CN107392755A - Credit risk merges appraisal procedure and system - Google Patents
Credit risk merges appraisal procedure and system Download PDFInfo
- Publication number
- CN107392755A CN107392755A CN201710551098.3A CN201710551098A CN107392755A CN 107392755 A CN107392755 A CN 107392755A CN 201710551098 A CN201710551098 A CN 201710551098A CN 107392755 A CN107392755 A CN 107392755A
- Authority
- CN
- China
- Prior art keywords
- parameter
- background server
- fraud
- credit risk
- 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
- G06Q40/03—Credit; Loans; Processing thereof
Abstract
The present invention provides a kind of credit risk fusion appraisal procedure and system, including being verified to live body, device-fingerprint, facility information, the collection of personal essential information and environmental information and the fusion based on strategy judge, realize risk approval process before a loan to lending and borrowing business application, pass through the synthetic determination of facility information, personal information, environmental information and device-fingerprint information, with reference to the risk policy and rank of specific different cooperative enterprises, the identification and analysis of risk before borrowing are realized, plays expected risk control effect.
Description
Technical field
The present invention relates to computer system and its applied technical field, merges and assesses in particular to a kind of credit risk
Method and system.
Background technology
The internet consumer finance, i.e., the examination ＆ verification entered by network on line, the operation flow such as make loans, consume, refunding.Relatively
In the user group of conventional banking facilities, Long Tail Market user is that the internet consumer finance brings bigger risk control and risk
The challenge of price.
From the point of view of open source information, the bad credit rate of internet consumer finance company reaches more than 10%, and emerging Hu Jin enterprises are general
The problems such as store-through lags in air control strategy, and air control solution is immature, so as to cause user's dolus malus, overdue etc. to upset gold
Melt the behavior of market order.
The content of the invention
It is an object of the invention to propose a kind of credit risk fusion appraisal procedure, comprise the steps：
Step 1, user open Mobile solution APP by client and asked for a loan request to background server；
Step 2, background server call living body authentication module to carry out living body authentication；
Step 3, in response to living body authentication fingerprint module is called by, subscription client, obtain the upper of subscription client
The equipment of fingerprint collecting, environment, geographical location information, and upload to background server；
And personal essential information is inserted in prompting on the debt-credit interface of subscription client, uploads to background server；
Step 4, subscription client collect user propose the user client device parameters of loan requests, ambient parameter and on
Reach background server；
The device parameter, personal essential information and ambient parameter are packaged by step 5, background server, are called
Result after encapsulation is sent to anti-fraud evaluation module by interface；
Step 6, anti-fraud evaluation module according to the parameter received prejudge whether the parameter that stealpass enters legal, parameter whether
Whether missing and parameter format correct, confirm it is errorless after, into next step, otherwise return to step 3；
Step 7, inquiry are registered in the identification code of the enterprise of business side of anti-fraud evaluation module, and determine it according to identification code
Corresponding scene ID, then, parameter information corresponding to enterprise of business side is transmitted to regulation engine, wherein pre- under different scenes ID
The combination of different anti-fraud rules is put；
After step 8, regulation engine receive the parameter of enterprise of business side, the preset anti-fraud rule of the enterprise of business side are matched
Then, the parameter after aforementioned encapsulation is run according to rule and obtains the result of decision.
In further embodiment, the personal essential information inserted at debt-credit interface includes：Name, identification card number
Code, address, phone number, bank card, bank card reserve cell-phone number information.
In further embodiment, the user client device parameter includes：International mobile equipment identity, international movement
CUSTOMER ID, the machine mac addresses, the machine type, the available machine time of the machine cpu models and the machine.
In further embodiment, the ambient parameter includes：Loan platform, user are applied in the url addresses of face, user
The mac addresses for the wifi that request source ip, the machine connect, the wifi of the machine connection title, gps geographical location information and base
Stand information
In further embodiment, the anti-fraud rule include natural person's strategy, equipment class strategy, add up class strategy,
Blacklist class strategy, use at least one of exception and class of algorithms strategy.
In further embodiment, natural person's strategy includes identification card number to be believed with name mismatch, GPS geographical position
Breath with address is inconsistent, the geographical position of GPS geographical location information and fingerprint collecting mismatches, identification card number and phone number not
Matching, phone number are at least one of inconsistent commonly using ground and GPS geographical location information.
In further embodiment, the equipment class strategy is including the use of simulator.
In further embodiment, the blacklist class strategy records black including the overdue blacklist of net loan, main strategies
List.
In further embodiment, the accumulative class strategy corresponds to multiple names, same including same identification card number
Identification card number corresponds to multiple phone numbers, same identification card number corresponds at least one of multiple addresses.
In further embodiment, the class of algorithms strategy includes lr models.
It is described to be included using exception class strategy in further embodiment：Phone number consumer record is abnormal, identification card number
Corresponding name is at least one of abnormal using abnormal, mobile phone use state.
A kind of it is proposed according to the present invention to credit risk fusion assessment system, it is characterised in that including subscription client, after
Platform server and it is counter cheat evaluating server, anti-cheat is provided with anti-fraud evaluation module, user client in evaluating server
End and background server, background server and anti-fraud evaluating server by network interaction data, wherein subscription client, after
Platform server and anti-fraud evaluating server are configured to realize that credit risk fusion is assessed in the following manner：
Step 1, user open Mobile solution APP by client and asked for a loan request to background server；
Step 2, background server call living body authentication module to carry out living body authentication；
Step 3, in response to living body authentication fingerprint module is called by, subscription client, obtain the upper of subscription client
The equipment of fingerprint collecting, environment, geographical location information, and upload to background server；
And personal essential information is inserted in prompting on the debt-credit interface of subscription client, uploads to background server；
Step 4, subscription client collect user propose the user client device parameters of loan requests, ambient parameter and on
Reach background server；
The device parameter, personal essential information and ambient parameter are packaged by step 5, background server, are called
Result after encapsulation is sent to anti-fraud evaluation module by interface；
Step 6, anti-fraud evaluation module according to the parameter received prejudge whether the parameter that stealpass enters legal, parameter whether
Whether missing and parameter format correct, confirm it is errorless after, into next step, otherwise return to step 3；
Step 7, inquiry are registered in the identification code of the enterprise of business side of anti-fraud evaluation module, and determine it according to identification code
Corresponding scene ID, then, parameter information corresponding to enterprise of business side is transmitted to regulation engine, wherein pre- under different scenes ID
The combination of different anti-fraud rules is put；
After step 8, regulation engine receive the parameter of enterprise of business side, the preset anti-fraud rule of the enterprise of business side are matched
Then, the parameter after aforementioned encapsulation is run according to rule and obtains the result of decision.
In the risk fusion appraisal procedure of the present invention, for loan application link, a set of credit wind is provided for credit agency
Control service plan.The service deployment big data technology, face recognition technology, with reference to data mining algorithm by population key element parameter,
The various dimensions information such as human face portrait parameter, facility environment parameter, human-computer behavior parameter are integrated, and effectively help credit agency's drop
Low-risk, reduce monetary losses.
It should be appreciated that as long as all combinations of aforementioned concepts and the extra design described in greater detail below are at this
Sample design it is not conflicting in the case of can be viewed as the disclosure subject matter a part.In addition, required guarantor
All combinations of the theme of shield are considered as a part for the subject matter of the disclosure.
Can be more fully appreciated from the following description with reference to accompanying drawing present invention teach that foregoing and other aspect, reality
Apply example and feature.The feature and/or beneficial effect of other additional aspects such as illustrative embodiments of the present invention will be below
Description in it is obvious, or by according to present invention teach that embodiment practice in learn.
Brief description of the drawings
Accompanying drawing is not intended to drawn to scale.In the accompanying drawings, each identical or approximately uniform group shown in each figure
It can be indicated by the same numeral into part.For clarity, in each figure, not each part is labeled.
Now, by example and the embodiment of various aspects of the invention will be described in reference to the drawings, wherein：
Fig. 1 is the schematic flow sheet of the credit risk fusion appraisal procedure of the present invention.
Fig. 2 is a specific implementation schematic diagram of the credit risk fusion appraisal procedure of the present invention.
Embodiment
In order to know more about the technology contents of the present invention, especially exemplified by specific embodiment and institute's accompanying drawings are coordinated to be described as follows.
Each side with reference to the accompanying drawings to describe the present invention in the disclosure, shown in the drawings of the embodiment of many explanations.
It is not intended to cover all aspects of the invention for embodiment of the disclosure.It should be appreciated that a variety of designs presented hereinbefore and reality
Apply example, and those designs described in more detail below and embodiment can in many ways in any one come it is real
Apply, because design disclosed in this invention and embodiment are not limited to any embodiment.In addition, disclosed by the invention one
A little aspects can be used alone, or otherwise any appropriately combined be used with disclosed by the invention.
With reference to shown in Fig. 1, Fig. 2, according to disclosure of the invention, a kind of credit risk fusion appraisal procedure is proposed, including under
State step：
Step 1, user open Mobile solution APP by client and asked for a loan request to background server；
Step 2, background server call living body authentication module to carry out living body authentication；
Step 3, in response to living body authentication fingerprint module is called by, subscription client, obtain the upper of subscription client
The equipment of fingerprint collecting, environment, geographical location information, and upload to background server；
And personal essential information is inserted in prompting on the debt-credit interface of subscription client, uploads to background server；
Step 4, subscription client collect user propose the user client device parameters of loan requests, ambient parameter and on
Reach background server；
The device parameter, personal essential information and ambient parameter are packaged by step 5, background server, are called
Result after encapsulation is sent to anti-fraud evaluation module by interface；
Step 6, anti-fraud evaluation module according to the parameter received prejudge whether the parameter that stealpass enters legal, parameter whether
Whether missing and parameter format correct, confirm it is errorless after, into next step, otherwise return to step 3；
Step 7, inquiry are registered in the identification code of the enterprise of business side of anti-fraud evaluation module, and determine it according to identification code
Corresponding scene ID, then, parameter information corresponding to enterprise of business side is transmitted to regulation engine, wherein pre- under different scenes ID
The combination of different anti-fraud rules is put；
After step 8, regulation engine receive the parameter of enterprise of business side, the preset anti-fraud rule of the enterprise of business side are matched
Then, the parameter after aforementioned encapsulation is run according to rule and obtains the result of decision.
In further embodiment, the personal essential information inserted at debt-credit interface includes：Name, identification card number
Code, address, phone number, bank card, bank card reserve cell-phone number information.
In further embodiment, the user client device parameter includes：International mobile equipment identity, international movement
CUSTOMER ID, the machine mac addresses, the machine type, the available machine time of the machine cpu models and the machine.
In further embodiment, the ambient parameter includes：Loan platform, user are applied in the url addresses of face, user
The mac addresses for the wifi that request source ip, the machine connect, the wifi of the machine connection title, gps geographical location information and base
Stand information
In further embodiment, the anti-fraud rule include natural person's strategy, equipment class strategy, add up class strategy,
Blacklist class strategy, use at least one of exception and class of algorithms strategy.
In further embodiment, natural person's strategy includes identification card number to be believed with name mismatch, GPS geographical position
Breath with address is inconsistent, the geographical position of GPS geographical location information and fingerprint collecting mismatches, identification card number and phone number not
Matching, phone number are at least one of inconsistent commonly using ground and GPS geographical location information.
In further embodiment, the equipment class strategy is including the use of simulator.
In further embodiment, the blacklist class strategy records black including the overdue blacklist of net loan, main strategies
List.
In further embodiment, the accumulative class strategy corresponds to multiple names, same including same identification card number
Identification card number corresponds to multiple phone numbers, same identification card number corresponds at least one of multiple addresses.
In further embodiment, the class of algorithms strategy includes lr models.
It is described to be included using exception class strategy in further embodiment：Phone number consumer record is abnormal, identification card number
Corresponding name is at least one of abnormal using abnormal, mobile phone use state.
Business/equipment/the ambient parameter for being related to and using in foregoing implementation process is as follows：
Request content：
Response results
| Parameter name | Parameter type | Description |
| guid | string | Session number, the unique mark of service request |
| code | int | Public error code, 0 represents successfully, and other represent failure |
| codeDesc | string | Specific error reason description |
| riskDecision | int | The result of decision：0 passes through；1 refusal；2 manual examination and verification |
| riskDetail | stirng | Decision-making detail, the reason for being such as intercepted |
Strategy and regular collection
| Classification | Citing |
| Natural person's strategy | Identification card number mismatches with name |
| Equipment class strategy | Use simulator |
| Accumulative class strategy | Same identification card number corresponds to multiple names |
| Blacklist class strategy | Net borrows overdue blacklist |
| Class of algorithms strategy | Lr models |
With reference to shown in Fig. 2, a specific implementation process of the above method is as follows：
1. for user when entering the debt-credit page, user's Background scheduling live body service end asks whether that needs are verified, living
Body service end returns to Y | N；
2. when needing to carry out live body checking, user backstage obtains checking token, and subscription client is aroused using token
Live body sdk is verified that live body sdk returns to the result to client；
3. subscription client calls fingerprint sdk to obtain finger print information, such as available machine time, installation number of applications etc.
4. user debt-credit the page fill in personal essential information, be directed to parameter include but is not limited to name, cell-phone number,
Bank's card number, contact person etc., the user profile that different enterprises are related to has different, while user initiates debit request；
5. service parameter, device-fingerprint parameter, ambient parameter are sent to wind by internet finance backstage (background server)
Dangerous evaluation module, risk evaluation module are asked according to this, and the result of this request is judged by backstage strategy set and model,
And return to the enterprise of called side；
After 6. risk evaluation module receives corresponding parameter, whether the parameter that anticipation stealpass enters is legal, and whether parameter lacks,
Whether parameter format is correct, after confirmation is errorless, into next step；
7. risk evaluation module obtains changing scene corresponding to enterprise according to the enterprise's identification code arranged with cooperative enterprise, inquiry
ID, parameter information corresponding to enterprise is transmitted to regulation engine, after regulation engine receives parameter, into next step；
8. after regulation engine receives the parameter of cooperative enterprise, matching the preset rule of the cooperative enterprise, transported according to rule
Row obtains the result of decision；
9. the returning result that mutually gold backstage basis is neglected, with reference to own business, the actual implementing result of decision-making, and return
To client.
According to foregoing disclosure, it is also proposed that a kind of credit risk merges assessment system, including subscription client, background server
And it is counter cheat evaluating server, anti-cheat is provided with anti-fraud evaluation module, subscription client and backstage in evaluating server
Server, background server and anti-fraud evaluating server pass through network interaction data, wherein subscription client, background server
And anti-fraud evaluating server is configured to realize that credit risk fusion is commented in the following manner (foregoing combination Fig. 1 mode)
Estimate：
Step 1, user open Mobile solution APP by client and asked for a loan request to background server；
Step 2, background server call living body authentication module to carry out living body authentication；
Step 3, in response to living body authentication fingerprint module is called by, subscription client, obtain the upper of subscription client
The equipment of fingerprint collecting, environment, geographical location information, and upload to background server；
And personal essential information is inserted in prompting on the debt-credit interface of subscription client, uploads to background server；
Step 4, subscription client collect user propose the user client device parameters of loan requests, ambient parameter and on
Reach background server；
The device parameter, personal essential information and ambient parameter are packaged by step 5, background server, are called
Result after encapsulation is sent to anti-fraud evaluation module by interface；
Step 6, anti-fraud evaluation module according to the parameter received prejudge whether the parameter that stealpass enters legal, parameter whether
Whether missing and parameter format correct, confirm it is errorless after, into next step, otherwise return to step 3；
Step 7, inquiry are registered in the identification code of the enterprise of business side of anti-fraud evaluation module, and determine it according to identification code
Corresponding scene ID, then, parameter information corresponding to enterprise of business side is transmitted to regulation engine, wherein pre- under different scenes ID
The combination of different anti-fraud rules is put；
After step 8, regulation engine receive the parameter of enterprise of business side, the preset anti-fraud rule of the enterprise of business side are matched
Then, the parameter after aforementioned encapsulation is run according to rule and obtains the result of decision.
In this way, realizing to risk approval process before the loan of lending and borrowing business application, pass through facility information, personal information, environment
The synthetic determination of information and device-fingerprint information, with reference to the risk policy and rank of specific different cooperative enterprises, realize before borrowing
The identification and analysis of risk, play expected risk control effect.
Although the present invention is disclosed above with preferred embodiment, so it is not limited to the present invention.Skill belonging to the present invention
Has usually intellectual in art field, without departing from the spirit and scope of the present invention, when can be used for a variety of modifications and variations.Cause
This, the scope of protection of the present invention is defined by those of the claims.
Claims (12)
1. a kind of credit risk merges appraisal procedure, it is characterised in that comprises the steps：
Step 1, user open Mobile solution APP by client and asked for a loan request to background server；
Step 2, background server call living body authentication module to carry out living body authentication；
Step 3, in response to living body authentication fingerprint module is called by, subscription client, obtain the upper fingerprint of subscription client
The equipment of collection, environment, geographical location information, and upload to background server；
And personal essential information is inserted in prompting on the debt-credit interface of subscription client, uploads to background server；
Step 4, the user client device parameter of subscription client collection user's proposition loan requests, ambient parameter are simultaneously uploaded to
Background server；
The device parameter, personal essential information and ambient parameter are packaged by step 5, background server, calling interface
Result after encapsulation is sent to anti-fraud evaluation module；
Step 6, anti-fraud evaluation module prejudge whether the parameter that stealpass enters legal, whether parameter lacks according to the parameter received
And whether parameter format correct, confirm it is errorless after, into next step, otherwise return to step 3；
Step 7, inquiry are registered in the identification code of the enterprise of business side of anti-fraud evaluation module, and determine that it is corresponding according to identification code
Scene ID, then, parameter information corresponding to enterprise of business side is transmitted to regulation engine, wherein being prefixed under different scenes ID
The combination of different anti-fraud rules；
After step 8, regulation engine receive the parameter of enterprise of business side, the preset anti-fraud rule of the enterprise of business side is matched,
The parameter after aforementioned encapsulation is run according to rule and obtains the result of decision.
2. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the individual inserted at debt-credit interface is substantially
Information includes：Name, ID card No., address, phone number, bank card, bank card reserve cell-phone number information.
3. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the user client device parameter includes：
International mobile equipment identity, international mobile subscriber identity, the machine mac addresses, the machine type, the machine cpu models and the machine
Available machine time.
4. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the ambient parameter includes：The url of face
The wifi's that the mac addresses for the wifi that loan platform, user ask source ip, the machine to connect, the machine connect applied in address, user
Title, gps geographical location information and base station information.
5. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the anti-fraud rule includes natural person's plan
Summary, equipment class strategy, add up class strategy, blacklist class strategy, use at least one of exception and class of algorithms strategy.
6. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that natural person strategy include identification card number with
Name mismatches, GPS geographical location information and address are inconsistent, the geographical position of GPS geographical location information and fingerprint collecting not
Matching, identification card number and phone number mismatch, phone number commonly using ground and GPS geographical location information it is inconsistent in extremely
Few one kind.
7. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the equipment class strategy is including the use of simulation
Device.
8. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that it is overdue that the blacklist class strategy includes net loan
Blacklist, main strategies record blacklist.
9. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the accumulative class strategy includes same identity
Card corresponds to multiple names, same identification card number corresponds to multiple phone numbers, same identification card number is corresponded in multiple addresses
At least one.
10. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that the class of algorithms strategy includes lr models.
11. appraisal procedure is merged according to the credit risk of right 1, it is characterised in that described to include using exception class strategy：Mobile phone
Number consumer record is abnormal, name corresponding to identification card number is at least one of abnormal using abnormal, mobile phone use state.
12. credit risk merges assessment system, it is characterised in that is commented including subscription client, background server and anti-fraud
Estimate server, anti-cheat is provided with anti-fraud evaluation module in evaluating server, subscription client takes with background server, backstage
Business device is commented with anti-fraud evaluating server by network interaction data, wherein subscription client, background server and anti-cheat
Estimate server to be configured to realize that credit risk fusion is assessed in the following manner：
Step 1, user open Mobile solution APP by client and asked for a loan request to background server；
Step 2, background server call living body authentication module to carry out living body authentication；
Step 3, in response to living body authentication fingerprint module is called by, subscription client, obtain the upper fingerprint of subscription client
The equipment of collection, environment, geographical location information, and upload to background server；
And personal essential information is inserted in prompting on the debt-credit interface of subscription client, uploads to background server；
Step 4, the user client device parameter of subscription client collection user's proposition loan requests, ambient parameter are simultaneously uploaded to
Background server；
The device parameter, personal essential information and ambient parameter are packaged by step 5, background server, calling interface
Result after encapsulation is sent to anti-fraud evaluation module；
Step 6, anti-fraud evaluation module prejudge whether the parameter that stealpass enters legal, whether parameter lacks according to the parameter received
And whether parameter format correct, confirm it is errorless after, into next step, otherwise return to step 3；
Step 7, inquiry are registered in the identification code of the enterprise of business side of anti-fraud evaluation module, and determine that it is corresponding according to identification code
Scene ID, then, parameter information corresponding to enterprise of business side is transmitted to regulation engine, wherein being prefixed under different scenes ID
The combination of different anti-fraud rules；
After step 8, regulation engine receive the parameter of enterprise of business side, the preset anti-fraud rule of the enterprise of business side is matched,
The parameter after aforementioned encapsulation is run according to rule and obtains the result of decision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710551098.3A CN107392755A (en) | 2017-07-07 | 2017-07-07 | Credit risk merges appraisal procedure and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710551098.3A CN107392755A (en) | 2017-07-07 | 2017-07-07 | Credit risk merges appraisal procedure and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107392755A true CN107392755A (en) | 2017-11-24 |
Family
ID=60334342
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710551098.3A Pending CN107392755A (en) | 2017-07-07 | 2017-07-07 | Credit risk merges appraisal procedure and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107392755A (en) |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107886430A (en) * | 2017-11-29 | 2018-04-06 | 南京甄视智能科技有限公司 | Risk control method and system after loan |
| CN107993146A (en) * | 2018-01-25 | 2018-05-04 | 深圳市前海吉顺信科技发展有限公司 | The air control method and system of financial big data |
| CN108229790A (en) * | 2017-12-06 | 2018-06-29 | 信用宝金融信息服务（北京）有限公司 | Air control automates platform |
| CN108364224A (en) * | 2018-01-12 | 2018-08-03 | 深圳壹账通智能科技有限公司 | Credit risk joint control method, apparatus, equipment and readable storage medium storing program for executing |
| CN108399573A (en) * | 2017-12-20 | 2018-08-14 | 平安科技（深圳）有限公司 | Electronic device, long-range declaration form go out folk prescription method and storage medium |
| CN108416672A (en) * | 2018-05-11 | 2018-08-17 | 试金石信用服务有限公司 | Financial risks appraisal procedure, system, server and storage medium |
| CN108846737A (en) * | 2018-04-19 | 2018-11-20 | 长春市万易科技有限公司 | A kind of fraud measure and system |
| CN109118247A (en) * | 2018-08-01 | 2019-01-01 | 安徽好车到家信息科技有限公司 | The anti-fake system of automobile consumption credit and analysis method based on LBS database and customer portrait |
| CN109255623A (en) * | 2018-07-27 | 2019-01-22 | 重庆小雨点小额贷款有限公司 | A kind of business approval method, server, client and storage medium |
| CN109255624A (en) * | 2018-07-27 | 2019-01-22 | 重庆小雨点小额贷款有限公司 | A kind of business approval method, apparatus, server and storage medium |
| CN109377347A (en) * | 2018-09-27 | 2019-02-22 | 深圳先进技术研究院 | Network credit method for early warning, system and electronic equipment based on feature selecting |
| CN109410031A (en) * | 2018-09-27 | 2019-03-01 | 苏宁消费金融有限公司 | The consumptive credit intelligence measures and procedures for the examination and approval and system |
| CN109472439A (en) * | 2018-09-13 | 2019-03-15 | 深圳市买买提信息科技有限公司 | Credit estimation method, device, equipment and system |
| CN109472692A (en) * | 2018-10-31 | 2019-03-15 | 厦门市七星通联科技有限公司 | It is a kind of based on the anti-auditing system of task by stages cheated and method |
| CN109558592A (en) * | 2018-11-29 | 2019-04-02 | 上海点融信息科技有限责任公司 | The method and apparatus of customer Credit Risk assessment information is obtained based on artificial intelligence |
| CN109660507A (en) * | 2018-10-16 | 2019-04-19 | 深圳壹账通智能科技有限公司 | Method, apparatus, equipment and the readable storage medium storing program for executing communicated with user terminal |
| CN109711973A (en) * | 2018-11-09 | 2019-05-03 | 深圳壹账通智能科技有限公司 | Methods of risk assessment and device, storage medium, computer equipment |
| CN110120933A (en) * | 2018-02-07 | 2019-08-13 | 阿里巴巴集团控股有限公司 | Air control, man-machine identification and data processing method, equipment and system |
| CN110135700A (en) * | 2019-04-23 | 2019-08-16 | 北京淇瑀信息科技有限公司 | Credit Risk Assessment method and device based on expandtabs data |
| CN110189134A (en) * | 2019-05-17 | 2019-08-30 | 同济大学 | Based on doubtful fraudulent trading referring to the anti-fake system architecture design method of network payment of tagmeme |
| CN110555762A (en) * | 2019-08-27 | 2019-12-10 | 上海易点时空网络有限公司 | Blacklist data processing method and device and big data loan platform optimization method |
| CN111222981A (en) * | 2020-01-16 | 2020-06-02 | 中国建设银行股份有限公司 | Credibility determination method, device, equipment and storage medium |
| CN111311136A (en) * | 2020-05-14 | 2020-06-19 | 深圳索信达数据技术有限公司 | Wind control decision method, computer equipment and storage medium |
| CN111861732A (en) * | 2020-07-31 | 2020-10-30 | 重庆富民银行股份有限公司 | Risk assessment system and method |
| CN112308578A (en) * | 2020-12-30 | 2021-02-02 | 南京智闪萤科技有限公司 | Method, electronic device and storage medium for task anti-fraud |
| CN113256408A (en) * | 2021-07-06 | 2021-08-13 | 中证信用云科技（深圳）股份有限公司 | Risk control method and system based on consumption finance and computer equipment |
| CN113553555A (en) * | 2021-06-28 | 2021-10-26 | 吉林亿联银行股份有限公司 | Client security authentication method and device |
| CN114971598A (en) * | 2022-08-01 | 2022-08-30 | 天津金城银行股份有限公司 | Wind-controlled approval system, method, equipment and medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101576869A (en) * | 2009-05-31 | 2009-11-11 | 北京富邦科讯信息咨询有限公司 | Intelligent expert consulting system based on backboard model and method |
| US20110106693A1 (en) * | 2001-11-13 | 2011-05-05 | Halper Steven C | Automated Loan Risk Assessment System and Method |
| CN102722814A (en) * | 2012-06-01 | 2012-10-10 | 汪德嘉 | Self-adaption controllable management system for cheat risk of online transaction |
| US20130110692A1 (en) * | 2009-04-16 | 2013-05-02 | Brad Nightengale | System and method for pushing advanced warning alerts |
| US20140012724A1 (en) * | 2011-03-23 | 2014-01-09 | Detica Patent Limited | Automated fraud detection method and system |
| CN105847270A (en) * | 2016-04-18 | 2016-08-10 | 贵州安易和信科技有限公司 | Loan authentication system |
-
2017
- 2017-07-07 CN CN201710551098.3A patent/CN107392755A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110106693A1 (en) * | 2001-11-13 | 2011-05-05 | Halper Steven C | Automated Loan Risk Assessment System and Method |
| US20130110692A1 (en) * | 2009-04-16 | 2013-05-02 | Brad Nightengale | System and method for pushing advanced warning alerts |
| CN101576869A (en) * | 2009-05-31 | 2009-11-11 | 北京富邦科讯信息咨询有限公司 | Intelligent expert consulting system based on backboard model and method |
| US20140012724A1 (en) * | 2011-03-23 | 2014-01-09 | Detica Patent Limited | Automated fraud detection method and system |
| CN102722814A (en) * | 2012-06-01 | 2012-10-10 | 汪德嘉 | Self-adaption controllable management system for cheat risk of online transaction |
| CN105847270A (en) * | 2016-04-18 | 2016-08-10 | 贵州安易和信科技有限公司 | Loan authentication system |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107886430A (en) * | 2017-11-29 | 2018-04-06 | 南京甄视智能科技有限公司 | Risk control method and system after loan |
| CN108229790A (en) * | 2017-12-06 | 2018-06-29 | 信用宝金融信息服务（北京）有限公司 | Air control automates platform |
| CN108399573A (en) * | 2017-12-20 | 2018-08-14 | 平安科技（深圳）有限公司 | Electronic device, long-range declaration form go out folk prescription method and storage medium |
| CN108364224A (en) * | 2018-01-12 | 2018-08-03 | 深圳壹账通智能科技有限公司 | Credit risk joint control method, apparatus, equipment and readable storage medium storing program for executing |
| CN107993146A (en) * | 2018-01-25 | 2018-05-04 | 深圳市前海吉顺信科技发展有限公司 | The air control method and system of financial big data |
| CN110120933A (en) * | 2018-02-07 | 2019-08-13 | 阿里巴巴集团控股有限公司 | Air control, man-machine identification and data processing method, equipment and system |
| CN108846737A (en) * | 2018-04-19 | 2018-11-20 | 长春市万易科技有限公司 | A kind of fraud measure and system |
| CN108846737B (en) * | 2018-04-19 | 2021-03-23 | 长春市万易科技有限公司 | Fraud measurement method |
| CN108416672A (en) * | 2018-05-11 | 2018-08-17 | 试金石信用服务有限公司 | Financial risks appraisal procedure, system, server and storage medium |
| CN109255623A (en) * | 2018-07-27 | 2019-01-22 | 重庆小雨点小额贷款有限公司 | A kind of business approval method, server, client and storage medium |
| CN109255624A (en) * | 2018-07-27 | 2019-01-22 | 重庆小雨点小额贷款有限公司 | A kind of business approval method, apparatus, server and storage medium |
| CN109118247A (en) * | 2018-08-01 | 2019-01-01 | 安徽好车到家信息科技有限公司 | The anti-fake system of automobile consumption credit and analysis method based on LBS database and customer portrait |
| CN109472439A (en) * | 2018-09-13 | 2019-03-15 | 深圳市买买提信息科技有限公司 | Credit estimation method, device, equipment and system |
| CN109377347A (en) * | 2018-09-27 | 2019-02-22 | 深圳先进技术研究院 | Network credit method for early warning, system and electronic equipment based on feature selecting |
| CN109410031A (en) * | 2018-09-27 | 2019-03-01 | 苏宁消费金融有限公司 | The consumptive credit intelligence measures and procedures for the examination and approval and system |
| CN109660507B (en) * | 2018-10-16 | 2022-05-17 | 深圳壹账通智能科技有限公司 | Method, device and equipment for communicating with user side and readable storage medium |
| CN109660507A (en) * | 2018-10-16 | 2019-04-19 | 深圳壹账通智能科技有限公司 | Method, apparatus, equipment and the readable storage medium storing program for executing communicated with user terminal |
| CN109472692A (en) * | 2018-10-31 | 2019-03-15 | 厦门市七星通联科技有限公司 | It is a kind of based on the anti-auditing system of task by stages cheated and method |
| CN109711973A (en) * | 2018-11-09 | 2019-05-03 | 深圳壹账通智能科技有限公司 | Methods of risk assessment and device, storage medium, computer equipment |
| CN109558592A (en) * | 2018-11-29 | 2019-04-02 | 上海点融信息科技有限责任公司 | The method and apparatus of customer Credit Risk assessment information is obtained based on artificial intelligence |
| CN110135700A (en) * | 2019-04-23 | 2019-08-16 | 北京淇瑀信息科技有限公司 | Credit Risk Assessment method and device based on expandtabs data |
| CN110189134A (en) * | 2019-05-17 | 2019-08-30 | 同济大学 | Based on doubtful fraudulent trading referring to the anti-fake system architecture design method of network payment of tagmeme |
| CN110555762A (en) * | 2019-08-27 | 2019-12-10 | 上海易点时空网络有限公司 | Blacklist data processing method and device and big data loan platform optimization method |
| CN111222981A (en) * | 2020-01-16 | 2020-06-02 | 中国建设银行股份有限公司 | Credibility determination method, device, equipment and storage medium |
| CN111311136A (en) * | 2020-05-14 | 2020-06-19 | 深圳索信达数据技术有限公司 | Wind control decision method, computer equipment and storage medium |
| CN111861732A (en) * | 2020-07-31 | 2020-10-30 | 重庆富民银行股份有限公司 | Risk assessment system and method |
| CN112308578A (en) * | 2020-12-30 | 2021-02-02 | 南京智闪萤科技有限公司 | Method, electronic device and storage medium for task anti-fraud |
| CN112308578B (en) * | 2020-12-30 | 2021-05-07 | 南京智闪萤科技有限公司 | Method, electronic device and storage medium for task anti-fraud |
| CN113553555A (en) * | 2021-06-28 | 2021-10-26 | 吉林亿联银行股份有限公司 | Client security authentication method and device |
| CN113256408A (en) * | 2021-07-06 | 2021-08-13 | 中证信用云科技（深圳）股份有限公司 | Risk control method and system based on consumption finance and computer equipment |
| CN114971598A (en) * | 2022-08-01 | 2022-08-30 | 天津金城银行股份有限公司 | Wind-controlled approval system, method, equipment and medium |
| CN114971598B (en) * | 2022-08-01 | 2022-11-22 | 天津金城银行股份有限公司 | Wind-controlled approval system, method, equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107392755A (en) | Credit risk merges appraisal procedure and system | |
| CN108596434B (en) | Fraud detection and methods of risk assessment, system, equipment and storage medium | |
| US7523051B2 (en) | Information processing apparatus, information processing method, and program | |
| CN107730377A (en) | Qualification of providing a loan screening technique, device and computer-readable recording medium | |
| CN110428279A (en) | Vehicle insurance recommended method, device, equipment and computer readable storage medium | |
| CN107705036A (en) | Dynamic credit estimation method and system based on multi-dimensional data | |
| CN106603327B (en) | Behavioral data analysis method and device | |
| CN111461857A (en) | Personal online credit method, device, system, equipment and medium for small and medium-sized banks | |
| CN110147803A (en) | Customer churn early-warning processing method and device | |
| CN107423987A (en) | A kind of local type Express Logistics real-name authentication system and authentication method | |
| CN107644098A (en) | A kind of fraud recognition methods, device, equipment and storage medium | |
| CN108268996A (en) | Task tracking processing method, system, computer equipment and storage medium | |
| JP2012529095A (en) | How to get data from users at the time of card payment made using payment terminal | |
| CN109858919A (en) | Determination method and device, online ordering method and the device of abnormal account | |
| CN110166416A (en) | A method of it is assessed based on Internet authentication and credit | |
| CN109685639A (en) | Loan checking method, device, equipment and computer readable storage medium | |
| CN110675252A (en) | Risk assessment method and device, electronic equipment and storage medium | |
| CN117252429A (en) | Risk user identification method and device, storage medium and electronic equipment | |
| CN108777749A (en) | A kind of fraudulent call recognition methods and device | |
| CN107679383A (en) | A kind of auth method and device based on geographical position and contact pressure area | |
| CN108376303A (en) | internet management system and management method | |
| KR101342673B1 (en) | Matching system for marry information in on-line and method thereof | |
| CN109711984B (en) | Pre-loan risk monitoring method and device based on collection urging | |
| CN107911331A (en) | Data verification method and server | |
| KR100856823B1 (en) | System for Planning Perform by Using Popularity Vote |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20171124 |