189 results about "Credit limit" patented technology

The invention discloses a supply chain data-oriented enterprise upstream and downstream collaborative risk control prediction method and system, and relates to the technical field of supply chain financial risk management, and the method comprises the steps: extracting a payment time deviation value between enterprises to construct a payment behavior sequence, calculating a supply chain collaborative credit rating based on a fluctuation amplitude and a historical default record, and recognizing a risk source enterprise; adopting a recursive partition time window to extract a period index and a sudden change index of the payment behavior, and constructing a payment behavior portrait; identifying associated enterprises through the phase overlapping degree of the payment behavior track to form a risk conduction sequence; and based on the evolution law and the risk conduction sequence of the payment behavior portrait, predicting a risk triggering node, and generating a risk prevention and control scheme including a credit line regulation and control instruction and a behavior monitoring strategy. The method can effectively identify the supply chain risk source, predict the risk conduction path, and improve the financial risk control capability of the supply chain.

The invention discloses a dynamic credit limit adjustment system and method based on reinforcement learning, and the system comprises a real-time data lake which collects a user behavior flow, obtains a macroeconomic index, and transmits the macroeconomic index to a reinforcement learning decision core; the federated learning engine and each participating mechanism locally deploy an RL agent, upload a model gradient to the coordination server through a security aggregation protocol, quantify the contribution degree of data of each mechanism to the model performance based on an improved Shapley value algorithm for income distribution, and transmit the contribution degree to the reinforcement learning decision core; the learning decision core is reinforced, state space modeling is carried out based on the user behavior flow and the macroeconomic index, and limit adjustment and risk early warning are realized by combining state space modeling output actions; providing an adaptive algorithm; an interpretability engine; the multi-target reward function dynamically balances interest revenue, risk suppression and user life cycle value, gradient limit adjustment avoids user perception mutation, and loss risks are reduced.

The invention provides an intelligent self-adaptive RDMA message processing method for power grid data transmission, and the method comprises the steps: S1, obtaining parameters, carrying out the initialization of a credit line value and a retransmission priority, and creating a data transmission task; s2, sending the data message to a second RDMA network card of an opposite terminal device through the first RDMA network card; after receiving the data message, the second RDMA network card generates a corresponding response message and sends the response message back to the sending end; s3, analyzing the response message, determining the type of the response message, and storing correctly received data; s4, performing error detection and retransmission processing by analyzing the PSN serial number of the RDMA message; and S5, dynamically adjusting and optimizing the credit line and the retransmission priority of the data message. According to the method, resource allocation can be dynamically optimized according to the real-time state of the network, priority transmission of high-priority data including power control instructions and the like is ensured, and the transmission efficiency and reliability of key data are remarkably improved.

A machine learning based computing method for automatic managing credit risks of first users, is disclosed. The machine learning based computing method includes: receiving inputs from electronic devices associated with second users; retrieving data associated with first users from databases; preprocessing the data to remove noises, outliers, and missing values, from datasets; determining, the credit risks of the entities based on the pre-processed data by machine learning models; generating credit decisions for the entities; generating confidence scores for credit decisions to classify the credit decisions, based on correlation between the data and credit decisions; determining recommended credit values, recommended first credit limits, and recommended second credit limits, based on classification of the credit decisions; and providing an output of the credit decisions, the recommended credit values and the recommended credit limits, to the second users on user interfaces associated with electronic devices.

A system can include processing circuitry and memory coupled to the processing circuitry. The memory can have instructions stored therein that are executable by the processing circuitry to cause the system to perform operations. The operations can include determining that an individual made a non-gaming purchase. The operations can further include determining that a gaming-related condition has been met subsequent to the non-gaming purchase. The operations can further include applying a credit to an account associated with the individual based on determining that the gaming-related condition has been met.

A system that facilitates a transfer of an amount of funds associated with a line of credit to a gaming establishment account and then to a gaming device, such as an EGM, wherein the amount of funds associated with the line of credit is separately tracked by the system.

The invention provides a comprehensive contribution assessment method and system based on reinforcement learning, and belongs to the technical field of financial science and technology. According to the method, multi-source heterogeneous data of enterprise customers are integrated, and financial index feature vectors and transaction behavior feature vectors are extracted to construct a fusion feature matrix. And a judgment mechanism of a fluctuation amplitude threshold is introduced, and when the fluctuation amplitude of the fusion feature matrix exceeds a limit, a transaction behavior trend is predicted to generate a dynamic risk vector. The contribution score is determined by fusing the feature matrix and the dynamic risk vector, and the industry enterprise feature data is grouped to calibrate the contribution score and correct the evaluation deviation by comparing the industry contribution benchmark and the condition exceeding the preset deviation threshold, so that the industry suitability and objectivity of contribution level judgment are ensured. And finally, the credit line is determined according to the contribution level, so that the bank credit resource optimization configuration is realized on the premise of dynamic risk controllability, the fund configuration efficiency and the risk control capability are improved, and the real operation demand of an enterprise can be accurately matched.

The invention discloses a credit payment processing method and system for a transaction platform, and relates to the technical field of credit payment, and the method comprises the steps: obtaining basic data of all tenants in the transaction platform, and calculating a dynamic weight for each tenant according to the basic data; creating an independent policy sandbox environment for each tenant according to the dynamic weight; inputting the basic data of the tenant into a preset mixed credit assessment model, and executing credit assessment of the tenant based on a policy sandbox environment; dynamically adjusting the credit line of the tenant according to the mixed evaluation result; and executing compliance verification and calculating a risk score according to the real-time credit line of the tenant and the basic data, if the risk score exceeds a preset threshold, triggering risk interception, and executing a credit payment isolation instruction. Through dynamic weight calculation, independent strategy sandbox isolation, a mixed credit evaluation model and block chain compliance verification, the problems of credit strategy coupling, data leakage, evaluation lag and compliance dependence on manpower in a multi-tenant environment are solved.

The invention discloses a PCIe virtual channel credit line adjustment method, communication equipment and system, and the method comprises the steps: pre-building a traffic prediction model to predict the probability of burst traffic of a virtual channel in a next period, and enabling the model to take multiple transmission features of the virtual channel as input features, each feature is configured with a corresponding optimal weight obtained through machine learning; predicting the probability of burst traffic of each virtual channel by using a traffic prediction model; configuring an initial virtual credit limit for each virtual channel, calculating a limit change amount according to the probability of the corresponding burst traffic, and further determining a virtual credit budget of each virtual channel; and generating a credit line distribution scheme for each virtual channel by integrating the virtual credit budget of each virtual channel and the remaining credit line of the global credit pool. According to the invention, the credit line is dynamically adjusted according to the real-time flow prediction result, and the response efficiency and the communication stability when the flow is burst are ensured.

The invention relates to the technical field of data processing, can be applied to the field of science and technology finance, and discloses a credit evaluation method and device for multi-source data co-processing and related equipment. The method comprises the steps of receiving credit evaluation data of at least two types of preset data sources, and performing cleaning, time scale unification and numerical standardization processing on the credit evaluation data to form standardized multi-source feature data; constructing credit portrait data based on the standardized multi-source feature data; performing scoring reasoning on the credit portrait data through a pre-trained credit extension evaluation model, and generating credit extension evaluation result data through a limit mapping rule; receiving the use behavior data and the performance behavior data, performing time sequence analysis and deviation identification, and generating credit use behavior analysis data; and updating credit extension evaluation model parameters based on the credit use behavior analysis data, loading the credit extension evaluation model parameters to a model operation state, outputting credit extension evaluation result data, and recording model parameter updating information. According to the method, the dynamic adaptive capacity of multi-source credit assessment is improved.

The present disclosure provides systems and methods for computerized methods and systems for processing transaction disputes and processing transactions associated with compromised accounts. According to one exemplary method, a server receives, from a user device, an instruction for disputing a transaction associated with a user account. The server can initiate, based on a record of the transaction, a dispute investigation, and issue provisional credit to the user account for the transaction. Based on a transaction history associated with the user account, the server can determine a plurality of expected transactions associated with the user account and cause the determined expected transactions to be displayed on the user device. After receiving a transaction authorization for at least one of the plurality of expected transactions, the server can process payments for the at least one of the plurality of expected transactions, and generate a notification indicating a status of the dispute investigation.

The invention discloses an intelligent auxiliary credit granting method for consumer finance, and relates to the technical field of monitoring analysis, and the method comprises the steps: constructing a customer initial feature set, carrying out the null value feature vector mapping analysis and intelligent completion of the customer initial feature set, generating a customer complete feature matrix, and carrying out the cross-scene model parameter migration of a risk prediction initial model. A risk prediction optimization model is obtained; performing real-time risk scanning on the customer complete feature matrix based on a risk prediction optimization model, generating a risk feature fluctuation map, extracting a key risk influence factor, performing risk conduction path simulation according to the key risk influence factor, and generating a dynamic risk early warning signal; performing credit extension decision tree reconstruction according to the dynamic risk early warning signal, generating an adaptive credit extension decision model, generating a gradient credit extension limit interval, performing decision weight fusion on the gradient credit extension limit interval and the dynamic risk early warning signal, and outputting an intelligent credit extension decision report. The method has the effect of improving the auxiliary credit extension efficiency.

An online system displays an ordering interface and, responsive to receiving a request from a client device associated with a user to place an order, retrieves information describing a set of unused credits provided to the user by each of one or more programs. The system identifies a set of the program(s), wherein the set of unused credits provided by each identified program is eligible to be used for acquiring an item in the order. The system accesses and applies a machine-learning model to predict an expiration of the set of unused credits provided to the user by each identified program based on the retrieved information and a current time. The system ranks the set of programs based on the prediction(s), determines a default allocation of a subset of each set of unused credits to the order based on the ranking, and updates the interface to include the default allocation.

The invention discloses an account quotation management method and system based on account credible authentication. The method comprises the following steps: extracting account information from a data warehouse, extracting a credible factor, and constructing an account credible file; acquiring flow data of the account; performing account risk level evaluation according to the account credible file and the flow data to obtain an account risk level; determining a rating decision according to the account risk level to obtain a decision result; when the decision result is passing or manual service, the flow data is input to an account credibility rating model for credibility rating evaluation to obtain an account credibility rating; acquiring account related information; calculating the account quota according to the account credibility level and the account related information to obtain a calculation result; and outputting the account risk level and the calculation result. By implementing the method provided by the invention, more comprehensive, flexible and efficient risk control and limit management can be realized, various transaction and account risks can be better handled, and financial security and user experience are improved.

The invention relates to the technical field of ticket management, in particular to a civil aviation electronic ticket system, a ticket management method, a storage medium and a program product, and the system comprises a ticket number taking interface, a quota consumption module and a ticket number consumption module; wherein the ticket number taking interface is configured to update quota data and ticket number data of a sales unit and distribute blank ticket numbers for the sales unit through the quota consumption module and the ticket number consumption module after receiving a ticket number application instruction sent by the sales unit. According to the civil aviation electronic ticket system, in the distribution process, a traditional ticket number segment is abandoned, the quota represented by numbers is used, ticket number distribution in advance is changed into ticket number distribution according to needs, the distribution process is simplified, number segment sharing is achieved, meanwhile, ticket number segments do not need to be recycled step by step, and number segment recycling is facilitated.

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

An online system displays an ordering interface and, responsive to receiving a request from a client device associated with a user to place an order, retrieves information describing a set of unused credits provided to the user by each of one or more programs. The system identifies a set of the program(s), wherein the set of unused credits provided by each identified program is eligible to be used for acquiring an item in the order. The system accesses and applies a machine-learning model to predict an expiration of the set of unused credits provided to the user by each identified program based on the retrieved information and a current time. The system ranks the set of programs based on the prediction(s), determines a default allocation of a subset of each set of unused credits to the order based on the ranking, and updates the interface to include the default allocation.

A method performed by at least one computing device. The method includes receiving a credit request from a self-checkout device before a customer completes an instore checkout process and sending a request for a Uniform Resource Locator (“URL”) to one or more authentication computing devices. The authentication computing device(s) send the URL to the mobile device. The method includes sending a credit application to the mobile device after the customer selects the URL, receiving a submission of the credit application from the mobile device, approving credit based on the submission, and forwarding a code to the mobile device. The code indicates that the credit is to be used to complete the instore checkout process when scanned by the scanner.

The embodiment of the invention provides a user account management method and system, a storage medium and a program product. The method comprises the steps of determining whether a target user opens a financial credit line service or not when it is monitored that a communication account is triggered to arrearage and shut down for the target user; if the target user opens the financial credit line service, a financial line deduction request is sent to a financial credit granting system according to the communication account insufficiency of the target user, and the financial line deduction request is used for requesting to deduct a financial account line corresponding to the communication account insufficiency for a financial account of the target user; and when a deduction success response sent by the financial credit extension system is received, the communication account arrearage of the target user is updated to 0, and the execution of the communication account arrearage shutdown on the target user is canceled, and the deduction success response corresponds to the financial credit line deduction request. According to the method, malicious overdraft risks can be effectively prevented and controlled, and the shutdown probability of the user can be reduced.

The present invention discloses a cross-border e-commerce data asset management method, device, computer equipment and medium, and the method implementation includes: constructing a data sandbox in a customs system; the e-commerce platform labels the business data of merchants and sends it to the data sandbox; in the data sandbox, the labeled business data and the customs data corresponding to the e-commerce platform in the customs system are compared and verified to determine the data error value; the labeled business data, customs data and data error value are field-merged to generate a data report, and the data report is sent to a bank risk control system; the bank risk control system selects target merchants that meet the preset credit rules according to the data report, and determines the credibility coefficient according to the data error value, so as to determine the actual credit limit of the target merchant according to the credibility coefficient. The security and reliability of data transmission are improved, the efficiency is high, the security is strong, and it is traceable.

The invention discloses an intelligent credit line dynamic regulation and control method based on causal inference and reinforcement learning, and the method comprises the steps: collecting multi-source heterogeneous data of a customer, and carrying out the structural integration and causal modeling; based on the constructed causal graph, performing intervention analysis on a quota adjustment behavior by using a causal inference method, and evaluating the real causal influence of quota adjustment on customer default risk PD and revenue REV; the method comprises the following steps: abstracting credit line regulation into a Markov decision process for modeling, and converting a line management problem into a dynamic sequence decision optimization problem; after modeling is completed, a reinforcement learning algorithm is adopted to carry out optimization training on a quota regulation and control strategy; and after the quota decision is completed, outputting an adjustment result and generating an interpretability analysis report. According to the scheme, key credit factors are identified through causal inference, intervention analysis is carried out, the real influence of limit adjustment on the risk is quantified, the causal rationality of decision making is ensured, and dynamic regulation and control and risk control of the credit limit are realized in combination with reinforcement learning.

The invention discloses a real estate business financial business risk control management method based on a big data model, and relates to the technical field of financial science and technology and risk management, and the method comprises the steps: collecting multi-dimensional original data, carrying out the fusion cleaning, and generating a structured risk control basic data set; extracting three feature sequences of application subject static portraits, real estate dynamic values and associated enterprise risks from the application subject static portraits and the real estate dynamic values; inputting the multi-source risk control feature fusion model into a pre-trained multi-source risk control feature fusion model for interactive modeling, and generating a comprehensive risk feature map; constructing a hierarchical risk decision network by using the map, and outputting a multi-dimensional vector containing credit and collateral risks and associated party conduction risk scores; and based on the vector, generating an adaptive credit line, a guarantee rate floating and a post-loan monitoring scheme through a dynamic strategy matching engine, and converting the adaptive credit line, the guarantee rate floating and the post-loan monitoring scheme into an executable instruction set to be issued and executed. According to the invention, the deep correlation analysis of the multi-source risk information and the automatic and accurate generation of the risk management strategy are realized.

This application relates to a method, system, storage medium, and electronic device for calculating a customer's credit limit, belonging to the field of data processing technology. The method includes: obtaining information on the actual assets, actual annual transaction targets, and actual collateral value of the approved target customer; determining the target customer's basic credit limit based on the actual assets, actual annual transaction targets, and actual collateral value; obtaining information on multiple historical downstream enterprises of the target customer where payment has stalled, and the historical payment nodes where payment stalls occurred; determining the target customer's collection difficulty index based on the historical downstream enterprises and historical payment nodes; and adjusting the basic credit limit based on the collection difficulty index to obtain the target customer's target credit limit. This application improves the accuracy of customer credit limit calculation.

To provide a billing amount processor, a billing amount processing method, and a billing amount processing program capable of setting a monthly repayment sum on the customer side in an extreme-type loan contract, thereby shortening a repayment term and reducing a bad debt risk on the financing side.SOLUTION: A billing amount processor comprises: a monthly repayment sum setting mask in which an outstanding loan balance is associated with a monthly repayment sum and registered; a storage area for storing contract data including customer identification, the date of contract, the date of cancellation, an extreme amount, a credit limit, a credit frame, a loan interest rate, a delinquency interest rate, and the monthly repayment sum updated on the basis of the outstanding loan balance after loan transactions by referring to the monthly repayment sum setting mask, and monthly repayment sum change data which is set upon the customer's request and includes the customer identification and the monthly repayment sum after the change; and a billing amount calculation unit for comparing the monthly repayment sum in the contract data with the monthly repayment sum after the change in the monthly repayment sum change data for a target month and year and determining the higher amount as the billing amount.SELECTED DRAWING: Figure 1

The invention relates to a dynamic aggregation code payment system, and the system comprises a front-end interaction module which is responsible for the display and interaction of a user interface; the business logic processing module is used for processing core business logic; the data access module is used for interacting with a database; the interface service module is in butt joint service with an external system and realizes data exchange and synchronization; the dynamic aggregation code generation module is used for calling an interface to generate a dynamic aggregation code; and the advance payment and credit line management module is used for managing a receipt of advance payment and managing a credit line. According to the invention, flexible processing of a plurality of orders, support of a plurality of payment modes, real-time query of payment results, automatic verification of order states, and effective management of advance payment and credit lines are realized, the payment efficiency is significantly improved, the operation complexity is reduced, the payment security is enhanced, and the user experience is improved. And a more convenient, efficient and secure payment solution is provided for enterprises.

The invention provides a credit card risk control method, device, medium and system, and the method comprises the steps: constructing and updating a multi-dimensional user portrait, the multi-dimensional user portrait comprises a risk type of a credit card holder determined on the basis of real-time transaction flow data, multi-source credit investigation data, behavior biological characteristic data and social graph data under the condition of user authorization; generating a risk user management list according to the risk type; and under the condition that the repayment event is detected, adjusting the credit line of the card holder of the credit card according to the information in the risk user management list. The problem that risk intervention of the credit card depends on manual judgment in the prior art, so that the risk control effect is difficult to meet the expectation is solved.

The present application relates to the technical field of communications, and discloses a credit updating method, an apparatus, a device, a system, and a storage medium. For example, when a first apparatus performs the method, the first apparatus determines a first credit limit of the first apparatus, wherein the first credit limit indicates a limit, updated by the first apparatus, on the amount of data allowed to be received; and the first apparatus sends first instruction information to a second apparatus, wherein the first instruction information instructs the second apparatus to update a credit limit of the second apparatus according to the first credit limit. The method provides a means for updating a credit limit, such that the configuration of the credit limit can be dynamically adjusted on the basis of a change in the limit on the amount of data that a first apparatus allows to receive. Moreover, during implementation of update of the credit limit, the method does not require interruption of a data stream between the first apparatus and a second apparatus, thereby avoiding the effect of the update of the credit limit on the transmission of the data stream.

The invention provides a quota application processing method and device, equipment, a storage medium and a program product, and can be applied to the technical field of artificial intelligence. The method comprises the steps of obtaining basic auditing information related to a target project under the condition that a target object is authorized; determining a matching degree between the project progress information, the project type information, the object type information and the limit information and a preset auditing rule; under the condition that the matching degree between at least one of the project progress information, the project type information, the object type information and the limit information and a preset auditing rule is inconsistent, determining a target reconnaissance end matched with the target object from a plurality of reconnaissance ends; obtaining first real-time investigation sub-information related to the target project; acquiring second real-time investigation sub-information, and taking the first real-time investigation sub-information and the second real-time investigation sub-information as real-time investigation information; and determining an application result of the quota application according to the real-time investigation information.

To provide a redemption machine and a game system that can easily achieve card recycling.SOLUTION: A redemption machine is provided, including: a recording medium reading unit for receiving a recording medium where a point balance and the number of game media are mapped, the point balance indicating a balance number left by subtracting the points used by games from the points corresponding to a credited amount, the number of game media indicating a balance number acquired by games based on the game media lent for games; an acquisition unit for acquiring the point balance and the number of game media recorded in the recording medium; a redemption amount acquisition unit for acquiring a redemption amount to be exchanged for cash based on the point balance; a receipt issuing unit capable of outputting a paper receipt where information is printed that can specify the number of game media; a recording medium recovery unit for recovering the received recording medium; and payout unit for paying out cash corresponding to the redemption amount.SELECTED DRAWING: Figure 1

To provide an evaluation apparatus, evaluation method, and program for granting a credit line based on inventory.SOLUTION: An evaluation apparatus in a storage that stores articles, as inventory goods, under high-humidity low-temperature conditions includes: a receiving unit which accepts inventory goods delivered from a producer; and a credit line granting unit which grants a credit line to the producer in accordance with the inventory goods.SELECTED DRAWING: Figure 1A