Method and system for determining credit line

Abstract

The invention relates to a method and system for determining a credit line, and the method comprises the steps: starting a monitoring node to monitor key monitoring items in borrower data information, and generating a key monitoring item data curve; constructing a correlation function, and generating a correlation curve; the key monitoring item data curve and the correlation function are calibrated in the same coordinate system, and abnormal nodes of the key monitoring item are monitored according to the calibrated coordinate system; fitting monitoring item abnormal nodes obtained in a period of time, predicting an abnormal point appearance range coordinate in a next set time period, if the occurrence of the abnormal point in the next time period accords with a prediction result, performing no action, and otherwise, triggering a credit line adjustment process; the key monitoring item can be monitored in real time, and the abnormal condition of the key monitoring item can be fed back in time, so that a reliable data basis is provided for determining and adjusting the credit line, the reliability of a credit extension system is improved, and the credit extension risk faced by a bank party is reduced.

Description

Technical Field

[0001] The present invention relates to the field of smart banking technology, and more specifically, to a method and system for determining a credit limit. Background Art

[0002] The credit line refers to the maximum risk limit that the bank can bear for the customer. When the bank grants a certain credit line to the customer, the customer can apply for a loan and other financial activities within this limit. The calculation method of the credit line is relatively complicated. Generally speaking, as long as the customer's behavior has credit risk, it will be included in the credit management by the bank. For example, loans, trade financing, bill acceptance and discount, overdraft, factoring, guarantees, loan commitments, opening letters of credit and other on-balance sheet businesses are all within the scope of credit lines.

[0003] The credit line can be divided into the maximum credit line and the sub-credit line; the maximum credit line is the total amount of risk that the customer can bear after the bank conducts a comprehensive assessment of the user's specific financial situation and risks, and quantifies it into a specific credit line. The sum of the credit lines of various credits provided by the bank to each user cannot be higher than the maximum credit line of the user. In other words, the maximum credit line is the basis for the sub-credit line. Sub-credit refers to the classification and limit of different financial businesses under the maximum credit line according to different risk generation and risk characteristics.

[0004] At present, the credit limit for users is mostly based on the information of the users in the previous period of time and is analyzed by relying on specific analysis models. However, this will lead to a more serious problem. That is, when the user encounters unexpected situations before the next evaluation, such as financial problems, this may or may not bring risks to the bank. For such emergencies, the existing methods are difficult to provide good reminders and responses. A method and system for determining the credit limit that can be flexibly targeted at this method is needed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for determining a credit limit and a system for determining a credit limit in view of the above-mentioned defects of the prior art.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A method for determining a credit limit is constructed, wherein the method comprises the following steps:

[0008] Step 1: After the initial credit line is issued to the borrower, the monitoring node is started to monitor the key monitoring items in the borrower's information and generate a data curve for the key monitoring items;

[0009] Step 2: Based on the factors that have an impact on the key monitoring items, construct a correlation function and generate a correlation curve;

[0010] Step 3: Calibrate the key monitoring item data curve and the correlation function in the same coordinate system, and monitor the abnormal nodes of the key monitoring items according to the calibrated coordinate system;

[0011] Step 4: If an abnormal node of a key monitoring item appears within a set time period, an alert will be issued to the outside, and it will be reviewed manually or by a set comparison algorithm. Otherwise, no action will be taken and the monitoring status will continue;

[0012] Step 5: If the abnormal node is approved, proceed to the next step; otherwise, the credit limit adjustment process is triggered;

[0013] Step 6: Fit the abnormal nodes of the monitoring items obtained within a period of time, and predict the range coordinates of the abnormal points in the next set time period. If the abnormal points in the next time period are consistent with the prediction results, no action will be taken, otherwise the credit limit adjustment process will be triggered.

[0014] The method for determining a credit limit of the present invention, wherein the step 1 further comprises the steps of:

[0015] Receive the personal information provided by the borrower and send it to the manual post for manual review of the personal information item by item;

[0016] The monitoring module receives the key monitoring item information input manually and generates monitoring nodes;

[0017] According to the scoring model, the personal information after review is scored item by item according to the set scoring criteria;

[0018] The initial credit limit is obtained by processing multiple scores based on the credit limit generation model.

[0019] In the method for determining a credit limit of the present invention, the monitoring module receives manually input key monitoring item information and generates a monitoring node using the following method:

[0020] Create a monitoring node and name it according to the borrower information;

[0021] Configure monitoring storage space and monitoring procedures;

[0022] Set reasonable value range for the monitoring program, set the source address of monitoring information, set the generation and transmission of monitoring item data curve data, and set the alarm.

[0023] The method for determining a credit limit of the present invention, wherein the method for constructing a correlation function based on factors that have an associated influence on key monitoring items and generating a correlation curve adopts the following method:

[0024] Constructing a correlation function in the monitoring module, the correlation function is a correlation function between the key monitoring items and the factors that have a correlation and influence on the key monitoring items;

[0025] The source of the factors that are associated with the key monitoring items is set by the monitoring module.

[0026] The correlation curve takes the factor data that has an associated impact on the key monitoring items as the horizontal axis and the key monitoring item data calculated by the correlation function as the vertical axis.

[0027] The method for determining a credit limit of the present invention, wherein the key monitoring item data curve and the correlation function are calibrated in the same coordinate system, and the abnormal nodes of the key monitoring items are monitored according to the calibrated coordinate system using the method:

[0028] Establish a calibration coordinate system, where the X-axis is time and the Y-axis is key monitoring item data;

[0029] Calibrate the data curve of key monitoring items in the calibration coordinate system;

[0030] The acquisition time of the factor data that has an associated impact on the key monitoring items and the corresponding key monitoring item data calculated by the correlation function are calibrated in the calibration coordinate system.

[0031] In the method for determining a credit limit of the present invention, the judgment criteria for abnormal nodes of key monitoring items are:

[0032] In a set time period, in the calibration coordinate system, if there is a node whose key monitoring item data is higher than the key monitoring item data calculated by the correlation function, the node is identified as an abnormal key monitoring item node.

[0033] The method for determining a credit limit of the present invention, wherein the process for adjusting the credit limit includes the following method:

[0034] According to the abnormal situations of key monitoring items and the weights of key monitoring items, the credit limit generation model is re-entered to evaluate the credit limit.

[0035] A system for determining a credit limit is applied to implement the method for determining a credit limit as described above, wherein the system comprises a credit limit adjustment unit, a monitoring unit and a reminder and warning unit;

[0036] After the credit line adjustment unit sends the initial credit line to the borrower, the monitoring unit starts the monitoring node to monitor the key monitoring items in the borrower's information, and generates a data curve for the key monitoring items; constructs a correlation function based on factors that have an associated impact on the key monitoring items, and generates a correlation curve; calibrates the key monitoring item data curve and the correlation function in the same coordinate system, and monitors the abnormal nodes of the key monitoring items based on the calibrated coordinate system; if an abnormal node of the key monitoring item appears within a set time period, the reminder warning unit sends a reminder to the outside, and the key monitoring item is reviewed manually or by a set comparison algorithm, otherwise no action is taken and the monitoring status is maintained; if the abnormal node is reviewed, the next step is entered, otherwise the credit line adjustment process is triggered; the abnormal nodes of the monitoring items obtained within a period of time are fitted, and the range coordinates of the abnormal points within the next set time period are predicted. If the abnormal points within the next time period meet the prediction results, no action is taken, otherwise the credit line adjustment process is triggered.

[0037] The beneficial effects of the present invention are as follows: by applying the method and approach of the present application, not only can real-time monitoring be performed on key monitoring items, but also abnormal situations of key monitoring items can be fed back in a timely manner. In addition, it also has the function of distinguishing whether the abnormal situation is reasonable, thereby achieving continuous tracking of borrowers after the initial credit line is issued, and identifying the rationality of abnormalities, providing a reliable data basis for determining and adjusting the credit line, improving the reliability of the credit system, and reducing the credit risks faced by banks. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. The drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work:

[0039] Figure 1 is a flow chart of a method for determining a credit limit in a preferred embodiment of the present invention;

[0040] Figure 2 It is a flow chart of initial credit line generation of a method for determining credit line in a preferred embodiment of the present invention;

[0041] Figure 3 It is a block diagram of the system principle for determining the credit limit of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the following will be described clearly and completely in combination with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0043] The method for determining the credit limit in a preferred embodiment of the present invention is as follows: Figure 1 See also Figure 2 , including the following steps:

[0044] S01: After the initial credit line is issued to the borrower, the monitoring node is started to monitor the key monitoring items in the borrower's information, and a data curve of the key monitoring items is generated;

[0045] S02: Construct correlation functions and generate correlation curves based on factors that are associated with key monitoring items;

[0046] S03: Calibrate the key monitoring item data curve and the correlation function in the same coordinate system, and monitor the abnormal nodes of the key monitoring items according to the calibrated coordinate system;

[0047] S04: If an abnormal node of a key monitoring item appears within a set time period, a reminder will be issued to the outside, and it will be reviewed manually or by a set comparison algorithm. Otherwise, no action will be taken and the monitoring status will continue;

[0048] S05: If the abnormal node is reviewed and passed, the next step will be entered; otherwise, the credit limit adjustment process will be triggered;

[0049] S06: Fit the abnormal nodes of the monitoring items obtained within a period of time, and predict the range coordinates of the abnormal points in the next set time period. If the abnormal points in the next time period meet the prediction results, no action is taken, otherwise the credit limit adjustment process is triggered;

[0050] By applying the method and approach of this application, not only can real-time monitoring of key monitoring items be carried out, but also abnormal situations of key monitoring items can be fed back in a timely manner. In addition, it also has the function of distinguishing whether the abnormal situation is reasonable, thereby achieving continuous tracking of borrowers after the initial credit line is issued, and identifying the rationality of abnormalities, providing a reliable data basis for credit line determination and adjustment, improving the reliability of the credit system, and reducing the credit risks faced by banks.

[0051] Preferably, step one further comprises the steps of:

[0052] S10: receiving the personal information provided by the borrower and sending it to a manual post for manual review of the personal information item by item;

[0053] S11: The monitoring module receives the key monitoring item information input manually and generates a monitoring node;

[0054] S12: Scoring the personal information after review item by item according to the set scoring criteria based on the scoring model;

[0055] S13: obtaining an initial credit limit based on multiple scores obtained through processing of the credit limit generation model;

[0056] In this step, manual review is still required, and monitoring nodes are generated based on the set important information. Possible important information includes: income, deposits, consumption, etc., which can be customized according to actual conditions;

[0057] Correspondingly, the following factors that are associated with the key monitoring items are factors that have a direct or indirect impact on the above important information. Taking income as an example, possible associated factors may include job changes, stock price fluctuations and other related situations. The associated factors can be determined based on the income composition of the borrower, and the proportion of each factor can be used to generate the associated function;

[0058] Of course, new content has been added to the information filling in for loan approval. In addition to providing the above-mentioned influencing factors, it is also necessary to provide a monitoring address for network monitoring. It can be set according to actual conditions, such as setting up a special account for easy supervision, etc. If the filled-in conditions do not meet the loan conditions or are false, the loan can be rejected and the credit will not be approved.

[0059] Preferably, the monitoring module receives the manually input key monitoring item information and generates the monitoring node using the method:

[0060] Create a monitoring node and name it according to the borrower information;

[0061] Configure monitoring storage space and monitoring procedures;

[0062] Set reasonable value ranges for monitoring programs, set monitoring information source addresses, set monitoring item data curve data generation and transmission settings, and set alarms;

[0063] The system configuration can be reasonably configured according to actual conditions.

[0064] Preferably, a correlation function is constructed based on factors that are associated with key monitoring items, and a correlation curve is generated using the following method:

[0065] Constructing a correlation function in the monitoring module, the correlation function is a correlation function between the key monitoring items and the factors that have a correlation and influence on the key monitoring items;

[0066] The source of the factors that are associated with the key monitoring items is set by the monitoring module.

[0067] The correlation curve takes the factor data that has an associated impact on the key monitoring items as the horizontal axis and the key monitoring item data calculated by the correlation function as the vertical axis.

[0068] The data curve of key monitoring items and the correlation function are calibrated in the same coordinate system, and the abnormal nodes of key monitoring items are monitored according to the calibrated coordinate system. The method is as follows:

[0069] Establish a calibration coordinate system, where the X-axis is time and the Y-axis is key monitoring item data;

[0070] Calibrate the data curve of key monitoring items in the calibration coordinate system;

[0071] The acquisition time of the factor data that has an associated impact on the key monitoring items and the corresponding key monitoring item data calculated by the correlation function are calibrated in the calibration coordinate system.

[0072] The criteria for judging abnormal nodes of key monitoring items are:

[0073] Within a set time period, in the calibration coordinate system, if there is a node whose key monitoring item data is higher than the key monitoring item data calculated by the correlation function, then the node is identified as an abnormal key monitoring item node;

[0074] By adopting the above-mentioned methods, data integration and calibration can be carried out more reasonably, making it easier for computer programs to carry out supervision.

[0075] Preferably, the process of adjusting the credit limit includes the following methods:

[0076] According to the abnormal situations of key monitoring items and the weights of key monitoring items, the credit limit generation model is re-entered to evaluate the credit limit.

[0077] A system for determining a credit limit is used to implement the method for determining a credit limit as described above, such as Figure 3 As shown, the system includes a credit limit adjustment unit 100, a monitoring unit 101 and a warning unit 102;

[0078] After the credit limit adjustment unit 100 sends the initial credit limit to the borrower, the monitoring unit 101 starts the monitoring node to monitor the key monitoring items in the borrower's information and generates a data curve of the key monitoring items;

[0079] According to the factors that have an impact on the key monitoring items, a correlation function is constructed and a correlation curve is generated; the key monitoring item data curve and the correlation function are calibrated in the same coordinate system, and the abnormal nodes of the key monitoring items are monitored according to the calibrated coordinate system;

[0080] If an abnormal node of a key monitoring item appears within a set time period, the warning unit 102 will issue a reminder to the outside, which will be reviewed manually or by a set comparison algorithm. Otherwise, no action will be taken and the monitoring status will continue to be maintained. If the abnormal node is reviewed and passed, the next step will be entered, otherwise the credit limit adjustment process will be triggered;

[0081] Fit the abnormal nodes of the monitored items obtained within a period of time, and predict the range coordinates of the abnormal points in the next set time period. If the abnormal points in the next time period are consistent with the predicted results, no action will be taken, otherwise the credit limit adjustment process will be triggered.

[0082] By applying the method and approach of this application, not only can real-time monitoring of key monitoring items be carried out, but also abnormal situations of key monitoring items can be fed back in a timely manner. In addition, it also has the function of distinguishing whether the abnormal situation is reasonable, thereby achieving continuous tracking of borrowers after the initial credit line is issued, and identifying the rationality of abnormalities, providing a reliable data basis for credit line determination and adjustment, improving the reliability of the credit system, and reducing the credit risks faced by banks.

[0083] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for determining a credit limit, characterized in that: The method comprises the following steps: Step 1: After the initial credit line is issued to the borrower, the monitoring node is started to monitor the key monitoring items in the borrower's information and generate a data curve for the key monitoring items; Step 2: Based on the factors that have an impact on the key monitoring items, construct a correlation function and generate a correlation curve; Step 3: Calibrate the key monitoring item data curve and the correlation function in the same coordinate system, and monitor the abnormal nodes of the key monitoring items according to the calibrated coordinate system; Step 4: If an abnormal node of a key monitoring item appears within a set time period, an alert will be issued to the outside, and it will be reviewed manually or by a set comparison algorithm. Otherwise, no action will be taken and the monitoring status will continue; Step 5: If the abnormal node is approved, proceed to the next step; otherwise, the credit limit adjustment process is triggered; Step 6: Fit the abnormal nodes of the monitoring items obtained within a period of time, and predict the range coordinates of the abnormal points in the next set time period. If the abnormal points in the next time period are consistent with the prediction results, no action will be taken, otherwise the credit limit adjustment process will be triggered.

2. The method for determining a credit limit according to claim 1, characterized in that: The step one also includes the steps of: Receive the personal information provided by the borrower and send it to the manual post for manual review of the personal information item by item; The monitoring module receives the key monitoring item information input manually and generates monitoring nodes; According to the scoring model, the personal information after review is scored item by item according to the set scoring criteria; The initial credit limit is obtained by processing multiple scores based on the credit limit generation model.

3. The method for determining a credit limit according to claim 2, characterized in that: The monitoring module receives the key monitoring item information input manually and generates the monitoring node using the method: Create a monitoring node and name it according to the borrower information; Configure monitoring storage space and monitoring procedures; Set reasonable value range for the monitoring program, set the source address of monitoring information, set the generation and transmission of monitoring item data curve data, and set the alarm.

4. The method for determining a credit limit according to claim 3, characterized in that: The method of constructing a correlation function and generating a correlation curve based on the factors that are associated with the key monitoring items is as follows: Constructing a correlation function in the monitoring module, the correlation function is a correlation function between the key monitoring items and the factors that have a correlation and influence on the key monitoring items; The source of the factors that are associated with the key monitoring items is set by the monitoring module. The correlation curve takes the factor data that has an associated impact on the key monitoring items as the horizontal axis and the key monitoring item data calculated by the correlation function as the vertical axis.

5. The method for determining a credit limit according to claim 4, characterized in that: The method of calibrating the key monitoring item data curve and the correlation function in the same coordinate system and monitoring the abnormal nodes of the key monitoring items according to the calibrated coordinate system is as follows: Establish a calibration coordinate system, where the X-axis is time and the Y-axis is key monitoring item data; Calibrate the data curve of key monitoring items in the calibration coordinate system; The acquisition time of the factor data that has an associated impact on the key monitoring items and the corresponding key monitoring item data calculated by the correlation function are calibrated in the calibration coordinate system.

6. The method for determining a credit limit according to claim 5, characterized in that: The judgment criteria for abnormal nodes of key monitoring items are: Within a set time period, in the calibration coordinate system, if there is a node whose key monitoring item data is higher than the key monitoring item data calculated by the correlation function, then the node is identified as an abnormal key monitoring item node.

7. The method for determining a credit limit according to claim 1, characterized in that: The credit limit adjustment process includes the following methods: According to the abnormal situations of key monitoring items and the weights of key monitoring items, the credit limit generation model is re-entered to evaluate the credit limit.

8. A system for determining a credit limit, applied to implement the method for determining a credit limit as claimed in any one of claims 1 to 7, characterized in that: The system includes a credit limit adjustment unit, a monitoring unit and a reminder and warning unit; After the credit line adjustment unit sends the initial credit line to the borrower, the monitoring unit starts the monitoring node to monitor the key monitoring items in the borrower's information, and generates a data curve of the key monitoring items; constructs a correlation function based on the factors that have an associated impact on the key monitoring items, and generates a correlation curve; calibrates the key monitoring item data curve and the correlation function in the same coordinate system, and monitors the abnormal nodes of the key monitoring items based on the calibrated coordinate system; if an abnormal node of the key monitoring item appears within a set time period, the warning unit sends a reminder to the outside, which is reviewed manually or by a set comparison algorithm, otherwise no action is taken and the monitoring state is maintained; If the review of the abnormal node passes, proceed to the next step, otherwise it triggers the credit limit adjustment process; fit the abnormal nodes of the monitoring items obtained within a period of time, and predict the range coordinates of the abnormal points in the next set time period. If the abnormal points in the next time period meet the prediction results, no action will be taken, otherwise it will trigger the credit limit adjustment process.

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