CN116977062B - Risk label management system and method for financial business - Google Patents
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Abstract
The invention relates to the technical field of financial data label management, in particular to a risk label management system and a risk label management method for financial business, comprising the steps of carrying out label management on risk labels of various risk users; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events; extracting all feature change events, and setting and adjusting the weight occupied by the risk value corresponding to each wind control dimension in the process of calculating the comprehensive risk value based on the risk value occupation deviation condition presented by the risk users which are mutually reference objects in different wind control dimensions in each feature change event; predicting and evaluating the probability of risk label change of each risk user; and (5) assisting the wind control personnel to timely complete risk monitoring processing business for all risk users of which the residual risk labels are not cleared.
Description
Technical Field
The invention relates to the technical field of financial data label management, in particular to a risk label management system and method for financial business.
Background
For financial credit business, loan approval is an important link of a loan flow, the efficiency and accuracy of loan approval are directly related to loan risks, and the premise of approval is that an air control person is required to complete risk assessment; financial big data wind control refers to the fact that a financial manager uses big data technology to analyze and judge the degree of business risk of financial business data, and effectively controls influence caused by the risk.
Financial wind control scenes are usually changeable, and business personnel are often required to adjust strategies in time according to business changes and risk needs of users under the changeable wind control scenes, and follow-up financial business processing is completed based on risk labels of the users, so the risk labels of the users are a premise of developing a plurality of financial businesses.
Disclosure of Invention
The present invention is directed to a risk tag management system and method for financial business, so as to solve the problems set forth in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a risk tag management method for financial transactions, the method comprising:
step S100: collecting user data of each user in a financial service system, expanding multidimensional risk assessment on each user by setting a plurality of wind control rules to obtain comprehensive risk values presented by each user on a plurality of wind control dimensions, judging whether the user is a risk user or not based on the comprehensive risk values, and carrying out label management on risk labels of each risk user;
step S200: acquiring marked time of risk labels corresponding to all risk users, and setting two risk users with the marked time difference smaller than a difference threshold and risk labels removed as a group of risk users which are mutually reference objects; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events;
step S300: extracting all feature change events, and setting and adjusting the weight occupied by the risk value corresponding to each wind control dimension in the process of calculating the comprehensive risk value based on the risk value occupation deviation condition presented by the risk users which are mutually reference objects in different wind control dimensions in each feature change event;
step S400: based on the weight value corresponding to each wind control dimension after adjustment, acquiring a risk value presented by each risk user of which the residual risk label is not cleared in the wind control dimension corresponding to each wind control rule, calculating the comprehensive risk value of each risk user again, and predicting and evaluating the probability of risk label change of each risk user;
step S500: and acquiring the probability of risk label change corresponding to each risk user of which the residual risk label is not cleared, sequencing according to the probability value from large to small, feeding back a wind control personnel port, and assisting in timely completing risk monitoring processing service for each risk user of which the residual risk label is not cleared.
Further, step S100 includes:
step S101: setting a wind control rule corresponding to one wind control dimension, and respectively carrying out risk evaluation on the user data of each user by each wind control rule to obtain a risk value presented by the user data of each user in the wind control dimension corresponding to each wind control rule; accumulating the risk values presented by the user data of each user on different wind control dimensions to obtain the comprehensive risk value of each user;
step S102: marking risk labels for users with comprehensive risk values greater than or equal to a risk threshold value, and setting the risk labels as risk users; whenever the comprehensive risk value corresponding to any risk user is captured to be smaller than the risk threshold value, the risk label of the risk user is cleared; one user is cleared from the marked risk tag to the risk tag, and one risk tag change occurs on one user.
Further, step S200 includes:
step S201: if there is a group of risk users A, B who are reference objects, the risk user A is marked with a risk tag for a time of TA 1 The time for which risk user B is marked with a risk tag is TB 1 Wherein, |TA 1 -TB 1 Tr is smaller than or equal to Tr, and Tr represents a difference threshold;
step S202: the captured risk user A is reduced from the comprehensive risk value a to be smaller than the risk threshold h, and the time when the corresponding risk label is cleared is set as TA 2 Reducing the comprehensive risk value B of the risk user B to be smaller than the risk threshold h, and setting the time when the corresponding risk label is cleared as TB 2 ；
Step S203: acquiring the time length of changing the risk label of the corresponding risk user A as LA (a-h) =TA 2 -TA 1 Acquiring a time length LB (B- > h) =TB when the corresponding risk user B generates the risk label change 2 -TB 1 ；
Step S204: when a > b is satisfied and LA < LB, it is determined that there is a feature change event between risk users A, B who are mutually reference targets.
Further, step S300 includes:
step S301: capturing risk values x and corresponding comprehensive risk values Q which are presented in different wind control dimensions when the risk labels are marked for risk users which are mutually reference objects in each characteristic change event, and obtaining a occupation ratio P=x/Q corresponding to each wind control dimension;
step S302: the risk users which are mutually referred objects in a certain characteristic change event are D, E respectively, and the comprehensive risk value D when the risk label is marked by D and the comprehensive risk value E when the risk label is marked by E are set to satisfy D>e, performing the step of; respectively carrying out deviation comparison on the occupancy values corresponding to D, E in each wind control dimension, and setting an occupancy deviation threshold g; when D is the ratio P in the ith wind control dimension D (i) Ratio P of E to the ith windage dimension E (i) Satisfy P E (i)-P D (i)>g, judging the ith wind control dimension as a characteristic influence dimension in a certain characteristic change event;
step S303: calculating a characteristic index beta=n/M for each wind control dimension respectively; wherein n represents the total number of times each wind control dimension is determined to be a characteristic influencing dimension; m represents the total number of feature change events; sequencing all the wind control dimensions from large to small based on the corresponding characteristic indexes, and endowing the wind control dimensions with corresponding weight values based on the corresponding sequences;
in the risk assessment process, the risk values obtained by different risk dimension assessments are different in value on the premise that the risk values obtained by different risk dimension assessments are the same in value, namely, the importance degree is different, so that the risk values are inaccurate according to the same data measurement standard.
Further, step S400 includes:
step S401: extracting weight values corresponding to all risk dimensions, and calculating the weight value ratio alpha=f/G of each risk dimension; wherein f represents weight values corresponding to all risk dimensions, and G represents the total weight value obtained by accumulating the weight values corresponding to all risk dimensions; acquiring a comprehensive risk value W obtained by recalculating each risk user of the remaining uncleaned risk labels, acquiring a risk value F of each risk dimension corresponding to each risk user of the remaining uncleaned risk labels, and calculating a risk contribution threshold value psi=W×alpha corresponding to each risk dimension for each risk user of the remaining uncleaned risk labels;
step S402: when a risk user with some remaining risk labels not cleared corresponds to the risk value F of the jth risk dimension j Risk contribution threshold value ψ corresponding to jth risk dimension j Satisfy F between j >Ψ j Judging the j-th risk dimension as a risk dimension in which risk unbalance exists for a certain risk user with a residual risk label which is not cleared;
step S403: acquiring the total number K of risk dimensions with risk unbalance of each risk user with the risk labels not removed, calculating the average weight value H corresponding to the K risk dimensions, and calculating the probability value delta=1/(K+H) of each risk user with risk label change;
the larger the total number K of risk dimensions with risk unbalance is, the longer the risk problem of a risk user is, the longer the time required for recovering to be normal is, the smaller the probability of risk label change is, the weight value is obtained by analyzing and extracting feature change events in the steps, the influence degree value of the risk of the user for recovering to be normal is affected, the larger the weight value corresponding to one risk dimension is, the more important the risk evaluation of the dimension is for the user, and if the risk value exists in the dimension, the longer the time required for recovering to be normal is, and the smaller the probability of risk label change is in the same time.
In order to better implement the method, a risk tag management system is also provided, and the system comprises: the system comprises a label management module, a characteristic change event identification management module, a dimension weight analysis management module, a risk label change probability estimation module and a risk monitoring service management module;
the label management module is used for collecting user data for each user in the financial business system, expanding multidimensional risk evaluation for each user by setting a plurality of wind control rules to obtain comprehensive risk values presented by each user on a plurality of wind control dimensions, judging whether the user is a risk user or not based on the comprehensive risk values, and carrying out label management on risk labels for each risk user;
the feature change event identification management module is used for acquiring marked time of the risk labels corresponding to all risk users, and setting two risk users with the risk labels being cleared as a group of risk users which are mutually reference objects, wherein the difference between the marked time is smaller than a difference threshold; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events;
the dimension weight analysis management module is used for extracting all feature change events, and setting and adjusting weights of risk values corresponding to all the wind control dimensions in the process of calculating and obtaining comprehensive risk values based on the risk value proportion deviation conditions of risk users which are mutually reference objects in different wind control dimensions in all the feature change events;
the risk tag change probability estimation module is used for acquiring the risk value presented by each risk user of which the risk tag is not cleared in the wind control dimension corresponding to each wind control rule according to the adjusted weight value corresponding to each wind control dimension, calculating the comprehensive risk value of each risk user again, and carrying out prediction evaluation on the probability of each risk user for risk tag change;
the risk monitoring service management module is used for acquiring the probability of risk label change corresponding to each risk user of which the residual risk label is not cleared, sequencing the risk labels according to the probability value from big to small, feeding back the air control personnel port, and assisting in timely completing the risk monitoring processing service for each risk user of which the residual risk label is not cleared.
Further, the label management module comprises a risk user judgment management unit and a risk label mark management unit;
the risk user judging and managing unit is used for collecting user data for all users in the financial service system, expanding multidimensional risk assessment for all users by setting a plurality of wind control rules to obtain comprehensive risk values presented by all users in a plurality of wind control dimensions, and judging whether the users are risk users based on the comprehensive risk values;
the risk tag label management unit is used for marking a risk tag for a user with a comprehensive risk value greater than or equal to a risk threshold value as a risk user; whenever the comprehensive risk value corresponding to any risk user is captured to be smaller than the risk threshold value, the risk label of the risk user is cleared;
further, the characteristic change event identification management module comprises a risk tag change comparison unit and a characteristic change event capturing and identifying unit;
the risk tag change comparison unit is used for obtaining marked time of the risk tag corresponding to each risk user, and setting two risk users with the risk tag removed as a group of risk users which are mutually reference objects, wherein the difference between the marked time is smaller than a difference threshold; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects;
feature change event capturing and identifying means for analyzing risk tag change information among risk users who are mutually referred to each other, and capturing feature change events.
Compared with the prior art, the invention has the following beneficial effects: according to the risk management method and the risk management system, the monitoring of the label change information is carried out on the users marked with the risk labels in real time, the risk assessment strategy which needs to be adjusted in the changeable financial wind control scene is captured from the dimension of the data, the data analysis on the comprehensive risk values obtained by the users in multiple dimensions can be realized, the probability value prediction assessment of risk change is carried out on the users which do not eliminate the risk labels based on the label change condition of the historical risk users, and the service management of the user risk labels is realized more scientifically, orderly and efficiently by assisting wind control personnel.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a risk tag management method for financial transactions according to the present invention;
fig. 2 is a schematic diagram of a risk tag management system for financial services according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: a risk tag management method for financial transactions, the method comprising:
step S100: collecting user data of each user in a financial service system, expanding multidimensional risk assessment on each user by setting a plurality of wind control rules to obtain comprehensive risk values presented by each user on a plurality of wind control dimensions, judging whether the user is a risk user or not based on the comprehensive risk values, and carrying out label management on risk labels of each risk user;
wherein, step S100 includes:
step S101: setting a wind control rule corresponding to one wind control dimension, and respectively carrying out risk evaluation on the user data of each user by each wind control rule to obtain a risk value presented by the user data of each user in the wind control dimension corresponding to each wind control rule; accumulating the risk values presented by the user data of each user on different wind control dimensions to obtain the comprehensive risk value of each user;
step S102: marking risk labels for users with comprehensive risk values greater than or equal to a risk threshold value, and setting the risk labels as risk users; whenever the comprehensive risk value corresponding to any risk user is captured to be smaller than the risk threshold value, the risk label of the risk user is cleared; clearing a user from the marked risk tag to the risk tag, and setting the risk tag as a risk tag change occurring on the user;
step S200: acquiring marked time of risk labels corresponding to all risk users, and setting two risk users with the marked time difference smaller than a difference threshold and risk labels removed as a group of risk users which are mutually reference objects; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events;
wherein, step S200 includes:
step S201: if there is a group of risk users A, B who are reference objects, the risk user A is marked with a risk tag for a time of TA 1 The time for which risk user B is marked with a risk tag is TB 1 Wherein, |TA 1 -TB 1 Tr is smaller than or equal to Tr, and Tr represents a difference threshold;
step S202: the captured risk user A is reduced from the comprehensive risk value a to be smaller than the risk threshold h, and the time when the corresponding risk label is cleared is set as TA 2 Reducing the comprehensive risk value B of the risk user B to be smaller than the risk threshold h, and setting the time when the corresponding risk label is cleared as TB 2 ；
Step S203: acquiring the time length of changing the risk label of the corresponding risk user A as LA (a-h) =TA 2 -TA 1 Acquiring a time length LB (B- > h) =TB when the corresponding risk user B generates the risk label change 2 -TB 1 ；
Step S204: when a > b is satisfied and LA < LB, determining that there is a feature change event between risk users A, B who are mutually reference objects;
step S300: extracting all feature change events, and setting and adjusting the weight occupied by the risk value corresponding to each wind control dimension in the process of calculating the comprehensive risk value based on the risk value occupation deviation condition presented by the risk users which are mutually reference objects in different wind control dimensions in each feature change event;
wherein, step S300 includes:
step S301: capturing risk values x and corresponding comprehensive risk values Q which are presented in different wind control dimensions when the risk labels are marked for risk users which are mutually reference objects in each characteristic change event, and obtaining a occupation ratio P=x/Q corresponding to each wind control dimension;
step S302: the risk users which are mutually referred objects in a certain characteristic change event are D, E respectively, and the comprehensive risk value D when the risk label is marked by D and the comprehensive risk value E when the risk label is marked by E are set to satisfy D>e, performing the step of; respectively carrying out deviation comparison on the occupancy values corresponding to D, E in each wind control dimension, and setting an occupancy deviation threshold g; when D is the ratio P in the ith wind control dimension D (i) Ratio P of E to the ith windage dimension E (i) Satisfy P E (i)-P D (i)>g, judging the ith wind control dimension as a characteristic influence dimension in a certain characteristic change event;
step S303: calculating a characteristic index beta=n/M for each wind control dimension respectively; wherein n represents the total number of times each wind control dimension is determined to be a characteristic influencing dimension; m represents the total number of feature change events; sequencing all the wind control dimensions from large to small based on the corresponding characteristic indexes, and endowing the wind control dimensions with corresponding weight values based on the corresponding sequences;
for example, there are five wind control rules in total, namely five wind control dimensions, where all wind control dimensions are ordered from large to small based on corresponding feature indices:
the corresponding order of the wind control rule a is 1, namely the corresponding characteristic index is the largest, and the weight occupied by the risk value obtained based on the wind control rule a in the process of calculating the comprehensive risk value is set to be the largest;
the corresponding order of the wind control rule b is 2, namely the corresponding characteristic index is smaller than a, and the weight of the risk value obtained based on the wind control rule b in the process of calculating the comprehensive risk value is set to be inferior to a;
the corresponding order of the wind control rule c is 3, namely the corresponding characteristic index is smaller than b, and the weight of the risk value obtained based on the wind control rule c in the process of calculating the comprehensive risk value is set to be inferior to b;
the corresponding order of the wind control rule d is 4, namely the corresponding characteristic index is smaller than c, and the weight of the risk value obtained based on the wind control rule d in the process of calculating the comprehensive risk value is set as the weight inferior to c;
the corresponding order of the wind control rule e is 5, namely the corresponding characteristic index is smaller than d, and the weight of the risk value obtained based on the wind control rule b in the process of calculating the comprehensive risk value is set to be inferior to d;
step S400: based on the weight value corresponding to each wind control dimension after adjustment, acquiring a risk value presented by each risk user of which the residual risk label is not cleared in the wind control dimension corresponding to each wind control rule, calculating the comprehensive risk value of each risk user again, and predicting and evaluating the probability of risk label change of each risk user;
wherein, step S400 includes:
step S401: extracting weight values corresponding to all risk dimensions, and calculating the weight value ratio alpha=f/G of each risk dimension; wherein f represents weight values corresponding to all risk dimensions, and G represents the total weight value obtained by accumulating the weight values corresponding to all risk dimensions; acquiring a comprehensive risk value W obtained by recalculating each risk user of the remaining uncleaned risk labels, acquiring a risk value F of each risk dimension corresponding to each risk user of the remaining uncleaned risk labels, and calculating a risk contribution threshold value psi=W×alpha corresponding to each risk dimension for each risk user of the remaining uncleaned risk labels;
step S402: when a risk user with some remaining risk labels not cleared corresponds to the risk value F of the jth risk dimension j Risk contribution threshold value ψ corresponding to jth risk dimension j Satisfy F between j >Ψ j Judging the j-th risk dimension as a risk dimension in which risk unbalance exists for a certain risk user with a residual risk label which is not cleared;
step S403: acquiring the total number K of risk dimensions with risk unbalance of all risk users with risk labels not removed, calculating the average weight value H corresponding to the K risk dimensions, and calculating the probability value delta=1/(K+H) of each risk user with risk label change.
Step S500: and acquiring the probability of risk label change corresponding to each risk user of which the residual risk label is not cleared, sequencing according to the probability value from large to small, feeding back a wind control personnel port, and assisting in timely completing risk monitoring processing service for each risk user of which the residual risk label is not cleared.
In order to better implement the method, a risk tag management system is also provided, and the system comprises: the system comprises a label management module, a characteristic change event identification management module, a dimension weight analysis management module, a risk label change probability estimation module and a risk monitoring service management module;
the label management module is used for collecting user data for each user in the financial business system, expanding multidimensional risk evaluation for each user by setting a plurality of wind control rules to obtain comprehensive risk values presented by each user on a plurality of wind control dimensions, judging whether the user is a risk user or not based on the comprehensive risk values, and carrying out label management on risk labels for each risk user;
the label management module comprises a risk user judging management unit and a risk label marking management unit;
the risk user judging and managing unit is used for collecting user data for all users in the financial service system, expanding multidimensional risk assessment for all users by setting a plurality of wind control rules to obtain comprehensive risk values presented by all users in a plurality of wind control dimensions, and judging whether the users are risk users based on the comprehensive risk values;
the risk tag label management unit is used for marking a risk tag for a user with a comprehensive risk value greater than or equal to a risk threshold value as a risk user; whenever the comprehensive risk value corresponding to any risk user is captured to be smaller than the risk threshold value, the risk label of the risk user is cleared;
the feature change event identification management module is used for acquiring marked time of the risk labels corresponding to all risk users, and setting two risk users with the risk labels being cleared as a group of risk users which are mutually reference objects, wherein the difference between the marked time is smaller than a difference threshold; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events;
the feature change event identification management module comprises a risk tag change comparison unit and a feature change event capturing and identifying unit;
the risk tag change comparison unit is used for obtaining marked time of the risk tag corresponding to each risk user, and setting two risk users with the risk tag removed as a group of risk users which are mutually reference objects, wherein the difference between the marked time is smaller than a difference threshold; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects;
feature change event capturing and identifying means for respectively analyzing risk tag change information among risk users who are mutually reference objects, and capturing feature change events;
the dimension weight analysis management module is used for extracting all feature change events, and setting and adjusting weights of risk values corresponding to all the wind control dimensions in the process of calculating and obtaining comprehensive risk values based on the risk value proportion deviation conditions of risk users which are mutually reference objects in different wind control dimensions in all the feature change events;
the risk tag change probability estimation module is used for acquiring the risk value presented by each risk user of which the risk tag is not cleared in the wind control dimension corresponding to each wind control rule according to the adjusted weight value corresponding to each wind control dimension, calculating the comprehensive risk value of each risk user again, and carrying out prediction evaluation on the probability of each risk user for risk tag change;
the risk monitoring service management module is used for acquiring the probability of risk label change corresponding to each risk user of which the residual risk label is not cleared, sequencing the risk labels according to the probability value from big to small, feeding back the air control personnel port, and assisting in timely completing the risk monitoring processing service for each risk user of which the residual risk label is not cleared.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A risk tag management method for a financial transaction, the method comprising:
step S100: collecting user data of each user in a financial service system, expanding multidimensional risk assessment on each user by setting a plurality of wind control rules to obtain comprehensive risk values presented by each user on a plurality of wind control dimensions, judging whether the user is a risk user or not based on the comprehensive risk values, and carrying out label management on risk labels of each risk user;
step S200: acquiring marked time of risk labels corresponding to all risk users, and setting two risk users with the marked time difference smaller than a difference threshold and risk labels removed as a group of risk users which are mutually reference objects; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events;
step S300: extracting all feature change events, and setting and adjusting the weight occupied by the risk value corresponding to each wind control dimension in the process of calculating the comprehensive risk value based on the risk value occupation deviation condition presented by the risk users which are mutually reference objects in different wind control dimensions in each feature change event;
step S400: based on the weight value corresponding to each wind control dimension after adjustment, acquiring a risk value presented by each risk user of which the residual risk label is not cleared in the wind control dimension corresponding to each wind control rule, calculating the comprehensive risk value of each risk user again, and predicting and evaluating the probability of risk label change of each risk user;
step S500: acquiring the probability of risk label change corresponding to each risk user of which the risk labels are not removed, sequencing according to the probability value from large to small, feeding back a wind control personnel port, and assisting in timely completing risk monitoring processing service for each risk user of which the risk labels are not removed;
the step S100 includes:
step S101: setting a wind control rule corresponding to one wind control dimension, and respectively carrying out risk evaluation on the user data of each user by each wind control rule to obtain a risk value presented by the user data of each user in the wind control dimension corresponding to each wind control rule; accumulating the risk values presented by the user data of each user on different wind control dimensions to obtain the comprehensive risk value of each user;
step S102: marking risk labels for users with comprehensive risk values greater than or equal to a risk threshold value, and setting the risk labels as risk users; each time the comprehensive risk value corresponding to any risk user is captured to be smaller than the risk threshold value, the risk label of the risk user is cleared; clearing a user from the marked risk tag to the risk tag, and setting the risk tag as a risk tag change occurring on the user;
the step S200 includes:
step S201: if a group of risk users A, B which are mutually reference objects exist, the time when the risk user A is marked with a risk tag is TA1, and the time when the risk user B is marked with a risk tag is TB1, wherein the I TA1-TB 1I is less than or equal to Tr, and Tr represents the difference threshold;
step S202: the method comprises the steps of reducing a comprehensive risk value a of a captured risk user A to be smaller than a risk threshold value h, setting time when a corresponding risk tag is cleared to be TA2, reducing a comprehensive risk value B of a risk user B to be smaller than the risk threshold value h, and setting time when the corresponding risk tag is cleared to be TB2;
step S203: acquiring the time length of the risk label change of the corresponding risk user A as LA (a-h) =TA 2-TA1, and acquiring the time length of the risk label change of the corresponding risk user B as LB (B-h) =TB 2-TB1;
step S204: when a > b is satisfied and LA < LB, determining that there is a feature change event between risk users A, B who are mutually reference objects;
the step S300 includes:
step S301: capturing risk values x and corresponding comprehensive risk values Q which are presented in different wind control dimensions when the risk labels are marked for risk users which are mutually reference objects in each characteristic change event, and obtaining a occupation ratio P=x/Q corresponding to each wind control dimension;
step S302: setting D, E risk users which are mutually referred objects in a certain characteristic change event, and setting a comprehensive risk value D when the risk label is marked by D and a comprehensive risk value E when the risk label is marked by E so as to meet D > E; respectively carrying out deviation comparison on the occupancy values corresponding to D, E in each wind control dimension, and setting an occupancy deviation threshold g; when the ratio PD (i) of the D on the ith wind control dimension and the ratio PE (i) of the E on the ith wind control dimension meet PE (i) -PD (i) > g, judging the ith wind control dimension as a characteristic influence dimension in the certain characteristic change event;
step S303: calculating a characteristic index beta=n/M for each wind control dimension respectively; wherein n represents the total number of times each of the wind control dimensions is determined to be a characteristic influencing dimension; m represents the total number of feature change events; and sequencing all the wind control dimensions from large to small based on the corresponding characteristic indexes, and endowing the wind control dimensions with corresponding weight values based on the corresponding sequences.
2. The risk tag management method for a financial service according to claim 1, wherein the step S400 includes:
step S401: extracting weight values corresponding to all risk dimensions, and calculating the weight value ratio alpha=f/G of each risk dimension; wherein f represents weight values corresponding to all risk dimensions, and G represents the total weight value obtained by accumulating the weight values corresponding to all risk dimensions; acquiring a comprehensive risk value W obtained by recalculating each risk user of the remaining uncleaned risk labels, acquiring a risk value F of each risk dimension corresponding to each risk user of the remaining uncleaned risk labels, and calculating a risk contribution threshold value psi=W×alpha corresponding to each risk dimension for each risk user of the remaining uncleaned risk labels;
step S402: when a risk user with some remaining risk labels not cleared corresponds to the risk value F of the jth risk dimension j A risk contribution threshold value ψ corresponding to the jth risk dimension j Satisfy F between j >Ψ j Judging the j-th risk dimension as a risk dimension in which risk unbalance exists for the risk users of the certain residual risk labels which are not cleared;
step S403: acquiring the total number K of risk dimensions with risk unbalance of all risk users with risk labels not removed, calculating the average weight value H corresponding to the K risk dimensions, and calculating the probability value delta=1/(K+H) of each risk user with risk label change.
3. A risk tag management system applied to a risk tag management method for financial services according to any one of claims 1 to 2, characterized in that the system comprises: the system comprises a label management module, a characteristic change event identification management module, a dimension weight analysis management module, a risk label change probability estimation module and a risk monitoring service management module;
the label management module is used for collecting user data for each user in the financial service system, expanding multidimensional risk evaluation for each user by setting a plurality of wind control rules to obtain comprehensive risk values presented by each user on a plurality of wind control dimensions, judging whether the user is a risk user or not based on the comprehensive risk values, and carrying out label management on risk labels for each risk user;
the feature change event identification management module is used for acquiring marked time of risk labels corresponding to all risk users, and setting two risk users with the risk labels removed as a group of risk users which are mutually reference objects, wherein the difference between the marked time is smaller than a difference threshold; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects, and capturing feature change events;
the dimension weight analysis management module is used for extracting all feature change events, and setting and adjusting weights of risk values corresponding to all the wind control dimensions in the process of calculating and obtaining comprehensive risk values based on the deviation condition of the risk values presented by risk users which are mutually reference objects in different wind control dimensions in each feature change event;
the risk tag change probability estimation module is used for acquiring the risk value presented by each risk user of which the risk tag is not cleared in the wind control dimension corresponding to each wind control rule according to the adjusted weight value corresponding to each wind control dimension, calculating the comprehensive risk value of each risk user again, and predicting and evaluating the probability of each risk user for risk tag change;
the risk monitoring service management module is used for acquiring the probability of risk label change corresponding to each risk user with the remaining risk labels not removed, sequencing the risk labels according to the probability value from big to small, feeding back the air control personnel port, and assisting in timely completing the risk monitoring processing service for each risk user with the remaining risk labels not removed.
4. The risk tag management system of claim 3, wherein the tag management module comprises a risk user judgment management unit, a risk tag label management unit;
the risk user judging and managing unit is used for collecting user data for all users in the financial service system, expanding multidimensional risk evaluation for all users by setting a plurality of wind control rules to obtain comprehensive risk values presented by all users in a plurality of wind control dimensions, and judging whether the users are risk users based on the comprehensive risk values;
the risk tag label management unit is used for marking the risk tag of the user with the comprehensive risk value being greater than or equal to the risk threshold value as a risk user; and clearing the risk label of the risk user every time the comprehensive risk value corresponding to any risk user is captured to be smaller than the risk threshold value.
5. The risk tag management system of claim 3, wherein the characteristic change event identification management module comprises a risk tag change comparison unit and a characteristic change event capture identification unit;
the risk tag change comparison unit is used for obtaining marked time of the risk tag corresponding to each risk user, and setting two risk users with the risk tag removed as a group of risk users which are mutually reference objects, wherein the difference between the marked time is smaller than a difference threshold; respectively comparing and analyzing risk label changes among risk users which are mutually reference objects;
the feature change event capturing and identifying unit analyzes the risk tag change information in each group of risk users who are mutually reference objects, and captures a feature change event.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109389486A (en) * | 2018-08-27 | 2019-02-26 | 深圳壹账通智能科技有限公司 | Loan air control rule adjustment method, apparatus, equipment and computer storage medium |
| CN109858737A (en) * | 2018-12-18 | 2019-06-07 | 平安科技（深圳）有限公司 | Rating Model method of adjustment, device and computer equipment based on model deployment |
| CN110647527A (en) * | 2019-08-30 | 2020-01-03 | 北京百度网讯科技有限公司 | Big data-based invalid label clearing method and device, equipment and readable medium |
| CN111353702A (en) * | 2020-02-28 | 2020-06-30 | 中国工商银行股份有限公司 | Change operation risk calculation method and device |
| CN115102834A (en) * | 2022-04-27 | 2022-09-23 | 浙江大学 | Change risk assessment method, equipment and storage medium |
| CN115146890A (en) * | 2021-03-31 | 2022-10-04 | 顺丰科技有限公司 | Enterprise operation risk warning method and device, computer equipment and storage medium |
| CN115391701A (en) * | 2022-07-26 | 2022-11-25 | 杭州又拍云科技有限公司 | Internet content risk analysis and early warning method |
| CN115965233A (en) * | 2021-10-09 | 2023-04-14 | 北京房江湖科技有限公司 | Risk early warning method and device for transaction complaints, storage medium and computer program product |
| CN116485185A (en) * | 2023-04-23 | 2023-07-25 | 深圳市精锐纵横网络技术有限公司 | Enterprise risk analysis system and method based on comparison data |
-
2023
- 2023-08-04 CN CN202310974918.5A patent/CN116977062B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109389486A (en) * | 2018-08-27 | 2019-02-26 | 深圳壹账通智能科技有限公司 | Loan air control rule adjustment method, apparatus, equipment and computer storage medium |
| CN109858737A (en) * | 2018-12-18 | 2019-06-07 | 平安科技（深圳）有限公司 | Rating Model method of adjustment, device and computer equipment based on model deployment |
| CN110647527A (en) * | 2019-08-30 | 2020-01-03 | 北京百度网讯科技有限公司 | Big data-based invalid label clearing method and device, equipment and readable medium |
| CN111353702A (en) * | 2020-02-28 | 2020-06-30 | 中国工商银行股份有限公司 | Change operation risk calculation method and device |
| CN115146890A (en) * | 2021-03-31 | 2022-10-04 | 顺丰科技有限公司 | Enterprise operation risk warning method and device, computer equipment and storage medium |
| CN115965233A (en) * | 2021-10-09 | 2023-04-14 | 北京房江湖科技有限公司 | Risk early warning method and device for transaction complaints, storage medium and computer program product |
| CN115102834A (en) * | 2022-04-27 | 2022-09-23 | 浙江大学 | Change risk assessment method, equipment and storage medium |
| CN115391701A (en) * | 2022-07-26 | 2022-11-25 | 杭州又拍云科技有限公司 | Internet content risk analysis and early warning method |
| CN116485185A (en) * | 2023-04-23 | 2023-07-25 | 深圳市精锐纵横网络技术有限公司 | Enterprise risk analysis system and method based on comparison data |
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|---|---|
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