CN118967305A - A financial risk control method based on big data - Google Patents
A financial risk control method based on big data Download PDFInfo
- Publication number
- CN118967305A CN118967305A CN202411119194.7A CN202411119194A CN118967305A CN 118967305 A CN118967305 A CN 118967305A CN 202411119194 A CN202411119194 A CN 202411119194A CN 118967305 A CN118967305 A CN 118967305A
- Authority
- CN
- China
- Prior art keywords
- data
- financial
- risk
- model
- centrality
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Business, Economics & Management (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Strategic Management (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Marketing (AREA)
- Economics (AREA)
- Pure & Applied Mathematics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Evolutionary Biology (AREA)
- Tourism & Hospitality (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Development Economics (AREA)
- Technology Law (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
本发明公开了一种基于大数据的金融风控方法，涉及金融风控技术领域，一种基于大数据的金融风控方法，包括数据收集、数据预处理、建立图数据库、风险分析、风险预估模型建立、预警机制建立、预警实施。通过结合社交网络数据与金融数据，提高了风险识别的全面性，并引入复杂网络分析技术提升风险控制的智能化水平。同时，实现了实时风险监控与预警，降低了运营成本，并增强了决策支持。总体而言，本发明为金融机构提供了一种更加高效、准确的风险控制解决方案。
The present invention discloses a financial risk control method based on big data, which relates to the technical field of financial risk control. The financial risk control method based on big data includes data collection, data preprocessing, establishment of a graph database, risk analysis, establishment of a risk estimation model, establishment of an early warning mechanism, and early warning implementation. By combining social network data with financial data, the comprehensiveness of risk identification is improved, and the introduction of complex network analysis technology improves the intelligent level of risk control. At the same time, real-time risk monitoring and early warning are achieved, operating costs are reduced, and decision support is enhanced. In general, the present invention provides a more efficient and accurate risk control solution for financial institutions.
Description
Technical Field
The invention relates to the technical field of financial wind control, in particular to a financial wind control method based on big data.
Background
In the financial credit business, the repayment capability of high-risk customers and the identification of fraudulent customers are main risk points, so that a set of wind control decision engine is needed for the wind control link of the financial credit business, and the principle is that basic indexes are derived into variables, rules and rule sets, and the basic indexes are comprehensively combined into a strategy to carry out wind control audit and output wind control conclusion.
With the development of big data technology, the financial wind control development based on big data technology is rapid, and big data technology can effectively improve financial data risk control's accuracy, and risk control process is efficient and intelligent degree is high.
However, the current financial wind control big data system mainly relies on financial data to perform risk control management, and for users with fewer financial activities, the supervision capability of the system is relatively weak, and the system has the defect of comprehensiveness.
Disclosure of Invention
The invention aims at: in order to solve the problem that the current financial wind control big data system mainly relies on financial data to carry out risk control management, and has relatively weak supervision capability and defects in comprehensiveness for users with fewer financial activities, the invention provides a financial wind control method based on big data.
The invention adopts the following technical scheme for realizing the purposes:
a financial wind control method based on big data comprises the following steps:
S1, collecting data, namely collecting user data from a social network platform;
S2, data preprocessing, namely cleaning the collected data, removing irrelevant information, and processing missing values and abnormal values. Then, mapping the user data into nodes and edges in the graph database, and ensuring that the data is converted into a format required by the graph database;
s3, establishing a graph database, selecting a graph database system, and importing the preprocessed data into the graph database, wherein each user can be represented as a node in the graph database, and the relationship among the users is represented as the edges among the nodes;
S4, risk analysis, namely analyzing a network structure in a graph database by using a complex network analysis technology, calculating a centrality index of the network, and identifying key nodes and potential community structures;
S5, establishing a risk prediction model, and establishing a financial risk prediction model by using a multiple linear regression algorithm based on the complex network analysis result and combining financial risk indexes (such as credit scores, transaction behaviors, historical default records and the like), wherein the financial risk indexes are used as dependent variables, and key nodes and community structure indexes are used as independent variables;
S6, an early warning mechanism is established, the early warning mechanism is established according to a risk prediction model, when the network structure change or the user behavior mode is detected, the network structure change or the user behavior mode after the change is calculated according to the risk prediction model, a corresponding predicted financial risk index is output, and when the financial risk index is in a risk range, the early warning system can timely send out a risk warning;
and S7, early warning implementation, namely integrating an early warning mechanism into a financial risk management platform, monitoring social network data and user behaviors in real time, and automatically notifying related personnel to take measures by the system once an early warning condition is triggered.
Further, the user data comprises user basic information and user behavior data, the user behavior data comprises friend relations and interaction behaviors among users, and the interaction behaviors comprise comment, forwarding, praise, sharing and publishing of content.
Further, the graph database system selection ArangoDB, arangoDB can build a multi-model database for distributed graphics data.
Further, the centrality index includes centrality, proximity centrality and intermediacy, the calculating the centrality index of the network includes counting the number of connections (i.e., the number of edges) of each node, calculating the proximity centrality by measuring an average shortest path length of one node to all other nodes, and calculating the intermediacy by measuring the frequency of occurrence of one node in all shortest paths, and the calculating the centrality index of the network is calculated using NetworkX graph analysis library provided by the graph database.
Further, the identifying key nodes and potential community structures includes the following steps:
A1, identifying key nodes according to the ranking of centrality indexes (centrality, near centrality and intermediacy centrality);
A2, community structure discovery, namely identifying a community structure in a network by using Louva i n community detection algorithm, and finding out densely connected node groups in the network.
Further, the establishing the financial risk prediction model by using the multiple linear regression algorithm comprises the following steps:
B1, collecting data, namely collecting user historical data including credit scores, transaction behaviors, historical violation records and key node and community structure data obtained through complex network analysis;
B2, data preprocessing: cleaning data, processing missing values and abnormal values, and carrying out data standardization or normalization to ensure data quality;
b3, feature selection: selecting one or more features related to financial risk from the collected data, including key node indicators and community structure indicators in the network analysis;
b4, constructing a model; constructing a model by using a multiple linear regression algorithm, wherein credit scores, transaction behaviors and historical violations are used as dependent variables, and key nodes and community structure indexes are used as independent variables;
B5, training a model, namely training the model by using historical data, and estimating model parameters, namely regression coefficients;
b6, evaluating the model, namely evaluating the performance of the model through cross validation, R square value adjustment, mean Square Error (MSE) and Root Mean Square Error (RMSE) statistical indexes;
b7, model optimization, namely adjusting model parameters according to an evaluation result, adding or deleting characteristics, and preventing overfitting by using LASSO function regularization;
and B8, deploying the model, namely deploying the optimized model into a financial wind control system.
Further, the LASSO function expression is as follows:
J(θ)＝1/2n(Xθ-Y)T(Xθ-Y)+α||θ||1
where n is the number of samples, α is a constant coefficient, and θ 1 is the L1 norm.
The beneficial effects of the invention are as follows:
1. According to the risk identification method, the comprehensiveness of risk identification is improved by combining social network data and financial data, and the intelligent level of risk control is improved by introducing a complex network analysis technology. Meanwhile, real-time risk monitoring and early warning are realized, the operation cost is reduced, and decision support is enhanced. In general, the present invention provides a more efficient and accurate risk control solution for financial institutions.
Drawings
FIG. 1 is a flow chart of the financial pneumatic control of the present invention;
FIG. 2 is a flow chart of the present invention identifying key nodes and potential community structures;
FIG. 3 is a flow chart of the present invention for creating a financial risk prediction model using a multiple linear regression algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Referring to fig. 1, the invention provides a financial wind control method based on big data, which comprises the following steps:
S1, collecting data, namely collecting user data from a social network platform;
S2, data preprocessing, namely cleaning the collected data, removing irrelevant information, and processing missing values and abnormal values. Then, mapping the user data into nodes and edges in the graph database, and ensuring that the data is converted into a format required by the graph database;
s3, establishing a graph database, selecting a graph database system, and importing the preprocessed data into the graph database, wherein each user can be represented as a node in the graph database, and the relationship among the users is represented as the edges among the nodes;
S4, risk analysis, namely analyzing a network structure in a graph database by using a complex network analysis technology, calculating a centrality index of the network, and identifying key nodes and potential community structures;
s5, establishing a risk prediction model, namely, based on the result of complex network analysis, combining financial risk indexes (such as credit scores, transaction behaviors, historical default records and the like), acquiring data from multiple channels of financial institution internal systems, public data sets, third party service providers and the like by using a multiple linear regression algorithm, and establishing the financial risk prediction model, wherein the financial risk indexes are used as dependent variables, and key nodes and community structure indexes are used as independent variables;
S6, an early warning mechanism is established, the early warning mechanism is established according to a risk prediction model, when the network structure change or the user behavior mode is detected, the network structure change or the user behavior mode after the change is calculated according to the risk prediction model, a corresponding predicted financial risk index is output, and when the financial risk index is in a risk range, the early warning system can timely send out a risk warning; risk ranges include credit reduction to bad and transaction violations, etc.;
and S7, early warning implementation, namely integrating an early warning mechanism into a financial risk management platform, monitoring social network data and user behaviors in real time, and automatically notifying related personnel to take measures by the system once an early warning condition is triggered.
The invention has the following steps when in use:
1. Improving the comprehensiveness of risk identification: by combining the social network data and the financial data, the invention can carry out more comprehensive risk assessment on users with fewer financial activities, and makes up the defects of the traditional financial wind control system.
2. Promote intelligent degree of risk control: based on a complex network analysis technology, the method can identify the key nodes and the potential community structure, so that financial risks can be estimated more accurately, and the intelligent level of risk control is improved.
3. Real-time risk monitoring and early warning: the invention integrates the early warning mechanism into the financial risk management platform, can monitor social network data and user behaviors in real time, discover and early warn potential risks in time, and effectively reduce the occurrence probability of financial risk events.
4. The operation cost is reduced: through automatic early warning mechanism, financial institution can reduce the needs of manual monitoring, reduces the running cost, improves the efficiency of risk control simultaneously.
5. Enhancing decision support: the risk prediction model can provide more scientific and accurate risk assessment results for financial institutions, and help decision makers to formulate more reasonable credit policies and risk control strategies.
In summary, the financial wind control method based on big data provided by the invention not only can improve the comprehensiveness and the intelligent degree of risk identification, but also can realize real-time risk monitoring and early warning, reduce the operation cost and enhance the decision support, thereby providing a more efficient and accurate risk control solution for financial institutions.
In this embodiment, preferably, the user data includes user basic information and user behavior data, where the user behavior data includes a friend relationship between users and an interaction behavior, and the interaction behavior includes comment, forwarding, praying, sharing and publishing content.
In this embodiment, preferably, the graph database system selection ArangoDB, arangoDB can build a multi-model database for distributed graph data, and ArangoDB graph database system allows not only to use one database for all operations, but also allows you to run special queries on data stored in different models, can minimize network jump problems, and can run queries efficiently even for distributed graph data.
In this embodiment, preferably, the centrality index includes centrality, proximity centrality and intermediacy, the calculating the centrality index of the network includes calculating the centrality by counting the number of connections (i.e., the number of edges) of each node, calculating the proximity centrality by measuring an average shortest path length from one node to all other nodes, and calculating the intermediacy index by measuring the frequency of occurrence of one node in all shortest paths, and the calculating the centrality index of the network is calculated using NetworkX graph analysis library provided by the graph database.
Referring to fig. 2, in this embodiment, preferably, the identifying key nodes and potential community structures includes the following steps:
A1, identifying key nodes according to the ranking of centrality indexes (centrality, near centrality and intermediacy centrality);
A2, community structure discovery, namely identifying a community structure in a network by using Louva i n community detection algorithm, and finding out densely connected node groups in the network.
Python code examples identifying key nodes and potential community structures:
```python
import commun ity as commun ity_l ouva i n
part i t i on＝commun ity_l ouva i n.best_part it i on(G)
for ii n range(l en(part it i on)):
pr i nt("Node{}be l ongs to commun ity{}".format(i,part i t i on[i]))
```
referring to fig. 3, in this embodiment, preferably, the method for establishing the financial risk prediction model by using the multiple linear regression algorithm includes the following steps:
B1, collecting data, namely collecting user historical data including credit scores, transaction behaviors, historical violation records and key node and community structure data obtained through complex network analysis;
B2, data preprocessing: cleaning data, processing missing values and abnormal values, and carrying out data standardization or normalization to ensure data quality;
b3, feature selection: selecting one or more features related to financial risk from the collected data, including key node indicators and community structure indicators in the network analysis;
b4, constructing a model; constructing a model by using a multiple linear regression algorithm, wherein credit scores, transaction behaviors and historical violations are used as dependent variables, and key nodes and community structure indexes are used as independent variables;
B5, training a model, namely training the model by using historical data, and estimating model parameters, namely regression coefficients;
b6, evaluating the model, namely evaluating the performance of the model through cross validation, R square value adjustment, mean Square Error (MSE) and Root Mean Square Error (RMSE) statistical indexes;
b7, model optimization, namely adjusting model parameters according to an evaluation result, adding or deleting characteristics, and preventing overfitting by using LASSO function regularization;
and B8, deploying the model, namely deploying the optimized model into a financial wind control system.
In this embodiment, preferably, the expression of the LASSO function is as follows:
J(θ)＝1/2n(Xθ-Y)T(Xθ-Y)+α||θ||1
where n is the number of samples, α is a constant coefficient, and θ 1 is the L1 norm.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A financial wind control method based on big data is characterized in that: the method comprises the following steps:
S1, collecting data, namely collecting user data from a social network platform;
S2, preprocessing data, namely cleaning the collected data, removing irrelevant information, processing missing values and abnormal values, and then mapping user data into nodes and edges in a graph database to ensure that the data is converted into a format required by the graph database;
s3, establishing a graph database, selecting a graph database system, and importing the preprocessed data into the graph database, wherein each user can be represented as a node in the graph database, and the relationship among the users is represented as the edges among the nodes;
S4, risk analysis, namely analyzing a network structure in a graph database by using a complex network analysis technology, calculating a centrality index of the network, and identifying key nodes and potential community structures;
S5, establishing a risk prediction model, and establishing the financial risk prediction model by using a multiple linear regression algorithm based on the complex network analysis result and combining financial risk indexes (credit score, transaction behavior and history default records), wherein in the model, the financial risk indexes are used as dependent variables, and the key nodes and community structure indexes are used as independent variables;
S6, an early warning mechanism is established, the early warning mechanism is established according to a risk prediction model, when the network structure change or the user behavior mode is detected, the network structure change or the user behavior mode after the change is calculated according to the risk prediction model, a corresponding predicted financial risk index is output, and when the financial risk index is in a risk range, the early warning system can timely send out a risk warning;
and S7, early warning implementation, namely integrating an early warning mechanism into a financial risk management platform, monitoring social network data and user behaviors in real time, and automatically notifying related personnel to take measures by the system once an early warning condition is triggered.
2. The financial wind control method based on big data according to claim 1, wherein: the user data comprises user basic information and user behavior data, the user behavior data comprises friend relations and interaction behaviors among users, and the interaction behaviors comprise comment, forwarding, praise, sharing and publishing of content.
3. The financial wind control method based on big data according to claim 1, wherein: the graph database system selection ArangoDB, arangoDB can build a multi-model database for distributed graph data.
4. The financial wind control method based on big data according to claim 1, wherein: the centrality index includes centrality, proximity centrality and intermediacy, the calculating the centrality index of the network includes counting a connection number calculation centrality of each node, measuring an average shortest path length calculation proximity centrality of one node to all other nodes, and measuring a frequency calculation intermediacy centrality of one node occurring in all shortest paths, and the calculating the centrality index of the network is calculated using NetworkX graph analysis library provided by a graph database.
5. The financial wind control method based on big data according to claim 4, wherein: the identification of key nodes and potential community structures comprises the following steps:
A1, identifying key nodes according to the ranking of centrality indexes (centrality, near centrality and intermediacy centrality);
a2, community structure discovery, namely identifying a community structure in a network by using a Louvain community detection algorithm, and finding a densely connected node group in the network.
6. The financial wind control method based on big data according to claim 1, wherein: the method for establishing the financial risk prediction model by using the multiple linear regression algorithm comprises the following steps of:
B1, collecting data, namely collecting user historical data including credit scores, transaction behaviors, historical violation records and key node and community structure data obtained through complex network analysis;
B2, data preprocessing: cleaning data, processing missing values and abnormal values, and carrying out data standardization or normalization to ensure data quality;
b3, feature selection: selecting one or more features related to financial risk from the collected data, including key node indicators and community structure indicators in the network analysis;
b4, constructing a model; constructing a model by using a multiple linear regression algorithm, wherein credit scores, transaction behaviors and historical violations are used as dependent variables, and key nodes and community structure indexes are used as independent variables;
B5, training a model, namely training the model by using historical data, and estimating model parameters, namely regression coefficients;
b6, evaluating the model, namely evaluating the performance of the model through cross validation, R square value adjustment, mean Square Error (MSE) and Root Mean Square Error (RMSE) statistical indexes;
b7, model optimization, namely adjusting model parameters according to an evaluation result, adding or deleting characteristics, and preventing overfitting by using LASSO function regularization;
and B8, deploying the model, namely deploying the optimized model into a financial wind control system.
7. The financial wind control method based on big data according to claim 1, wherein: the LASSO function expression is as follows:
J(θ)＝1/2n(Xθ-Y)T(Xθ-Y)+α||θ||1
where n is the number of samples, α is a constant coefficient, and θ 1 is the L1 norm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411119194.7A CN118967305A (en) | 2024-08-15 | 2024-08-15 | A financial risk control method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411119194.7A CN118967305A (en) | 2024-08-15 | 2024-08-15 | A financial risk control method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118967305A true CN118967305A (en) | 2024-11-15 |
Family
ID=93388908
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411119194.7A Pending CN118967305A (en) | 2024-08-15 | 2024-08-15 | A financial risk control method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118967305A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119379427A (en) * | 2024-12-31 | 2025-01-28 | 杭州字节方舟科技有限公司 | Risk control behavior early warning prediction method and system based on AI identification |
| CN119624661A (en) * | 2024-12-12 | 2025-03-14 | 绍兴鸿脉科技有限公司 | A financial risk early warning method and system based on big data technology |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023142424A1 (en) * | 2022-01-25 | 2023-08-03 | 国网江苏省电力有限公司南京供电分公司 | Power financial service risk control method and system based on gru-lstm neural network |
| CN117611335A (en) * | 2023-11-15 | 2024-02-27 | 中银金融科技有限公司 | Financial risk identification method, apparatus, electronic device and storage medium |
| CN118195760A (en) * | 2024-03-18 | 2024-06-14 | 上海维信荟智金融科技有限公司 | Intelligent prediction method and system for personal credit default |
-
2024
- 2024-08-15 CN CN202411119194.7A patent/CN118967305A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023142424A1 (en) * | 2022-01-25 | 2023-08-03 | 国网江苏省电力有限公司南京供电分公司 | Power financial service risk control method and system based on gru-lstm neural network |
| CN117611335A (en) * | 2023-11-15 | 2024-02-27 | 中银金融科技有限公司 | Financial risk identification method, apparatus, electronic device and storage medium |
| CN118195760A (en) * | 2024-03-18 | 2024-06-14 | 上海维信荟智金融科技有限公司 | Intelligent prediction method and system for personal credit default |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119624661A (en) * | 2024-12-12 | 2025-03-14 | 绍兴鸿脉科技有限公司 | A financial risk early warning method and system based on big data technology |
| CN119379427A (en) * | 2024-12-31 | 2025-01-28 | 杭州字节方舟科技有限公司 | Risk control behavior early warning prediction method and system based on AI identification |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN118967305A (en) | A financial risk control method based on big data | |
| CN113538126B (en) | Fraud risk prediction method and device based on GCN | |
| JP2004528628A (en) | Artificial intelligence trend system | |
| CN113449964A (en) | Enterprise financial risk monitoring and early warning system and monitoring and early warning method | |
| CN117034143A (en) | Distributed system fault diagnosis method and device based on machine learning | |
| CN117827593A (en) | Method and system for determining abnormality cause based on abnormality recognition result | |
| CN106952190A (en) | False source of houses typing Activity recognition and early warning system | |
| CN114155096B (en) | A method for banks to detect illegal transfer of online gambling funds based on tripartite graphs | |
| CN119668990B (en) | Distributed system fault location and diagnosis method and system based on log analysis | |
| CN118798743A (en) | A multi-dimensional collaborative urban physical examination intelligent decision-making system | |
| CN118279034A (en) | Internet financial wind control report analysis method and system based on artificial intelligence | |
| CN118735498A (en) | Predictive maintenance model construction method and system for coking industry equipment | |
| CN115766096A (en) | Network security protection system based on big data | |
| CN117974321A (en) | Financial product risk management and control method based on rule engine | |
| CN118917840A (en) | Intelligent data optimization management platform based on electric power operation and maintenance | |
| CN118886997B (en) | Funds chain risk behavior identification method and system based on artificial intelligence | |
| CN114118793B (en) | A local exchange risk warning method, device and equipment | |
| CN120219062A (en) | Electricity customer credit rating management method and system based on differentiated service strategy | |
| CN115660410A (en) | Enterprise risk prediction method, control device and storage medium based on big data | |
| CN119850347A (en) | Risk assessment and treatment method based on tax payer virtual opening behavior | |
| CN119558645A (en) | Method, device, electronic device and storage medium for screening potential risk data | |
| CN116894684B (en) | Big data-based computer data processing method and system | |
| CN118917938A (en) | Information processing method, apparatus, device, storage medium, and program product | |
| CN118378131A (en) | Smart meter data analysis and anomaly detection method and system | |
| CN117114686B (en) | Credit supervision method and system based on bulk transaction platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20241230 Address after: Three road 610059 Sichuan city of Chengdu Province, No. 1 East Applicant after: Chengdu University of Technology Country or region after: China Address before: Chengdu University of technology, No.1, East Third Road, erxianqiao, Chenghua District, Chengdu, Sichuan 610051 Applicant before: Zhao Xianxu Country or region before: China |