21 results about "Credit analysis" patented technology

The invention discloses a bank credit evaluation system and method based on deep learning, and relates to the technical field of data analysis and evaluation, and the system comprises a data acquisition module, a credit evaluation module, a credit analysis module, and a decision output module. According to the method, customer groups are divided in a layered and detailed manner through data acquisition, a dynamic updating and exception rechecking mechanism is applied, a data set of key information is constructed, potential risks of daily demand expenditure of customers on credit are evaluated, and meanwhile income stability analysis is performed according to work to judge the stability of the work development trend of the customers; considering the influence of work on customer credit conditions, establishing a skill equivalent replacement risk model, simulating equivalent work replacement execution, performing customer economic risk evaluation, predicting credit risks generated by work, and integrating a historical credit data set and the work stability and development trend reflected by a work trend change coefficient to obtain a credit risk evaluation result. And a comprehensive customer credit assessment value is generated according to the fund demand and the risk condition embodied by the daily demand prediction risk value.

The invention relates to the technical field of residential engineering detection institution credit evaluation, and discloses a residential engineering detection institution credit grading evaluation method and system, and the method comprises the steps: obtaining the basic qualification and historical business data of a detection institution, collecting the full-dimensional supervision data, cleaning and classifying, and building a multi-dimensional credit evaluation model. Forming an initial credit evaluation model; superposing the market environment parameters and the policy constraint conditions to generate a comprehensive credit evaluation model, and outputting dynamic credit evaluation indexes; a credit evaluation pre-training framework is constructed and optimized, a credit adjustment coefficient is extracted, and a credit state evolution graph is generated; and establishing a regulation and control model to optimize supervision strategy parameters, and realizing credit grading dynamic management and control through a grading decision model. The system comprises a data integration module, a model construction module, a credit analysis module, an evolution deduction module, a strategy optimization module and a grading execution module. The system can comprehensively and dynamically evaluate the credit of the detection mechanism, optimizes the supervision strategy, and guarantees the quality safety of residential engineering.

The invention discloses a credit data mining processing system and method based on block chain fusion, relates to the technical field of credit data mining processing, and solves the technical problem that in the prior art, multi-stage data acquisition and mining cannot be performed for an acquisition stage, a development stage and a retention stage of a user. Specifically, an acquisition stage analysis unit performs credit data mining analysis on a user in an acquisition stage, acquires acquisition stage selection data and acquisition stage development data, and deduces whether the current user is further expanded or not through data analysis; after it is determined that an authorization request is initiated to the development user after expansion, and after authorization is completed, the current development user is set as a financial auxiliary user; the development stage analysis unit performs risk assessment on the development users in the development stage; and the retention stage analysis unit performs user credit analysis on the retention stage.

A system and method for soft credit approval using text redirect. The first step is engagement by a user computing device, such as a mobile phone, with a call-to-action element that may be embedded in various media including a QR code or NFC-enabled beacon and that triggers a text message to auto-populate on the user computing device. The auto-populated message comprises a unique identifier and consumer financial information. The second step is for the user to tap to send the auto-populated message to a credit analysis manager, which evaluates the consumer financial information according to the lender's lending criteria and instructions. Optionally, the system may integrate with a universal identification and passport manager, send pre-qualified credit offers to the consumer, and send an automatically completed credit application to the lender upon consumer approval.

The invention discloses a risk control system suitable for Internet machine rental, relates to the technical field of machine rental credit analysis, and solves the technical problem that in machine rental risk control, whether a user defaults is judged only according to a fixed threshold, ignoring the fact that the fixed threshold cannot adapt to the personalized needs of different users; the invention obtains the credit factor of the user by analyzing the number of machine rentals and the default rate; obtains the basic judgment threshold by analyzing basic information, obtains the regional influence factor based on the resident area and the credit factor, and obtains the time influence factor based on the machine rental time and the credit factor; comprehensively calculates the basic judgment threshold, the regional influence factor and the time influence factor to obtain the dynamic judgment threshold; judges the credit factor and the dynamic judgment threshold to obtain an operation instruction, and sets a deposit and a charging method based on the operation instruction and the current machine rental amount; the invention can increase the flexibility and adaptability of the credit evaluation system.

The invention discloses a credit scene risk identification method and device based on a big data model, and a medium, and relates to the technical field of risk identification, and the method comprises the steps: obtaining a safety credit feature based on the historical debit and credit data of a safety credit website; acquiring risk data features by using a security credit analysis method; establishing a credit analysis model; performing risk identification based on a credit analysis model; the method is used for solving the problems that in an existing credit scene risk identification method, when the number of users and the number of transactions of a website are large, due to the fact that the updating frequency of user data and website data in the website is high, the extraction speed of the user data is low, and the risk identification speed is low; therefore, the problem that the loan risk of the website cannot be effectively and accurately identified in real time based on the loan data of the user is solved.

The invention discloses a credit data processing method and device, a storage medium and a program product, and relates to the technical field of credit data processing, and the method comprises the steps: obtaining credit process data of a plurality of credit products, and obtaining operation data of different bottom layer credit extension systems corresponding to the plurality of credit products; converting the credit process data into a first standard data record based on the first standard data template, and converting the operation data into a second standard data record based on the second standard data template; analyzing the first standard data record and the second standard data record according to the target analysis requirement to obtain a credit analysis result; wherein the first standard data template is generated based on standard credit processes corresponding to the plurality of credit products, and the first standard data record and the second standard data record are associated based on the business application identifier. The invention aims to improve the accuracy of whole-process analysis of a plurality of credit businesses related to a financial platform so as to provide core data support for operation and risk control.

The invention discloses an enterprise credit analysis and rating method based on multi-source soft information processing, and belongs to the technical field of data processing, and the method comprises the steps: obtaining multi-source information data containing soft information and hard information; preprocessing the multi-source information data to generate preprocessed data; extracting feature data used for representing credit conditions from the preprocessed data; comparing different source features in the feature data to generate a consistency evaluation result between the feature data; performing dynamic weighted fusion on the feature data based on a consistency evaluation result to generate fused feature data; and inputting the fused feature data into the credit rating model to generate a credit rating result, and adopting a technical means of generating a consistency evaluation result by comparing different source features in the feature data and performing dynamic weighted fusion on the feature data based on the result to effectively improve the accuracy and reliability of enterprise credit rating.

The invention discloses a financial credit intelligent analysis and risk management method based on artificial intelligence, belongs to the technical field of credit analysis, and obtains a risk assessment result by establishing a financial credit association analysis model and utilizing credit data entry vectors and the like. Model establishment comprises the steps of data collection and preprocessing, feature selection and representation, model training, parameter optimization and the like. And the risk management model covers risk behavior analysis, processing limitation and result feedback. Processing limitation is carried out by obtaining the uniqueness information of the loan subject, risks are evaluated according to preset risk management parameters, and a final risk management result is obtained, fed back and updated. According to the method, internal and external credit data can be efficiently processed, standardization and feature selection and representation are carried out, after risk behavior analysis is carried out, borrowing subject information is fed back and updated, and dynamic adjustment and continuous optimization are realized.

The application provides an enterprise credit intelligent analysis method and system fusing a knowledge graph and a dialogue engine, and relates to the technical field of enterprise credit risk analysis.The application firstly acquires multi-source credit records of a target enterprise and associates source information fields to construct an enterprise credit knowledge graph; analyzes a credit query request to obtain a credit conclusion unit and a graph query condition; searches a candidate fact set and forms an evidence chain data package; groups according to a preset conflict rule and calculates a confidence score based on a preset scoring rule to determine a main conclusion and an alternative conclusion and generate a conflict explanation, which is output to a dialogue engine to generate a dialogue reply.The application associates the credit conclusion with the corresponding evidence chain, introduces a conflict grouping and confidence sorting mode, and improves the structured expression ability and conclusion reliability of the enterprise credit analysis result.

The invention discloses a credit data analysis system and method based on big data, and belongs to the field of big data analysis and mining, and the system comprises the following modules: a tag lag adjustment module, a feature drift detection module and a true and false drift adjustment module. According to the credit data analysis system and method based on the big data, accurate correspondence of the features and the tags in the time dimension is ensured, misjudgment caused by tag confirmation delay is effectively reduced, the features which change rapidly can be captured in time, the stable features keep a long-term trend, the pertinence and accuracy of feature extraction are improved, and the credit data analysis efficiency is improved. False drift caused by real distribution change and lag is effectively distinguished, the reliability of drift detection is enhanced, finally, weight adjustment is performed according to the characteristics of real drift, the response capability of the system to the latest data trend is improved, interference of old characteristics is reduced, dynamic self-adaption and intelligent management of credit analysis are realized, and the reliability of credit analysis is improved. Therefore, the accuracy, the stability and the real-time performance of credit assessment are improved.

The invention provides an enterprise credit assessment system and method, electronic equipment and a storage medium. The system is connected with a preset overall database. The enterprise credit evaluation system comprises an input module, an acquisition module, a processing module and an analysis module. The input module is used for inputting an enterprise identifier of a target enterprise of the to-be-assessed medical creative type; the acquisition module is used for acquiring various to-be-evaluated data of the target enterprise from the overall database according to the enterprise identifier of the target enterprise; wherein the to-be-evaluated data comprises data under different dimensions, and the to-be-evaluated data covers the whole life cycle of the target enterprise; the processing module is used for processing the to-be-evaluated data to obtain sub-processing results of the to-be-evaluated data; and the analysis module is used for analyzing the sub-processing result to obtain an enterprise credit analysis result. According to the invention, full-life-cycle data analysis is carried out on the medical and invasive enterprises, and the problem that the credit rating and early warning of the enterprises cannot cover the industry pain points in the beginning period and the growth period is solved.

The invention discloses an enterprise credit granting processing method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the comprehensive data of a to-be-tested enterprise; the enterprise to be tested belongs to a large-scale or medium-scale enterprise; inputting the comprehensive data into a pre-constructed admission model, and outputting a result whether admission is performed or not; when the result is admission, inputting the comprehensive data of the to-be-tested enterprise into a pre-constructed credit analysis model, and outputting a credit risk prediction value; the credit granting analysis model is used for measuring and calculating enterprise financial risk, an enterprise contract fulfillment capability value and an enterprise operation turnover capability value; and adjusting the credit granting risk prediction value by using the medical and invasive financial associated data of the to-be-tested enterprise in the target bank, and outputting a credit granting evaluation result of the target bank for the to-be-tested enterprise. According to the invention, the credit granting processing efficiency of large and medium-sized science and technology enterprises can be improved.

The invention discloses a data security risk assessment method based on unsupervised learning, and the method comprises the steps: firstly carrying out the analysis of typical marketing business scenes, such as user behavior portraits, directional activities, credit analysis, and the like, and obtaining a full-life-cycle security protection demand and a corresponding risk; and establishing an evaluation index set according to risk factor analysis, risk index selection and risk grade division. In consideration of low efficiency and limitation that a traditional fuzzy comprehensive evaluation method depends on expert scoring, the semantic similarity technology based on an improved Transform encoder is adopted, an interactive attention mechanism is combined, matching scoring of text service data and risk assessment elements is achieved, and a risk factor weight vector is effectively provided for the fuzzy comprehensive method. Then, a K-Means + + algorithm introducing a CLT (Center Limit Theorem) is utilized to optimize selection of a clustering center, unstable and inaccurate results caused by the defect of randomly selecting an initial center point are avoided, and finally, risk factor clustering is carried out according to the Euclidean distance. According to the method, the K-Means + + clustering algorithm introducing the CLT is adopted for risk assessment of the power business data to cluster risk factors, the clustering method that subjective and objective combination weight calculation is adopted for weighting the Euclidean distance is adopted for clustering instability, and the method can be effectively applied to safety risk assessment of the power business data.

The invention relates to the technical field of credit analysis, in particular to a method, a device and equipment for analyzing user credit based on a graph neural network, and the method comprises the steps: fusing the multi-source data of a user in a period of time, such as object transaction data, equipment use data, credit data, associated user data and other data of different dimensions; according to the method and the device, the credit condition of the user is determined by analyzing the nodes of different dimensions and the credit characteristics of the nodes, so that the analysis accuracy of the credit condition of the user can be improved, and the processing accuracy and efficiency of user credit decision are improved; for example, the lease admission analysis accuracy is improved, the guarantee deposit adjustment efficiency and accuracy are improved, and the overdue risk prediction accuracy and reliability are improved.

The invention discloses a multi-level processing method and system for credit big data based on data warehouse modeling, and the method comprises the steps: carrying out the classification according to the channel source and characteristics of original data, and employing different uploading modes to a data platform; the platform performs standardization processing and dirty data cleaning on the uploaded original data to construct standard data; classifying standard data based on a business process, establishing corresponding business models, analyzing relevance between the models, and performing multi-level development; carrying out standardized definition on credit indexes and binding the credit indexes to a business model to realize credit analysis and monitoring; and matching a data output mode according to the service scene, constructing a full-link data quality assurance system, and generating a data quality report. Therefore, the data processing efficiency can be improved, repeated processing can be avoided, the data reusability is improved through a multi-level development model, intuitive and visual data analysis and monitoring services can be provided, the business processing efficiency is improved, and data service requirements can be responded more quickly.

The invention relates to the technical field of automobile financial evaluation, and discloses an automobile financial service data analysis platform based on a block chain technology, which comprises a data acquisition module, a data analysis and evaluation module, a risk management module and a privacy protection module, the data acquisition module comprises a user credit data acquisition unit, a user loan data acquisition unit and a user insurance data acquisition unit, and the data analysis and evaluation module comprises a credit analysis unit, a loan analysis unit and a consumption analysis unit. The platform acquires data from different financial service platforms, lending platforms and insurance service platforms through the user credit data acquisition unit, the user lending data acquisition unit and the user insurance data acquisition unit, so that activity information of users in multiple financial fields can be comprehensively covered, and limitation caused by a single data source is avoided; the financial institution can more comprehensively know the financial behavior and credit status of the user, so that the financial institution can more comprehensively know the financial behavior and credit status of the user.

The invention belongs to the technical field of project management, and discloses a customer behavior prediction method based on big data analysis. The method comprises the following steps: collecting enterprise management big data of a customer, and constructing a customer fund return database according to a preset customer fund return index; constructing a credit analysis and evaluation model and a customer fund return behavior prediction model; performing credit analysis and evaluation by using the credit analysis and evaluation model based on the client fund return database to obtain a client credit score; based on the client fund return database and the corresponding client credit score, using a client fund return behavior prediction model to perform client fund return behavior prediction to obtain a client fund return behavior prediction result; and formulating a corresponding customer maintenance and collection strategy according to a customer fund collection behavior prediction result. According to the invention, the problems of view limitation, poor adaptability, fragmentation of customer credit management and poor overall effect in the prior art are solved.

The invention belongs to the technical field of project data analysis, and discloses a customer credit analysis and evaluation method based on full life cycle data and an AI algorithm. The method comprises the following steps: constructing a customer credit database according to first full life cycle data of a plurality of first engineering projects, and constructing a customer credit analysis and evaluation model by using an AI algorithm; collecting second full life cycle data of a second engineering project, and storing the second full life cycle data to the customer credit database; and performing customer credit analysis and evaluation on the second full life cycle data stored in the customer credit database by using the customer credit analysis and evaluation model to obtain a second customer credit analysis and evaluation result. According to the invention, the problems of weak default risk identification capability, poor accuracy, poor comprehensiveness and low efficiency in the prior art are solved.

The invention provides an order approval method based on a real-time risk control decision and related equipment, and the method comprises the steps: carrying out the data collection and standardization processing of multiple systems of an e-commerce platform through an API gateway, and obtaining the preprocessing order data; obtaining a bank statement file according to the merchant identifier, and performing field extraction and credit analysis to obtain bank statement analysis data; constructing risk features by adopting a stream-oriented computing technology, and carrying out anomaly detection through graph correlation analysis to obtain a risk scoring report; and based on the risk score, executing a multi-level approval strategy through a block chain smart contract, and adopting a distributed lock to control a concurrent process to obtain an approval decision result. According to the method, real-time acquisition of a multi-dimensional data source is realized through the API gateway, the problem of data islands is avoided, real-time fusion processing of multi-dimensional features is realized through a streaming computing technology and graph correlation analysis, the problem of time delay of traditional offline analysis is avoided, and automatic execution of an approval process is realized through a block chain smart contract.

The invention discloses a contract generation method and device, computer equipment and a computer storage medium, and relates to the technical field of contract generation. The method comprises the following steps: constructing a multi-source knowledge fusion engine according to multi-dimensional contract basic data, wherein the multi-dimensional contract basic data comprises legal dimension text information, industry dimension text information, historical contract case information and enterprise dimension text information; obtaining contract signing party information and contract prompt words, wherein the contract signing party information comprises enterprise credit analysis data and enterprise abnormal data; inputting the signing party information and the contract prompt word into a multi-source knowledge fusion engine, and performing semantic reasoning on the signing party information and the contract prompt word through the multi-source knowledge fusion engine to obtain an initial contract file; and revising the initial contract file based on an AI technology to obtain a target contract file. Therefore, through term semantic reasoning, it is ensured that the generated contract not only meets the legal requirements, but also reflects the industry characteristics and the enterprise risk control strategy, and meanwhile, the contract generation and review efficiency is improved.