CN110610413A - Loan transaction risk control method and system - Google Patents
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Abstract
The invention provides a loan transaction risk control method, which receives loan application data of an applicant; reviewing loan application data; if the examination is passed, setting the credit limit; if not, refusing the loan application; before depositing money, obtaining the data of the applicant again, and if the risk exists, refusing to batch the money to the applicant; if no risk exists, the money is batched to the applicant according to the credit limit; the invention also provides a loan transaction risk control system, which greatly reduces the risk of loan transaction and ensures that the loan service withdraws the debt to the maximum extent.
Description
Technical Field
The invention relates to a loan transaction risk control method and system.
Background
Today, the internet finance is rapidly developing, the pure online loan service has gradually replaced the traditional offline loan, and in the online loan approval process, the automatic examination rules of each financial institution become the key point for controlling the credit risk due to the lack of offline investigation of the financial institution employees. At present, partial risk cases exist, customers use the convenience of bank online loan, and partial student groups are organized to apply for loan and then move the loan, so that money is not paid when repayment days come; in the existing mode, when the credit related information of a client is lost, the credit risk of the client is difficult to discover; and after the client has been granted credit limit, the client is paid directly without further risk control.
Disclosure of Invention
The invention aims to provide a loan transaction risk control method and system, which can reduce the risk of personal online credit service and provide better financial service for students at school.
One of the present invention is realized by: a loan transaction risk control method, comprising:
step 1, receiving loan application data of an applicant;
step 2, checking loan application data;
step 3, if the examination is passed, setting the credit line of the mobile phone; if not, refusing the loan application;
step 4, before depositing money, obtaining the data of the applicant again, and if the risk exists, refusing to batch the money to the applicant; if no risk exists, money is batched to the applicant according to the credit limit.
Further, the step 2 is further specifically: the applicant's loan application data is received,
if the mobile phone identification number or the binding mobile phone number in the loan application data appears in the loan application data with the first limited number, the loan applicant is examined;
if the limited number of people in the limited time of the mobile banking opening website in the loan application data is opened and the loan application is carried out, the loan applicant is examined;
if the IP address in the loan application data exceeds a second limited number of loan applications within a limited time, the loan applicant is examined.
Further, the step 1, the step 2 or the step 3 is followed by a step a of obtaining data of the applicant by a third party data company, and if the applicant has network loan risk transaction or excessive consumption, refusing the loan application and ending the step; if not, the next step is carried out.
Further, the step 4 is further specifically: before depositing money, obtaining the data of the applicant again, if the applicant is a student and the set point appears, the class escaping time exceeds the limited class time or the average score is reduced to the limited score, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
if the applicant is a non-student and the student leaves the job or is in time sharing with a set place, refusing to batch the money to the applicant; otherwise, the money is batched to the applicant according to the credit line.
And further, step 5, acquiring the data of the applicant by a third party data company, and processing the applicant if the applicant has online credit risk transaction or excessive consumption.
The second invention is realized by the following steps: a loan transaction risk control system comprising:
a receiving module for receiving loan application data of an applicant;
the examination module is used for examining the loan application data;
the credit module sets the credit limit if the examination is passed; if not, refusing the loan application;
the money putting module acquires the data of the applicant again before money putting, and refuses to batch the money to the applicant if the risk exists; if no risk exists, money is batched to the applicant according to the credit limit.
Further, the review module is further specifically configured to: the applicant's loan application data is received,
if the mobile phone identification number or the binding mobile phone number in the loan application data appears in the loan application data with the first limited number, the loan applicant is examined;
if the limited number of people in the limited time of the mobile banking opening website in the loan application data is opened and the loan application is carried out, the loan applicant is examined;
if the IP address in the loan application data exceeds a second limited number of loan applications within a limited time, the loan applicant is examined.
Further, a risk control module is further included behind the receiving module, the reviewing module or the credit granting module, the data of the applicant is obtained through a third party data company, if the applicant has online loan risk transaction or excessive consumption, the loan application is refused, and the step is ended; if not, the next step is carried out.
Further, the deposit module is further specifically: before depositing money, obtaining the data of the applicant again, if the applicant is a student and the set point appears, the class escaping time exceeds the limited class time or the average score is reduced to the limited score, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
if the applicant is a non-student and the student leaves the job or is in time sharing with a set place, refusing to batch the money to the applicant; otherwise, the money is batched to the applicant according to the credit line.
And further, a deposit follow-up module is further included, the data of the applicant is obtained through a third party data company, and the applicant is processed if the applicant has online credit risk transaction or excessive consumption.
The invention has the following advantages: the invention reduces the risk of personal online credit service, can perform full risk control before paying, and can continue tracking after paying, if the system is at risk, the system can respond immediately, thereby reducing the risk to the maximum extent for banks.
Drawings
The invention will be further described with reference to the following examples with reference to the accompanying drawings.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a diagram of the intra-row architecture for credit investigation and white-user credit granting and wind control according to the present invention.
FIG. 3 is a diagram illustrating the docking of the in-line and out-of-line data query system according to the present invention.
Fig. 4 is a flow chart of loan application of the invention.
FIG. 5 is a flowchart illustrating the flow of the invention for the outbound confirmation.
FIG. 6 is a flowchart illustrating how to build a model of credit in accordance with the present invention.
Fig. 7 is a flow chart of the loan wind control of the invention.
Detailed Description
As shown in fig. 1, one of the present invention is implemented as follows: a loan transaction risk control method, comprising:
step 1, receiving loan application data of an applicant;
step 2, receiving the loan application data of the applicant,
if the mobile phone identification number or the binding mobile phone number in the loan application data appears in the loan application data with the first limited number, the loan applicant is examined;
if the limited number of people in the limited time of the mobile banking opening website in the loan application data is opened and the loan application is carried out, the loan applicant is examined;
if the IP address in the loan application data exceeds a second limited number of loan applications within a limited time, the loan applicant is examined;
step 3, if the examination is passed, setting the credit line of the mobile phone; if not, refusing the loan application;
step 4, before depositing money, obtaining the data of the applicant again, if the applicant is a student and the set point appears, the class escaping class time exceeds the limited class time or the average score is reduced to the limited score, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
if the applicant is a non-student and the student leaves the job or is in time sharing with a set place, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
and 5, acquiring the data of the applicant through a third party data company, and processing the applicant if the applicant has online credit risk transaction or excessive consumption.
Step 1, step 2 or step 3 is followed by a step a of obtaining data of the applicant by a third party data company, if the applicant has online loan risk transaction or excessive consumption, refusing the loan application, and ending the step; if not, the next step is carried out.
The invention discloses a loan transaction risk control system, which comprises:
a receiving module for receiving loan application data of an applicant;
an audit module that receives the applicant's loan application data,
if the mobile phone identification number or the binding mobile phone number in the loan application data appears in the loan application data with the first limited number, the loan applicant is examined;
if the limited number of people in the limited time of the mobile banking opening website in the loan application data is opened and the loan application is carried out, the loan applicant is examined;
if the IP address in the loan application data exceeds a second limited number of loan applications within a limited time, the loan applicant is examined;
the credit module sets the credit limit if the examination is passed; if not, refusing the loan application;
the money paying module acquires the data of the applicant again before paying money, and refuses to batch money to the applicant if the applicant is a student and the given place, the class escaping class time exceed the limited class time or the average score is reduced to a limited score; if not, the money is batched to the applicant according to the credit line;
if the applicant is a non-student and the student leaves the job or is in time sharing with a set place, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
and the deposit follow-up module acquires the data of the applicant through a third party data company, and processes the applicant if the applicant has online credit risk transaction or excessive consumption.
The receiving module or the reviewing module or the credit granting module also comprises a risk control module, the data of the applicant is obtained through a third party data company, if the applicant has online loan risk transaction or excessive consumption, the loan application is refused, and the step is ended; if not, the next step is carried out.
The invention realizes a risk identification method for credit investigation blank users, which carries out scene analysis on a bank personal loan process, carries out classification analysis on clients when personal credit investigation of the clients is blank in online loan service, and records the result in a client relationship management system in a row for subsequent client management. The specific information is as follows:
1. and carrying out data interface with the education system, and identifying whether the credit investigation blank user is a student at a school or not.
2. Different risk identification schemes are used for the client groups at school and not at school.
3. And identifying the risks of the associated persons of the clients by utilizing the inline and out-of-line data, and judging whether the loan is in suspicion of appropriation.
4. And carrying out risk identification on loan applications of the same related client in a short time.
The system function of the invention specifically comprises the following contents:
1. according to the general younger characteristics of credit investigation blank users, the clients are divided into two types of clients, namely, school students and non-school students. The method comprises the steps of establishing an information sharing mechanism with a regional education system, pushing the information to the education system for inquiry when a client initiates a credit application, confirming whether students are students at school, acquiring information such as whether the students are poverty students or not, and pushing the information of the clients for using credit and subsequent repayment to the education system after giving credit financial services to the client. And the bank and the school together control the credit risk of the students by utilizing the related information and the information sharing mechanism of the education system.
2. For the clients who are not at school or have graduated and cannot obtain information from the education system, the resources sharing mechanism is established with the social human resource system to obtain data such as working data of the clients, the third-party data company is connected to obtain whether the clients have online loan and associated client risks, and the data is imported into the inline big data platform to carry out comprehensive judgment so as to identify the credit risks of the clients.
3. And obtaining the comprehensive default risk of the customer by using a Bayesian model and a big data platform according to the analysis result of different risk factor combinations.
(I) Integrated System architecture
As shown in fig. 2, the overall system architecture of the present patent combines inline data, campus data, and social human resource data of a client as risk factors of a client trust model. And a customer relationship management system, a customer information system, a retail credit system, a big data platform and a mobile phone bank as a loan application channel in the associated line are used as system supports of patents. And combining a flow calculation engine and big data modeling, and screening and blocking the risks before, during and after the loan of the client.
(II) information sharing mechanism with educational system and social human resources
According to the ' notice about further strengthening the campus loan standard management work ' issued by the China banking and supervising department, the education department, the human resource department and the social security department in a united way ', the normal order of campus loans is maintained, and the commercial bank and the policy bank are required to purposefully develop financial products such as college-learning assistance, training, consumption, entrepreneurship and the like on the premise of controllable risks, and the interest rate of credit limits is reasonably set. Banks need to gradually replace various loan network and loan platforms in campuses to provide financial services related to students. The bank system needs to complete sharing of information resources together with the education system, and data transmission between the bank system and the education system can be completed in a special line butt joint mode, so that safety in data transmission is guaranteed. The bank is connected with an education system database through a bank external connection system to complete inquiry and information push, and whether the education inquiry information content is limited to the poverty and poverty school or not is judged, and the school-time score is obtained under the condition of the school awards and punishment.
For non-student client groups, such as graduate clients, a mechanism for establishing information sharing with the social and human resources system is needed. The bank system is similar to the interaction of the social human resource system, the data interaction inside and outside the bank is completed through the external connection system, and the data transmission is completed through a special line mode, so that the data safety is ensured. The social human resource system query information includes but is not limited to the employment situation, salary situation and administrative reward and punishment of customers.
As shown in fig. 3, the data query relationship for the inline system and the inline system.
(III) Risk control Scenario partitioning
The system participation in the credit transaction scenario is as follows:
as shown in fig. 4 and 5, the effective credit requirement of a credit investigation blank client needs to be confirmed, a credit scene needs to be divided, and after a data sharing mechanism is established, the credit scene targeted by the patent is a scene of online loan application of a personal client, including a loan application link, a fund accounting environment and a post-loan risk early warning link. The specific business process is as follows:
1. when applying for loan, the client uses the mobile phone bank to supplement and assist the verification of identity materials and loan purpose information materials, and the authorized bank can inquire the relevant data of the client (whether the student client is poor or not, shared information such as school scores and the like, and the working client inquires payment condition shared data such as on duty, social security, tax and the like).
2. And judging couples or parent-child relations recorded in the customer relationship management system in the customer association bank according to information inside and outside the bank, and frequently contacting risks of the commuters recorded in the data outside the bank (the frequently contacting risks of the commuters comprise abnormal transfer behaviors in the bank, credit default behaviors of credit investigation, other risks of credit investigation and open court judgment).
3. And inquiring the relevant information of the client from the education system and the social human resource system for credit granting. And (4) adopting different risk models to respectively carry out loan line credit for student groups and social employees by judging the category of the client groups. (details of the model information are described below in the model establishment calculation credit line)
4. When the account is confirmed, whether the client has high risk operation behavior is judged, if the risk transaction occurs, the client is blocked or early warned in time (the specific risk treatment part is detailed in the real-time risk prevention and control system establishment below)
(IV) establishing a model to calculate the credit line
As shown in FIG. 6, the establishment of the credit model is as follows:
firstly, establishing model factors, and using the internal and external data of the client as credit model factors. The out-of-line data used by the student client group includes: whether poverty is poverty or living, whether the poverty is in school or not, and whether the score (shared information) is in school or not are used as credit line model factors, wherein the existing data in the line comprises the following data: and (5) associating the trust records in the rows. The extravehicular data used by the social staff includes: the specific occupation, the period of employment, the average income (nearly March, nearly half year, nearly one year), the social security payment condition, the tax payment condition, the existing data in the row includes: the purchase condition of the intra-row financial products and the contract condition of the intra-row business. The specific credit formula is as follows:
student client group credit line + a poor credit line + a number of awards at school-b number of awards at school + c number of awards at leveling average performance point + d MAX (credit line granted in related personnel)
Wherein: a. b, c and d are respectively related coefficients, and when a plurality of related persons in the customer have credit lines, the highest credit line is taken as a credit factor. The highest credit limit does not exceed the consumption limit of the credit scene.
The credit line of the social on-duty personnel is the trade line + a the time of entry + b MAX (average income in nearly three months, average income in nearly half a year) + c monthly social security payment transaction line + d monthly public deposit payment transaction line + e tax payment transaction line + f the internal financial product purchase share + g the internal business subscription number.
Wherein: a. b, c, d, e, f and g are respectively correlation coefficients, and the average income of customers is the highest value of the average value of about March, about half year and about one year. The credit line does not exceed the consumption line of the credit scene.
Next, a look-ahead mode is used, and relevant weight coefficient values are set by using expert experience, and data accumulation is performed.
As in the student formula above: in the line, an expert model is used to set 1000 yuan for poor survival, a coefficient a is 100, a coefficient b is 100, a coefficient c is 100, and a coefficient d is 0.005, then, a student is poor survival, the number of times of correction and reward is 3, the number of times of division is 1, the average performance point is 3.5, the highest credit limit of the inter-line related person is 100000 yuan, and the credit amount of the student is 1000+100, 3-100, 1 is 100, 3.5+0.005, 100000, 2050 yuan. If the consumption amount of the current credit granting scene of the student client is 2000 yuan (namely the amount of the loan applied by the applicant) and is lower than the amount calculated by the formula, the final credit granting amount is 2000 yuan.
And finally, training the data in an incremental mode by adopting a Bayesian model according to the credit amount of the customer in the historical data and default risk condition data serving as the basis of big data model analysis, calculating the risk occurrence probability of the customer loan after establishing an automatic credit model, and automatically adjusting the weight coefficient of each factor in the credit model and the amounts of poverty, industry credit and the like according to the risk occurrence probability. And comparing the credit amount obtained by the big data model with the credit amount obtained by the original scoring card, and continuously optimizing the model.
After certain data accumulation and model training are carried out, due to the fact that default risks of clients are considered in the model, the coefficients in the formula are optimized and adjusted, for example, the poor living quota is adjusted to 800 yuan from 1000 yuan of an original expert model, the division coefficient b is adjusted to 200 yuan, and the performance point coefficient c is adjusted to 80 yuan.
The adjusted credit amount of the student is 800+100 x 3-200 x 1+80 x 3.5+0.005 x 100000-1680 yuan. Because the credit amount does not exceed the consumption scene amount, the final credit amount of the student client after model optimization is 1680 yuan.
And continuously adding new model factors according to the service operation condition and the service data accumulation, and then adding the new model factors into the limit test formula.
(V) establishment of real-time risk prevention and control system
Besides the pre-loan risk early warning and the limit measuring and calculating rule, a set of complete real-time wind control system needs to be established to carry out prevention and control in the loan, and the bank's own real-time risk prevention and control system is utilized to apply for risky centralized credit and adopt real-time transaction blocking with credit.
As shown in fig. 7, the real-time wind control process of the bank loan service is as follows:
in the steps of loan application, examination and approval, loan and loan, wind control precaution rule control is added, real-time prevention and control are carried out on the generated risk, and particularly in the loan process, when the risky transaction is inquired, the loan process is blocked in real time, so that the credit risk is avoided. Meanwhile, a big data wind control platform is used for calculating risk data in real-time services, storing the risk data and providing a data basis for an optimization model.
Since the credit investigation blank user group is mainly a young customer group, special treatment needs to be performed on the high-risk items and the risk characteristics of the young group in the aspect of risk prevention and control. The high risk of the young group comprises excessive advanced consumption, high-risk network credit and campus credit behaviors. The risks are not recorded to the people to assess credit, which is one of the reasons that the risk of assessing blank user groups is difficult to find, and under the condition that data is lack in the rows, the data needs to be shared with campus and social human resource data, and a third party cooperates with the campus and social human resource data, and the campus and social human resource data comprises an e-commerce platform, a data company and the like, so that the records of the high-risk behaviors of the customers are obtained and blocked in time.
Specific prevention and control rules are currently available as follows:
TABLE 5.1 wind control rules
The above rule actual risk handling case is as follows: when a client applies for a loan, the big data real-time computing engine finds that a large number of applications occur in the IP used by the client in a short time, judges that the IP has a centralized loan application risk, carries out early warning in the application stage, adds the IP to an IP blacklist, and rejects the loan application of the client.
The method comprises the steps of calculating risk probability in the money paying process and after money paying of a customer by adopting a Bayesian model, setting a risk threshold, conducting real-time blocking processing on risk transactions exceeding the threshold, conducting registration and reminding processing on transactions in the threshold, prompting default risks of the customer after credit, and enabling personnel in a bank to conduct risk prevention and control processing as soon as possible. And meanwhile, continuously extracting risk factors in the case to serve as risk factors of a client risk model, and optimizing and perfecting the model and the prevention and control rules.
Although specific embodiments of the invention have been described above, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, and that equivalent modifications and variations can be made by those skilled in the art without departing from the spirit of the invention, which is to be limited only by the appended claims.
Claims (10)
1. A loan transaction risk control method is characterized by comprising the following steps: the method comprises the following steps:
step 1, receiving loan application data of an applicant;
step 2, checking loan application data;
step 3, if the examination is passed, setting the credit line of the mobile phone; if not, refusing the loan application;
step 4, before depositing money, obtaining the data of the applicant again, and if the risk exists, refusing to batch the money to the applicant; if no risk exists, money is batched to the applicant according to the credit limit.
2. The loan transaction risk control method according to claim 1, wherein: the step 2 is further specifically as follows: the applicant's loan application data is received,
if the mobile phone identification number or the binding mobile phone number in the loan application data appears in the loan application data with the first limited number, the loan applicant is examined;
if the limited number of people in the limited time of the mobile banking opening website in the loan application data is opened and the loan application is carried out, the loan applicant is examined;
if the IP address in the loan application data exceeds a second limited number of loan applications within a limited time, the loan applicant is examined.
3. The loan transaction risk control method according to claim 1, wherein: step 1, step 2 or step 3 is followed by a step a of obtaining data of the applicant by a third party data company, if the applicant has online loan risk transaction or excessive consumption, refusing the loan application, and ending the step; if not, the next step is carried out.
4. The loan transaction risk control method according to claim 1, wherein: the step 4 is further specifically as follows: before depositing money, obtaining the data of the applicant again, if the applicant is a student and the set point appears, the class escaping time exceeds the limited class time or the average score is reduced to the limited score, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
if the applicant is a non-student and the student leaves the job or is in time sharing with a set place, refusing to batch the money to the applicant; otherwise, the money is batched to the applicant according to the credit line.
5. The loan transaction risk control method according to claim 1, wherein: and step 5, acquiring the data of the applicant through a third party data company, and processing the applicant if the applicant has online credit risk transaction or excessive consumption.
6. A loan transaction risk control system, characterized by: the method comprises the following steps:
a receiving module for receiving loan application data of an applicant;
the examination module is used for examining the loan application data;
the credit module sets the credit limit if the examination is passed; if not, refusing the loan application;
the money putting module acquires the data of the applicant again before money putting, and refuses to batch the money to the applicant if the risk exists; if no risk exists, money is batched to the applicant according to the credit limit.
7. The loan transaction risk control system according to claim 6, wherein: the review module is further specifically: the applicant's loan application data is received,
if the mobile phone identification number or the binding mobile phone number in the loan application data appears in the loan application data with the first limited number, the loan applicant is examined;
if the limited number of people in the limited time of the mobile banking opening website in the loan application data is opened and the loan application is carried out, the loan applicant is examined;
if the IP address in the loan application data exceeds a second limited number of loan applications within a limited time, the loan applicant is examined.
8. The loan transaction risk control system according to claim 6, wherein: the receiving module or the reviewing module or the credit granting module also comprises a risk control module, the data of the applicant is obtained through a third party data company, if the applicant has online loan risk transaction or excessive consumption, the loan application is refused, and the step is ended; if not, the next step is carried out.
9. The loan transaction risk control system according to claim 6, wherein: the deposit module further comprises: before depositing money, obtaining the data of the applicant again, if the applicant is a student and the set point appears, the class escaping time exceeds the limited class time or the average score is reduced to the limited score, refusing to batch the money to the applicant; if not, the money is batched to the applicant according to the credit line;
if the applicant is a non-student and the student leaves the job or is in time sharing with a set place, refusing to batch the money to the applicant; otherwise, the money is batched to the applicant according to the credit line.
10. The loan transaction risk control system according to claim 6, wherein: the system also comprises a deposit follow-up module, the data of the applicant is obtained through a third party data company, and if the applicant has online credit risk transaction or excessive consumption, the applicant is processed.
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| CN112767131A (en) * | 2021-01-26 | 2021-05-07 | 福建卓融信息技术有限公司 | Loan approval-based risk control method and device |
| CN113052672A (en) * | 2019-12-26 | 2021-06-29 | 北京宸信征信有限公司 | Processing method and system for fighting illegal fraud based on loan application association relation |
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