CN111008086A - Anti-fraud policy optimization method based on message queue - Google Patents
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Abstract
The invention provides a message queue-based anti-fraud policy optimization method, which comprises the following steps: acquiring an anti-fraud strategy to be processed, and initiating a champion challenger task; optimizing the anti-fraud policy by adopting one of the following optimization modes: (1) a gray running mode; proportional mode. The invention gradually tests and optimizes the target strategy through the grey running mode and the proportional mode, performs optimization test while finishing entering a piece, and judges the optimization condition according to the generated piece entering test report. The invention can greatly improve the optimized test efficiency of the related strategies, is beneficial to shortening the development period, saves the consumption of manpower and material resources and helps enterprises to better and more quickly finish the business requirements.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an anti-fraud strategy optimization method based on a message queue.
Background
Internet finance is a novel financial business mode for realizing fund financing, investment, loan and information auditing data analysis by utilizing internet technology and information big data in the traditional financial industry and internet enterprises, and along with the development of the traditional financial industry and the internet enterprises, more and more banks, credit companies and financial institutions select internet finance to realize related financial business.
In the pre-loan review aspect, in most cases, the user conducts information review through the provided rule engine, decision engine and model. Under different business scenes and user requirements, a user usually completes corresponding requirements in a way of customizing strategies (rules, decisions, models and third-party interfaces), but with the change of company strategies and the change of business scenes, the customized strategy method can greatly fail to meet the business requirements, and related strategies and strategy tests need to be established again, so that a large amount of time is spent on the optimization measures, and more manpower and material resources are wasted, thereby affecting the business efficiency of enterprises.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide a message queue-based anti-fraud policy optimization method.
In order to achieve the above object, an embodiment of the present invention provides a message queue-based anti-fraud policy optimization method, including the following steps:
step S1, acquiring an anti-fraud strategy to be processed, and initiating a champion challenger task;
step S2, performing optimization processing on the anti-fraud policy by using one of the following optimization modes:
(1) grey running mode:
defining a task name of an optimization test;
importing an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
all piece incoming information is fed through the anti-fraud strategy A to be processed and the newly created optimization strategy B to be tested of the anti-fraud strategy, and corresponding piece incoming reports are respectively generated;
obtaining a comparison result, and judging whether to replace the champion strategy or not; if yes, replacing the champion strategy with the grey running strategy; otherwise, retaining the champion strategy;
(2) proportional mode:
defining a task name of an optimization test;
importing an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
respectively setting the incoming proportion of an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
adding a task, carrying out incoming piece processing on all incoming piece information through the anti-fraud strategy A to be processed and a newly-created optimization strategy B to be tested of the anti-fraud strategy, and respectively generating corresponding incoming piece reports;
in the process of multiple tests, adjusting the incoming piece proportion of the strategy B according to the generated incoming piece report until the strategy B defeats the strategy A or the strategy B fails;
obtaining a comparison result, and judging whether to replace the champion strategy or not; if yes, replacing the champion strategy with the grey running strategy; otherwise, the champion strategy is reserved.
Further, in the grey running mode, generating a piece-in report of an anti-fraud strategy and returning the piece-in report to the client for viewing; and generating a incoming report of the to-be-tested optimization strategy of the anti-fraud strategy for testing and analyzing.
Further, in the proportion mode, the sum of the incoming proportion of the anti-fraud policy A to be processed and the incoming proportion of the newly created optimization policy B to be tested for the anti-fraud policy is 100%.
Further, in the proportional mode, the file feeding sequence of the strategy A and the strategy B is set to be consistent with the sequence in the file feeding file, wherein the file feeding file information is generated by random sequencing, and the file feeding according to the sequence is equal to the random file feeding.
Further, in the proportion mode, after the piece feeding proportion of the strategy A and the strategy B is set, a piece feeding file is imported; and downloading the file to be delivered through the corresponding template, carrying out user-defined editing and uploading.
Furthermore, the file to be uploaded is uploaded in two modes of dragging and uploading or clicking and uploading.
Further, in the proportional mode, when the incoming proportion is set for the first time, the incoming proportion of the strategy A is set to be higher than that of the strategy B.
Further, in the proportional mode, a plurality of servers are set to listen to the incoming information while adding tasks, and different policy contents are executed according to different marks.
Further, if the mark is a champion strategy, executing the champion strategy; if the flag is a challenger policy, the challenger policy is executed.
Further, in the grayrunning mode, the piece entering information includes: firstly, downloading a corresponding excel template according to a specific strategy, wherein the template comprises user information required by the strategy, and the templates corresponding to different strategies have different information.
According to the anti-fraud strategy optimization method based on the message queue, the target strategy is tested and optimized step by step through a grey running mode and a proportional mode (champion challenger), optimization testing is carried out while the work entering is completed, and the optimization condition is judged according to a generated work entering test report. The invention can greatly improve the optimized test efficiency of the related strategies, is beneficial to shortening the development period, saves the consumption of manpower and material resources and helps enterprises to better and more quickly finish the business requirements. Compared with other strategy optimization schemes, the method has the advantages that the user can use a grey-race strategy optimization method or a champion challenger strategy optimization method according to actual requirements, iteration is continuously optimized in the testing process, the service efficiency is improved, and solid guarantee is provided for rapid and efficient iterative optimization of related services of enterprises.
The invention provides a plurality of strategy (rules, decisions, models and third-party interfaces) optimization schemes, which enable users to individually optimize and modify strategy schemes, wherein the optimization schemes comprise a grey-race strategy optimization method and a champion challenger strategy optimization method. The invention can quickly respond to and continuously optimize the existing strategy according to the continuously changing company strategy and user requirements, provides guarantee for the iteration cycle of the normal scheme of an enterprise, is also beneficial to saving manpower and material resources, and greatly solves the problems of inflexible modification, long optimization cycle, slow deployment and the like of the traditional optimization scheme.
The invention provides services such as an anti-fraud strategy optimization, ' grey-race ' strategy optimization method, champion challenger ' strategy optimization method and the like for various financial anti-fraud systems in the fields of financial credit investigation, marketing, information security and the like, and a user can carry out strategy optimization based on an own rule set, a decision set, a scoring model or a third-party interface. According to the continuous change of the business scene and the user requirements, the invention provides a method which can slightly and accurately optimize, conveniently and rapidly test in real time, ensure the safe and efficient operation of the own business, simultaneously carry out the autonomous controllable test optimization, and gradually carry out the iterative optimization on the test strategy, thereby reducing the generation of risk events to the maximum extent, ensuring the working efficiency of enterprises and helping the enterprises to better and more quickly finish the business requirements.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a message queue based anti-fraud policy optimization method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a message queue-based anti-fraud policy optimization method according to an embodiment of the present invention;
fig. 3 is a diagram of message queue architecture according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The invention provides an anti-fraud strategy optimization method based on a message queue, which is used for carrying out optimization test while finishing entering a piece and judging the optimization condition according to a generated piece entering test report.
As shown in fig. 1 and fig. 2, the method for optimizing anti-fraud policy based on message queue according to the embodiment of the present invention includes the following steps:
and step S1, acquiring the anti-fraud strategy to be processed, and initiating a champion challenger task.
In an embodiment of the present invention, champion challenger tasks include: champion strategy and challenger strategy.
Step S2, performing optimization processing on the anti-fraud policy by using one of the following optimization modes:
(1) grey running mode:
(11) defining a task name of an optimization test;
(12) importing an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
(13) all piece incoming information is fed through an anti-fraud strategy A to be processed and a newly created optimization strategy B to be tested for the anti-fraud strategy, and corresponding piece incoming reports are respectively generated;
in the embodiment of the present invention, the incoming information is described as follows: firstly, a corresponding excel template is downloaded according to a specific strategy, wherein the template contains user information required by the strategy, such as name, age, location and the like. The template containing information corresponding to different strategies is different.
(14) Obtaining a comparison result, and judging whether to replace the champion strategy or not; if yes, replacing the champion strategy with the grey running strategy; otherwise, the champion strategy is reserved.
In particular to a strategy optimization method of a grey running mode, which is realized by a method similar to a grey test. The greyscale test is (on which a/Btesting can be performed, i.e. a part of the users continue to use product property a, a part of the users start to use product property B, if the users do not have any objection to B, the range is gradually expanded, and all users are migrated to B.
In the embodiment of the invention, A represents a policy (such as a rule, a decision, a model or a third-party interface) needing to be optimized, B is newly created to be optimized for A, and when a user enters a 'grey-running' policy optimization method, a task name of an optimization test needs to be defined first, and then the policy A and the policy B are imported. The gray running mode of the invention is different from the gray test in that all piece entering information enters through the strategy A and the strategy B, a corresponding strategy A piece entering report and a corresponding strategy B piece entering report are generated while a task is added, but only the strategy A piece entering report is returned to a client, and the strategy B piece entering report is not fed back to the user and is only used for test analysis, so that the relevant characteristics of the gray test are reflected. The strategy optimization method is suitable for a service scene with the main purpose of testing a new strategy, can effectively reduce the operation steps of a user, generates a service report and a test report by one key, and can effectively improve the updating period of the optimization strategy.
(2) Proportional mode:
(21) defining a task name of an optimization test;
firstly, performing request access:
user accesses corresponding function entrance of system through browser in web application [ container
Then define the task name, import A, B policy:
in an optimization task custom interface, firstly defining a task name of an optimization test, sequentially importing a champion strategy A and a challenger strategy B, simultaneously defining a piece-entering proportion according to business requirements, importing a piece-entering file after setting is finished, and uploading the piece-entering file after downloading, customizing and editing through a corresponding template. The file can be uploaded in two modes of dragging and uploading or clicking and uploading.
(22) Importing an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
(23) and respectively setting the incoming proportion of the anti-fraud policy A to be processed and the newly created optimization policy B to be tested for the anti-fraud policy. And the sum of the incoming proportion of the anti-fraud policy A to be processed and the incoming proportion of the optimization policy B to be tested of the newly-created anti-fraud policy is 100%.
In the embodiment of the invention, when the feeding proportion is set for the first time, the feeding proportion of the strategy A is set to be higher than that of the strategy B.
Specifically, in the initial testing process, the challenger policy proportion can be set to be a lower proportion, and the champion policy can be set to be a higher proportion, so that the user data can be scientifically analyzed and the user experience can be improved by gradually increasing the part entering proportion of the challenger policy.
(24) And adding a task, carrying out incoming piece on all incoming piece information through the anti-fraud strategy A to be processed and the newly-created optimization strategy B to be tested of the anti-fraud strategy, and respectively generating corresponding incoming piece reports.
After the piece feeding proportion of the strategy A and the strategy B is set, importing a piece feeding file; and downloading the file to be delivered through the corresponding template, carrying out user-defined editing and uploading.
In the embodiment of the invention, the file to be uploaded is uploaded in two modes of dragging and uploading or clicking and uploading.
In the grey running mode, generating a piece-in report of an anti-fraud strategy and returning the piece-in report to a client for viewing; and generating a incoming report of the to-be-tested optimization strategy of the anti-fraud strategy for testing and analyzing.
In the embodiment of the invention, the file feeding sequence of the strategy A and the strategy B is set to be consistent with the sequence in the file feeding file, wherein the file feeding file information is generated by random sequencing, and the file feeding according to the sequence is equal to the random file feeding.
It should be noted that, while adding a task, the incoming list imported by the user is pushed into the message queue, the incoming information is marked as "a (champion policy)" or "B (challenger policy)" according to the incoming proportion, and the server monitors through the set server, once the task is added, the server starts to process the incoming information, and executes different policy contents according to different marks. In order to improve the work-piece feeding efficiency, the invention sets a plurality of servers to monitor the message list at the same time, and can simultaneously execute work-piece feeding tasks. Fig. 3 is a schematic diagram of a message queue. For example, if the label is a champion policy, then a champion policy is implemented; if the flag is a challenger policy, the challenger policy is executed.
In the embodiment of the invention, after the optimization task is finished, the corresponding report is generated, and the user can simultaneously receive the champion strategy report and the challenger strategy report, so that the optimization condition can be scientifically and visually displayed. The user is helped to improve the optimization efficiency.
(25) In the process of multiple tests, adjusting the incoming piece proportion of the strategy B according to the generated incoming piece report until the strategy B defeats the strategy A or the strategy B fails;
the 'champion challenger' strategy optimization method is characterized in that after a basic first name and an import strategy are completed, an import proportion needs to be set according to needs, an import strategy is distributed according to the proportion by an import file, simultaneously generated reports are fed back to a client, the optimization method is suitable for being used by an optimization strategy which is subjected to a 'grey-running' strategy optimization scheme and the optimization performance can be basically guaranteed, and meanwhile, in the process of continuously debugging and optimizing, the import proportion of a challenger strategy can be continuously changed according to the generated import report until the challenger strategy defeats the champion strategy or the challenger strategy fails. And a solid guarantee is provided for the rapid and efficient iterative optimization of enterprise related services.
(26) Obtaining a comparison result, and judging whether to replace the champion strategy or not; if yes, replacing the champion strategy with the grey running strategy; otherwise, the champion strategy is reserved.
In an embodiment of the invention, the comparison result comprises the respective incoming reports of the "champion" strategy and the "challenger" strategy (the report result comprises the incoming quantity, the number of passed pieces, the number of rejected pieces, the number of manual audits and the number of failure pieces, and simultaneously the percentage of the number of each state is shown by a pie chart), the report contains the same content, and the comparison result is generated by the transverse comparison of the two.
In summary, the "champion challenger" policy optimization method in the proportional mode is based on the "grey-runner" policy optimization method to perform related implementation method optimization, when a user enters the "champion challenger" policy optimization module, firstly, the task name of an optimization test is defined, and the operation of importing the policy a and the policy B is performed, except that the part entering proportion of an A, B policy needs to be specified, the sum of the two proportions is one hundred percent, the meaning of the part entering proportion is the percentage of the total number of parts entering, and the part entering sequence is consistent with the sequence in the part entering file (the part entering file information is generated by random ordering, and the part entering according to the sequence is equivalent to random part entering). Among them, the a policy is called "champion" policy, and the B policy is called "challenger policy". In the process of multiple tests, the piece entering proportion of the challenger strategy can be continuously changed according to the generated piece entering report until the challenger strategy overcomes the champion strategy or the challenger strategy fails. In the process, optimization is carried out while testing, so that the optimization of the target strategy is completed.
According to the anti-fraud strategy optimization method based on the message queue, the target strategy is tested and optimized step by step through a grey running mode and a proportional mode (champion challenger), optimization testing is carried out while the work entering is completed, and the optimization condition is judged according to a generated work entering test report. The invention can greatly improve the optimized test efficiency of the related strategies, is beneficial to shortening the development period, saves the consumption of manpower and material resources and helps enterprises to better and more quickly finish the business requirements. Compared with other strategy optimization schemes, the method has the advantages that the user can use a grey-race strategy optimization method or a champion challenger strategy optimization method according to actual requirements, iteration is continuously optimized in the testing process, the service efficiency is improved, and solid guarantee is provided for rapid and efficient iterative optimization of related services of enterprises.
The invention provides a plurality of strategy (rules, decisions, models and third-party interfaces) optimization schemes, which enable users to individually optimize and modify strategy schemes, wherein the optimization schemes comprise a grey-race strategy optimization method and a champion challenger strategy optimization method. The invention can quickly respond to and continuously optimize the existing strategy according to the continuously changing company strategy and user requirements, provides guarantee for the iteration cycle of the normal scheme of an enterprise, is also beneficial to saving manpower and material resources, and greatly solves the problems of inflexible modification, long optimization cycle, slow deployment and the like of the traditional optimization scheme.
The invention provides services such as an anti-fraud strategy optimization, ' grey-race ' strategy optimization method, champion challenger ' strategy optimization method and the like for various financial anti-fraud systems in the fields of financial credit investigation, marketing, information security and the like, and a user can carry out strategy optimization based on an own rule set, a decision set, a scoring model or a third-party interface. According to the continuous change of the business scene and the user requirements, the invention provides a method which can slightly and accurately optimize, conveniently and rapidly test in real time, ensure the safe and efficient operation of the own business, simultaneously carry out the autonomous controllable test optimization, and gradually carry out the iterative optimization on the test strategy, thereby reducing the generation of risk events to the maximum extent, ensuring the working efficiency of enterprises and helping the enterprises to better and more quickly finish the business requirements.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A fraud prevention strategy optimization method based on a message queue is characterized by comprising the following steps:
step S1, acquiring an anti-fraud strategy to be processed, and initiating a champion challenger task;
step S2, performing optimization processing on the anti-fraud policy by using one of the following optimization modes:
(1) grey running mode:
defining a task name of an optimization test;
importing an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
all piece incoming information is fed through the anti-fraud strategy A to be processed and the newly created optimization strategy B to be tested of the anti-fraud strategy, and corresponding piece incoming reports are respectively generated;
obtaining a comparison result, and judging whether to replace the champion strategy or not; if yes, replacing the champion strategy with the grey running strategy; otherwise, retaining the champion strategy;
(2) proportional mode:
defining a task name of an optimization test;
importing an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
respectively setting the incoming proportion of an anti-fraud policy A to be processed and a newly created optimization policy B to be tested for the anti-fraud policy;
adding a task, carrying out incoming piece processing on all incoming piece information through the anti-fraud strategy A to be processed and a newly-created optimization strategy B to be tested of the anti-fraud strategy, and respectively generating corresponding incoming piece reports;
in the process of multiple tests, adjusting the incoming piece proportion of the strategy B according to the generated incoming piece report until the strategy B defeats the strategy A or the strategy B fails;
obtaining a comparison result, and judging whether to replace the champion strategy or not; if yes, replacing the champion strategy with the grey running strategy; otherwise, the champion strategy is reserved.
2. The message queue-based anti-fraud policy optimization method of claim 1, wherein in the grey-running mode, an incoming report of the anti-fraud policy is generated and returned to the customer for viewing; and generating a incoming report of the to-be-tested optimization strategy of the anti-fraud strategy for testing and analyzing.
3. The message queue-based anti-fraud policy optimization method of claim 1, wherein in the proportional mode, the sum of the incoming proportion of the anti-fraud policy a to be processed and the incoming proportion of the optimization policy B to be tested for the newly created anti-fraud policy is 100%.
4. The message queue-based anti-fraud policy optimization method of claim 1, wherein in the proportional mode, the piece feeding sequence of the policy a and the policy B is set to be consistent with the sequence in a piece feeding file, wherein the piece feeding file information is generated by random ordering, and the piece feeding in the sequence is equal to the random piece feeding.
5. The message queue-based anti-fraud policy optimization method according to claim 1, wherein in the proportional mode, after setting the incoming proportion of the policy a and the policy B, importing an incoming file; and downloading the file to be delivered through the corresponding template, carrying out user-defined editing and uploading.
6. The message queue-based anti-fraud policy optimization method of claim 5, wherein the incoming file is uploaded by two modes of dragging and uploading or clicking and uploading.
7. The message queue-based fraud prevention policy optimization method of claim 1, wherein in the proportional mode, when the incoming proportion is initially set, the incoming proportion of policy a is set higher than the incoming proportion of policy B.
8. The message queue-based anti-fraud policy optimization method of claim 1, wherein in the proportional mode, a plurality of server listeners are set to listen to incoming messages while adding tasks, and different policy contents are executed according to different tags.
9. The message queue-based anti-fraud policy optimization method of claim 8, wherein if the flag is a champion policy, the champion policy is executed; if the flag is a challenger policy, the challenger policy is executed.
10. The message queue-based anti-fraud policy optimization method of claim 1, wherein in the grayrunning mode, the piece-in information comprises: firstly, downloading a corresponding excel template according to a specific strategy, wherein the template comprises user information required by the strategy, and the templates corresponding to different strategies have different information.
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