CN111861730A - Risk report early warning system and method - Google Patents
Risk report early warning system and method Download PDFInfo
- Publication number
- CN111861730A CN111861730A CN202010758383.4A CN202010758383A CN111861730A CN 111861730 A CN111861730 A CN 111861730A CN 202010758383 A CN202010758383 A CN 202010758383A CN 111861730 A CN111861730 A CN 111861730A
- Authority
- CN
- China
- Prior art keywords
- loan
- early warning
- unit
- risk
- data
- 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
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000012544 monitoring process Methods 0.000 claims abstract description 36
- 238000004458 analytical method Methods 0.000 claims abstract description 25
- 238000001514 detection method Methods 0.000 claims abstract description 6
- 230000005012 migration Effects 0.000 claims description 4
- 238000013508 migration Methods 0.000 claims description 4
- 238000011084 recovery Methods 0.000 claims description 4
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 4
- 230000006872 improvement Effects 0.000 abstract description 3
- 230000009467 reduction Effects 0.000 abstract description 3
- 230000002159 abnormal effect Effects 0.000 description 9
- 230000008520 organization Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000004080 punching Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000000638 stimulation Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004898 kneading Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention belongs to the technical field of report management, and particularly relates to a risk report early warning system and a risk report early warning method, wherein the system comprises the following steps: the input unit is used for inputting reference values of all detection indexes; the pre-loan monitoring unit is used for monitoring pre-loan data and analyzing the relation between the pre-loan data and an input reference value; the system comprises a credit monitoring unit, a credit analysis unit and a credit analysis unit, wherein the credit monitoring unit is used for monitoring the credit data and analyzing the relation between the credit data and an input reference value; the post-loan monitoring unit is used for monitoring post-loan data and analyzing the relationship between the post-loan data and an input reference value; and the early warning unit is used for giving out early warning when the analysis result of the pre-loan supervision unit, the mid-loan supervision unit or the post-loan supervision unit is that the risk exists. By using the system, the operation risk report can be supervised in a full flow and all-around way, and automatic early warning is realized, so that the effects of cost reduction and efficiency improvement are achieved.
Description
Technical Field
The invention belongs to the technical field of report management, and particularly relates to a risk report early warning system and method.
Background
For most financial institutions, the loan transaction is one of the core transactions. However, as the types of loan transactions increase at present, it is becoming increasingly difficult to supervise the loan transactions.
At present, the existing report systems are mainly divided into two categories: the method mainly comprises a risk monitoring mode based on post-loan, mainly comprises client overdue risk and compensation statistical data, and is also used for statistical analysis (including simple user figures, addresses, age stages and other indexes) of the article advance and the passing rate of the loan.
With loan administration in this manner, the following problems remain in terms of operational risk management and control: first, lack unified operation risk management platform, present each department makes risk operation statement by oneself, from the company's aspect, lacks a unified operation risk management platform, leads to "information isolated island" to produce and the processing method is different, is unfavorable for standardized construction. Secondly, the risk report information is incomplete. Pre-and post-loan monitoring is relatively scattered and unique, lacking connections to longitudinal data (as in different periods of a project), and cannot function as data and accurately locate project problems. And thirdly, the risk operation report form lacks an automatic early warning mechanism. When a certain index of a project is abnormal, the index is difficult to find in time and cannot respond in time.
Therefore, a risk report form early warning system is needed at present, which can perform full-flow and omnibearing real-time monitoring on the operation risk report form.
Disclosure of Invention
The invention aims to provide a risk report early warning system and method, which can carry out full-flow and all-around supervision on an operation risk report.
The basic scheme provided by the invention is as follows:
risk report early warning system includes:
the input unit is used for inputting reference values of all detection indexes;
the pre-loan monitoring unit is used for monitoring pre-loan data and analyzing the relation between the pre-loan data and an input reference value;
the system comprises a credit monitoring unit, a credit analysis unit and a credit analysis unit, wherein the credit monitoring unit is used for monitoring the credit data and analyzing the relation between the credit data and an input reference value;
the post-loan monitoring unit is used for monitoring post-loan data and analyzing the relationship between the post-loan data and an input reference value;
and the early warning unit is used for giving out early warning when the analysis result of the pre-loan supervision unit, the mid-loan supervision unit or the post-loan supervision unit is that the risk exists.
Basic scheme theory of operation and beneficial effect:
when the system is used, the staff can input specific reference values of various detection indexes according to actual factors such as actual service types, duty ratios of various services and the like.
The pre-loan monitoring unit is used for monitoring the pre-loan data and analyzing whether the pre-loan data has risks or not by combining with an input reference value; the in-credit monitoring unit can monitor the in-credit data and analyze whether the in-credit data has risks or not by combining with the input reference value; the post-loan unit will supervise the post-loan data and analyze whether the post-loan data is risky in combination with the input reference values. In this way, the loan information of the institution can be supervised in a full flow and all directions.
And when the analysis result of the pre-loan supervision unit, the mid-loan supervision unit or the post-loan supervision unit indicates that the risk exists, the early warning unit sends out early warning to remind the staff of the existence of the risk. Through the mode, when a certain index of the loan is abnormal, the system can find the abnormal index in time, remind workers through an early warning mode, process abnormal conditions in time and prevent the abnormal conditions from expanding to cause hidden troubles for the business development of institutions.
Compared with the prior art, the system can be used for carrying out full-flow and all-around supervision on the operation risk report, and automatically early warning, so that the effects of cost reduction and efficiency improvement are achieved.
Further, the device also comprises a receiving unit; the early warning mode of the early warning unit is to send an early warning signal to the receiving unit.
Through holding receiving element integration at staff's intelligence, even the staff is not in the office, also can in time know the condition, in time make the correspondence.
Further, the content of the early warning signal includes a risk type, a risk content and a risk degree.
The staff of being convenient for knows the condition fast, summons corresponding personnel and prepares corresponding countermeasure.
Further, the receiving unit is also used for sending out a prompt when receiving the early warning signal.
Through the mode of reminding, let the staff notice the early warning signal.
Furthermore, the reminding mode is voice.
Compared with text reminding, voice reminding has stronger stimulation and can attract the attention of workers.
Further, the input unit is also used for modifying the reference values of the indexes.
With the change of the operation condition, the specific numerical value of the reference value suitable for each index also changes, and by the arrangement, when the change occurs, a worker can modify the reference value of each index in time through the input unit, so that the system can continuously keep effectiveness and timeliness.
Further, the input unit is also used for increasing or decreasing the index type.
When the business composition of the organization changes, the worker can modify the type of the monitoring index through the input unit, and the effectiveness of the monitoring index is ensured.
Further, the pre-loan data includes the amount of the granted application, the throughput, and the rate of passage of the project; the data in the loan includes the amount of the put money, the amount of the return money and the uniform amount of the piece; the post-loan data comprises first-term overdue rate, repayment overdue rate, loan surplus at the end of a month, loan recovery rate, loan hastening rate and migration rate.
By monitoring the data, the current risk conditions of the organization can be comprehensively known. When risks occur, the overall consideration can also be carried out, and the decision which is most beneficial to the whole business is made.
The system further comprises a user analysis unit which is used for analyzing the user and classifying the user according to a preset type, wherein the preset type comprises a blacklist and a multi-head loan.
Through the user analysis unit, the working personnel can timely know the current user composition type and the proportion occupied by each type of user, and can be used as a reference basis for the tightness of each loan policy of the institution.
The invention provides a second basic scheme: the risk report early warning method uses the risk report early warning system.
Drawings
Fig. 1 is a logic block diagram of a risk early warning report system according to a first embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
As shown in fig. 1, the risk early warning report system includes an input unit, a pre-loan supervision unit, a mid-loan supervision unit, a post-loan supervision unit, an early warning unit, and a receiving unit.
The input unit and the receiving unit are integrated at a management end, and the pre-loan supervision unit, the mid-loan supervision unit, the post-loan supervision unit and the early warning unit are integrated at a server. In this embodiment, the management end is a smart phone loaded with a corresponding APP, and in other embodiments, the management end may also select a tablet or a PC loaded with a corresponding APP. In this embodiment, the server is an Tencent cloud server. And the management terminal is communicated with the server through the 5G module.
The input unit is used for inputting reference values of various detection indexes; the input unit is also used for modifying the reference value of each index and increasing or decreasing the index type.
The pre-credit supervision unit is used for supervising the pre-credit data and analyzing the relation between the pre-credit data and the input reference value. The pre-credit data includes the amount of credit applications, throughput, and project throughput.
The in-credit supervision unit is used for supervising in-credit data and analyzing the relation between the in-credit data and an input reference value. The lending data includes the amount of the money put, the amount of the money returned and the amount of the uniform of the piece.
The post-loan monitoring unit is used for monitoring post-loan data and analyzing the relationship between the post-loan data and an input reference value. The post-loan data comprises first-term overdue rate, repayment overdue rate, loan surplus at the end of a month, loan recovery rate, loan hastening rate and migration rate.
The early warning unit is used for giving out early warning when the analysis result of the pre-loan supervision unit, the mid-loan supervision unit or the post-loan supervision unit is that the risk exists. Specifically, the early warning mode of the early warning unit is to send an early warning signal to the receiving unit, and the content of the early warning signal includes a risk type, a risk content and a risk degree.
The receiving unit is used for receiving the early warning signal. The receiving unit is also used for sending out a prompt when the early warning signal is received. Through the mode of reminding, let the staff notice the early warning signal. The reminding mode in the embodiment is voice, and compared with character reminding, the voice reminding mode is strong in stimulation and can attract the attention of workers.
The specific implementation process is as follows:
when the system is used, the staff can input specific reference values of various detection indexes according to actual factors such as actual service types, duty ratios of various services and the like. When the business composition of the organization changes, the worker can modify the type of the monitoring index through the input unit, and the effectiveness of the monitoring index is ensured. Similarly, with the change of the operation condition, the specific numerical value of the reference value suitable for each index also changes, and by the arrangement, when the change occurs, a worker can modify the reference value of each index in time through the input unit, so that the system can continuously keep effectiveness and timeliness.
Then, the pre-loan supervision unit supervises the pre-loan data and analyzes whether the pre-loan data has risks by combining with the input reference value; the in-credit monitoring unit can monitor the in-credit data and analyze whether the in-credit data has risks or not by combining with the input reference value; the post-loan unit will supervise the post-loan data and analyze whether the post-loan data is risky in combination with the input reference values. Specifically, the pre-loan data includes the amount of the granted application, the throughput, and the rate of the passing of the project; the data in the loan includes the amount of the put money, the amount of the return money and the uniform amount of the piece; the post-loan data comprises first-term overdue rate, repayment overdue rate, loan surplus at the end of a month, loan recovery rate, loan hastening rate and migration rate. By monitoring the data, the current risk conditions of the organization can be comprehensively known. When risks occur, the overall consideration can also be carried out, and the decision which is most beneficial to the whole business is made.
In this way, the loan information of the institution can be supervised in a full flow and all directions.
When the analysis result of the pre-loan supervision unit, the mid-loan supervision unit or the post-loan supervision unit indicates that a risk exists (if certain index data exceeds a preset threshold), the early warning unit sends out early warning to remind a worker of the existence of the risk. Through the mode, when a certain index of the loan is abnormal, the system can find the abnormal index in time, remind workers through an early warning mode, process abnormal conditions in time and prevent the abnormal conditions from expanding to cause hidden troubles for the business development of institutions.
Through the management end of receiving element integration at the staff, the staff also can in time know the condition even not in the office, in time makes the correspondence. Through reminding, the staff of being convenient for knows the condition fast, summons corresponding personnel and prepares corresponding countermeasure.
The content of the early warning signal comprises a risk type, risk content and risk degree, so that the staff can conveniently and quickly know the situation and call corresponding staff to prepare corresponding countermeasures.
Compared with the prior art, the system can be used for carrying out full-flow and all-around supervision on the operation risk report, and automatically early warning, so that the effects of cost reduction and efficiency improvement are achieved.
The invention also provides a risk report early warning method using the risk report early warning system.
Example two
In this embodiment, the system further includes a user analysis unit, and the user analysis unit is integrated on the server.
The user analysis unit is used for analyzing the user and classifying the user according to a preset type, wherein the preset type comprises a blacklist and a multi-head loan. Through the user analysis unit, the working personnel can timely know the current user composition type and the proportion occupied by each type of user, and can be used as a reference basis for the tightness of each loan policy of the institution.
EXAMPLE III
The working condition of the user has a little influence on the paying out of the mechanism, and if the working condition of the user is fake, for example, one working unit is kneaded by a mode of stamping a fake seal (or a support relationship). Because the working unit is manufactured by kneading, the repayment ability and the repayment consciousness of the user cannot be guaranteed after the payment is released. Thus, the loan transaction of the institution is greatly attacked if the number of the users reaches a certain order of magnitude.
In order to avoid such a situation, in this embodiment, the system further includes a user side, where the user side is a smart phone loaded with a corresponding APP; the user data of the user is stored in the user side, and the user data comprises a working company; the user side is used for positioning the current position when receiving the work card punching voice information and comparing the current position with the positioning information of the company on the user data;
the user side is also used for carrying out work marking on the user; specifically, when the number of comparison times reaches a preset value, the user side calculates the ratio of address consistency, if the ratio of address consistency exceeds the preset ratio, the user is marked as work authentication, otherwise, the user is marked as work doubt; the management terminal also comprises a storage unit used for receiving and storing the working marks.
When a user punches a card on duty, the user needs to be located near the card punch, namely, at a company address, the user side is triggered to acquire the current coordinates of the user through the sound of the work punching (such as nailing Bluetooth punching), and the acquired coordinate position can ensure that the current coordinate position is the real address of the company where the user is located. And then, the user side compares the acquired current coordinate with the positioning information of the company in the user data. It can know whether the actual working address of the user is consistent with the company address in the document.
When the number of comparison times reaches a preset value (e.g. 30 times), the user side calculates the ratio of address consistency, and if the ratio exceeds the preset ratio (e.g. 80%, considering the situation that the worker may go out sometimes), the working unit of the user is true. Thus, the user is marked as "job authenticated". Otherwise, the user is marked as "in doubt with work". After the storage unit structure stores the working marks, the manager of the financial institution can accurately know whether the working condition of the user is true or not through the management terminal.
By the method, the real work unit information of the user can be obtained, the work unit information of the user is verified, and negative influence on loan service of an organization due to the work unit information of the user is prevented.
Example four
Different from the third embodiment, in the present embodiment, the user side is further configured to screen the short message received by the user side, and the screening manner is to screen the short message content with the keywords related to finance or loan and the short message of the sender of the financial institution;
the user side is also used for carrying out semantic analysis on the screened short messages, and when the result of the semantic analysis is a forward result, the result is recorded as a forward score; when the result of the semantic analysis is a negative result, recording as a negative one; the user side is also used for carrying out financial marking on the user; specifically, when the screening time exceeds the preset time, the user side performs score statistics; when the statistical score is lower than a preset score, marking the user as a problem user, otherwise, marking the user as a normal user; the storage unit is also used for receiving and storing the golden mark.
The specific implementation process comprises the following steps:
when a user performs a financial loan, the user usually fills in personal telephone data, and the related content of the financial loan or repayment is also notified to the user by a short message.
By using the system, the user side can screen the short messages received by the user side, and particularly, the screening mode is to screen out the short messages with key words related to finance or loan and the short messages of senders of financial institutions. In such a way, short messages related to the loan behaviors of the users can be screened out.
Then, the user side carries out semantic analysis on the screened short messages, and records the result as a positive score when the result of the semantic analysis is a positive result (such as on-schedule payment, advance payment and the like); when the result of semantic analysis is a negative result (such as overdue payment), the record is negative one.
And when the screening time exceeds the preset time, the user end carries out financial marking on the user. Specifically, the user side carries out score statistics, and when the statistical score is lower than a preset score, the user is marked as a problem user; otherwise, the user is marked as a normal user.
After the storage unit receives and stores the financial marks, the staff of the financial institution can accurately know the real loan condition of the user. In addition, because the system only sends the financial mark condition of the user to the storage unit, the specific loan behavior of the user is not exposed, and the privacy of the user is also respected.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. Risk statement early warning system, its characterized in that includes:
the input unit is used for inputting reference values of all detection indexes;
the pre-loan monitoring unit is used for monitoring pre-loan data and analyzing the relation between the pre-loan data and an input reference value;
the system comprises a credit monitoring unit, a credit analysis unit and a credit analysis unit, wherein the credit monitoring unit is used for monitoring the credit data and analyzing the relation between the credit data and an input reference value;
the post-loan monitoring unit is used for monitoring post-loan data and analyzing the relationship between the post-loan data and an input reference value;
and the early warning unit is used for giving out early warning when the analysis result of the pre-loan supervision unit, the mid-loan supervision unit or the post-loan supervision unit is that the risk exists.
2. The risk report early warning system according to claim 1, wherein: also includes a receiving unit; the early warning mode of the early warning unit is to send an early warning signal to the receiving unit.
3. The risk report early warning system according to claim 2, wherein: the content of the early warning signal comprises a risk type, a risk content and a risk degree.
4. The risk report early warning system according to claim 2, wherein: the receiving unit is also used for sending out a prompt when the early warning signal is received.
5. The risk report early warning system according to claim 4, wherein: the reminding mode is voice.
6. The risk report early warning system according to claim 1, wherein: the input unit is also used for modifying the reference values of the indexes.
7. The risk report early warning system according to claim 1, wherein: the input unit is also used for increasing and decreasing index types.
8. The risk report early warning system according to claim 1, wherein: the pre-loan data comprises the amount of the credit application, the throughput and the item passing rate; the data in the loan includes the amount of the put money, the amount of the return money and the uniform amount of the piece; the post-loan data comprises first-term overdue rate, repayment overdue rate, loan surplus at the end of a month, loan recovery rate, loan hastening rate and migration rate.
9. The risk report early warning system according to claim 1, wherein: the system also comprises a user analysis unit which is used for analyzing the user and classifying the user according to a preset type, wherein the preset type comprises a blacklist and a multi-head loan.
10. The risk report form early warning method is characterized by comprising the following steps: use of the risk report early warning system according to any of the claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010758383.4A CN111861730A (en) | 2020-07-31 | 2020-07-31 | Risk report early warning system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010758383.4A CN111861730A (en) | 2020-07-31 | 2020-07-31 | Risk report early warning system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111861730A true CN111861730A (en) | 2020-10-30 |
Family
ID=72953819
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010758383.4A Pending CN111861730A (en) | 2020-07-31 | 2020-07-31 | Risk report early warning system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111861730A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435787A (en) * | 2021-07-21 | 2021-09-24 | 北京融和友信科技股份有限公司 | Risk management index early warning method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109086887A (en) * | 2018-08-06 | 2018-12-25 | 贵州大学 | Method for early warning of the depth RBF neural in conjunction with the AHP based on entropy weight |
| CN109191283A (en) * | 2018-08-30 | 2019-01-11 | 成都数联铭品科技有限公司 | Method for prewarning risk and system |
| CN109214617A (en) * | 2017-06-29 | 2019-01-15 | 格局商学教育科技（深圳）有限公司 | A kind of internet financial risks qualitative assessment auditing system |
| CN109785100A (en) * | 2018-12-27 | 2019-05-21 | 大象慧云信息技术有限公司 | A kind of method and system for taxation risk progress early warning |
| CN110298745A (en) * | 2019-07-03 | 2019-10-01 | 安徽省鼎众金融信息咨询服务有限公司 | A kind of overdue case classified estimation management system of credit customer |
| CN110349002A (en) * | 2019-07-02 | 2019-10-18 | 北京淇瑀信息科技有限公司 | A kind of method, apparatus and electronic equipment of consumer finance whole process monitoring and early warning |
| CN111127213A (en) * | 2019-11-19 | 2020-05-08 | 泰康保险集团股份有限公司 | Information processing method and device, storage medium and electronic equipment |
-
2020
- 2020-07-31 CN CN202010758383.4A patent/CN111861730A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109214617A (en) * | 2017-06-29 | 2019-01-15 | 格局商学教育科技（深圳）有限公司 | A kind of internet financial risks qualitative assessment auditing system |
| CN109086887A (en) * | 2018-08-06 | 2018-12-25 | 贵州大学 | Method for early warning of the depth RBF neural in conjunction with the AHP based on entropy weight |
| CN109191283A (en) * | 2018-08-30 | 2019-01-11 | 成都数联铭品科技有限公司 | Method for prewarning risk and system |
| CN109785100A (en) * | 2018-12-27 | 2019-05-21 | 大象慧云信息技术有限公司 | A kind of method and system for taxation risk progress early warning |
| CN110349002A (en) * | 2019-07-02 | 2019-10-18 | 北京淇瑀信息科技有限公司 | A kind of method, apparatus and electronic equipment of consumer finance whole process monitoring and early warning |
| CN110298745A (en) * | 2019-07-03 | 2019-10-01 | 安徽省鼎众金融信息咨询服务有限公司 | A kind of overdue case classified estimation management system of credit customer |
| CN111127213A (en) * | 2019-11-19 | 2020-05-08 | 泰康保险集团股份有限公司 | Information processing method and device, storage medium and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435787A (en) * | 2021-07-21 | 2021-09-24 | 北京融和友信科技股份有限公司 | Risk management index early warning method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9349016B1 (en) | System and method for user-context-based data loss prevention | |
| CN110457195B (en) | Method and device for obtaining local log of client, server and storage medium | |
| US6965886B2 (en) | System and method for analyzing and utilizing data, by executing complex analytical models in real time | |
| US10318894B2 (en) | Conformance authority reconciliation | |
| US11627152B2 (en) | Real-time classification of content in a data transmission | |
| US10326748B1 (en) | Systems and methods for event-based authentication | |
| US8230445B2 (en) | Event management method and system | |
| US20070124255A1 (en) | Pluggable heterogeneous reconciliation | |
| WO2020253348A1 (en) | Abnormality information identification method and apparatus, computer device and storage medium | |
| US20040138931A1 (en) | Trend detection in an event management system | |
| US20110185280A1 (en) | Computerized Toolset for Use with Oracle Forms | |
| US11297023B2 (en) | Distributed messaging aggregation and response | |
| US20210256120A1 (en) | Utilization of deceptive decoy elements to identify data leakage processes invoked by suspicious entities | |
| US20040139452A1 (en) | Dynamic recipients in an event management system | |
| CN113516337A (en) | Method and device for monitoring data security operation | |
| CN112598513B (en) | Method and device for identifying stockholder risk transaction behaviors | |
| US20210125272A1 (en) | Using Inferred Attributes as an Insight into Banking Customer Behavior | |
| CN112184238A (en) | Anti-money laundering monitoring method and device for financial leasing industry, electronic equipment and medium | |
| CN112116273A (en) | Employee query behavior risk monitoring method, device, equipment and storage medium | |
| Rahmatullah | The legal protection of sharia financial technology in Indonesia (Analysis of regulation, structure and law enforcement) | |
| Saemann et al. | Investigating GDPR fines in the light of data flows | |
| CN111861730A (en) | Risk report early warning system and method | |
| CN113706092A (en) | Engineering project supervision method and system | |
| US20140122163A1 (en) | External operational risk analysis | |
| US20220210163A1 (en) | Techniques for deployment of deceptive decoy elements in computing environments |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201030 |