality of personal attributes about the user from the loan application, querying a machine learning model via an application programming interface (API) based on the plurality of attributes about the user to identify one or more rules for auto-adjudicating the loan application, determining whether or not to approve the loan application based on the one or more rules identified via the machine learning model, and transmitting notice of the determination to a device associated with the user.

The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data. For example, an apparatus may receive a plurality of messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.

The invention discloses a risk control credit monitoring method based on cloud computing, and belongs to the technical field of cloud computing, and the risk control credit monitoring method based on cloud computing comprises the following steps: S1, collecting user transaction data and behavior track data in real time; s2, cleaning and standardizing the data; s3, constructing a multi-dimensional risk assessment model based on machine learning; s4, dynamically generating a credit score according to the risk characteristics; s5, triggering an early warning mechanism for abnormal transactions in real time; and S6, generating a visual risk control report and updating a monitoring strategy. According to the method, multi-source heterogeneous data are integrated through federated learning, hierarchical privacy protection is realized in combination with homomorphic encryption and differential privacy, risk assessment real-time performance is improved by using a hybrid cloud resource scheduling and dynamic model updating technology, and a compliance audit closed loop is constructed based on a block chain and interpretability analysis.

The present invention generally relates to computer systems, methods and program products that allow a digital asset exchange to provide an option to customers to lend all or a portion of the digital assets associated with at least a first blockchain and held by the exchange on behalf of the customer in exchange for payments such as interest.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention relates to the technical field of data processing, and discloses a multi-dimensional financial pressure testing and financial early warning method and system. The method comprises the following steps: forming a financial situation awareness data set through multi-dimensional data acquisition integration and standardization processing; adaptive weight distribution and data dimension reduction are carried out on the five dimension indexes, and a risk measurement standard spectrum is output; a third-level pressure scene is established according to the standard spectrum, a risk conduction path is analyzed, and a financial pressure fluctuation spectrum is generated; a deep learning early warning model is constructed based on the fluctuation pedigree, an early warning threshold value is determined, and a risk pulsation graph is formed; constructing a multi-level early warning system based on the pulsating diagram, and establishing a financial early warning intelligent chain; and matching historical cases based on the early warning intelligent chain, designing a prevention and control strategy, and constructing a risk solution scheme library. According to the invention, by constructing a multi-level risk assessment system, accurate identification, dynamic monitoring and intelligent disposal of financial risks are realized, and the accuracy and timeliness of risk early warning are improved.

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

The invention provides a reimbursement data management system and method based on AI artificial intelligence, and the system comprises a multi-modal data collection and fusion module which is used for providing an access reimbursement application entrance; the auditing and compliance judgment module is used for invoice authenticity identification, reason rationality identification, reimbursement item classification and automatic auditing; the approval process optimization module is used for learning and optimizing a reimbursement approval process and automatically simplifying or increasing approval levels according to reimbursement application features; the analysis and prediction module is used for performing multi-dimensional analysis on the reimbursement data based on a big data processing framework and a data mining algorithm and generating a visual report and result; and the data storage and block chain module is used for constructing a distributed and non-tampering data storage account book by adopting a block chain technology, and executing a reimbursement process automation rule by utilizing a block chain intelligent contract. The efficiency, quality and safety of reimbursement data management are comprehensively improved, and enterprise financial management is promoted to develop towards the intelligent and refined direction.

The invention discloses an AI-based automatic financial information processing method, and relates to the field of financial automation, and the method comprises the steps: achieving the automatic collection and storage of structured and unstructured data through the access of enterprise multi-source financial data; systematic preprocessing is carried out on the collected multi-source heterogeneous financial data, and a unified and high-quality financial data set is constructed; based on natural language processing and a knowledge graph technology, performing text semantic understanding, transaction automatic classification, field standardization and label generation on the cleaned and integrated financial data; comprehensively quantifying enterprise operation and financial performance based on the structured transaction data and the semantic annotation result; based on historical financial indexes, establishing a multi-model architecture to predict key financial variables; and based on the structured data, the prediction result and the historical rule, identifying potential financial abnormity and risk behaviors, and realizing intelligent early warning. According to the method, the intelligence, the real-time performance and the accuracy of financial information processing can be remarkably improved.