CN110675240A - Monitoring method and system for risk radar early warning - Google Patents
Monitoring method and system for risk radar early warningDownload PDFInfo
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- CN110675240A CN110675240ACN201910751410.2ACN201910751410ACN110675240ACN 110675240 ACN110675240 ACN 110675240ACN 201910751410 ACN201910751410 ACN 201910751410ACN 110675240 ACN110675240 ACN 110675240A
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Abstract
Description
Technical Field
The invention relates to the field of finance, in particular to a risk radar early warning monitoring method and system.
Background
The patent with application number 201610474448.6 relates to a credit risk monitoring method and device, and the data types are deposit business data, loan business data, insurance business data and credit card business data; the risk identification can only simply distinguish abnormal or normal, qualitative analysis of the risk, namely, which type of risk belongs to, and quantitative analysis of the risk, namely, the grade evaluation of the risk are not available, and the risk evaluation system is too simple. However, the practical application scenario of credit warning is complicated, and simple risk assessment is difficult to adapt to.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a novel monitoring method and a novel monitoring system for risk radar early warning.
The invention solves the technical problems through the following technical scheme:
the invention provides a monitoring method for risk radar early warning, which is characterized by comprising the following steps:
s1, configuring a monitoring list, a monitoring period, a monitoring rule and monitoring contents under the monitoring rule, and determining a monitoring range, wherein the monitoring rule comprises a risk event monitoring mode and a key label threshold monitoring mode;
s2, acquiring the transaction data of each monitoring user in the current monitoring period in the monitoring list, cleaning the transaction data of each monitoring user, and restoring the loan behavior of each monitoring user;
s3, analyzing the monitoring content of the corresponding monitoring user in the current monitoring period based on the loan behavior of each monitoring user;
s4, comparing the monitoring content of each monitoring user in the current monitoring period with the monitoring content of the corresponding monitoring user in the previous monitoring period;
aiming at monitoring contents in a risk event monitoring mode: if the state of the monitoring content of the monitoring user in the current monitoring period changes compared with the state of the monitoring content of the monitoring user in the previous monitoring period, go to step S5;
aiming at the monitoring contents in the key label threshold monitoring mode: if the state of the monitoring content of the monitoring user in the current monitoring period changes compared with the state of the monitoring content of the monitoring user in the previous monitoring period and the threshold value in the monitoring content of the monitoring user in the current monitoring period meets the preset early warning condition, entering step S6;
s5, pushing the early warning information that the monitoring content of the monitoring user changes;
and S6, pushing the early warning information that the monitoring content of the monitoring user changes and the threshold value reaches the preset early warning condition.
Preferably, in step S4, the loan behavior data of the monitoring user in the current monitoring period is substituted into the risk scoring model for training to obtain the risk level and the default probability of the monitoring user.
Preferably, in step S1, configuring a corresponding warning tag when the monitoring content changes in the risk event monitoring mode;
in step S5, the warning information with the corresponding warning label when the monitored content of the monitoring user changes is pushed.
Preferably, in step S1, configuring a corresponding early warning tag when the monitored content changes in the key tag threshold monitoring mode and the threshold reaches a preset early warning condition;
in step S6, the warning information with the warning label corresponding to the monitoring content of the monitoring user changing and the threshold reaching the preset warning condition is pushed.
Preferably, the monitoring contents in the risk event monitoring mode include newly-increased application behaviors, newly-increased loan orders, newly-increased overdue orders, newly-increased withholding failure records, newly-increased abnormal repayment behaviors, newly-increased hit risk lists and increased risk levels;
the key tag threshold monitoring mode comprises that the current debt mechanism number is larger than a threshold, the current unbundled total amount is larger than a threshold, the current overdue mechanism number is larger than a threshold, the current overdue total amount is larger than a threshold, the overdue amount of nearly 12 months is larger than a threshold, the number of failed money deduction strokes of nearly 12 months is larger than a threshold, and the number of abnormal money repayment actions of nearly 6 months is larger than a threshold, wherein the threshold monitoring can self-define the early warning threshold of each tag.
The invention also provides a monitoring system for risk radar early warning, which is characterized by comprising a configuration module, a cleaning and restoring module, an analysis module, a comparison module, a first pushing module and a second pushing module;
the configuration module is used for configuring a monitoring list, a monitoring period, a monitoring rule and monitoring contents under the monitoring rule, and determining a monitoring range, wherein the monitoring rule comprises a risk event monitoring mode and a key tag threshold monitoring mode;
the cleaning and restoring module is used for acquiring the transaction data of each monitoring user in the current monitoring period in the monitoring list, cleaning the transaction data of each monitoring user and restoring the loan behavior of each monitoring user;
the analysis module is used for analyzing the monitoring content of the corresponding monitoring user in the current monitoring period based on the loan and loan behavior of each monitoring user;
the comparison module is used for comparing the monitoring content of each monitoring user in the current monitoring period with the monitoring content of the corresponding monitoring user in the previous monitoring period;
aiming at monitoring contents in a risk event monitoring mode: calling a first pushing module when the state of the monitoring content of the monitoring user in the current monitoring period is changed compared with the state of the monitoring content of the monitoring user in the previous monitoring period;
aiming at the monitoring contents in the key label threshold monitoring mode: calling a second pushing module when the state of the monitoring content of the monitoring user in the current monitoring period is changed compared with the state of the monitoring content of the monitoring user in the previous monitoring period and a threshold value in the monitoring content of the monitoring user in the current monitoring period meets a preset early warning condition;
the first pushing module is used for pushing the early warning information of the change of the monitoring content of the monitoring user;
the second pushing module is used for pushing the early warning information that the monitoring content of the monitoring user changes and the threshold value reaches the preset early warning condition.
Preferably, the comparison module is configured to substitute the loan behavior data of the monitoring user in the current monitoring period into the risk scoring model for training to obtain the risk level and the default probability of the monitoring user.
Preferably, the configuration module is configured to configure an early warning tag corresponding to a change in monitoring content in the risk event monitoring mode;
the first pushing module is used for pushing the early warning information with the corresponding early warning label when the monitoring content of the monitoring user changes.
Preferably, the configuration module is configured to configure the pre-warning tag corresponding to the condition that the monitored content changes and the threshold reaches a preset pre-warning condition in the key tag threshold monitoring mode;
the second pushing module is used for pushing the early warning information of the corresponding early warning label when the monitoring content of the monitoring user changes and the threshold value reaches the preset early warning condition.
Preferably, the monitoring contents in the risk event monitoring mode include newly-increased application behaviors, newly-increased loan orders, newly-increased overdue orders, newly-increased withholding failure records, newly-increased abnormal repayment behaviors, newly-increased hit risk lists and increased risk levels;
the key tag threshold monitoring mode comprises that the current debt mechanism number is larger than a threshold, the current unbundled total amount is larger than a threshold, the current overdue mechanism number is larger than a threshold, the current overdue total amount is larger than a threshold, the overdue amount of nearly 12 months is larger than a threshold, the number of failed money deduction strokes of nearly 12 months is larger than a threshold, and the number of abnormal money repayment actions of nearly 6 months is larger than a threshold, wherein the threshold monitoring can self-define the early warning threshold of each tag.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the invention can continuously monitor the actions of credit application, performance, overdue and the like of the user in the monitoring list provided by the credit agency, and send early warning information to the merchant when the monitoring rule is hit.
Drawings
Fig. 1 is a flowchart of a method for monitoring risk radar warning according to a preferred embodiment of the present invention.
Fig. 2 is a block diagram of a monitoring system for risk radar warning according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present embodiment provides a method for monitoring risk radar warning, which includes the following steps:
The early warning label is specifically as follows:
1) risk rating
The risk rating part comprises 3 tags in total, and is used for evaluating the comprehensive risk level and default probability of the user.
2) Multi-head application
The multi-head application part comprises 4 tags in total, and is statistics of the multi-head application condition of the user.
3) Multi-head debt
The multiple liability part comprises 4 tags in total, and is statistics of the multiple liability condition of the user.
4) Deterioration of score
The scoring deterioration part is 9 labels, and the scoring deterioration part is used for counting the user overdue, withholding failure, abnormal repayment and other risk behaviors.
5) Performance of contract
The performance module contains 5 tags, which are statistics of the user performance.
6) Risk list
The risk list refers to whether the user hits the blacklist.
The monitoring contents in the risk event monitoring mode comprise newly increased application behaviors, newly increased loan orders, newly increased overdue orders, newly increased withholding failure records, newly increased abnormal repayment behaviors, newly increased hit risk lists and increased risk levels.
The key tag threshold monitoring mode comprises that the current debt mechanism number is larger than a threshold, the current unbundled total amount is larger than a threshold, the current overdue mechanism number is larger than a threshold, the current overdue total amount is larger than a threshold, the overdue amount of nearly 12 months is larger than a threshold, the number of failed money deduction strokes of nearly 12 months is larger than a threshold, and the number of abnormal money repayment actions of nearly 6 months is larger than a threshold, wherein the threshold monitoring can self-define the early warning threshold of each tag.
And 102, acquiring the transaction data of each monitoring user in the current monitoring period in the monitoring list, cleaning the transaction data of each monitoring user, and restoring the loan behavior of each monitoring user.
And 103, analyzing the monitoring content of the corresponding monitoring user in the current monitoring period based on the loan behavior of each monitoring user.
And 104, comparing the monitoring content of each monitoring user in the current monitoring period with the monitoring content of the corresponding monitoring user in the previous monitoring period.
Aiming at monitoring contents in a risk event monitoring mode: if the state of the monitoring content of the monitoring user in the current monitoring period changes from the state of the monitoring content of the monitoring user in the previous monitoring period,step 105 is performed.
Aiming at the monitoring contents in the key label threshold monitoring mode: if the state of the monitoring content of the monitoring user in the current monitoring period changes compared with the state of the monitoring content of the monitoring user in the previous monitoring period and the threshold value in the monitoring content of the monitoring user in the current monitoring period meets the preset early warning condition, the process proceeds to step 106.
Instep 104, the loan behavior data of the monitoring user in the current monitoring period is substituted into the risk scoring model for training to obtain the risk level and default probability of the monitoring user.
And 105, pushing the early warning information with the corresponding early warning label when the monitoring content of the monitoring user changes.
Risk event early warning:
when a user generates a new risk behavior, the early warning information is pushed to a merchant, the risk event list is as shown in the following table, the label of the user is updated every day, and the new risk behavior is judged whether to occur or not by comparing with the previous day.
And 106, pushing the early warning information of the corresponding early warning label when the monitoring content of the monitoring user changes and the threshold value reaches a preset early warning condition.
Early warning of key labels:
and when the key label of the user changes and exceeds a threshold value compared with the key label of the user in the previous day, triggering early warning, and pushing early warning information to the merchant. The key labels are listed as follows, the new customer provides a set of default thresholds, and the merchant can also self-define the thresholds according to the self condition.
| Early warning type coding | Reason for early warning | Default threshold |
| R2001 | The current liability institution number is larger than the threshold value | |
| R2002 | The current sum of outstanding money is larger than the threshold value | |
| R2003 | The number of current overdue mechanisms is larger than the threshold value | |
| R2004 | The current total overdue amount is larger than the threshold value | |
| R2005 | The last order number of the last 12 months (M1+) is larger than the threshold value | |
| R2006 | The number of the losing money deducting strokes in the last 12 months is more than the threshold value | |
| R2007 | The number of abnormal repayment behaviors in the last 6 months is larger than a threshold value |
As shown in fig. 2, the embodiment further provides a monitoring system for risk radar warning, which includes a configuration module 1, a cleaning and restoring module 2, an analysis module 3, a comparison module 4, a first pushing module 5, and a second pushing module 6.
The configuration module 1 is configured to configure a monitoring list, a monitoring period, a monitoring rule and monitoring contents under the monitoring rule, and determine a monitoring range, where the monitoring rule includes a risk event monitoring mode and a key tag threshold monitoring mode, configure an early warning tag corresponding to a change of the monitoring contents under the risk event monitoring mode, and configure an early warning tag corresponding to a change of the monitoring contents under the key tag threshold monitoring mode and a threshold reaching a preset early warning condition.
The cleaning and restoring module 2 is used for acquiring the transaction data of each monitoring user in the current monitoring period in the monitoring list, cleaning the transaction data of each monitoring user, and restoring the loan behavior of each monitoring user.
The analysis module 3 is used for analyzing the monitoring content of the corresponding monitoring user in the current monitoring period based on the loan behavior of each monitoring user.
The comparison module 4 is used for comparing the monitoring content of each monitoring user in the current monitoring period with the monitoring content of the corresponding monitoring user in the previous monitoring period.
Aiming at monitoring contents in a risk event monitoring mode: and calling the first pushing module 5 when the state of the monitoring content of the monitoring user in the current monitoring period is changed compared with the state of the monitoring content of the monitoring user in the previous monitoring period.
Aiming at the monitoring contents in the key label threshold monitoring mode: and calling the second pushing module 6 when the state of the monitoring content of the monitoring user in the current monitoring period is changed compared with the state of the monitoring content of the monitoring user in the previous monitoring period and the threshold value in the monitoring content of the monitoring user in the current monitoring period meets the preset early warning condition.
The first pushing module 5 is configured to push the early warning information with the corresponding early warning tag when the monitoring content of the monitoring user changes.
The second pushing module 6 is configured to push the warning information of the warning label corresponding to the change of the monitoring content of the monitoring user and the threshold reaching the preset warning condition.
The invention relates to an in-credit risk monitoring product, namely radar early warning, which is used for cleaning, analyzing, monitoring and early warning risk of credit transactions of specified users. The product cleans and restores transaction data in each monitoring period (usually days) to obtain loan and repayment records of a user, cleans the travel into related labels according to the records, calculates the comprehensive risk level of the user by using a risk scoring card model, scans early warning monitoring rules of merchants one by one, and sends early warning information to the merchants in the form of URL or mail if the early warning rules are hit.
The system can form real-time monitoring and early warning in the credit unlike the traditional mode that the wind control confirms that the credit is collected again after the expiration. For credit institutions, timely risk early warning and comprehensive user portrait can help institutions to make and implement reasonable user care mechanisms, collection promotion strategies and the like in time, bad account capital loss can be effectively reduced, and collection promotion cost is saved.
The evaluation index system of the risk is relatively complete, qualitative and quantitative analysis on the risk is realized, the grading abnormity capable of early warning comprises four categories of multi-head application, multi-head liability, deterioration of the grading value, hit risk list and the like, the early warning message is sent when the early warning rule is hit, the early warning message comprises the specific reason of hitting the early warning rule, the related detailed label and comprehensive risk rating of the user and the like, and the credit agency is helped to evaluate the risk of the user more comprehensively.
The product related to the invention can accurately evaluate the category and the grade of the grading risk and output the specific label and the grade related to the risk point instead of simply returning the risk grade; meanwhile, the product provides two monitoring models of risk event monitoring and key index threshold monitoring, monitoring rules and thresholds can be configured by merchants, and the use modes of the product are more flexible and diversified; the risk evaluation system is more comprehensive and stereoscopic, and the system is more complete.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A monitoring method for risk radar early warning is characterized in that the behavior of a user is continuously monitored and early warned in time, and the method comprises the following steps:
s1, configuring a monitoring list, a monitoring period, a monitoring rule and monitoring contents under the monitoring rule, and determining a monitoring range, wherein the monitoring rule comprises a risk event monitoring mode and a key label threshold monitoring mode;
s2, acquiring the transaction data of each monitoring user in the current monitoring period in the monitoring list, cleaning the transaction data of each monitoring user, and restoring the loan behavior of each monitoring user;
s3, analyzing the monitoring content of the corresponding monitoring user in the current monitoring period based on the loan behavior of each monitoring user;
s4, comparing the monitoring content of each monitoring user in the current monitoring period with the monitoring content of the corresponding monitoring user in the previous monitoring period;
aiming at monitoring contents in a risk event monitoring mode: if the state of the monitoring content of the monitoring user in the current monitoring period changes compared with the state of the monitoring content of the monitoring user in the previous monitoring period, go to step S5;
aiming at the monitoring contents in the key label threshold monitoring mode: if the state of the monitoring content of the monitoring user in the current monitoring period changes compared with the state of the monitoring content of the monitoring user in the previous monitoring period and the threshold value in the monitoring content of the monitoring user in the current monitoring period meets the preset early warning condition, entering step S6;
s5, pushing the early warning information that the monitoring content of the monitoring user changes;
and S6, pushing the early warning information that the monitoring content of the monitoring user changes and the threshold value reaches the preset early warning condition.
2. The method for monitoring radar warning of risks according to claim 1, wherein in step S4, the lending behavior data of the monitoring user in the current monitoring period is substituted into the risk scoring model for training to obtain the risk level and the default probability of the monitoring user.
3. The method for monitoring risk radar warning as claimed in claim 1, wherein in step S1, configuring a warning tag corresponding to a change in monitoring content in the risk event monitoring mode;
in step S5, the warning information with the corresponding warning label when the monitored content of the monitoring user changes is pushed.
4. The method for monitoring radar risk early warning according to claim 1, wherein in step S1, configuring a corresponding early warning tag when the monitored content changes and the threshold reaches a preset early warning condition in a key tag threshold monitoring mode;
in step S6, the warning information with the warning label corresponding to the monitoring content of the monitoring user changing and the threshold reaching the preset warning condition is pushed.
5. The monitoring method of risk radar warning as claimed in claim 1, wherein the monitoring contents in the risk event monitoring mode include a newly added application behavior, a newly added loan order, a newly added overdue order, a newly added withholding failure record, a newly added abnormal repayment behavior, a newly added hit risk list and a risk level increase;
the key tag threshold monitoring mode comprises that the current debt mechanism number is larger than a threshold, the current unbundled total amount is larger than a threshold, the current overdue mechanism number is larger than a threshold, the current overdue total amount is larger than a threshold, the overdue amount of nearly 12 months is larger than a threshold, the number of failed money deduction strokes of nearly 12 months is larger than a threshold, and the number of abnormal money repayment actions of nearly 6 months is larger than a threshold, wherein the threshold monitoring can self-define the early warning threshold of each tag.
6. A monitoring system for risk radar early warning is characterized by comprising a configuration module, a cleaning and restoring module, an analysis module, a comparison module, a first pushing module and a second pushing module;
the configuration module is used for configuring a monitoring list, a monitoring period, a monitoring rule and monitoring contents under the monitoring rule, and determining a monitoring range, wherein the monitoring rule comprises a risk event monitoring mode and a key tag threshold monitoring mode;
the cleaning and restoring module is used for acquiring the transaction data of each monitoring user in the current monitoring period in the monitoring list, cleaning the transaction data of each monitoring user and restoring the loan behavior of each monitoring user;
the analysis module is used for analyzing the monitoring content of the corresponding monitoring user in the current monitoring period based on the loan and loan behavior of each monitoring user;
the comparison module is used for comparing the monitoring content of each monitoring user in the current monitoring period with the monitoring content of the corresponding monitoring user in the previous monitoring period;
aiming at monitoring contents in a risk event monitoring mode: calling a first pushing module when the state of the monitoring content of the monitoring user in the current monitoring period is changed compared with the state of the monitoring content of the monitoring user in the previous monitoring period;
aiming at the monitoring contents in the key label threshold monitoring mode: calling a second pushing module when the state of the monitoring content of the monitoring user in the current monitoring period is changed compared with the state of the monitoring content of the monitoring user in the previous monitoring period and a threshold value in the monitoring content of the monitoring user in the current monitoring period meets a preset early warning condition;
the first pushing module is used for pushing the early warning information of the change of the monitoring content of the monitoring user;
the second pushing module is used for pushing the early warning information that the monitoring content of the monitoring user changes and the threshold value reaches the preset early warning condition.
7. The risk radar warning monitoring system of claim 6, wherein the comparison module is configured to substitute loan behavior data of the monitoring user in the current monitoring period into the risk scoring model for training to obtain the risk level and the default probability of the monitoring user.
8. The risk radar warning monitoring system of claim 6, wherein the configuration module is configured to configure a warning tag corresponding to a change in monitoring content in the risk event monitoring mode;
the first pushing module is used for pushing the early warning information with the corresponding early warning label when the monitoring content of the monitoring user changes.
9. The risk radar warning monitoring system of claim 6, wherein the configuration module is configured to configure a warning tag corresponding to a condition that monitoring content changes and a threshold reaches a preset warning condition in a key tag threshold monitoring mode;
the second pushing module is used for pushing the early warning information of the corresponding early warning label when the monitoring content of the monitoring user changes and the threshold value reaches the preset early warning condition.
10. The monitoring system for risk radar warning as recited in claim 6, wherein the monitoring contents in the risk event monitoring mode include a newly added application behavior, a newly added loan order, a newly added overdue order, a newly added withholding failure record, a newly added abnormal repayment behavior, a newly added hit risk list and a risk level increase;
the key tag threshold monitoring mode comprises that the current debt mechanism number is larger than a threshold, the current unbundled total amount is larger than a threshold, the current overdue mechanism number is larger than a threshold, the current overdue total amount is larger than a threshold, the overdue amount of nearly 12 months is larger than a threshold, the number of failed money deduction strokes of nearly 12 months is larger than a threshold, and the number of abnormal money repayment actions of nearly 6 months is larger than a threshold, wherein the threshold monitoring can self-define the early warning threshold of each tag.
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