CN106600413A - Cheat recognition method and system - Google Patents
Cheat recognition method and system Download PDFInfo
- Publication number
- CN106600413A CN106600413A CN201510680303.7A CN201510680303A CN106600413A CN 106600413 A CN106600413 A CN 106600413A CN 201510680303 A CN201510680303 A CN 201510680303A CN 106600413 A CN106600413 A CN 106600413A
- Authority
- CN
- China
- Prior art keywords
- business
- event
- object event
- information
- reference information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention relates to a cheat recognition method and system. The method comprises the steps of receiving a service processing request corresponding to a target event, wherein an event mark of the target event is carried in the service processing request; acquiring service reference information related to the target event according to the event mark; and calculating a service evaluation value of the target event according to the service reference information, and determining that the target event is a cheat event when the service evaluation value conforms to a preset evaluation condition. With the method disclosed by the invention, the recognition accuracy and the recognition efficiency of the cheat event are improved.
Description
Technical field
It relates to network technology, more particularly to a kind of fraud recognition methods and system.
Background technology
When Claims Resolution event is processed, the examination ＆ verification generally settled a claim by manpower intervention such as, is audited for insurance company
Judge whether the Claims Resolution event meets reparation regulation, to prevent the generation of insurance fraud behavior.But, this people
The mode of work examination ＆ verification, the factor for not only auditing institute's foundation is relatively simple, it is impossible to accurately comprehensively judge Claims Resolution
The authenticity of event, and review efficiency is also than relatively low so that and the processing speed of Claims Resolution event is slow,
Even occurs substantial contribution loss when extensive insurance fraud event occurs.
The content of the invention
To overcome problem present in correlation technique, the disclosure to provide a kind of fraud recognition methods and system,
To improve the degree of accuracy and recognition efficiency to fraud identification.
According to the first aspect of the embodiment of the present disclosure, there is provided one kind fraud recognition methods, including：
The corresponding Business Processing request of object event is received, is carried in the Business Processing request：The mesh
The event identifier of mark event；
According to the event identifier, the business reference information associated with the object event is obtained；
According to the business reference information, the business assessed value of the object event is calculated, when the business
When assessed value meets default evaluation condition, determine that the object event is fraud.
According to the second aspect of the embodiment of the present disclosure, there is provided one kind fraud identifying system, including：
Request receiving module, for receiving the corresponding Business Processing request of object event, the Business Processing
Carry in request：The event identifier of the object event；
Data obtaining module, for according to the event identifier, obtaining the industry associated with the object event
Business reference information；
Identification module is calculated, for according to the business reference information, calculating the business of the object event
Assessed value, when business assessed value meets default evaluation condition, determines that object event is fraud.
The technical scheme that embodiment of the disclosure is provided can include following beneficial effect：By acquisition and mesh
The business reference information of mark event correlation, and business assessed value is calculated accordingly, can accord with business assessed value
Determine that object event is fraud when closing default evaluation condition, due to considering business reference information,
And it is the differentiation that fraud is carried out according to the method for being quantized into business assessed value such that it is able to improve right
The degree of accuracy of fraud identification and recognition efficiency.
It should be appreciated that the general description of the above and detailed description hereinafter are only exemplary and explanatory
, the disclosure can not be limited.
Description of the drawings
Accompanying drawing herein is merged in specification and constitutes the part of this specification, shows and meets the present invention
Embodiment, and together with specification be used for explain the present invention principle.
Fig. 1 is the application architecture of the fraud identifying system according to an exemplary embodiment；
Fig. 2 is a kind of flow chart of the fraud recognition methods according to an exemplary embodiment；
Fig. 3 is the flow chart of another kind of fraud recognition methods according to an exemplary embodiment；
Fig. 4 is a kind of structure chart of the fraud identifying system according to an exemplary embodiment；
Fig. 5 is the structure chart of another kind of fraud identifying system according to an exemplary embodiment.
Specific embodiment
Here exemplary embodiment will be illustrated in detail, its example is illustrated in the accompanying drawings.Following
When description is related to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represent same or analogous
Key element.Embodiment described in following exemplary embodiment does not represent the institute consistent with the present invention
There is embodiment.Conversely, they are only and being described in detail in such as appended claims, of the invention one
The example of the consistent apparatus and method of a little aspects.
In order to improve for the review efficiency of Claims Resolution event, and the degree of accuracy of auditing result is improved, this Shen
Please embodiment provide a kind of fraud recognition methods, as shown in figure 1, the method can be by fraud identification system
System 11 is performed, and the embodiment of the fraud identifying system 11 is exemplary, can be mounted to one
Processing system on server or a computer, or can also be by being distributed on multiple servers
Multiple module compositions.The flow process of the method can be with reference to shown in Figure 2：
In step 201, the corresponding Business Processing request of object event, the Business Processing request are received
Middle carrying：The event identifier of the object event.
For example, object event can be shopping online transaction, be bought by the A side as seller and conduct
The B side of family completes this transaction jointly；Or, object event can also be once other kinds of same
The event participated in by two sides, such as, and vehicle accidents.In the present embodiment, object event can be related to
And the both sides for arriving are referred to as " the event side of execution of object event ".
For example, the Business Processing request in this step, is corresponding with object event, that is, to be asked to hold
Capable business is related to object event.Such as, can be request for shopping online transaction event
This transaction is settled a claim, then be likely to be the buyer in this transaction and receive loss, it is right to need
Buyer is compensated, then the process business that as request is performed compensated.
With reference to shown in Fig. 1, the Business Processing request that identifying system 11 is received is cheated, can be by terminal
12 transmissions.The terminal 12 for example can be the equipment that PC, notebook, smart mobile phone etc. can surf the Net.
By taking above-mentioned shopping online transaction event as an example, user can browse the sheet for completing using the notebook of oneself
Secondary event, and select to carry out Claims Resolution application to this transaction event.Exemplary, user can click on page
The similar option of one " application Claims Resolution " in face, to trigger Business Processing request (request is settled a claim)
Transmission.The Business Processing request can be received by the fraud identifying system 11 in Fig. 1.
In Business Processing request, the event identifier of object event, the effect of the event identifier can be carried
It is mainly used for enabling cheating identifying system and learns handled business for which event, than
Such as, in the example of Claims Resolution application, need to know and which time transaction is settled a claim.For example, the event
Mark can be the order number of shopping online transaction.
In step 202., according to event identifier, the business reference information associated with object event is obtained.
For example, business reference information be when Business Processing is carried out can according to or reference information, such as,
For the example that above-mentioned shopping online is concluded the business, when application Claims Resolution is carried out, business reference information can be
When Claims Review is carried out according to reference information, it is exemplary, can include trading activity information,
Social network information etc., specific acquisition of information will be described in detail in follow-up embodiment.
In this step, after cheating Business Processing request of the identifying system in step 201 is received, will
Automatically according to event identifier, the business reference information of object event association can be obtained.Obtain these business
The mode of reference information, can with reference to the example of Fig. 1, cheat identifying system 11 can by server 13,
Collect in the grade equipment of server 14.Different equipment can be stored with different types of business reference letter
Breath, such as, server 13 can store social networks with store transaction behavioural information, server 14
Information.Storage of the server to business reference information, can just record when object event occurs.
In step 203, according to business reference information, the business assessed value of object event is calculated, works as industry
When business assessed value meets default evaluation condition, determine that the object event is fraud.
In this step, identifying system was cheated before Business Processing is carried out, whether can judge object event
Asked business can be carried out.Such as, when the Claims Resolution application for certain purchase transaction is received,
It is also required to judge the condition whether this transaction meets Claims Resolution.Object event is likely to be a fraud,
Fraud is that object event does not meet the condition for carrying out above-mentioned business, when object event is fraud,
Business Processing will not gone on.By shopping online transaction as a example by, if this purchase transaction be seller and
Buyer's malice insurance fraud, wants to gain insurance money by cheating, and that can identify this by the method for the embodiment of the present application
It is the transaction of a fraud property, will not continue to Claims Resolution.
For example, in the identification of fraud, can be by the method for quantum chemical method, will be in step 202
In the business reference information that obtains be converted into the business assessed value after quantifying, and one can be set preset
Evaluation condition, when business assessed value meets evaluation condition, then can be determined that as fraud.Example
Property, default evaluation condition can be the threshold value of a point value of evaluation, when business assessed value is one point
Value and during higher than the threshold value, can be defined as fraud by object event.
The fraud recognition methods of the present embodiment, the business reference information meter associated with object event by basis
Calculation business assessed value, and fraud is judged according to business assessed value, this mode can by with target thing
The various reference informations of part association are more to be taken in, and also has carried out the information processing of quantum chemical method,
The degree of accuracy to fraud identification can be improved；And cheat identifying system receive Business Processing please
Automatically the identification of fraud is carried out when asking, relevant information is obtained automatically and calculating process is carried out, to fraud
The recognition efficiency of event is improved.
In following example, by by taking the Claims Resolution of online transaction as an example illustrating how with the application reality
Apply the fraud recognition methods identification fraud of example.Wherein, in the example of online transaction Claims Resolution, target
The corresponding Business Processing of event, that is, certain corresponding Claims Resolution Business Processing of once concluding the business；And it is to be identified
Fraud, then be by defraud of Claims Resolution money for the purpose of dolus malus property transaction；Business reference information,
As in the reference information for judging object event when that whether is fraud institute's foundation.Fig. 3 illustrates the net
The identification process of the fraud in upper transaction Claims Resolution：
In step 301, the corresponding Claims Resolution request of target transaction is received, is carried in the Claims Resolution request：
The transaction ID of the target transaction.
For example, it is assumed that the execution side of this transaction includes buyer and the seller, the seller can be the business for selling clothes
Family, buyer can be the consumer for buying clothes.Assume that buyer needs to be settled a claim in this transaction, then may be used
To submit Claims Resolution application on computers.User can click on the order concluded the business before, carry out item of settling a claim
Selection is filled in.The Claims Resolution application is as settled a claim and is asked, and can be sent to fraud identifying system by computer terminal.
In this step, Claims Resolution request can also carry the order number of targeted transaction when sending, as
The transaction of this request Claims Resolution can also be referred to as target transaction by transaction ID, the present embodiment.
In step 302, according to transaction ID, business reference information of the target transaction when occurring is obtained,
Including：Behavior record, the corresponding history in execution side of transaction of operating environment information, transaction when occurring
Transaction Claims Resolution record or, the corresponding social network information in transaction execution side.
For example, identifying system is cheated after Claims Resolution application is received, by the examination ＆ verification before being settled a claim, can be with
Obtain in this step some examination ＆ verification can foundation reference information.The type of the business reference information can be with
Have various, several reference informations including but not limited to particularized below：
Operating environment information：The operating environment information, for example, it may be seller and buyer are in transaction
End message, such as, and the information such as IP address, MAC Address of computer.Can also be located including computer
Region, such as, and Beijing, Shanghai, or specific to the more detailed band of position.
The behavior record concluded the business when occurring：For example, payment time, the time of receiving can be included；May be used also
To include：Credit grade, account hour of log-on of buyer etc..
Above-mentioned operating environment information and trading activity is recorded, and can be when seller and buyer are traded
Just store.Such as, seller and buyer are handed in the dealing that commodity are carried out using oneself computer or mobile phone
Yi Shi, by client, (e.g., Taobao's client, of company of Taobao offer can be used for answering for shopping
With client) IP address of terminal or terminal iidentification that user uses are obtained, and when transaction proceeds to certain
When individual flow process, friendship incident time, such as payment time, the time of receiving are recorded.Therefore, this
A little information are just recorded when transaction event occurs.
The corresponding historical trading Claims Resolution record in the execution side of transaction：For example, the record can be used to indicate that right
In historical each time transaction, whether buyer has carried out Claims Resolution application to the transaction.Such case can be used
In the abnormal safeguarding-rights act for identifying counterparty.Such as, new account soon has just been registered, by history
The analysis of transaction Claims Resolution record, it can be found that the account has carried out ten transactions, but has eight all to carry out
Claims Resolution application, Claims Resolution rate has reached 80%, and this is just likely to the problem account that the account is intentional registration.
The corresponding social network information in transaction execution side：For example, social network information, can participate in handing over
Easy seller and the social networks of buyer, such as, the friend relation of instant messaging, work relationship, microblogging
Friend relation etc..These information can be safeguarded by certain social interaction server device, when the account for submitting seller and buyer to
Number when, it is possible to get friend relation, such as, it is known that seller and buyer are good friends.
Above-mentioned polytype business reference information, can be stored in same equipment, it is also possible to store
In different equipment.Such as, trading activity record and operating environment information can be deposited by Taobao's server
Storage, is to be obtained to be uploaded to after these information by the Taobao's client in the terminal used installed in user operation
Taobao's server；And social network information can obtain storage by other servers.In the present embodiment,
When these information are stored, can be with record information and the incidence relation of target transaction；When receive Claims Resolution Shen
Please when, these information of above-mentioned record can be extracted and used by fraud identifying system according to transaction ID.
Such as, the mode of extraction, can be according to the order number carried in Claims Resolution application, initial record information
When, these information are all associated with order number, then this step just can accordingly extract information.
In step 303, according to default quantization transformation rule, respectively by various types of business
Reference information, is converted to the sub- assessed value of the correspondence type.
Can be quantified the miscellaneous service reference information obtained in step 302 in this step, and
Value after quantization is referred to as into sub- assessed value.Quantify transformation rule, can be flexible according to the characteristics of actual scene
Setting, a kind of quantification manner particularized below, but be not limited thereto in being embodied as.
Assume the sub- assessed value after representing to operating environment information quantization with X1, X2 is represented to being transaction row
Sub- assessed value after quantifying for record, X3 represents the sub- assessed value after record quantization of settling a claim to historical trading,
X4 represents the sub- assessed value after the quantization to social network information, and with X5 both parties' essential information is represented.
Each sub- assessed value quantization is as follows：
X1：For example, when the computer that seller and buyer are judged according to the MAC Address of computer is same electricity
During brain, then X1 is defined as into 1, otherwise, X1 can be defined as 0.Or, when according to computer institute
When the computer distance of the region at place, judgement seller and buyer is nearer, the score value of X1 is arranged into higher.
X2：For example, can by payment time, confirm that the time of receiving obtains time interval, and can be by
Value of the numerical value of the time interval as X2.Or, or, if above-mentioned time interval exists
During less than time threshold, what is differed with threshold value is more, then the score value of X2 arranges higher.Additionally, setting
When putting time threshold, it is also contemplated that the threshold value arranged to the transaction characteristics of different industries, difference transaction
Numerical value can be determined with difference according to the average exchange hour of industry.
X3：For example, when recording according to the Claims Resolution of the historical trading of certain Buyer ID, actual Claims Resolution rate is carried out
Analysis, and according to the Claims Resolution rate that obtains of analysis, when Claims Resolution rate is higher, can be set to X3 point
Value is higher；Or, it is also possible to value of the Claims Resolution rate score for obtaining analysis as X3.
X4：For example, according to social network information, it is possible to determine that the intimate degree of seller and buyer.Than
Such as, if both have a financial transactions behavior, or chat number of times is more frequent etc., it is believed that both
It is relatively more intimate, then X3 is set to into score value higher.
X5：Both parties' essential information, can include：Age, sex, city, risk partiality etc..
Exemplary, can be summarized in certain age according to some the history Claims Resolution events for having processed
The likelihood ratio of people's fraud of section or certain area is higher, or with higher risk of fraud, therefore,
When both parties are in the information characteristics of these excessive risk high probabilities, can be represented by the value of X5.
In step 304, it is comprehensive according to each sub- assessed value, calculate the business assessed value of target transaction.
For example, if business assessed value represented with Score, the Score can be calculated with equation below：
Score=W1*X1+W2*X2+ ...+Wi*Xi
In the present embodiment, four kinds of business reference information are listed, i.e., X1, X2 in step 303,
X3, X4 and X5；Sub- assessed value after quantization can be substituted in above-mentioned formula.
For the calculating of weight W1, W2 in formula etc., can be with empirically determined, or according to patrolling
Collect and return and IV analyses, finally determine each weight；Generally more important factor can be arranged it
Weight is higher.It is exemplary, such as, after some Claims Resolution events were processed, will can have been acknowledged for
The historical events of fraud carries out model training and obtains all kinds of business references as the sample of model training
The weight of information.When after a period of time, being updated to the sample of fraud, and again
Model training is carried out, corresponding weight is updated.
In step 305, judge business assessed value whether more than default threshold value；
For example, default evaluation condition can be set as a threshold value by this step, in step 304 basis
Calculated assessed value Score of formula, can be compared with the threshold value.Exemplary, set threshold
It is worth for 30.If Score is more than or equal to 30, can determine that this Claims Resolution is a fraud, i.e.,
Target transaction is a fraudulent trading, draws the result of step 306.Otherwise, if Score is less than 30,
Can determine that this is not fraud, continue with according to normal flow.
Such as, with reference to the step of above 303 grades process, for a fraudulent trading, possible buyer with sell
Family is closer to the distance two executor, in some instances it may even be possible to be to use same terminal operation, then X1 will take
High level；And payment time and confirmation are received, the time interval of time is shorter, and when averagely concluding the business less than industry
Between, then this transaction more there may be fraud, then X2 values are higher；In the same manner, the pass of seller and buyer
System is more intimate, and the Claims Resolution rate of Buyer ID is higher, and the value that can all promote X3 or X4 is improved.Therefore,
Also just there is such a trend, when the score value of Score is higher, this transaction is possible of fraudulent trading
Property is higher.And the present embodiment sets a condition for needing to remind fraud by predetermined threshold value.
Within step 306, determine that the object event is fraud.
When identify fraud after, the fraud identifying system of the present embodiment, can to audit terminal feedback result,
Such as, remind auditor that this is once suspicious Claims Resolution event.
In above-mentioned example, trading activity record, operating environment information, social network information are combined
The identification of fraud is carried out Deng many factors.It should be understood that, in being embodied as,
Can be recognized according to other factors, or, can to carry out fraud identification according to some factors, for example,
Perhaps fraud can just be identified according to operating environment information sometimes, or, according to transaction row
It is assured that this is once fraud Claims Resolution for information.
The fraud recognition methods of the present embodiment, the business reference information meter associated with object event by basis
Calculation business assessed value, and fraud is judged according to business assessed value, this mode can by with target thing
The various reference informations of part association are more to be taken in, and also has carried out the information processing of quantum chemical method,
The degree of accuracy to fraud identification can be improved；And cheat identifying system receive Business Processing please
Automatically the identification of fraud is carried out when asking, relevant information is obtained automatically and calculating process is carried out, to fraud
The recognition efficiency of event is improved.
Fig. 4 illustrates the structure of the fraud identifying system of the present embodiment, and the system can include：Request connects
Receive module 41, data obtaining module 42 and calculate identification module 43.
Request receiving module 41, for receiving the corresponding Business Processing request of object event, at the business
Carry in reason request：The event identifier of the object event；
Data obtaining module 42, for according to the event identifier, obtaining what is associated with the object event
Business reference information；
Identification module 43 is calculated, for according to the business reference information, calculating the industry of the object event
Business assessed value, when business assessed value meets default evaluation condition, determines that object event is fraud.
Further, data obtaining module 42, the business reference information of acquisition, including：The mesh
Operating environment information of the mark event when occurring.
Further, data obtaining module 42, the business reference information of acquisition, including：The mesh
Event behavior record of the mark event when occurring；Or, the event execution side of the object event is corresponding
Historical events transaction log.
Further, data obtaining module 42, the business reference information of acquisition, including：The mesh
The corresponding social network information in event execution side of mark event.
Referring to Fig. 5, when the type of business reference information has various；The calculating identification module 43, can
To include：
Quantify transform subblock 431, for according to default quantization transformation rule, respectively by all kinds
The business reference information, be converted to the sub- assessed value of the correspondence type；
Comprehensive assessment submodule 432, for synthesis according to each sub- assessed value, is calculated the target
The business assessed value of event.
Those skilled in the art will readily occur to this after considering specification and putting into practice invention disclosed herein
Other embodiments of invention.The application is intended to any modification, purposes or the adaptability of the present invention
Change, these modifications, purposes or adaptations follow the general principle of the present invention and including this public affairs
Open undocumented common knowledge or conventional techniques in the art.Description and embodiments only by
It is considered as exemplary, true scope and spirit of the invention are pointed out by claim below.
It should be appreciated that the invention is not limited in be described above and be shown in the drawings accurate
Structure, and can without departing from the scope carry out various modifications and changes.The scope of the present invention is only by institute
Attached claim is limiting.
Claims (10)
1. a kind of fraud recognition methods, it is characterised in that include：
The corresponding Business Processing request of object event is received, is carried in the Business Processing request：The mesh
The event identifier of mark event；
According to the event identifier, the business reference information associated with the object event is obtained；
According to the business reference information, the business assessed value of the object event is calculated, when the business
When assessed value meets default evaluation condition, determine that the object event is fraud.
2. method according to claim 1, it is characterised in that the acquisition and the object event
The business reference information of association, including：
Obtain operating environment information of the object event when occurring.
3. method according to claim 1, it is characterised in that the acquisition and the object event
The business reference information of association, including：
Obtain event behavior record of the object event when occurring；
Or, obtain the corresponding historical events transaction log in event execution side of the object event.
4. method according to claim 1, it is characterised in that the acquisition and the object event
The business reference information of association, including：
Obtain the corresponding social network information in event execution side of the object event.
5. according to the arbitrary described method of Claims 1 to 4, it is characterised in that when the business reference
When the type of information has various；It is described according to the business reference information, calculate the industry of the object event
Business assessed value, including：
According to default quantization transformation rule, respectively by various types of business reference information, conversion
For the sub- assessed value of the correspondence type；
Comprehensively according to each sub- assessed value, the business assessed value of the object event is calculated.
6. it is a kind of to cheat identifying system, it is characterised in that to include：
Request receiving module, for receiving the corresponding Business Processing request of object event, the Business Processing
Carry in request：The event identifier of the object event；
Data obtaining module, for according to the event identifier, obtaining the industry associated with the object event
Business reference information；
Identification module is calculated, for according to the business reference information, calculating the business of the object event
Assessed value, when business assessed value meets default evaluation condition, determines that object event is fraud.
7. system according to claim 6, it is characterised in that
Described information acquisition module, the business reference information of acquisition, including：The object event exists
Operating environment information during generation.
8. system according to claim 6, it is characterised in that
Described information acquisition module, the business reference information of acquisition, including：The object event exists
Event behavior record during generation；Or, the corresponding historical events in event execution side of the object event
Transaction log.
9. system according to claim 6, it is characterised in that
Described information acquisition module, the business reference information of acquisition, including：The object event
The corresponding social network information in event execution side.
10. according to the arbitrary described system of claim 6～9, it is characterised in that when the business reference
When the type of information has various；The calculating identification module, including：
Quantify transform subblock, for according to default quantization transformation rule, respectively by various types of institutes
Business reference information is stated, the sub- assessed value of the correspondence type is converted to；
Comprehensive assessment submodule, for synthesis according to each sub- assessed value, is calculated the object event
Business assessed value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510680303.7A CN106600413A (en) | 2015-10-19 | 2015-10-19 | Cheat recognition method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510680303.7A CN106600413A (en) | 2015-10-19 | 2015-10-19 | Cheat recognition method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106600413A true CN106600413A (en) | 2017-04-26 |
Family
ID=58555106
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510680303.7A Pending CN106600413A (en) | 2015-10-19 | 2015-10-19 | Cheat recognition method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106600413A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109461068A (en) * | 2018-09-13 | 2019-03-12 | 深圳壹账通智能科技有限公司 | Judgment method, device, equipment and the computer readable storage medium of fraud |
| CN109670934A (en) * | 2018-09-26 | 2019-04-23 | 深圳壹账通智能科技有限公司 | Personal identification method, equipment, storage medium and device based on user behavior |
| CN110245960A (en) * | 2019-05-21 | 2019-09-17 | 何金星 | A kind of medical insurance antifraud system and method based on computer control |
| TWI686760B (en) * | 2018-04-12 | 2020-03-01 | 香港商阿里巴巴集團服務有限公司 | Data processing method, device, equipment and server for insurance fraud identification |
| CN111738867A (en) * | 2020-04-17 | 2020-10-02 | 世纪保众(北京)网络科技有限公司 | Method and device for quantifiably evaluating claim looseness of insurance product |
| CN112819611A (en) * | 2021-03-02 | 2021-05-18 | 成都新希望金融信息有限公司 | Fraud identification method, device, electronic equipment and computer-readable storage medium |
| CN114595997A (en) * | 2022-03-21 | 2022-06-07 | 联想(北京)有限公司 | Data processing method and device and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101577653A (en) * | 2008-05-08 | 2009-11-11 | 韩军 | System and method using positioning technique to carry out vehicle insurance claim assistant authentification |
| CN102013084A (en) * | 2010-12-14 | 2011-04-13 | 江苏大学 | System and method for detecting fraudulent transactions in medical insurance outpatient services |
| CN103038795A (en) * | 2009-12-24 | 2013-04-10 | 旅行者保险公司 | Risk assessment and control, insurance premium determinations, and other applications using busyness |
| CN103810637A (en) * | 2013-12-17 | 2014-05-21 | 深圳市般若计算机系统有限公司 | Motor vehicle insurance fraud detecting method and system |
-
2015
- 2015-10-19 CN CN201510680303.7A patent/CN106600413A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101577653A (en) * | 2008-05-08 | 2009-11-11 | 韩军 | System and method using positioning technique to carry out vehicle insurance claim assistant authentification |
| CN103038795A (en) * | 2009-12-24 | 2013-04-10 | 旅行者保险公司 | Risk assessment and control, insurance premium determinations, and other applications using busyness |
| CN102013084A (en) * | 2010-12-14 | 2011-04-13 | 江苏大学 | System and method for detecting fraudulent transactions in medical insurance outpatient services |
| CN103810637A (en) * | 2013-12-17 | 2014-05-21 | 深圳市般若计算机系统有限公司 | Motor vehicle insurance fraud detecting method and system |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI686760B (en) * | 2018-04-12 | 2020-03-01 | 香港商阿里巴巴集團服務有限公司 | Data processing method, device, equipment and server for insurance fraud identification |
| CN109461068A (en) * | 2018-09-13 | 2019-03-12 | 深圳壹账通智能科技有限公司 | Judgment method, device, equipment and the computer readable storage medium of fraud |
| CN109670934A (en) * | 2018-09-26 | 2019-04-23 | 深圳壹账通智能科技有限公司 | Personal identification method, equipment, storage medium and device based on user behavior |
| CN110245960A (en) * | 2019-05-21 | 2019-09-17 | 何金星 | A kind of medical insurance antifraud system and method based on computer control |
| CN111738867A (en) * | 2020-04-17 | 2020-10-02 | 世纪保众(北京)网络科技有限公司 | Method and device for quantifiably evaluating claim looseness of insurance product |
| CN112819611A (en) * | 2021-03-02 | 2021-05-18 | 成都新希望金融信息有限公司 | Fraud identification method, device, electronic equipment and computer-readable storage medium |
| CN114595997A (en) * | 2022-03-21 | 2022-06-07 | 联想(北京)有限公司 | Data processing method and device and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106600413A (en) | Cheat recognition method and system | |
| US11687938B1 (en) | Reducing false positives using customer feedback and machine learning | |
| Jagtiani et al. | Fintech lending and mortgage credit access | |
| Roberts | Can warranties substitute for reputations? | |
| US20100005013A1 (en) | Methods and systems for detecting fraudulent transactions in a customer-not-present environment | |
| CN109711955B (en) | Poor evaluation early warning method and system based on current order and blacklist base establishment method | |
| KR20200006967A (en) | Merchant evaluation method and system | |
| CN111461857A (en) | Personal online credit method, device, system, equipment and medium for small and medium-sized banks | |
| JP2004348536A (en) | History information addition program, fraudulent determination program using history information, and fraudulent determination system using history information | |
| WO2010037030A1 (en) | Evaluating loan access using online business transaction data | |
| CN111476559A (en) | Merchant authentication method and device, computer equipment and storage medium | |
| CN113469789A (en) | Abnormal order detection method and device and computer equipment | |
| Lamba et al. | Exchange listings and delistings: The role of insider information and insider trading | |
| CN104599175A (en) | Personal finance credit evaluation system based on big data | |
| CN112330355A (en) | Consumption ticket transaction data processing method, device, equipment and storage medium | |
| CN111489255A (en) | Data credit granting method, device, equipment and computer readable storage medium | |
| CN109271415B (en) | Data processing method and device for credit investigation database | |
| CN111062770A (en) | Merchant identification method, equipment and computer readable medium | |
| CN113935844A (en) | Financial wind control system based on big data and artificial intelligence | |
| US10528924B2 (en) | Self-aware token | |
| KR20200025074A (en) | Method, apparatus and program for money exchange matching between different games | |
| CN105956919A (en) | Business data application evaluation method and device | |
| JP2005346730A (en) | Method of determination of unauthorized utilization of credit card using history information | |
| CN111445325B (en) | Credit card information processing method, device, system and storage medium | |
| WO2022090999A1 (en) | System for pre-owned electronic device diagnostics, with sales and operation facilitation features |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170426 |