1726 results about "Risk Control" patented technology

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 invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention discloses a financial data risk control system and method based on big data, and relates to the technical field of financial science and technology, and the system comprises a multi-source data collection module which achieves cross-mechanism safety collection through federal learning; the data cleaning and preprocessing module is used for processing abnormal values and missing values by using an improved algorithm; the knowledge graph construction module is used for constructing a dynamic knowledge network based on an innovative algorithm; the risk assessment engine fuses various models to assess risks; the real-time monitoring and early warning module is used for realizing second-level response by utilizing multi-scale analysis; the decision support module is used for optimizing a strategy based on reinforcement learning; and the audit tracking module guarantees evidence storage and privacy through zero-knowledge proof, and all the modules cooperate to improve the risk control capability. According to the financial data risk control system and method, risks are accurately recognized, real-time monitoring and early warning are achieved, data security sharing is achieved, risk control strategies are dynamically optimized, risks and business development are balanced, the risk prevention and control capacity and economic benefits of financial institutions are improved, and data privacy and risk control transparency are guaranteed.

The invention provides a financial data risk analysis method and system based on a large model, and relates to the technical field of intelligent risk control, and the method comprises the steps: obtaining financial data from a participant, carrying out the standardization and aggregation through a federated learning framework, generating a federated feature vector, constructing a bidirectional knowledge distillation model, receiving a vector, and carrying out the risk analysis of the financial data. A teacher model and a student model are deployed, a risk conduction map is constructed, a generative adversarial network is utilized to monitor changes of an enterprise association network, a risk conduction edge weight is adjusted, a financial report time sequence and a transaction event sequence are aligned through a space-time coupling encoder based on an LSTM and Transform architecture, and a multi-modal fusion feature vector is generated and input to a bidirectional knowledge distillation model; and a final risk score is obtained by combining the atlas, a grading early warning mechanism is triggered, and a risk disposal suggestion and a conduction path are generated, so that the data standardization and aggregation efficiency can be improved, and the dynamic coupling capability of a multi-modal fusion mechanism and the adaptability of a risk conduction model can be enhanced.

The invention discloses a contract compliance examination method and platform based on multi-agent cooperation, relates to the field of artificial intelligence, and is used for improving the safety and accuracy of contract examination. The method comprises the following steps: acquiring latest rule data by a data collection agent; the data construction agent constructs retrieval data and a rule knowledge graph based on the rule data; the document analysis agent extracts key information of the target contract and retrieves related rules from the retrieval data according to the key information; the compliance review agent performs risk item assessment on the target contract according to the related rules and the rule knowledge graph; the risk management and control agent generates a response measure of the risk item based on the retrieval data; the report generation agent generates a compliance review report based at least on the evaluated risk items and corresponding countermeasures. According to the invention, the security, accuracy and efficiency of contract review are improved.

The invention relates to a steel logistics full-process real-time dynamic management method and device, and belongs to the technical field of steel logistics control methods and devices. According to the technical scheme of the invention, data acquisition and monitoring are carried out, and transport vehicle and cargo states are tracked; demand prediction and inventory dynamic adjustment are carried out, and high-frequency cargo response emergency orders are allocated according to shipment frequency; visual management of the supply chain is carried out, shared data of all links of the supply chain is established, a 3D logistics map is constructed, and inventory, transportation states and bottleneck nodes are displayed in real time; and risk control management: quickly starting the alternative scheme when the early warning is triggered. The method has the advantages that efficiency is optimized, limitation of a traditional single transportation mode is reduced, rolling updating and emergency response of demand prediction are achieved, data transparency and credibility are improved, rapid risk response is achieved, and logistics full-life-cycle management is achieved.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention discloses a digital twinning-based major engineering construction safety active control method, system and equipment, and belongs to the technical field of construction active control. The problems of low safety risk identification efficiency and inaccurate risk control means in the major engineering construction process are solved, major engineering construction site environment and structural feature data are obtained through space-air-ground-water multi-way intelligent means and equipment, a digital twinborn model of a real-time mapping construction site is established, and the safety risk identification efficiency of the major engineering construction site is improved. Correcting the digital twinborn model by using real-time data; a digital twinborn model is used as a basic model of finite element analysis, mechanical properties of different construction stages are analyzed, dynamic early warning is performed on an analysis result in combination with real-time data, a safety risk derivation model is established to perform real-time analysis on key problems such as a risk point location, a risk influence range and risk derivation prediction in the digital twinborn model, and the safety risk derivation prediction result is obtained. Data support is provided for engineering safety active control, an active control strategy model is established, a control strategy is output to a servo control device according to risk information, strategy dynamic adjustment is performed according to a real-time effect, and high-precision cooperative control of major engineering safety monitoring is realized.

The invention discloses a full-life-cycle underground engineering geological safety risk collaborative management and control method and system. The method comprises the following steps: performing regional geological structure identification and geological risk identification on a target construction region, and constructing a regional geological digital model; regional construction monitoring information is obtained, and construction risk analysis and cavity prediction are performed in combination with a regional geological digital model, so that regional construction risk analysis information is obtained; constructing a carrier risk assessment model, and analyzing whether the current construction risk affects a building object on the ground or not through the regional construction risk analysis information and the regional construction monitoring information to obtain carrier risk assessment information; and based on the regional construction risk analysis information and the carrier risk assessment information, judging whether construction risk management and control need to be carried out, and if so, generating a risk management and control scheme to carry out regional construction safety risk collaborative management and control. And timeliness, comprehensiveness and decision reliability requirements of underground engineering construction management and control in a complex geological environment are improved.

The invention belongs to the field of multi-modal artificial intelligence risk identification. The core comprises a multi-source heterogeneous data parallel acquisition module; a modal exclusive feature extraction module; a graph attention driven dynamic fusion module; and a risk classification module with an attention mechanism. A real-time fusion weight is generated through a cross-modal incidence matrix, adaptive feature weighting and hidden risk association mining are realized, and the recognition accuracy and interpretability of a complex scene are remarkably improved. The method is applied to the fields of financial risk control and industrial monitoring.

The invention belongs to the technical field of decision optimization, and relates to an enterprise-level intelligent risk control decision system combined with a real-time data stream, and the system comprises a heterogeneous data distribution module which is used for obtaining multi-source heterogeneous data streams inside and outside an enterprise; the behavior time sequence splicing module is used for executing cross-system user ID association and time sequence recombination on the real-time data flow to generate a user behavior chain with continuous time stamps; the feature fusing calculation module is used for receiving the user behavior chain, performing feature extraction and outputting a real-time feature vector with a quality flag bit; the incremental model updating module is used for respectively generating a baseline risk score and a dynamic risk score; and the dynamic weight decision module is used for generating final decision parameters. And the risk control processing execution module responds to the final decision parameter to trigger a processing action, and configures a manual auditing arbitration channel and a feedback data generation unit. According to the method, the problems that a dual-check algorithm is not deeply coupled with a business index, and abnormal data which passes hash check but has logic violation flows into a real-time channel are solved.

The invention discloses a digital product online leasing system based on a cloud native micro-service architecture and a construction method thereof, and the method specifically comprises the steps: firstly, carrying out containerized deployment and micro-service hierarchical design, including a plurality of business modules of user service, product management, leasing order, payment settlement, credit evaluation and recommendation, and enabling each module to operate in an independent service form; efficient cooperation is realized through service registration and discovery; a cloud native platform is utilized to realize elastic expansion and automatic operation and maintenance of the system, lease life cycle management is realized through an event-driven mechanism, links of ordering, payment, delivery, lease renewal, return and maintenance are covered, and a credit determination and risk control mechanism is introduced to support multiple lease modes such as detention-free lease and the like. The digital product leasing platform supports multi-terminal access and unified entrance management, has good system expansibility and leasing service flexibility, improves the concurrent processing capability, deployment efficiency and user experience of the digital product leasing platform, and has wide commercial application prospects.

The invention discloses a big data risk control decision support system based on multi-modal data, and the system comprises a data access module which is used for accessing and analyzing multi-source entity information, building a grouping calibration rule base, and generating a compliance multi-modal data set; the preprocessing and characterization module is used for preprocessing the data, generating basic characterization of each modal and establishing a micro verifier library; the causal hypergraph module is used for constructing a causal hypergraph and outputting a stable causal structure and a gating mask; the fusion and anti-fact module is used for executing cross-modal fusion to generate a risk representation, generating and verifying a minimum change anti-fact sample, and outputting a reachable identifier and an adjustment representation; the evidence chain module is used for calculating a shortest evidence overpass, generating a structured evidence chain and outputting an evidence index; and the strategy module is used for generating and correcting scores, triggering strategy actions and recording execution information. Through multi-modal data fusion and causal reasoning, high-precision, low-delay and interpretable big data risk control decision support is realized.

The invention discloses a bank loan business risk control system and method based on big data analysis, and relates to the technical field of financial risk control, and the method comprises the steps: collecting and preprocessing real-time behavior data, and obtaining a user behavior feature set; based on the user behavior feature set, calling a behavior map modeling engine to carry out structured mapping, matching with a risk anchor point rule base, identifying a potential risk mode and labeling an initial anchor point risk label; correcting the deviation between the initial risk anchor point tag and the actual default record by adopting a value function optimization method, and predicting the risk grade score of the current behavior of each user in combination with the historical behavior sample data and loan feedback data of the user; predicting probability distribution of migrating to a default state in the future through user risk grade scores and historical state evolution data; and in combination with the potential loss under each behavior path, evaluating the current loan business risk, and generating a risk control strategy through a risk level mapping rule and a strategy decision engine.

The invention discloses an intelligent expense reimbursement auditing system and method for a financial sharing center, and relates to the technical field of intelligent financial risk control, and the method comprises the steps: collecting reimbursement voucher data, and carrying out the preprocessing of the reimbursement voucher data, and generating standardized data; the method comprises the following steps: constructing a dual-task learning architecture based on an XGBoost and LightGBM framework, performing enhancement through a weight loss function and a multi-dimensional dynamic feature cross mechanism to generate an enhanced dual-task learning architecture, obtaining a credit-risk two-dimensional evaluation model through training, and performing credit score regression prediction and risk level classification prediction to obtain a credit score and a risk level; performing integrity verification on the credit score and the risk level through an intelligent shunting strategy engine, and performing credit-risk weight fusion, load dynamic adjustment and risk conduction analysis through a preset shunting strategy matrix to obtain a work order shunting result; according to the invention, through the credit-risk two-dimensional evaluation model, the problem of misjudgment caused by credit and risk separation analysis is reduced.

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

The invention relates to the technical field of financial risk control, and discloses a financial pre-opening account risk assessment system based on intelligent identification, and the system comprises a multi-dimensional situation feature collection module which is used for collecting multi-dimensional situation features in a pre-opening account process; the situation analysis processing module is used for converting the multi-dimensional situation features into situation feature vectors and carrying out hierarchical risk feature extraction on recognized similar situation cases; the risk assessment module is used for generating a risk score and a risk classification result; the knowledge graph updating module is used for performing quality evaluation on the newly extracted risk knowledge and dynamically updating the risk knowledge graph based on an evaluation result; the crowdsourcing knowledge collection module is used for forming a continuous optimization closed loop of risk assessment; according to the method, accurate identification of the financial account pre-opening risk is realized, the risk identification accuracy is improved, the risk knowledge updating period is shortened, the risk knowledge island problem is effectively solved, and a clear risk explanation path is provided.

The invention provides a data privacy protection method for a data governance system, and belongs to the technical field of data governance, and the method comprises the steps: carrying out the cross verification of multi-source feature data and the unique identification information of an object collected on site, generating an original data set, carrying out the sensitive information recognition and grading, and generating the preprocessing data with a sensitive grade label; core identification information in the preprocessed data is disassembled to generate standardized desensitized data conforming to privacy protection, a bidirectional encryption mapping relation is established between object unique identification information collected on site and the standardized desensitized data, an encryption index is formed, the encryption index and preset multi-dimensional compliance data are fused, and a data fusion result is obtained; generating standardized fusion data; and based on the access token, generating a differential authorization data set divided according to permission granularity, performing privacy disclosure risk assessment, generating a risk level, performing privacy processing on the risk level, and outputting the risk level to a risk control system. And the data management efficiency is improved.

The invention discloses a financial risk assessment method and device, a computer program product and electronic equipment. The method relates to the field of artificial intelligence, and comprises the following steps: obtaining interaction data between a target user and a financial institution, the interaction data comprising at least one of texts, images, voices and structured data; inputting the interaction data into a decision module of the risk control agent to obtain a sub-model scheduling strategy; scheduling the target expert sub-model to process the interaction data based on the sub-model scheduling strategy to obtain multi-modal features of the target user; inputting the multi-modal features into a feature alignment module in a risk control agent to obtain target alignment features; and inputting the target alignment feature into a target model in the risk control agent to obtain a risk assessment result. Through the financial risk assessment method and device, the problem that the accuracy of financial risk assessment of multi-modal data is low in the prior art is solved.

The invention provides a risk control strategy dynamic optimization method and system based on a large model, and the method comprises the steps: determining a current risk control strategy system and corresponding strategy execution data, calling the large model to carry out the conjoint analysis of the current risk control strategy system and the corresponding strategy execution data, and generating a strategy structure map and a deviation diagnosis report; based on the strategy structure map and the deviation diagnosis report, generating a strategy adjustment scheme containing a rule logic reconstruction suggestion and a rule priority optimization sequence, and according to the strategy adjustment scheme, updating the current risk control strategy system to obtain an optimized risk control strategy system; and finally, effect verification is carried out on the optimized risk control strategy system through a dynamic verification mechanism, and a verification result is used as newly added data of strategy execution data, so that dynamic optimization of the risk control strategy is realized, and the accuracy and adaptability of the risk control strategy are improved.

The invention relates to the technical field of chemical industry park risk control, and discloses a chemical industry park multi-source risk coupling evaluation system and method. A data acquisition and preprocessing unit of the system acquires and processes dangerous source state, environment and emergency resource information in real time in a standardized manner; the multi-source risk coupling evaluation model simulates risk evolution based on a coupling mechanism and outputs a quantitative index; the dynamic risk situation assessment unit calculates risk space-time propagation in combination with real-time meteorological geographic information to generate a dynamic map; the risk intention deduction unit deduces a risk evolution trend in combination with a historical accident scene library, and identifies short-term and long-term risks; the emergency strategy generation unit is combined with the emergency resource layout to generate a multi-level emergency response strategy; and the visual interaction unit comprehensively displays the dynamic graph and the emergency strategy. According to the system, accurate assessment and dynamic management and control of multi-source risks are realized, and the risk management and control and emergency response capabilities of the park are improved.

The invention relates to the technical field of information authentication management, and discloses an information processing method and system based on authentication and admission assessment, and the method comprises the steps: achieving the automatic extraction of key index data in enterprise registration information and data information based on an OCR technology, and carrying out the consistency verification of the relation between the two, and obtaining a verification result; the authenticity and integrity of submitted information are ensured; then, the system performs difference field marking in combination with third-party registration information and index data, and accurately identifies data inconsistent items; performing quantitative risk scoring based on the index data, and dynamically determining an auditing queue to which the enterprise belongs; finally, the risk score and the difference field marking result are integrated to judge whether to trigger a manual auditing process or not, approval, filing and storage are automatically completed for enterprises needing to be audited, closed-loop automatic management from information collection and verification to risk assessment and approval is achieved, and the authentication efficiency and risk control accuracy are remarkably improved.

The invention discloses a cross-border e-commerce block chain payment credit chain system and a method thereof, and realizes data sharing, information real-time synchronization and credit information dynamic transmission of each participant in a transaction by constructing an alliance block chain network. The system comprises a credit management module, a risk control module, an intelligent contract payment module, a digital currency exchange module and a security collaboration module, and multi-dimensional credit assessment, dynamic risk prediction and automatic payment are realized by using cross-domain technologies such as federated learning, trusted execution environment and homomorphic encryption. Through fund freezing, real-time digital currency exchange, payment triggered by performance conditions and other mechanisms, the cross-border payment clearing period is remarkably shortened, the transaction cost is reduced, the payment safety and supervision transparency can be improved, and the problems that traditional cross-border payment is low in efficiency, high in cost, difficult in risk control and the like are effectively solved.

The invention discloses an intelligent data management system, and relates to the field of intelligent data management. The system comprises a data acquisition module for acquiring and preprocessing multi-source data and extracting field information; the decoupling analysis module is used for calculating a mutual information index and an information cementation degree according to the field information; the map generation module is used for constructing an attribute fusion map according to the field information and generating risk mapping skewness and a risk control map; the behavior analysis module is used for collecting action execution logs, extracting field behavior sequences and calculating field behavior interference factors; and the quality evaluation module is used for extracting a key field missing proportion and information entropy based on the field information to calculate an information completeness index, and fusing multiple indexes to generate a comprehensive quality score. Through graph structure construction, behavior sequence analysis and quality scoring fusion, the multi-source data field fusion accuracy and risk identification precision are improved, and intelligent assessment and compression abstract generation of high-quality data are realized.

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

The invention discloses a multi-head time sequence intelligent risk control method and system based on weighted trend and fluctuation, and relates to the technical field of financial risk management. Comprising the following steps: S1, collecting multi-channel time sequence data in real time, and carrying out data preprocessing; s2, calculating the global time weight, quantifying the multi-scale fluctuation stability of the channel pair, and judging the asynchronous alignment degree of the channel pair; s3, extracting an effective frequency band interval, calculating frequency domain characteristic parameters of the signal, evaluating frequency domain energy phase characteristics of a channel signal, and quantifying fluctuation states of a channel under different scales; s4, constructing a sparse coupling relation graph, quantifying an edge weight in the sparse coupling relation graph, and obtaining a network average coupling weight; and S5, evaluating the dynamic evolution characteristics of the channel risk state, and generating risk trend prediction and control suggestions. The problem that risk control accuracy is affected due to the fact that trend and fluctuation feature extraction of multi-source heterogeneous high-noise multi-head time series data is unstable under the condition of concept drift and multi-scale coexistence is solved.

The invention provides a background authority management system and method for adaptive risk control. Comprising a data acquisition module used for acquiring behavior data and environment data associated with an account and converting the data into an event stream; the context management module is used for associating the context attribute set with the event flow; the feature construction module is used for generating a multi-dimensional feature vector based on the event stream and the context attribute set; the adaptive risk assessment module is used for calling at least two heterogeneous models to perform parallel reasoning on the multi-dimensional feature vectors; the decision engine module is used for mapping the risk weight into an access control instruction and an audit instruction; the auditing module is used for recording auditing data according to the auditing instruction; and the authority management module is used for calculating an authority weight according to the attenuation parameter and judging and generating an authority recovery instruction or a maintenance instruction according to a threshold value. According to the method, event stream unification and context binding, real-time risk assessment and strategy linkage and permission adaptive recovery closed loop can be realized.

The invention relates to the technical field of intelligent customer acquisition, in particular to a cross-border supply chain financial service AI intelligent customer acquisition system based on big data analysis. Functional components such as a big data access engine, an artificial intelligence analysis model, a customer portrait modeling engine, a preference setting interface, a public opinion and risk identification module, an intelligent screening engine and a docking output module are integrated; multi-source data such as customs data, trade data, enterprise industry and commerce data, judicial public data, international manufacture and cutting lists and internet public information from all places around the world are widely fused. The system performs modeling calculation on industry categories, regional distribution, export history, transaction records, public opinion trends and compliance risks of import and export enterprises, establishes customizable customer optimization rules, accurately outputs a target customer list which can be covered by credit insurance and financing service in combination with a machine learning algorithm, and attaches contact information. Various import and export related data resources can be accessed, automatic screening and output of credit insurance and account receivable financing adaptive exporters are realized through preference setting and AI analysis, the customer obtaining efficiency is remarkably improved, the marketing and risk control cost is reduced, and the economic benefit is improved. The problems that traditional cross-border supply chain financial service customer obtaining depends on manual recommendation, offline visit and a static customer library, the efficiency is low, and small and medium-sized export enterprises are difficult to cover are solved.

The invention discloses a reimbursement approval method and device, equipment and a storage medium. In the scheme, based on the tax identifier of the target enterprise, the electronic invoice data associated with the tax identifier is acquired through the third-party electronic invoice platform interface. And extracting target text information from the electronic invoice data according to a predefined field rule, and generating structured reimbursement data based on the target text information. And generating a reimbursement application form according to the structured reimbursement data, and performing compliance verification on the reimbursement application form to obtain a compliance verification result. And if the compliance verification result is that the compliance verification is passed, performing risk assessment on the reimbursement application form based on the intelligent risk control model, and generating an approval suggestion. And executing a corresponding reimbursement approval process operation according to the approval suggestion and a preset approval rule. According to the technical scheme, the efficiency and accuracy of reimbursement approval are remarkably improved while the financial risk of an enterprise is reduced.