CN111242632A - Method for identifying cash register account, storage medium and electronic equipment - Google Patents
Method for identifying cash register account, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method for identifying cash register accounts, a storage medium and electronic equipment, wherein the method comprises the following steps of: acquiring the transaction behavior detail of the target account; determining a transaction behavior probability ratio corresponding to each transaction behavior in the transaction behavior detail based on a likelihood function model; determining a transaction behavior detection value of a target account according to the product of the transaction behavior probability ratios corresponding to each transaction behavior, comparing the transaction behavior detection value with a preset threshold condition, and determining whether the target account belongs to a cash register account or a normal account according to a comparison result. The method and the device can judge whether the target account is a cash register account or a normal account according to the transaction behavior detection value of the target account obtained by multiplying the transaction behavior probability ratio corresponding to each transaction behavior of the target account, improve the accuracy of identifying the cash register account, filter out normal client cards and effectively identify employee cash register behaviors and employee cash register anti-cheating behaviors.
Description
Technical Field
The invention belongs to the technical field of application, and particularly relates to a method for identifying cash register accounts, a storage medium and electronic equipment.
Background
In recent years, cash register behaviors are very common, particularly, a refueling cash register behavior, for example, a refueling station employee performs the refueling cash register behavior by activating a plurality of cardless refueling accounts to simulate normal customer refueling, acquaintance the employee to carry out serial account refueling, and communication of social vehicles and the like, and the cash register behavior is low in recognition rate and easy to be misreported simply by refueling frequency, refueling time, refueling place or certain specific conditions.
From the manner of the fueling cash register, the fueling cash register can be divided into direct cash register and indirect cash register. Direct cash register is the behavior of personnel of a gas station to carry a user card to fill cash customers with cash in the filling field. Indirect cash register is the use of a refueling cash register in a refueling station by outside personnel (non-personnel at the station).
The measures of oiling cash register are diversified, and virtual assets such as points, coupons, and easy and quick coins are cash registered.
At present, a method for identifying a fueling cash register account includes: analyzing basic running water data of refueling, specifically, analyzing the refueling date, the refueling time, the refueling oil product and the transaction amount of a refueling account at a fuel station, calculating the consumption times ratio of the fuel station, the consumption times ratio of the fuel product and the distribution of the refueling ascending number, then calculating the entropy value of the consumption times ratio, and finally selecting the refueling cash-out behavior meeting the conditions according to the entropy value.
However, the above method for identifying the fueling cash-out account is complicated in analysis process and insufficient in accuracy.
There is a need for a method, storage medium, and electronic device for identifying cash-out accounts.
Disclosure of Invention
The invention aims to improve the accuracy of identifying cash register accounts, filter normal client cards and effectively identify employee cash register behaviors and employee cash register anti-cheating behaviors.
In view of the above problems, the present invention provides a method, a storage medium, and an electronic device for identifying a cash-out account.
In a first aspect, the present invention provides a method of identifying a cash-out account, comprising the steps of:
acquiring the transaction behavior detail of the target account;
determining a transaction behavior probability ratio corresponding to each transaction behavior in the transaction behavior detail based on a likelihood function model;
determining a transaction behavior detection value of a target account according to the product of the transaction behavior probability ratios corresponding to each transaction behavior, comparing the transaction behavior detection value with a preset threshold condition, and determining whether the target account belongs to a cash register account or a normal account according to a comparison result.
According to the embodiment of the present invention, preferably, the determining the transaction behavior probability ratio of each transaction behavior in the transaction behavior detail based on the likelihood function model includes the following steps:
for each transaction behavior in the transaction behavior detail, acquiring the probability that the current transaction behavior belongs to the normal transaction behavior and the probability of cash-out transaction behavior;
based on a likelihood function model, determining a transaction behavior probability ratio of the current transaction behavior according to the probability that the current transaction behavior belongs to the normal transaction behavior and the probability of cash-over transaction behavior; wherein the content of the first and second substances,
the likelihood function model is as follows:
Lr＝(1-Pr0)/(1-Pr1)
wherein Lr is the transaction behavior probability ratio of the current transaction behavior, Pr0Is the probability that the current transaction behavior belongs to the normal transaction behavior; pr (Pr) of1Is the probability that the current transaction activity belongs to the cash-out transaction activity.
According to an embodiment of the present invention, it is preferable that,
the probability that the current transaction behavior belongs to the normal account behavior is the ratio of the normal account with the current transaction behavior in the historical record data to all the normal accounts, and the probability that the current transaction behavior belongs to the cash-out transaction behavior is the ratio of the cash-out account with the current transaction behavior in the historical record data to all the cash-out accounts.
According to an embodiment of the present invention, preferably, comparing the transaction behavior detection value with a preset threshold condition, and determining whether the target account belongs to a cash-out account or a normal account according to the comparison result includes:
when the transaction behavior detection value is larger than or equal to a preset cash register account threshold value, determining that the target account is a cash register account;
and when the transaction behavior detection value is smaller than or equal to a preset normal account threshold value, determining that the target account is a normal account.
According to an embodiment of the present invention, preferably, the method further comprises:
and when the transaction behavior detection value is larger than the normal account threshold value and smaller than the cash register account threshold value, waiting for the next transaction behavior, taking the next transaction behavior as the current transaction behavior, and recalculating the transaction behavior detection value of the corresponding target account until determining whether the target account belongs to the cash register account or the normal account.
According to the embodiment of the present invention, preferably, the transaction behavior detection value is equal to a product of transaction behavior probability ratios corresponding to each transaction behavior.
According to an embodiment of the present invention, preferably, the method further comprises:
respectively establishing a blacklist and a white list of the accounts according to all the accounts determined to belong to the cash register account and all the accounts determined to belong to the normal accounts;
inquiring whether a target account with an account type to be determined exists in the blacklist or the white list;
when a target account with an account type to be determined exists in the blacklist, determining the target account as a cash-out account;
and when the target account with the account type to be determined exists in the white list, determining that the target account is a normal account.
According to an embodiment of the present invention, it is preferable that,
the blacklist and the whitelist both include: account, account registrar information, and transaction activity details.
In a second aspect, the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above method.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory has stored thereon a computer program which, when executed by the processor, performs the steps of the method described above.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
by applying the method for identifying the cash register account, the transaction behavior details of the target account are obtained; determining a transaction behavior probability ratio corresponding to each transaction behavior in the transaction behavior detail based on a likelihood function model; the method comprises the steps of determining a transaction behavior detection value of a target account according to the product of the transaction behavior probability ratios corresponding to each transaction behavior, comparing the transaction behavior detection value with a preset threshold condition, determining whether the target account belongs to a cash register account or a normal account according to a comparison result, judging whether the target account is the cash register account or the normal account according to the transaction behavior detection value of the target account obtained according to the product of the transaction behavior probability ratios corresponding to each transaction behavior of the target account, improving the accuracy of identifying the cash register account, filtering out normal client cards, and effectively identifying employee cash register behaviors and employee cash register anti-cheating behaviors.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 illustrates a flow diagram of a method of identifying a cash-out account in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for identifying a cash-out account according to a second embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
The theoretical basis of the invention is a hypothesis test based on sequential probability ratios. Let H0 denote that the account is assumed to be a normal account. H1 indicates that the account is assumed to be a cash-out account. The results of each observation form a different likelihood ratio under two different assumptions. Let Yi-0 denote normal consumption and Yi-1 denote cash-out consumption, where i-1, 2, …, n, mark each random event associated with a connection. Given the assumption that Hj, j is 0,1, there are two specific probabilities of these two types of possible events, and for each observed event i is 1, 2, …, n, the likelihood ratio for detecting an update can be defined as:
with each observed event i, this likelihood ratio will increase or decrease accordingly. If Λ (Y) >, eta1, then the fueling belongs to the set H1; belongs to the set H0 if Λ (Y) < ═ eta0, where eta0 and eta1 are the critical values for detection. The choice of these two thresholds defines the accuracy of the detection. It has been verified by testing data that the theoretical basis requires only a small number of observed events to successfully complete the cash-out detection.
Example one
In order to solve the technical problems in the prior art, the embodiment of the invention provides a method for identifying a cash-out account.
Referring to fig. 1, a method for identifying a cash-out account of the present embodiment includes the following steps:
s110, respectively establishing a blacklist and a white list of the accounts according to all the accounts determined to belong to the cash register account and all the accounts determined to belong to the normal accounts;
s120, inquiring whether the blacklist has a target account with an account type to be determined:
if yes, determining the target account as a cash register account;
if not, executing step S130;
s130, inquiring whether the white list has a target account with an account type to be determined:
if yes, determining the target account as a normal account;
if not, go to step S140;
s140, acquiring the transaction behavior details of the target account;
s150, determining a transaction behavior probability ratio corresponding to each transaction behavior in the transaction behavior detail based on a likelihood function model;
s160, determining a transaction behavior detection value of the target account according to the product of the transaction behavior probability ratios corresponding to each transaction behavior, comparing the transaction behavior detection value with a preset threshold condition, and determining whether the target account belongs to a cash-out account or a normal account according to the comparison result.
Example two
In order to solve the above technical problems in the prior art, an embodiment of the present invention provides a method for identifying a cash-out account based on the first embodiment, where the method for identifying a cash-out account in the first embodiment of the present invention improves steps S150 and S160 in the first embodiment.
Referring to fig. 2, a method for identifying a cash-out account of the present embodiment includes the following steps:
s210, respectively establishing a blacklist and a white list of the accounts according to all the accounts determined to belong to the cash register account and all the accounts determined to belong to the normal accounts;
s220, inquiring whether the blacklist has a target account with an account type to be determined:
if yes, determining the target account as a cash register account;
if not, go to step S230;
s230, inquiring whether the white list has a target account with the account type to be determined:
if yes, determining the target account as a normal account;
if not, go to step S240;
s240, acquiring the transaction behavior detail of the target account;
s251, for each transaction behavior in the transaction behavior detail, obtaining the probability that the current transaction behavior belongs to the normal transaction behavior and the probability of cash-out transaction behavior;
s252, based on the likelihood function model, determining the probability ratio of the transaction behaviors of the current transaction behaviors according to the probability that the current transaction behaviors belong to the normal transaction behaviors and the probability of the cash-out transaction behaviors;
s261, determining a transaction behavior detection value of the target account according to a product of the transaction behavior probability ratios corresponding to each transaction behavior;
s262, judging whether the transaction behavior detection value is larger than or equal to a preset cash register account threshold value:
if yes, determining that the target account is a cash register account;
if not, go to step S263;
s263, judging whether the transaction behavior detection value is smaller than or equal to a preset normal account threshold value:
if yes, judging that the target account is a normal account;
if not, waiting for the next transaction behavior, taking the next transaction behavior as the current transaction behavior, returning to the step S251, and recalculating the transaction behavior detection value of the corresponding target account until determining whether the target account belongs to the cash-out account or the normal account.
In the step S210, in the step S,
the blacklist and the whitelist both include: account, account registrar information, and transaction activity details.
In the step S251 of the present embodiment,
the probability that the current transaction behavior belongs to the normal account behavior is the ratio of the normal account with the current transaction behavior in the historical record data to all the normal accounts, and the probability that the current transaction behavior belongs to the cash-out transaction behavior is the ratio of the cash-out account with the current transaction behavior in the historical record data to all the cash-out accounts.
In step S252, the likelihood function model is as follows:
Lr＝(1-Pr0)/(1-Pr1)
wherein Lr is the transaction behavior probability ratio of the current transaction behavior, Pr0Is the probability that the current transaction behavior belongs to the normal transaction behavior; pr (Pr) of1Is the probability that the current transaction activity belongs to the cash-out transaction activity.
In step S261, the transaction behavior detection value is equal to the product of the probability ratios of the transaction behaviors corresponding to each transaction behavior, and specifically, the transaction behavior detection value of the target account is determined according to the product of the probability ratios of the transaction behaviors corresponding to each transaction behavior by the following expression:
wherein, ΛnFor the transaction behavior detection value, n is the number of transaction behaviors in the transaction behavior detail, LriAnd the transaction behavior probability ratio is the transaction behavior probability ratio corresponding to the ith transaction behavior.
EXAMPLE III
In order to solve the technical problems in the prior art, the embodiment of the present invention is an application scenario of the method for identifying a cash-out account provided in the second embodiment in fueling.
The method for identifying the cash-out account of the embodiment comprises the following steps:
firstly, constructing a characteristic Yi prior function.
The prior function is the event proportion (as related to data security protocols, the following numerical values are analog values) of the event (cash-out, normal) in the normal account and cash-out account in the historical event. Table 1 shows details of exemplary build events.
TABLE 1
| Event behavior | The event proportion Pr0 in normal account | The event proportion Pr1 in cash register account |
| Changing oil products | 2％ | 34％ |
| Multiple refuelling in approximately 24 hours | 1％ | 23％ |
| Money-saving oiling device | 11％ | 45％ |
| Changing registration plate information | 6％ | 34％ |
| Self-service refueling | 14％ | 2％ |
| Refueling station replacement | 45％ | 8％ |
| Occurrence of convenience store consumption | 13％ | 1％ |
The specific steps of constructing the prior function are as follows:
the transaction data detail of the previous year is collected, the data collection time range can be 1 year, and the regional limitation can be the Beijing area. A prior function is established for each transaction event in the transaction data detail, the prior function being an event probability reflecting cash-over and normal events.
The following is an example of oil replacement, and illustrates the calculation process of the event proportion Pr0 in the normal account and the event proportion Pr1 in the cash register account:
and calculating the proportion Pr0 of the oil products to be replaced in the normal account, namely using the account number of more than or equal to two oil products/the total normal account number, and the proportion is equal to about 2%.
And calculating the proportion Pr0 of the oil products to be replaced in the cash register account, namely using the account number of more than or equal to two oil products/the total cash register account number, and the proportion is equal to about 34 percent.
And secondly, configuring a white list and a black list, acquiring a cash-out account list and a normal account list verified in the previous year, configuring the white list and the black list, wherein the data acquisition time range can be 1 year, the area limit can be a Beijing area, and the acquired verified cash-out account list and the normal account list are imported as default values when the white list and the black list are initialized and are updated along with the detection result. Storing the judged or manually imported normal account in the white list; and the blacklist stores the judged or manually introduced cash register account and employee information.
Third, detecting each transaction event
1) And if the refueling account exists in the white list or the black list, directly judging the refueling account as a normal account or a cash register account.
2) And if the oil adding account does not exist in the white list or the black list, judging to extract the prior probability value Pr of the current transaction event from the first step.
3) If the transaction event is the first transaction in the current year, the calculation expression of the likelihood function lambda (Y) of the refueling account is as follows:
Λ(Y)＝1*(1-Pr0)/(1-Pr1)
if the transaction event is not the first transaction, the calculation expression of the likelihood function Λ (Y) of the fueling account is as follows:
Λn(Y)＝Λn-1(Y)*(1-Pr0)/(1-Pr1)
where Λ n (Y) is the likelihood function of the cutoff to the current transaction event, and Λ n-1(Y) is the likelihood function of the cutoff to the previous transaction event.
4) If the value of the likelihood function Λ (Y) of the fueling account exceeds eta1, then the fueling account is considered to be a cash-out account; lambda (Y) is lower than eta0, the fueling account is considered to be a normal account. Among them, eta1 may be 99, eta0 may be 0.01, and of course, eta1 and eta0 may also be adjusted for the severity of the scene.
5) When the condition in 4) is met, namely when the refueling account is a cash register account, recording the refueling account into a blacklist, recording all transaction details of the current year of the blacklist into a blacklist transaction details history table, and when the refueling account is a normal account, recording the refueling account into a white list, and recording all transaction details of the current year of the white list into a white list transaction details history table.
6) And when the condition in 4) is not met, namely when the fueling account does not belong to the cash register account or the normal account, waiting for the next transaction, and circulating to the step 2) until the fueling account is judged to be the normal account or the cash register account.
And fourthly, outputting the blacklist and the blacklist transaction detail history list for the service personnel to check and register for refueling.
The blacklist includes account, identification card, phone number, whether algorithm is determined, whether verification is performed manually, verifier is performed manually, whether addition is performed manually, and current card status (normal, disabled, and logged off).
The blacklist transaction detail history list comprises an account, a transaction oil station name, a transaction oil station code, an oil product number, an oil product type, a transaction amount, a real income amount, a preferential amount and transaction occurrence time.
And fifthly, outputting a white list and a white list transaction detail history list for business personnel to modify or add.
The white list includes account, identification card, phone number, whether algorithmic determination, whether manual verification, verifier, whether manual addition, addee, current card status (normal, disabled, logoff).
The white list transaction detail history list comprises an account, a transaction oil station name, a transaction oil station code, an oil product number, an oil product type, a transaction amount, a real income amount, a preferential amount and transaction occurrence time.
In the embodiment, the ratio of each transaction event in the transaction behavior details of the fueling account between the normal account and the cash register account, namely the prior cash register probability, is calculated first, and whether the fueling account is the cash register account is judged by comparing the transaction behavior details of the fueling account with the sequential probability.
Wherein the sequential probability ratio formula is
And judging whether the refueling account is a cash register account or not according to the stacking formula and the threshold condition.
According to the method and the device, the normal oil account or the cash register account can be calculated, the mistaken identification of the normal oil account is effectively prevented, and the identification rate of the cash register account is improved.
The embodiment can feed back the generated blacklist and the generated white list to the supervision personnel for cash register supervision, and especially feed back the information of the staff of the fuel filling station in the blacklist to the supervision personnel so as to monitor whether cash register behaviors occur again when the staff of the fuel filling station in the blacklist works at the gas station changing station in real time.
Example four
In order to solve the above technical problems in the prior art, an embodiment of the present invention further provides a storage medium.
The storage medium of the present embodiment has stored thereon a computer program that, when executed by a processor, implements the steps of the method of identifying a cash-out account of the above-described embodiments.
EXAMPLE five
In order to solve the technical problems in the prior art, the embodiment of the invention also provides electronic equipment.
The electronic device of the present embodiment includes a memory and a processor, the memory stores a computer program, and the computer program implements the steps of the method when executed by the processor.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method of identifying a cash-out account, comprising the steps of:
acquiring the transaction behavior detail of the target account;
determining a transaction behavior probability ratio corresponding to each transaction behavior in the transaction behavior detail based on a likelihood function model;
determining a transaction behavior detection value of a target account according to the product of the transaction behavior probability ratios corresponding to each transaction behavior, comparing the transaction behavior detection value with a preset threshold condition, and determining whether the target account belongs to a cash register account or a normal account according to a comparison result.
2. The method of claim 1, wherein determining a transaction behavior probability ratio for each transaction behavior in the transaction behavior specification based on a likelihood function model comprises:
for each transaction behavior in the transaction behavior detail, acquiring the probability that the current transaction behavior belongs to the normal transaction behavior and the probability of cash-out transaction behavior;
based on a likelihood function model, determining a transaction behavior probability ratio of the current transaction behavior according to the probability that the current transaction behavior belongs to the normal transaction behavior and the probability of cash-over transaction behavior; wherein the content of the first and second substances,
the likelihood function model is as follows:
Lr＝(1-Pr0)/(1-Pr1)
wherein Lr is the transaction behavior probability ratio of the current transaction behavior, Pr0Is the probability that the current transaction behavior belongs to the normal transaction behavior; pr (Pr) of1Is the probability that the current transaction activity belongs to the cash-out transaction activity.
3. The method of claim 2, wherein the probability that the current transaction activity belongs to the normal account activity is a ratio of normal accounts having the current transaction activity to all normal accounts in the history data, and the probability that the current transaction activity belongs to the cash-out transaction activity is a ratio of cash-out accounts having the current transaction activity to all cash-out accounts in the history data.
4. The method of claim 1, wherein comparing the transaction activity detection value to a predetermined threshold condition and determining whether the target account belongs to a cash-out account or a normal account based on the comparison comprises:
when the transaction behavior detection value is larger than or equal to a preset cash register account threshold value, determining that the target account is a cash register account;
and when the transaction behavior detection value is smaller than or equal to a preset normal account threshold value, determining that the target account is a normal account.
5. The method of claim 4, further comprising:
and when the transaction behavior detection value is larger than the normal account threshold value and smaller than the cash register account threshold value, waiting for the next transaction behavior, taking the next transaction behavior as the current transaction behavior, and recalculating the transaction behavior detection value of the corresponding target account until determining whether the target account belongs to the cash register account or the normal account.
6. The method of claim 1, wherein the transaction activity detection value is equal to a product of a transaction activity probability ratio corresponding to each transaction activity.
7. The method of claim 1, further comprising:
respectively establishing a blacklist and a white list of the accounts according to all the accounts determined to belong to the cash register account and all the accounts determined to belong to the normal accounts;
inquiring whether a target account with an account type to be determined exists in the blacklist or the white list;
when a target account with an account type to be determined exists in the blacklist, determining the target account as a cash-out account;
and when the target account with the account type to be determined exists in the white list, determining that the target account is a normal account.
8. The method of claim 7,
the blacklist and the whitelist both include: account, account registrar information, and transaction activity details.
9. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored thereon a computer program which, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 8.
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Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009144247A1 (en) * | 2008-05-27 | 2009-12-03 | Sign2Pass Technologies Ab | Method for authentication |
| JP2009289154A (en) * | 2008-05-30 | 2009-12-10 | Toshiba Corp | Apparatus and method for determining monitoring requirement, and monitoring and diagnostic apparatus |
| US20100169192A1 (en) * | 2008-12-31 | 2010-07-01 | Scott Zoldi | Detection Of Compromise Of Merchants, ATMS, And Networks |
| JP2010279644A (en) * | 2009-06-08 | 2010-12-16 | Nippon Telegr & Teleph Corp <Ntt> | State determining device, state determining method, and program |
| US20120290477A1 (en) * | 2011-05-12 | 2012-11-15 | Moneygram International, Inc. | Methods and System for Utilizing Cash with Online Activities |
| CN103974311A (en) * | 2014-05-21 | 2014-08-06 | 哈尔滨工业大学 | Condition monitoring data stream anomaly detection method based on improved gaussian process regression model |
| CN104636912A (en) * | 2015-02-13 | 2015-05-20 | 银联智惠信息服务（上海）有限公司 | Identification method and device for withdrawal of credit cards |
| CN106897509A (en) * | 2017-02-16 | 2017-06-27 | 大连理工大学 | A kind of dynamic Non-Gaussian structures Monitoring Data abnormality recognition method |
| CN107330776A (en) * | 2017-04-25 | 2017-11-07 | 阿里巴巴集团控股有限公司 | One kind book keeping operation and the detailed detection method and device of abnormal book keeping operation |
| CN107491970A (en) * | 2017-08-17 | 2017-12-19 | 北京三快在线科技有限公司 | Anti- cheating detection monitoring method and system and computing device in real time |
| CN107545422A (en) * | 2017-08-02 | 2018-01-05 | ***股份有限公司 | A kind of arbitrage detection method and device |
| JP2018010332A (en) * | 2016-07-11 | 2018-01-18 | 沖電気工業株式会社 | Monitoring device |
| CN107679204A (en) * | 2017-10-12 | 2018-02-09 | 中启行物联科技（北京）有限公司 | A kind of method for preventing oil card arbitrage |
| US20180315052A1 (en) * | 2015-09-14 | 2018-11-01 | Tongji University | System and method for measuring user behavior in electronic transaction based on an immunity system |
| JP2019101711A (en) * | 2017-12-01 | 2019-06-24 | 日本電信電話株式会社 | Abnormality class determination device, abnormality class determination method and program |
| CN109933608A (en) * | 2019-01-28 | 2019-06-25 | 石化盈科信息技术有限责任公司 | The arbitrage behavior of a kind of pair of gas filling card carries out knowledge method for distinguishing |
| CN110032859A (en) * | 2018-12-25 | 2019-07-19 | 阿里巴巴集团控股有限公司 | Abnormal account's discrimination method and device and medium |
| CN110046889A (en) * | 2019-03-20 | 2019-07-23 | 腾讯科技（深圳）有限公司 | A kind of detection method, device and the server of abnormal behaviour main body |
| CN110189228A (en) * | 2019-06-24 | 2019-08-30 | 深圳前海微众银行股份有限公司 | It is a kind of to monitor the method and apparatus traded extremely |
| CN110348850A (en) * | 2019-05-28 | 2019-10-18 | 深圳壹账通智能科技有限公司 | The arbitrage risk checking method and device, electronic equipment of polymerization payment trade company |
| CN110418173A (en) * | 2019-07-18 | 2019-11-05 | 北京达佳互联信息技术有限公司 | Determine method, apparatus, server and the storage medium of exception account |
| CN110443380A (en) * | 2019-06-18 | 2019-11-12 | 石化盈科信息技术有限责任公司 | Maintenance cycle statistical method, device and storage medium |
| CN110648214A (en) * | 2018-06-27 | 2020-01-03 | 银联数据服务有限公司 | Method and device for determining abnormal account |
-
2020
- 2020-01-07 CN CN202010014401.8A patent/CN111242632A/en active Pending
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2009144247A1 (en) * | 2008-05-27 | 2009-12-03 | Sign2Pass Technologies Ab | Method for authentication |
| JP2009289154A (en) * | 2008-05-30 | 2009-12-10 | Toshiba Corp | Apparatus and method for determining monitoring requirement, and monitoring and diagnostic apparatus |
| US20100169192A1 (en) * | 2008-12-31 | 2010-07-01 | Scott Zoldi | Detection Of Compromise Of Merchants, ATMS, And Networks |
| JP2010279644A (en) * | 2009-06-08 | 2010-12-16 | Nippon Telegr & Teleph Corp <Ntt> | State determining device, state determining method, and program |
| US20120290477A1 (en) * | 2011-05-12 | 2012-11-15 | Moneygram International, Inc. | Methods and System for Utilizing Cash with Online Activities |
| CN103974311A (en) * | 2014-05-21 | 2014-08-06 | 哈尔滨工业大学 | Condition monitoring data stream anomaly detection method based on improved gaussian process regression model |
| CN104636912A (en) * | 2015-02-13 | 2015-05-20 | 银联智惠信息服务（上海）有限公司 | Identification method and device for withdrawal of credit cards |
| US20180315052A1 (en) * | 2015-09-14 | 2018-11-01 | Tongji University | System and method for measuring user behavior in electronic transaction based on an immunity system |
| JP2018010332A (en) * | 2016-07-11 | 2018-01-18 | 沖電気工業株式会社 | Monitoring device |
| CN106897509A (en) * | 2017-02-16 | 2017-06-27 | 大连理工大学 | A kind of dynamic Non-Gaussian structures Monitoring Data abnormality recognition method |
| CN107330776A (en) * | 2017-04-25 | 2017-11-07 | 阿里巴巴集团控股有限公司 | One kind book keeping operation and the detailed detection method and device of abnormal book keeping operation |
| CN107545422A (en) * | 2017-08-02 | 2018-01-05 | ***股份有限公司 | A kind of arbitrage detection method and device |
| CN107491970A (en) * | 2017-08-17 | 2017-12-19 | 北京三快在线科技有限公司 | Anti- cheating detection monitoring method and system and computing device in real time |
| CN107679204A (en) * | 2017-10-12 | 2018-02-09 | 中启行物联科技（北京）有限公司 | A kind of method for preventing oil card arbitrage |
| JP2019101711A (en) * | 2017-12-01 | 2019-06-24 | 日本電信電話株式会社 | Abnormality class determination device, abnormality class determination method and program |
| CN110648214A (en) * | 2018-06-27 | 2020-01-03 | 银联数据服务有限公司 | Method and device for determining abnormal account |
| CN110032859A (en) * | 2018-12-25 | 2019-07-19 | 阿里巴巴集团控股有限公司 | Abnormal account's discrimination method and device and medium |
| CN109933608A (en) * | 2019-01-28 | 2019-06-25 | 石化盈科信息技术有限责任公司 | The arbitrage behavior of a kind of pair of gas filling card carries out knowledge method for distinguishing |
| CN110046889A (en) * | 2019-03-20 | 2019-07-23 | 腾讯科技（深圳）有限公司 | A kind of detection method, device and the server of abnormal behaviour main body |
| CN110348850A (en) * | 2019-05-28 | 2019-10-18 | 深圳壹账通智能科技有限公司 | The arbitrage risk checking method and device, electronic equipment of polymerization payment trade company |
| CN110443380A (en) * | 2019-06-18 | 2019-11-12 | 石化盈科信息技术有限责任公司 | Maintenance cycle statistical method, device and storage medium |
| CN110189228A (en) * | 2019-06-24 | 2019-08-30 | 深圳前海微众银行股份有限公司 | It is a kind of to monitor the method and apparatus traded extremely |
| CN110418173A (en) * | 2019-07-18 | 2019-11-05 | 北京达佳互联信息技术有限公司 | Determine method, apparatus, server and the storage medium of exception account |
Non-Patent Citations (3)
| Title |
|---|
| 姜盛;: "基于Logistic的***套现侦测评分模型" * |
| 宋晨;王远;王利明;: "受控账户检测技术研究" * |
| 赵梦非;谭瑛;白尚旺;: "概率关系模型的应用研究", 太原科技大学学报 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113569910A (en) * | 2021-06-25 | 2021-10-29 | 石化盈科信息技术有限责任公司 | Account type identification method and device, computer equipment and storage medium |
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