60 results about "Risk detection" patented technology

The invention relates to a digital finance and taxation auditing method based on artificial intelligence, and the method comprises the following steps: S1, obtaining electric finance and taxation multi-source data, and carrying out the preprocessing of the data, and obtaining the preprocessed electric finance and taxation multi-source data; s2, constructing a finance and taxation knowledge graph according to the preprocessed power finance and taxation multi-source data; s3, constructing a multi-modal risk detection model, and obtaining a risk event list and a whole-course data traceability path according to the finance and taxation knowledge graph and the real-time electric finance and taxation multi-source data; s4, according to the risk event and traceability, based on a rule model, generating a structured decision instruction set including all key information and traceability links; and S5, converting the structured decision instruction set and the finance and tax knowledge graph into a standard relation graph for input, and dynamically displaying fund flow, risk events, abnormal aggregation and traceability paths among enterprises through D3. Js rendering. According to the invention, the efficiency and reliability of electric power digital finance and taxation auditing are significantly improved.

The invention relates to the technical field of financial risk management, in particular to a big data-driven financial risk early warning system, which comprises a data detection module, a behavior risk rating module, a dynamic risk monitoring module, a behavior pattern clustering module and a financial risk early warning module. According to the method, the sensitivity and accuracy of risk detection are improved by analyzing small deviation between transaction behaviors and historical data, the dynamic risk monitoring function can realize quick response to abnormal transactions by continuously tracking the fund flow direction and the transaction frequency, and the risk detection accuracy is improved by analyzing the relationship between the fund flow direction and past risk events. According to the method, normal financial activities and potential risk behaviors can be effectively distinguished, an enterprise can take prevention measures before the risk actually occurs, and the clustering analysis of the risk behaviors can help to more accurately position a risk source, so that the measures are taken in a targeted manner, financial resources are optimized, and the risk prevention and control capability and decision-making efficiency are enhanced.

The invention relates to the technical field of blockchain risk control, and discloses an after-bid electronic warrant risk control method based on a blockchain smart contract. According to the method, historical warranty records are stored in a block chain distributed account book, and a risk background database is established; transaction data and state information of the electronic warrant after bidding are collected in real time, and a risk detection period is dynamically determined accordingly. And in the period, acquiring all the warranty event intensity indexes, and analyzing and marking suspected risk events in combination with the risk background database. If the suspected event exists, extracting features to judge whether the suspected event is a real risk event; if yes, calculating an initial risk coefficient according to the event frequency; extracting event feature vectors through an intelligent contract, and classifying risk events according to a distributed consistency algorithm; if the classification indicates a specific risk pattern, the initial risk coefficient is adjusted. And when the adjusted risk coefficient exceeds a preset threshold value, triggering the block chain smart contract to execute a hierarchical risk control strategy.

The invention discloses a classification model privacy disclosure risk black box detection method and device, and relates to the technical field of machine learning security, and the method comprises the steps: constructing an auxiliary data set; generating an auxiliary model covering different privacy risk levels; training a shadow model and a black box member reasoning attack model corresponding to the shadow model; evaluating the privacy risk score of each auxiliary model by using the attack model, and labeling a high-risk / low-risk label; calculating a correction prediction entropy difference, and screening the first k samples with the maximum difference to form a query data set; extracting a prediction result of each auxiliary model on the query set, calculating and correcting a prediction entropy and a mean value, a variance, a kurtosis and a skewness thereof, and splicing into a k + 4-dimensional risk feature vector; training a risk detection classifier; similar features of the model to be detected are extracted and input into the classifier, and the classifier performs five-level privacy disclosure risk level mapping according to the final risk probability prediction value and outputs the five-level privacy disclosure risk level mapping. The method is mainly applied to risk assessment of classification models in high-privacy sensitive fields such as financial credit investigation and medical diagnosis.

The invention discloses an intelligent expense reimbursement auditing system based on multi-modal identification, and relates to the technical field of artificial intelligence and financial informatization. According to the system, invoice images, structured transaction data and text description information are acquired through a multi-modal data acquisition module, multi-modal feature weighted fusion is realized by using an attention mechanism of a feature fusion module and a tensor fusion technology, and a multi-modal feature fusion model is established by combining Bayesian reasoning of an auditing decision module, Mahalanobis distance of an anomaly detection module and an auto-encoder algorithm. And a rule engine module, an interpretability analysis module and a federal learning architecture, so that the problems of low efficiency, high misjudgment rate, poor rule adaptability, insufficient risk detection capability and the like of a traditional system are solved. The system can shorten the single checking time from 5-10 minutes to a second level, the recognition accuracy rate exceeds 99%, the misjudgment rate is lower than 0.5%, 10 + type high-risk behaviors can be automatically recognized, dynamic policy updating and interpretability analysis are supported, data privacy is guaranteed through federal learning, and the system is suitable for enterprise financial reimbursement checking.

The invention discloses a financial report generation method and device, a program product and electronic equipment, and relates to the field of financial science and technology, and the method comprises the steps: generating L financial statements based on the key financial data of a target customer after the authorization information of the target customer is received; the L financial statements are subjected to articulation verification, and the articulation verification is used for detecting the logic relation between the key financial data included in each financial statement; generating a financial index table based on the L financial statements under the condition that the check of the articulation relationships of the L financial statements is passed; and carrying out risk detection on the L financial statements and the financial index table, and generating a target financial report based on a detection result and a preset report template. The technical problems of low efficiency and low accuracy of processing financial information in a manual mode in the prior art are solved.

Disclosed herein are methods and systems related to fraud detection, risk assessment, and intervention. More specifically, this is related to conducting and monitoring transactions on a blockchain through the use of artificial intelligence, AI, and machine learning. This combination can be applied to financial systems to create an Artificial Intelligence Financial Technology Blockchain, AIFTB. The AIFTB is able to receive a variety of data and use it to predict actions being taken by users and detect if there are any risks or legal concerns with the activity. The AIFTB is then able to apply its training to execute smart contracts on the blockchain based on the risk and legality of the data it received. Finally it will alert appropriate authorities based on the predictions.

The invention provides a business risk detection method and device, equipment, a medium and a program product, and relates to the field of financial science and technology or the field of big data. The method comprises the steps of obtaining multi-modal business data of a target industry chain, processing the multi-modal business data in each industry to generate a joint feature vector corresponding to each industry, determining business association information among the industries according to the joint feature vectors corresponding to all the industries, and sending the business association information to the target industry chain. Generating a risk detection result of the target industrial chain based on the business association information; according to the method, the multi-modal business data of the multi-industry associated enterprises in the target industry chain is acquired and subjected to targeted processing, the joint feature vector of each industry is generated so as to accurately mine the business associated information between the industries, efficient detection and accurate identification of the target industry chain risk are finally realized, and the efficiency and accuracy of industry chain risk detection are improved.

The embodiment of the invention provides a risk transaction early warning method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of obtaining transaction behavior data of a target transaction, generating behavior characteristic data based on the transaction behavior data, matching the behavior characteristic data with a plurality of preset rules in a preset rule base to obtain a rule deviation degree of the target transaction, and matching the behavior characteristic data with a plurality of historical behavior characteristics to obtain a rule deviation degree of the target transaction. According to the method, the historical case deviation degree of the target transaction is obtained, then the risk index of the target transaction is determined based on the rule deviation degree and the historical case deviation degree, a hierarchical early warning mechanism is triggered based on the risk index, implicit illegal behaviors such as improper commitment and an abnormal operation chain are timely and comprehensively identified, and a more accurate risk detection result is obtained. Therefore, the accuracy and timeliness of risk detection are improved, and workers can process corresponding risk transactions in time based on differential early warning.

The invention discloses a risk detection method, device and equipment for financial institution issuing items and a medium. The method comprises the following steps: calling an auxiliary platform data interface to obtain auxiliary service information according to a project identifier of a commanded item in response to the commanded item received from a payment gateway of a financial institution; synchronously acquiring associated business data for risk feature calculation; according to the auxiliary service information, performing compliance and authenticity verification on the issued item; if the verification is passed, taking a payer or a payee who issues the item on behalf as an analysis subject, and combining historical similar item records and associated business data of the analysis subject to generate multi-dimensional risk features; inputting the multi-dimensional risk features into a plurality of pre-constructed risk identification models, and generating corresponding risk signals; and according to the risk signal, generating risk alarm information of the issuing item. According to the embodiment of the invention, the intelligent, real-time and precise risk identification and early warning of the issuing items can be realized.

The invention belongs to the technical field of artificial intelligence and auditing science, and particularly relates to an intelligent auditing method and device based on large model and multi-modal data fusion. The method comprises the steps of firstly constructing a vector knowledge base, then extracting deep features of unstructured bill data, an original audit text and an original audit table, and based on cross-modal comparative learning and fusion of a dynamic weight algorithm based on risk awareness attention, obtaining multi-modal joint embedded representation features so as to obtain a vector knowledge base retrieval result, the method comprises the following steps: firstly, generating a standard auditing program template, then constructing an auditing prompt word, finally generating a risk detection score and a risk evidence chain based on a large language model, constructing an auditing program template through the standard auditing program template obtained by retrieving a vector knowledge base, and finally generating a structured auditing report in combination with the auditing program template, the risk detection score and the risk evidence chain. According to the method, the problems of low efficiency and low accuracy of fusion auditing based on multi-modal data such as texts, tables, images and the like are solved.

The invention belongs to the technical field of artificial intelligence, and relates to a business risk detection method and device, equipment and a storage medium thereof.The business risk detection method comprises the steps that processing equipment information is recognized by obtaining and analyzing a processing request message; inputting the handling equipment information into the risk detection model, and outputting a risk value of the handling equipment information; and processing according to the risk value detection result. Compared with a previous risk detection mode, the application directly takes the handling equipment information for sending the handling request messages as the risk detection information, each handling request message inevitably corresponds to the sending equipment, the detection limitation caused by less individual business data is broken through, and the handling equipment information is directly utilized, so that the risk detection efficiency is improved. Malicious handling robots can be conveniently identified and forbidden; the method is applied to an insurance buying service launched by finance or health care, risk detection is carried out at least once according to the handling equipment sending the insurance buying request, the detection range of risk detection is improved, and meanwhile, the insurance fraud risk can be reduced as much as possible.

The invention discloses a method and a system for determining financial risk information, and a computer program product. Relates to the field of financial science and technology, and the method comprises the steps: determining M financial risk data of a financial institution in a preset time period, and obtaining a case database, the case database comprising a plurality of financial risk cases, and M being a positive integer; matching each piece of financial risk data with a financial risk case in a case database to obtain M groups of rank-related data, inputting the M groups of rank-related data and the M pieces of financial risk data into a risk scoring model, and outputting a risk score; and inputting the risk score into a report generation model, and outputting a financial risk attribution report of the financial institution. Through the risk detection method and device, the technical problem that the detection accuracy is low when risk detection is carried out on a financial institution in the prior art is solved.

The invention discloses a risk detection method, device and equipment, a medium and a program product, and relates to the field of financial science and technology. The method comprises the steps of obtaining current text data, current time sequence data and current graph structure data of a to-be-detected party; vectorizing the current text data, the current time sequence data and the current graph structure data to obtain a current text vector, a current time sequence vector and a current graph structure vector; fusing the current text vector, the current time sequence vector and the current graph structure vector to obtain a current fusion vector; and obtaining a candidate risk vector and a current rule engine score, and performing risk detection on the to-be-detected party according to the current fusion vector, the candidate risk vector and the current rule engine score to obtain a current risk detection result of the to-be-detected party. According to the technical scheme provided by the embodiment of the invention, real-time risk detection in a high-concurrency scene is realized, the adaptability and comprehensiveness of risk detection are improved, and meanwhile, the risk detection precision is also improved.

The invention discloses a monitoring and safeguard system based on an auction platform. The monitoring and safeguard system comprises a user authentication module, a transaction monitoring module, a risk detection module, a data storage module, an alarm module, a safeguard processing module, a log recording module and a system control module. According to the invention, real-time monitoring and automatic guarantee of the auction platform are realized through modular design, and the safety and efficiency of the system are significantly improved. The system can integrate user behavior data, transaction flow and environment information, performs multi-dimensional risk analysis by using an intelligent algorithm, and timely identifies and processes abnormal events, thereby reducing the occurrence of fraudulent behaviors and transaction disputes. Meanwhile, rapid response of safeguard measures, such as automatic triggering of a compensation mechanism or alarming, is ensured through cooperative work among the modules, and user trust and platform stability are enhanced.

The invention discloses a financial data security management system and method based on artificial intelligence, and relates to the field of financial technology security management, and the system comprises a data preprocessing module, a security storage module, a risk detection module and a dynamic optimization module. The method comprises the steps of data preprocessing, secure storage, risk detection and dynamic optimization. The data preprocessing module encrypts standardized financial data by adopting a hybrid encryption mechanism in combination with symmetric and asymmetric encryption algorithms, and divides security levels through a BiLSTM and attention mechanism fusion model; the secure storage module stores the encrypted data in a block chain and IPFS dual-architecture system, the block chain adopts a practical Byzantine fault-tolerant consensus algorithm, and the IPFS stores data fragments through a Merkle-DAG structure; the risk detection module extracts long-term and short-term risk features based on a Transform-TCN fusion model and fuses the long-term and short-term risk features; and the dynamic optimization module dynamically adjusts a risk detection threshold value through a reinforcement learning algorithm to realize full-life-cycle safety management of financial data.

The invention provides an application risk detection method and device, equipment, a medium and a product, which can be applied to the technical field of artificial intelligence, the technical field of distribution and the field of financial science and technology. The method is applied to a target terminal, and comprises the following steps: determining an application risk detection model issued to the target terminal by a center terminal; inputting the information of the to-be-detected application into an application risk detection model, and determining to-be-detected application features output by a specified hidden layer in the application risk detection model when the application risk detection model predicts and determines that the to-be-detected application is a risk application; determining a first risk application feature set issued to the target terminal by the center terminal; and in the first risk application feature set, determining a target risk application feature of which the feature similarity with the to-be-detected application feature is greater than a first similarity threshold, and determining the risk degree of the to-be-detected application according to the to-be-detected application feature and the determined target risk application feature.

This application discloses a risk warning method, apparatus, equipment, and product, relating to the field of risk detection technology. The method includes: receiving transaction data and calculating the transaction data using a set of transaction risk control clauses to obtain an initial risk control result; performing mutual exclusion and joint relationship judgments on the initial risk control result to obtain mutual exclusion judgment results and joint judgment results; performing aggregate analysis on the mutual exclusion judgment results and joint judgment results to obtain a final risk control result, and generating alarm information based on the final risk control result. This solves the problem of inaccurate risk control judgments and low efficiency in asset management risk control systems due to rule conflicts and insufficient adaptation, thus improving the efficiency of risk warning.

The invention discloses a transaction risk detection method and device, an electronic device and a storage medium. According to the application, a heterogeneous information network representing a multi-class entity transaction relationship can be constructed based on a transaction subject, a transaction object and associated identification information in financial transaction flow data; structural roles and behavior modes of entities in a network are quantified by extracting graph structure features, structural features such as fund closed loop are accurately captured, comprehensive risk judgment is performed by using a model fusing unsupervised anomaly detection and graph structure reconstruction detection, single-point anomaly and collusion association anomaly are comprehensively covered, and the risk assessment accuracy is improved. The method makes up for the limitation of single-point statistical feature analysis, and achieves the technical effects of improving the comprehensiveness and precision of financial transaction abnormal risk identification, reducing the probability of missing detection of collusion fraud, accurately capturing structural abnormality, strengthening the financial anti-fraud prevention and control capability, and perfecting a serial number transaction monitoring system.

The invention relates to a fraud payment detection method and device, equipment and medium in the technical field of e-commerce, and the method comprises the steps: responding to a transaction payment event triggered by a user, and employing a local risk detection model to determine the real-time fraud risk degree of a payment order corresponding to the event; when the real-time fraud risk degree does not exceed a first preset threshold value or exceeds a second preset threshold value, the payment order is correspondingly confirmed to be a low-risk type order or a high-risk type order, and the second preset threshold value is greater than the first preset threshold value; when the payment order is not confirmed as the low-risk type order or the high-risk type order, adopting a third-party risk detection model to confirm that the payment order is the high-risk type order or the low-risk type order; and for the high-risk type order, pausing the transaction process, verifying the real identity of the user, and continuing the transaction process after the verification is passed. According to the invention, payment risk detection services of local and third parties can be reasonably scheduled, the payment security is ensured, and the detection cost of the third party is reduced.

The invention discloses a risk detection method and device for a transaction account and electronic equipment, and relates to the field of financial science and technology or other related technical fields, and the method comprises the steps: carrying out the clustering operation of L transaction accounts based on a target feature corresponding to each transaction account in the L transaction accounts, and obtaining K account sets; determining a first score, a second score and a third score of each account set based on transaction data of all transaction accounts included in each account set; and determining a risk detection result of the transaction account in each account set based on the first score, the second score and the third score of each account set. The technical problem of low accuracy of risk detection of the transaction account participating in the financial transaction in the prior art is solved.

The invention discloses a financial account processing method and device based on a large model and electronic equipment, and relates to the field of financial science and technology, and the method comprises the steps: determining a data collection strategy based on a historical risk score of a target account; after authorization information of a user of the target account is received, first data is collected based on a data collection strategy, and the first data is at least one of a verification code, a contact information verification result, a face image and transaction verification data of the user; performing feature extraction on the first data through the target model to obtain a first feature of the target account; determining a target risk score of the target account based on the first feature and the second feature of the target account; and under the condition that the target risk score is smaller than a preset risk threshold value, performing unfreezing processing on the target account. According to the method and the device, the technical problem of low user experience caused by the condition that the risk detection accuracy of the financial account is low and the financial account is frozen by mistake in the prior art is solved.

An information processing device is provided that can calculate an interpretation index for each explanatory variable in the process of calculating a risk score, thereby reducing the calculation load. [Solution] An accounting audit support device 1 detects risks that can be detected from accounting-related data using a machine learning model. The system is equipped with a learning model generation unit 32 that uses a plurality of training records that combine a plurality of explanatory variables and a dependent variable selected from the financial data to generate, as a machine learning model, a shape function that indicates the relationship between each explanatory variable and dependent variable; a learning model storage unit 41 that stores the shape function for each explanatory variable generated by the learning model generation unit; and a risk calculation unit 34 that calculates an interpretation index for each explanatory variable using the shape function for a new record that is the target of risk detection, and calculates a risk score from the plurality of interpretation indexes.

The invention relates to the technical field of artificial intelligence crossing, and particularly discloses a multi-modal data knowledge extraction method based on a large model, and the method comprises the steps: obtaining the multi-dimensional interaction data of a user in real time through a distributed interface; wherein the multi-dimensional interaction data is divided into structured data and time sequence characteristic data; inputting the multi-dimensional interaction data of the user into the multi-modal embedding layer for joint characterization learning; and calculating the account real-time risk probability of the user, and carrying out early warning operation based on the account real-time risk probability. The calculation of the real-time risk probability is combined with multi-dimensional features such as dynamic causal strength, time correlation score and geographic position offset, the abnormal degree of user behaviors can be accurately quantified, a refined risk control strategy from low-risk monitoring to high-risk interception is realized, the accuracy and response speed of risk detection are remarkably improved, and the risk detection efficiency is improved. And the maintainability and the expansibility of the system are enhanced through a modular design and a distributed architecture, and efficient and reliable technical support is provided for financial anti-fraud.

The embodiment of the invention discloses a safe deposit box business risk prediction method and device, equipment and a storage medium, and the method comprises the steps: obtaining safe deposit box business data of a target customer; wherein the safe deposit box business data comprises client data, safe deposit box equipment data and historical transaction data; and inputting the safe deposit box business data into a pre-trained target risk detection model to obtain a target risk detection result corresponding to the target customer. According to the technical scheme, the problem that in the prior art, analysis is carried out based on single customer behavior data, and an inaccurate risk detection result is likely to occur is solved, fusion analysis can be carried out based on multiple dimensions of customers, safe deposit boxes, historical transaction data and the like, different types of heterogeneous data are integrated, a multi-dimensional entity relationship is established, and the risk detection accuracy is improved. And the risk prediction accuracy is improved.

The embodiment of the invention provides a risk equipment identification method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the following steps: acquiring a plurality of login data of a current user in a service system and carrying out risk detection, when a potential risk device is found, further tracing a login user of the device and associated devices thereof in a historical time period, extracting a plurality of feature data for a non-main login device, and carrying out risk detection on the extracted feature data; the preset risk judgment model is used for carrying out equipment risk identification, the login behavior data of the user is used for carrying out multi-layer analysis and model judgment, and the privacy data that the user rejects authorization is not collected any more, but the login data which can be directly obtained by a service system is used, so that the feasibility and sufficiency of data acquisition are improved, and the user experience is improved. The problem that in the prior art, the identification efficiency is low due to the fact that privacy data are collected depending on user authorization is solved, and the identification efficiency of the risk equipment is improved.

The embodiment of the invention provides a bank outlet risk detection method and a related device, and the method comprises the steps: determining an account risk value of at least one user based on risk account information in a risk account database and account change information of the at least one user; determining at least one business handling risk value based on actual communication information between at least one business person of the bank outlet and at least one user and reference communication information corresponding to the account risk value of at least one user; and based on the account risk value of the at least one user and the at least one business handling risk value, carrying out risk detection on the bank outlet to obtain a risk detection result of the bank outlet. According to the embodiment of the invention, the risk possibly brought by the user and the risk possibly brought by the business personnel are comprehensively considered, and the risk detection is carried out on the bank outlet based on the account risk value and the business handling risk value corresponding to the two risk sources, so that the detection precision can be effectively improved, and the safety of the business handling process is ensured.

The invention discloses a risk data processing method and device, electronic equipment and a readable storage medium, and relates to the field of financial science and technology. The method comprises the following steps: extracting current video data from current multi-modal data, determining current face feature information according to a current face picture extracted from the current video data, and determining the current face feature information according to at least one of fingerprint data of user equipment, environment risk quality of an application program and transaction behavior features of current business; performing risk detection according to the current face feature information and the current face feature information to obtain initial risk quality and an initial risk label; according to the initial risk quality and the initial risk label, selecting a target expert detection model corresponding to a target modal from the candidate expert detection models, extracting data of the target modal from the multi-modal data, and inputting the expert detection model of the target modal to obtain predicted risk quality of the target modal; and determining a final risk detection result based on the predicted risk quality of the target mode.

The invention discloses a transaction risk detection method and device and a program product, and relates to the field of artificial intelligence, and the method comprises the steps: collecting commercial tenant information and transaction flow information of a target commercial tenant, and collecting external channel information from a preset channel, and obtaining a commercial tenant information set; performing data cleaning on information in the merchant information set based on the data type or the data meaning, and performing correction processing on abnormal information in the cleaned merchant information set to obtain processed information; performing feature extraction on the processed information based on multiple dimensions, and fusing the extracted features according to a target weight to obtain a fused feature vector; and inputting the fusion feature vector into a target detection model to obtain a detection result output by the target detection model. Through the method and the device, the problem that the detection accuracy is relatively low due to the fact that the transaction behavior characteristics of the commercial tenant with the financial risk are difficult to capture when the financial risk of the commercial tenant is detected based on a business rule or a statistical algorithm in the related technology is solved.

The application discloses a kind of data generation method and system of member privacy risk detection of credit investigation model, comprising: respectively estimating the average prediction confidence of member data and non-member data on each credit investigation category as judgment threshold;Randomly sample a sample from the sample space of the target credit investigation model to be measured, and for each feature dimension of the sample, a new feature value is randomly sampled from the feature value space of the feature dimension to replace the current feature value, and a seed sample is generated;The seed sample is input into the target credit investigation model to be measured, and the posterior probability prediction output is obtained;When the prediction probability of the seed sample in target label c satisfies the judgment threshold condition of member data or non-member data, then it corresponds as member detection sample or non-member detection sample;Generation satisfies the sample quantity of detection data.The application can more accurately capture the performance difference of credit investigation model on two different data, and guarantee the effectiveness of the generated detection data.